Innovation Diffusion in Australian State Owned Health - …eprints.qut.edu.au/15982/1/Ian_England_Thesis.pdf · · 2010-06-09Innovation Diffusion in State Owned Health A Study of

Innovation Diffusion in State Owned Health

A Study of IT Adoption

Ian England BSc (Hons) Dip Bus Adm Centre for Health Research

Submitted for the award of PhD

Tuesday, 14 December 2004

INNOVATION DIFFUSION IN AUSTRALIAN STATE OWNED HEALTH

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Keywords

Innovation Diffusion, Innovation Adoption, Innovation Diffusion Theory, IT, Health,

Hospital, Management, IT, Information Technology, IT Maturity, Banking,

Organisational Development


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Abstract

The health industry has acquired a reputation as lagging in the use of information

technology (IT). Therefore, this study has been undertaken to assess state health’s use

of IT and then to assess the causal factors of the differing usage rate, if any. The state

health industry was compared to the banking industry as a benchmark, on the basis

that the banking industry is widely perceived as a leading IT user.

A literature review summarised and critiqued current literature and informed the

subsequent research. The research comprised two related studies. The first study was

a qualitative study of the beliefs of senior state health executives. The second study

was based upon a survey of state health and banking managers.

The research confirmed that in these two ‘knowledge’ industries, state health is slower

to adopt IT with an apparent lower maturity level. This finding was observed across a

range of best-practice management, procedural and cultural topics as well as the level

of resources applied to IT.

Innovation-diffusion-theory helped understand why IT implementation has progressed

at a slower rate in state health than other industry sectors. The complexity of state

health organisations and their fragmented internal structure constrain their ability to

adopt traditional, hierarchical, organisation-wide IT. This is further impacted upon by

the relative immaturity of clinical health IT, which is complicated, incomplete and

unable to show quantifiable benefits. In addition, elements of the findings suggest

that health IT departments are poorly aligned to the needs of clinicians and managers.

Both organisational and technological factors lead to the slow adoption of health IT,

although measures suggest that the key factors relate to the unique organisational

nature of state health.

The recommendations for health and IT policy arising from this research are:


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• The effectiveness of state health IT departments needs comparing to those in

other sectors and improvement interventions implemented;

• The strongest way for state health IT to proceed is to focus on management

and social issues in preference to the ever-seductive technology. Research and

development funds should be allocated, as a priority, to benefits-analysis

methods, improved understanding of the true nature of health organisations

(formal and informal) and a rich understanding of clinical behaviours and

work.

Deeper knowledge in all of these areas will permit the development of more relevant

IT leading to greater value, more focussed implementation and new areas for business

development in the IT industry.


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i. Contents I. CONTENTS ................................................................................................................................ V II. TABLE OF FIGURES...............................................................................................................XI III. LIST OF TABLES................................................................................................................... XII IV. GLOSSARY OF TERMS..........................................................................................................XI V. PRESENTATIONS & PUBLICATIONS .............................................................................XIV VI. STATEMENT OF ORIGINAL AUTHORSHIP................................................................... XV VII. ACKNOWLEDGEMENTS ...................................................................................................XVI 1. BACKGROUND ..........................................................................................................................1

1.1. Introduction ...........................................................................................................................2 1.2. An Overview of the Health Systems .....................................................................................3 1.3. Why IT as a Focus? ...............................................................................................................6 1.4. Research Question .................................................................................................................6 1.5. Significance ...........................................................................................................................7 1.6. Relation to Previous Work in Same Field .............................................................................8 1.7. Positioning this research against existing knowledge..........................................................11 1.8. Definition of Terms and their Usage ...................................................................................11 1.9. Organisation of this Document............................................................................................12

2. LITERATURE REVIEW .........................................................................................................14 2.1. Introduction .........................................................................................................................15 2.2. Innovation Adoption Theory ...............................................................................................15

2.2.1. Introduction to Innovation & Change .......................................................................................... 15 2.2.2. Critique of the Theory.................................................................................................................. 22

2.3. The Features of Health Organisations .................................................................................27 2.3.1. Leader Characteristics.................................................................................................................. 28 2.3.2. Centralisation............................................................................................................................... 32 2.3.3. Complexity .................................................................................................................................. 33 2.3.4. Formalisation ............................................................................................................................... 33 2.3.5. Interconnectedness....................................................................................................................... 34 2.3.6. Organisational Slack .................................................................................................................... 35 2.3.7. Size .............................................................................................................................................. 35 2.3.8. External Characteristics of Organisation (Openness)................................................................... 35 2.3.9. Conclusions about Health Organisations ..................................................................................... 36

2.4. The Features of Health IT....................................................................................................41 2.4.1. Relative Advantage...................................................................................................................... 41 2.4.2. Complexity .................................................................................................................................. 46 2.4.3. Compatibility ............................................................................................................................... 49 2.4.4. Observability................................................................................................................................ 52 2.4.5. Trialability ................................................................................................................................... 53 2.4.6. Conclusions Regarding Health IT................................................................................................ 53


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2.5. Environment/Policy.............................................................................................................59 2.5.1. Consumer Expectations................................................................................................................ 60 2.5.2. Conclusions about Environmental/Policy Factors........................................................................ 61

2.6. Level of IT Adoption in Health care....................................................................................61 2.7. Innovation Diffusion Research............................................................................................63

2.7.1. Health Innovation Diffusion Research ......................................................................................... 64 2.7.2. IT Innovation Research................................................................................................................ 65 2.7.3. Health IT Innovation Research .................................................................................................... 65

2.8. Conclusions .........................................................................................................................66 2.8.1. Gaps and Weaknesses in the Literature........................................................................................ 66 2.8.2. Commentary & Relevance for this Study..................................................................................... 67

3. METHODS.................................................................................................................................69 3.1. Overview .............................................................................................................................70 3.2. Ethics ...................................................................................................................................72 3.3. Study One – Executive Interviews ......................................................................................72

3.3.1. Study One – Theory Revisited ..................................................................................................... 73 3.3.2. Study One - Target Population..................................................................................................... 73 3.3.3. Study One - Interviews Design .................................................................................................... 75 3.3.4. Study One – Analysis & Data Management ................................................................................ 76 3.3.5. Study One - Reliability, Validity ................................................................................................. 77

3.4. Study Two – Survey-Based Research..................................................................................79 3.4.1. Study Two - Research Directions ................................................................................................ 79 3.4.2. Study Two - Design ..................................................................................................................... 81 3.4.3. Study Two – Measurement Techniques ....................................................................................... 83 3.4.4. Study Two – Data Collection....................................................................................................... 86 3.4.5. Study Two - Output Scales & Derived values ............................................................................. 89 3.4.6. Study Two - Data Cleaning.......................................................................................................... 98 3.4.7. Study Two - Data Quality & Data Management .......................................................................... 98 3.4.8. Study Two - Analysis................................................................................................................... 98 3.4.9. Study Two - Validity & Reliability.............................................................................................. 99

4. STUDY ONE - EXECUTIVE INTERVIEWS ......................................................................100 4.1. The Interviews Described..................................................................................................101

4.1.1. Patterns of acceptances .............................................................................................................. 101 4.1.2. Features of the Interviews .......................................................................................................... 101

4.2. Coding described ...............................................................................................................102 4.2.1. The Coding Process ................................................................................................................... 102

4.3. Organisational factors........................................................................................................104 4.3.1. Slack .......................................................................................................................................... 104 4.3.2. Leader Characteristics................................................................................................................ 105 4.3.3. Size ............................................................................................................................................ 117 4.3.4. Centralisation............................................................................................................................. 119 4.3.5. Complexity of organisation........................................................................................................ 120


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4.3.6. Formalisation ............................................................................................................................. 121 4.3.7. Interconnectedness..................................................................................................................... 122 4.3.8. Openness.................................................................................................................................... 123 4.3.9. Organisation factors summarised ............................................................................................... 124

4.4. Technology factors ............................................................................................................124 4.4.1. Relative Value............................................................................................................................ 124 4.4.2. Complexity of technology.......................................................................................................... 127 4.4.3. Compatibility ............................................................................................................................. 129 4.4.4. Observability.............................................................................................................................. 130 4.4.5. Trialability ................................................................................................................................. 131 4.4.6. Technology conclusions............................................................................................................. 132

4.5. Social Factors (Environmental / Policy Factors) ...............................................................133 4.6. Type of decision ................................................................................................................136 4.7. Summary ...........................................................................................................................137

4.7.1. The Findings Summarised ......................................................................................................... 138 4.7.2. Implications for the Framework................................................................................................. 141

5. STUDY TWO - MANAGEMENT SURVEYS ......................................................................143 5.1. Study Two – Survey-Based Research................................................................................144

5.1.1. Introduction................................................................................................................................ 144 5.1.2. Response Profile ........................................................................................................................ 144 5.1.3. IT Maturity................................................................................................................................. 150 5.1.4. Organisation............................................................................................................................... 160 5.1.5. Technology ................................................................................................................................ 167 5.1.6. Environmental/Policy ................................................................................................................ 173

5.2. Implications for the Framework ........................................................................................176 6. CONCLUSIONS ......................................................................................................................191

6.1. Developing the Conceptual Framework ............................................................................192 6.2. Summarising Studies One & Two .....................................................................................195 6.3. Studies One & Two Compared & Contrasted ...................................................................196

6.3.1. Summary of Findings................................................................................................................. 198 6.4. Strengths & Limitations ....................................................................................................207

6.4.1. Strengths .................................................................................................................................... 207 6.4.2. Limitations................................................................................................................................. 208

6.5. Analysing the Research Questions ....................................................................................209 6.5.1. Is There a Difference in IT Adoption between Health and Banking? ........................................ 210 6.5.2. Are Policy Issues Significant? ................................................................................................... 210 6.5.3. Are IT Issues Significant?.......................................................................................................... 210 6.5.4. Are Organisation Issues Significant? ......................................................................................... 211

6.6. Summary Response ...........................................................................................................211 6.7. Concluding Remarks .........................................................................................................211

6.7.1. Next steps................................................................................................................................... 216


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7. REFERENCES ........................................................................................................................221 8. APPENDICES..........................................................................................................................247

8.1. Publications .......................................................................................................................248 8.1.1. England (2000) .......................................................................................................................... 249 8.1.2. England(2001) ........................................................................................................................... 254 8.1.3. Stewart (2002)............................................................................................................................ 258 8.1.4. England(2003) ........................................................................................................................... 268

8.2. Project Documents.............................................................................................................272 8.2.1. Health IT Survey........................................................................................................................ 273 8.2.2. Banking IT Survey..................................................................................................................... 279 8.2.3. Health Organisation Survey ....................................................................................................... 285 8.2.4. Banking Organisation Survey .................................................................................................... 288 8.2.5. Interview Invitation.................................................................................................................... 291 8.2.6. Health IT Survey Letter ............................................................................................................. 293 8.2.7. Banking IT Survey Letter .......................................................................................................... 294 8.2.8. Health Organisation Survey Letter............................................................................................. 295 8.2.9. Banking Organisation Survey Letter.......................................................................................... 296 8.2.10. Semi-Structured Interview Prompts ........................................................................................... 297 8.2.11. Interview Coding Sample........................................................................................................... 298


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ii. Table of Figures

Figure 2-1 The Innovation Process, adapted from Rogers (1995) ..........................................................18 Figure 2-2 Variables of Organisational Innovativeness, from Rogers (1995) ........................................20 Figure 2-3 Innovation Adoption Influences, derived from Rogers (1995) .............................................22 Figure 2-4 Theoretical Framework.........................................................................................................26 Figure 2-5 IT Strategic Disposition Model (DeLuca & Enmark Cagan, 1996)......................................30 Figure 2-6 Typical Returns on IT Investment in Health (DeLuca et al., 1996) ......................................43 Figure 3-1 Factor calculation map..........................................................................................................97 Figure 4-1 The Coding Tree .................................................................................................................103 Figure 4-2 Summary of Findings After Study One ..............................................................................140 Figure 4-3 Revised Conceptual Framework .........................................................................................142 Figure 5-1 Maturity and Variables By Industry....................................................................................153 Figure 5-2 Factors within maturity measures .......................................................................................158 Figure 5-3 Scatter graph of the two adoption factors ..........................................................................159 Figure 5-4 Graph of Organisation Innovation Drivers .........................................................................162 Figure 5-5 Graph of technology factors................................................................................................168 Figure 5-6 Graph of Policy Variables...................................................................................................175 Figure 5-7 Annotated Conceptual Framework with Correlations.........................................................179 Figure 5-8 Correlations with Pervasiveness ........................................................................................187 Figure 5-9 Correlations with Resource Commitment..........................................................................188 Figure 6-1 Final conceptual framework ...............................................................................................194 Figure 6-2 Strengths of this research project ........................................................................................207 Figure 6-3 Limitations of this research project.....................................................................................209


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iii. List of Tables

Table 2-1 Summary of determinants of innovation rate .........................................................................25 Table 2-2 Rogers’ organisational factors................................................................................................36 Table 2-3 Organisational factors derived from the literature.................................................................37 Table 2-4 Summary of Rogers’ technology factors................................................................................53 Table 2-5 Technology factors derived from the literature ......................................................................54 Table 3-1 Study One Validation Approach ............................................................................................78 Table 3-2 Subsidiary Elements to Detailed Questions ...........................................................................80 Table 3-3 Technology Measurements Described ...................................................................................91 Table 3-4 Deriving the Organisation Variables ......................................................................................92 Table 3-5 Maturity Factors Described....................................................................................................95 Table 3-6 Usage Weightings ..................................................................................................................96 Table 5-1 Characteristics of the major Australian banks......................................................................146 Table 5-2 Maturity Factors by Industry................................................................................................151 Table 5-3 Other Maturity Measures by Industry ..................................................................................152 Table 5-4 Maturity differences between industries ..............................................................................154 Table 5-5 Maturity Measures Correlated..............................................................................................156 Table 5-6 Component Variance............................................................................................................157 Table 5-7 Maturity measure component analysis .................................................................................157 Table 5-8 Organisational factors by industry - .....................................................................................161 Table 5-9 Analysis of organisational innovation drivers ......................................................................163 Table 5-10 Overall technology variables by industry with inverse coding for complexity ..................167 Table 5-11 Technology Factors Analysed ............................................................................................170 Table 5-12 Policy Questions.................................................................................................................174 Table 5-13 Correlations within the framework.....................................................................................178 Table 5-14 Noteworthy Correlations ....................................................................................................182 Table 6-1 Summary of Findings ...........................................................................................................198


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iv. Glossary of Terms

Term Description

Adoption The decision to take up and continue with an innovation.

Axial coding The second-level of coding in a qualitative study. Involves

categorising, re-categorising and condensation of all first

level codes by connecting a category and its sub-

categories.

Centralisation The degree to which the control of an organisation is via a

small central or wider distributed management team.

CEO Chief Executive Office

CFO Chief Financial Officer

CIO Chief Information Officer

Clinical Information

System (CIS)

A system used to manage patient diagnostic and treatment

related information. A generic phrase that incorporates a

number of systems including order entry, results reporting,

electronic medical record, clinical decision support etc

Compatibility The fit of an innovation to the current environment. This

considers all factors including cultural, social and

technical.

Complexity A variable in Innovation Diffusion Theory used as a

measure of how complex an organisation’s business,

process and environment are.

DP Data Processing, an outdated phrase replaced by IT or IS.

External openness The degree to which an organisation is open to ideas,

communication and interactions with other organisations

and elements of society.

Financial sector The banking and insurance industry

Formalisation The level to which an organisation operates through formal

processes and rules.

Health In general, this refers to Australian state health services.


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Term Description

Health care sector The industry of health care. In this study this means the

Australian government organised health organisations

predominantly being the state health departments that

provide acute care and other care services. When referring

to non-Australian health, the general meaning is the acute

care sector.

HMO An abbreviation for Health Maintenance Organisation, a

type of health insurer using managed care approaches,

combining insurance and care management.

IM Information Management

IM&T Information Management and Technology

Innovation The application of a technology or idea that is new to the

given community or organisation.

Innovation diffusion The spread of a new idea or technology.

Interconnectedness The degree to which internal sub-groups within an

organisation work together, communicate and co-operate.

IS Information Systems – the application of IT to create

business process. In common use this term is synonymous

with Information Technology.

IT Information Technology, the science and equipment used

to implement information systems. In common use this is

synonymous with Information Systems.

NPV Net Present Value, a financial measure of the current worth

of an investment based upon future cash flows and a

known interest rate.

Order entry In this context, a clinical information system used by

clinical staff to request patient related services such as tests

and patient specific supplies.

Rate of diffusion The speed at which a new technology or innovation

spreads through a community.

Relative advantage The benefit of an innovation compared to the status-quo.


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Term Description

Results reporting A clinical system used to transfer the result of patient

diagnostic procedures back to the requesting clinician.

ROI Return on Investment, the percentage gain on an

investment in an activity.

Slack Spare capacity in an organisation.


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v. Presentations & Publications Presentations England, I. W. R. (2001) Realizing Value From Clinical IS, CHIC CIS Forum,

Sydney, May 2001

England, I.W.R. (2001) The Status of Health IT Expenditure: A Qualitative Study of

Senior Executives in Regard to IT Investment, HIC, Canberra July 2001

England, I. W. R. (2002) Why Turkeys Soar and Eagles Don’t, or, Explaining the

Success and Failure of Innovations, IPENZ Annual Conference (Engineers for Social

Responsibility Interest Group), Wellington March 2002

Di Donato, J; England, I.W.R.; Scott, P.; Donker, A.; Walduck. A. (2004) Health IT

Uptake – The Research, The Policy, The Technology. Can the Planets Align on this

Issue? ACHSE Continuing Education Series, Brisbane February 2004

Published Papers & Books England, I. W. R., Stewart, D., & Walker, S. (2000). IT Adoption in Health Care:

When Organisations and Technology Collide. Australian Health Review, 23, 176-185.

England, I. W. R. (2001). The status of health IT expenditure: A qualitative study of

senior executives in regard to IT investment. In: P. James, J. Smith, & L. Smith

(Eds.), HIC 2001: Realising Quality Health Care (Melbourne Victoria: Health

Informatics Society of Australia.

Stewart, D. & England, I. W. R. (2002). The Contested Domain of Innovation. In A.

Twaddle (Ed.), Health Care Reform Efforts Around The World. Westport:

Greenwood.

England, I. W. R. & Stewart, D. (2003). Health: IT leader or laggard? A comparative

assessment of IT maturity. Australian Health Review 26.


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vi. Statement of Original Authorship The work contained in this thesis has not been previously submitted for a degree or

diploma at any other higher education institution. To the best of my knowledge and

belief, the thesis contains no material previously published or written by another

person except where due reference is made.

Signed:

Ian England

Date:


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vii. Acknowledgements

This research project has a long history with many ups and downs.

My Supervisor Assoc. Prof. Don Stewart, and Associate Supervisor Prof. Beth

Newman, made invaluable contributions to my thinking and the rigour of this project.

Don has proved a wonderful mentor, he challenged me to develop conceptual

thinking, gave me the space to think and create whilst providing gentle yet firm

motivation to ensure I reached the end. Beth brought rigour, focus, clarity and

precision to my work helping me develop my ideas from their conceptual roots to

reasoned, argued and defensible hypotheses. I thank them both for their dedication

and professionalism. Similarly I thank my examiners; their contribution, even when I

found it challenging, has led to a stronger outcome.

In addition, there were many people alongside me through this journey. Some helped

with ideas, some with sources of information, friends and family encouraged me and

gave me the space and time to work, whilst the truly dedicated read and re-read my

words, helping me to say what I meant. So, in no particular order, a big thank you to:

Alister, Louise, Heather, Robert England, Glenys England, Sue Walker, Terence

Seymour, Chris Kent, Sarah Warner, Ross Pitt.

Of course, I also send a big thank you to the managers who either were interviewed or

completed surveys. This research tells their story; I thank them for such openness. I

hope that the findings in this study assist you to deliver more and better health care.

1. Background

In my beginning is my end. T. S. Eliot (1888–1965). Four Quartets, ‘East Coker.’


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1.1. Introduction Organisations always face the tension generated between the unlimited demand for

new ways of doing things and the limited resources available. Part of management's

challenge is to decide the most appropriate ways to allocate the scarce resources and

which new activities and technologies to adopt. Health care organisations are no

different in this respect. This research looked at the way managers make decisions

about the allocation of resources for the adoption of information technology (IT) in

Australian state health care. This contributes to the deeper understanding of this

wider resource allocation process.

This research arose from an interest in the contribution that IT makes to health care

organisations. It is remarkably difficult to determine the connection between adoption

of IT and the resulting impact, if any, on patient care. In an ideal world, it would be

possible to evaluate innovations competing for resources in terms of their impact on

patient outcomes, and a logical approach would be to create a portfolio of health

investments that generate the optimum health status. Unfortunately, management

science is far from enabling this, and writers imply that policy makers are knowingly

setting policy and making decisions that lead to sub-optimal health status (Meyer,

Silow-Carroll, & Garrett, 1993). This research, however, attempts to be a step along

the way to achieving the goal of a rational, patient care-oriented resource allocation

process.

Understanding how management allocates its resources to adopt new practices leads

to further and deeper understanding of the connection between resource allocation and

health status. The focus of investigation was IT due to its life cycle stage and the

broad influence it can have on the state health sector. The research concentrated on

the Australian and New Zealand state health systems. As will be seen in the

following section, government funded and controlled state health systems are the

dominant and core parts of the overall Australian and New Zealand health system.

Due to the ability of this core to have the largest impact upon the health of the

population, and its fundamental position in implementing major IT innovations, such

as a national health record, this was the part of the system of most influence and

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therefore relevance for research. To enable a clearer understanding of state health’s

IT adoption process comparative data were collected from the banking industry, a

mature user of IT (CSC, 1999) that has achieved a significant degree of nationwide IT

adoption.

1.2. An Overview of the Health Systems1 This research was conducted in Australia with a focus on both the Australian and New

Zealand state health care industries. Australia has a unique history and

implementation for its health system. To assist readers unfamiliar with this system,

and therefore the context for this project, a brief overview of Australia’s health system

is provided. Comments about the differences in New Zealand will be made at the end.

Australia has a strong free-enterprise culture backed up by a belief in the need to

provide a moderately comprehensive welfare system. The health care system in

Australia is characterised by a mixture of public and private sector involvement.

Under the Australian Health Care Agreements between the Commonwealth and the

State and Territory governments, all eligible people are entitled to free services as

public patients in public hospitals. This socialised health care delivery system is

financed mostly by general taxes. A proportion of these taxes is raised by an income-

related Medicare levy. The Commonwealth has the responsibility for raising these

funds and, in turn, for disbursing them using a range of mechanisms, such as Health

Care Agreement Grants to the States and Territories, medical and pharmaceutical

benefits, and Health Program Grants. In 1997-98, total health service expenditure,

including both government and non-government sectors, was AUD$47,030 million

that represented 8.3 per cent of Gross Domestic Product (AIHW, 2001). In 1998-99

some AUD$5.6 billion was contributed by the Commonwealth to the States in public

hospital funding under the Australian Health Care Agreements.

Australians have consistently enjoyed relatively good access to medical care. There

have been notable inconsistencies in this, for example – Aged Care, Mental Health

and Indigenous Health, which are now subject to special Commonwealth programs.

With the more recent health care reform, significant issues have impinged on the

1 This review of the Australian Health System is based upon work by the author and others published in a US textbook on global health reforms (Stewart & England, 2002).


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delivery and quality of health care delivery. For example, after a decade of

management and fiscal policy guided by an economic rationalist philosophy aimed at

improving delivery of services, health care delivery has become stretched.

The Commonwealth Government plays a major role in policy formulation in such

areas as public health, research and national information management as well as the

planning and funding of health services, but rather less in their delivery. The

Commonwealth provides the funds for most non-hospital medical services,

pharmaceuticals and health research. Together with the States and Territories, it

jointly funds public hospitals as well as home and community care for the aged and

disabled. The Commonwealth also plays a role in funding residential facilities for

aged persons. Other areas of responsibility for the Commonwealth include Medicare

benefits, private health insurance, medical workforce issues, Aboriginal and Torres

Strait Islander health issues and the Health Insurance Commission.

The State and Territory governments play a major role in the public provision of

health services, such as public and psychiatric hospitals, public health and mental

health. They have the primary responsibility for delivering and managing public

health services and regulating health care providers. The health care organisations

provided by the State and Territory governments are the focus of this current research

project. In addition, local governments also provide health services, for example, they

deliver most environmental health programs.

The health sector is dominated by the medical profession that has been historically

and socially very influential. The profession regulates the number of trainees, for

example, thereby managing supply. Medical practitioners qualified overseas who

attempt to gain registration in Australia sometimes find this challenging due to

professional control.

Complementing the services provided by governments, private and non-government

organisations (both non-profit and for profit) provide health services. Approximately

one third of Australia’s health expenditure is on non-government delivered services.

For example, private, non-salaried practitioners, including self-employed general

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practitioners and specialists, provide most medical and dental care as well as some

allied health services such as physiotherapy and those involved in diagnostic imaging

and pathology. The private hospital system has developed from providing less

complex, elective surgery to increasingly complex high technology services, for

example day-only surgical procedures. Patients may choose to be private patients in

public hospitals (which allows them to choose their doctors), or they may elect to be

private patients in private hospitals. Such choices are usually governed by whether or

not the patient holds private health insurance.

Successive governments have committed themselves to a strong private health care

delivery sector through such means as direct and indirect subsidies. For example,

prescriptions for medicines dispensed by private sector pharmacies are directly

subsidised by the Commonwealth through the Pharmaceutical Benefits Scheme and

financial incentives are provided to encourage private health insurance.

New Zealand has a far simpler political environment, the lack of a federated structure

making for a relatively simple, centralised government. Health is centrally controlled

from a national ministry, with regional operating organisations running hospitals and

health services for their allocated geography. Like Australia, New Zealand has a

socialised health service that provides low-cost or free access to health services for all

residents. However, in contrast to Australia, New Zealand has a far weaker culture of

free enterprise in its health sector. The private hospital sector is far smaller than that

in Australia, whilst general practitioners, pharmaceuticals and diagnostic services are

subsidised by government funds. These funding mechanisms are transparent to the

health consumer, unlike Australia where Medicare, acting as a compulsory insurance

scheme, makes health costs highly visible.

Policy making for New Zealand health care is centrally controlled by the ministry,

whilst operational and tactical decisions are made by the regional operating entities.

Most significant decisions by the operating entities are subject to review and fiscal

control from the central ministry.


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1.3. Why IT as a Focus? IT is a relatively new, emerging technology that is developing at a great rate. The

“information age” is taking over from the “industrial age” driven by the

implementation of IT (Grove, 1996; Slywotzky, 1996; Hamel, 1998).

The ability of organisations to manage innovation with the appropriation of new

technologies is a key to organisations’ strategic behaviours (Rosegger, 1991).

Therefore, the processes by which innovations diffuse and resources are transferred

from one area of the health sector to another are important topics for research.

It is claimed IT is lagging in its uptake in health compared with other sectors of the

economy (Shortliffe, 1998; CHIC, 2000) yet it has been noted that IT has transformed

other industries, such as banking (Whaling, 1996).Therefore, this makes IT of interest

in the way state health is choosing to adopt its use. IT’s impact spans the entire health

sector. It can work as an enabling infrastructure, it can automate administrative

process, it can enable and facilitate entirely new ways of delivering care and it can

work as a direct care tool. This makes it a significant domain for innovation and

therefore innovation research.

1.4. Research Question Diffusion of innovations occurs at different rates for differing industries and

technologies based upon the decision making of managers. This research project

therefore addresses issues around the diffusion process for IT and its related

investment in Australian and New Zealand health care, the speed of IT diffusion in

state health and the factors that influence this.

The basic question for this research is therefore:

“What factors affect the adoption & diffusion of IT in state-owned health organisations: how do the policy, organisation & technology environment influence the rates of adoption/diffusion in state health?”

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Several issues arise from this question including:

a) What is the level of IT diffusion in state health care?

b) What are the policy, organisational and technology influences in state health

care?

c) What can accurately and reliably answer this question?

d) What comparisons will allow a better understanding of state health care’s

behaviour?

These issues are central to the structure and design of this research project. Whilst

these topics will be discussed more fully in chapters 2 and 3, it is timely to note that

the ideal source of information about IT adoption decisions is the managers with

delegated authority for approving these investments or making recommendations to

those with the financial authority. In the state health industry, this is a small and elite

group of very senior managers.

1.5. Significance The state health care industry and hospitals in particular are under continual pressure

to deliver more care with constrained resources. Current state government policies

hold budgets constant meaning that the only avenue to increase outputs is by

increasing the productivity of existing resources - especially personnel. In the U.S.,

the productivity of service industries has increased significantly since the early 1990s

(Brynjolfsson & Hitt, 1996a). The principal tool for ensuring productivity

improvement has been IT-enabled process redesign and many industries have invested

significantly to realise these benefits (Davenport and Short, 1990). However, there

has been systematic under-investment in IT in health care across the countries of the

Organisation for Economic Cooperation and Development (OECD) (Lazarus, 1993).

It seems that addressing this under-investment in Australia and New Zealand’s

government-controlled health systems may lead to productivity improvements.

Innovation is disruptive to an organisation. Therefore, innovation and change,

especially if protracted, are usually met with resistance and organisational politics

(Tushman & O'Reilly III, 1997). However, it is commonly considered that IT has the


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ability to change industries (Clemons & Row, 1988; Doll, 1989). The financial

services industry, as an example, illustrates this point. This industry has made heavy

use of IT and recent innovations in retail financial services have almost invariably

been driven by it (Channon, 1998). Of course, financial services and health are quite

different in many ways from their production processes; however, in both industries

technology is employed for the sake of efficiency and quality. The use of banking in

this project should be taken at face value, namely it is a relatively well known

baseline against which health can be compared in a field of research for which there

are, as yet, no firm measurements.

The appropriateness and effectiveness of the state health industry’s IT innovations are

therefore keys to future viability. Thus, it is important that the processes by which

state health management decides upon IT investments are well understood. To

facilitate this understanding, state health will be the focus of this investigation and

banking will be used as a counterpoint to assist in better understanding of the

processes occurring in state health.

1.6. Relation to Previous Work in Same Field The reasons for state health’s IT decision-making pattern is not well understood,

especially the reasons for decisions by the senior policy makers. Understanding this

requires information from a number of academic fields including:

a. Innovation Diffusion

b. Policy & Planning

c. IT Management

d. Management & Organisation

e. Finance & Economics

Innovation diffusion theory is a well-described body of literature and has been

applied to health care, technology and hospitals. Innovation diffusion explains the

processes by which innovations spread within and the factors that encourage adoption.

The theory provides explanations for speed of diffusion, adoption patterns, factors that

BACKGROUND

Page 9

encourage a technology to be adopted and factors that determine an organisation’s

level of innovativeness.

Policy and planning as well as management decision-making are both major topics

of academic research. These sit within the domain of business and management

theory and are substantial topics of research. Policy studies usually review reasons

for management decisions, policy alternatives in differing situations or techniques for

analytical policy development. As an example, Wiktorowicz and Deber (1997) look

at the impact technology assessment has made upon policy-making though find it has

made little impact due to other conflicts and organisational issues.

Decision-making is another major area of study with research in both the

psychological and management fields. Etzioni (1989) and Einhom & Hogarth (1987)

both present models for management decision-making, though these are only a small

proportion of the total work in this area. The work in this area provides processes for

improved decision-making as well as mapping processes used to gather data and reach

decisions.

The IT management literature includes many examples concerning IT planning and

management, (Davenport, 1994; Davenport, Hammer and Metsisto, 1989; Kovacevic

& Majluf, 1993; Avishai, 1989; Allen, 1987). These look at a broad range of topics

including different methods for planing IT , aligning IT with organisational strategy

(Boar, 1994), measuring IT results (Parker, Trainor, & Benson, 1989), achieving

value from IT investment (Thorp, 1998) and managing IT risk. There are a number of

models for strategic IT management, organisational issues and management

involvement in IT. This literature, however, tends towards advice and techniques

rather than presenting research into the effectiveness of the proposed advice, maybe

reflecting the relative youth of IT as a discipline. Other authors have looked at issues

of applying IT to organisations as an agent of change and improvement (Tyre and

Orlikowski, 1993; Benjamin and Levinson, 1993; Davenport and Short, 1990). Such

approaches particularly look at IT enabling change and business process

improvement.


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10

Many researchers have looked at facets of IT in health care (Chae, Kim, Lee, Choi, &

Kim, 1994; Dent, 1996; Dick, Steen, & Detmer, 1997). One study examined the

effects of health management information systems (IS) in Korean health centres and

found productivity and satisfaction improvements (Chae et al 1994). Griffin (1996)

proposed the use and design of information architectures to support health care

organisations. Wyatt and Friedman (1997) present a book that explains how to

structure objective and subjective evaluation studies of the effectiveness of medical

informatics projects. However, they do not present findings about the effectiveness of

systems; rather they present methods for the reader to use to do their own

effectiveness studies.

Another area of literature looks at the business impact of IT on health care and the

success of IT projects in health. Berkowitz (1998) looks at the challenges of getting

physician acceptance of systems and Collins (1998) looks at the risks of implementing

computerised patient records. Kimball-Baker (1998) and Blumberg (1997) each look

at financial aspects of IT in health care.

The management and organisation literature addresses a broad range of issues about

the way managers and organisations function. This literature, being an applied area,

tends to overlap many others including sociology and psychology. The management

literature tends to look at the role of the manager and ways of improving managerial

performance (Drucker, 1974; Mintzberg, 1980). This includes issues such as

organisation structures, organisational development and change, decision-making and

organisational communication.

Finance and economics are disciplines that look at the allocation of scarce resources,

particularly financial ones. They influence this research project as the rationalist

models of management assume that managers allocate resources for the optimum

benefit to the stakeholders. Financial (and accounting) research tends to look at the

measurement of cost and financial benefits of investments, whereas the economic

literature tends to look at resource allocation decisions.

BACKGROUND

Page 11

In summary, the literature surrounds the research question thoroughly. Risks,

benefits, strategies, methods are all reviewed. These are all important aspects and

features that are relevant to the research question - but none actually addresses the

heart of the question - why do hospital managers choose to invest in IT at the level

that they do?

1.7. Positioning the current research against existing knowledge

This research is being conducted at an early point in the research of IT adoption in

health care. Whilst specific, narrow studies have been conducted into the innovation

of specific systems (Halamka & Safran, 1998; Ash, Lyman, Carpenter, & Fournier,

2001; Gosling, Westbrook, & Braithwaite, 2003; Gladwin, Dixon, & Wilson, 2003) or

barriers to the adoption of specific systems (Rind & Safran, 1993; Kirveennummi &

Hirvo, 1998; Sobol, Alverson, & Lei, 1999), little or no research has been done on the

organisational context of health and the challenges it presents to enterprise-wide IT

systems.

This research, therefore, seeks to take a top-level view of the organisational and

technological influences on state health IT adoption aiming to develop, or at least

introduce, theory. To this point, little or no state health IT-specific theory has been

developed. Generic theories such as the Diffusion of Innovations (Rogers, 1962;

Rogers, 1995) exist, but these have not been tailored specifically for the state health

environment. Nor have these theories been tested in a state-controlled health system

such as Australia’s. This project aims to be the start of that theory-building process.

Its aim is to identify the major concepts and move the published knowledge along by

formalising information and proposing initial hypotheses. The sign that this research

has moved along this path comes from the more detailed and specific questions that

arise in the conclusion, pointing to more focussed areas requiring analysis.

1.8. Definition of Terms and their Usage This research uses a number of common phrases in particular contexts. The meaning

intended in this study is described in the following list:


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12

Health care This phrase, and many variants such as industry or health care sector,

refer to the industry that is the target of this study. In particular, it refers to

government-controlled state health care in Australia and New Zealand. Australian

and New Zealand government-controlled state health care is responsible for health

care delivery, via hospitals and community health services, as well as policy-making

and planning. In addition, the phrase health care is also used loosely to describe

health entities in other parts of the world. Due to the preponderance of literature, the

bias is towards hospitals, but the context of this study is intended to be hospitals,

community care and state health policy-making.

Information Technology The application of computer hardware and software to

business and organisational processes. Information Systems is used synonymously.

This phrase excludes computers embedded in other machines (such as CT scanners or

laboratory analysers) and telephone systems.

Executive A manager in the top two or three layers of management and part of the

central management team.

1.9. Organisation of this Document This document comprises six parts as follows:

Chapter 1, Background This is the introductory chapter that describes the nature of

the research, the reason it is worthwhile undertaking and positions the study within

other academic disciplines.

Chapter 2, Literature Review This section reviews the literature surrounding this

research project. It addresses the theoretical basis to build a theoretical framework,

and then performs an analysis of literature using the framework as the organising

concept.

Chapter 3, Method Describes the methods used to conduct the research and

underlying theories. The method section provides an overview of the overall research

design, and then documents in more detail the two studies that were conducted.

Chapter 4, Study One Presents the data, analysis and discussion of the executive

interview study making up the first study of this research project.

Chapter 5, Study Two Presents the data, analysis and discussion of the executive

surveys making up the second study of this research project.

BACKGROUND

Page 13

Chapter 6, Conclusions Presents the synthesis and integration of the findings from the

literature review and studies one and two. These are used to provide a concluding

answer to the research question.

2. Literature Review

Learning without thought is labour lost; thought without learning is perilous.

Confucius (551–479 BC) Chinese philosopher.

LITERATURE REVIEW

Page 15

2.1. Introduction This chapter explores the factors that influence management adoption of innovations

in organisations, particularly IT. The range of factors influencing such adoption

decisions is broad; therefore, an emphasis is placed upon IT in state health care.

Some factors are directly involved in the diffusion process whilst others are less direct

and form part of the environment. This chapter by necessity reviews a range of

topics. First, there will be a review of the Innovation Diffusion literature leading to

the development of a conceptual framework and a critique of Innovation Diffusion

Theory. This forms the basis for the structure of an analysis of the innovation factors

within health, IT and the environment. The next three sections, therefore, review the

literature about organisations, IT and the policy environment with a focus on health

entities. Each of these sections draws conclusions about the likely impact upon

health’s innovation behaviours with respect to IT. The final part of this chapter will

discuss the literature about IT diffusion and its progress in health care. This analysis

will present some of the basic IT diffusion models and measurements along with

studies about the current state of IT in health. This will provide grounding for the

methods developed in Chapter 3.

2.2. Innovation Adoption Theory The process of innovation adoption and diffusion is the subject of this study. The

diffusing technology is IT and the organisations compared for their adoption of IT are

health care and banking. This section will therefore set the scene by examining the

literature on innovation diffusion, determine the major concepts, review the major

theories and examine examples from health and finance. The conclusion of this

section will be a conceptual framework used throughout this study.

2.2.1. Introduction to Innovation & Change A number of definitions of innovation exist, including:

Innovation is a process involving producers and consumers in a dynamic interaction involving the forces of technological change and the forces of individual choice (Silverstone & Haddon, 1996); and


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An innovation is an idea, practice or object that is perceived as new by an individual or other unit of adoption (Rogers, 1995).

Therefore, the key concepts about innovation are the acceptance of new ideas or

changes by a “consumer.” In this study, the consumer is the executive who is making

the decision to adopt new IT in his/her organisation. Many writers look at the impact of change, especially technology changes, on

organisations (Rosegger, 1991; Grove, 1996; Slywotzky, 1996; Hamel, 1998). In

particular, impacts on the existing social structures have been examined and a number

of interactions between organisations, social factors and change identified (Zuboff,

1988; Rosegger, 1991; Rogers, 1995; Kotha, 1998). Organisations have to adapt to

handle these changes, however, organisations resist change and only change when a

range of necessary preconditions are in place (Toffler, 1985; Meyer & Gardner, 1992;

Snull, 1999):

Due to the complexity of innovation and change in organisations, a number of topics

are relevant to this study. These include beliefs about IT-led change, organisational

culture, organisational flexibility and acceptance of change and ability to manage

change. These factors have been synthesised into a body of knowledge known as

Innovation Diffusion Theory (Rogers, 1962; Rogers, 1995).

To underpin this study, a conceptual framework based upon Innovation Diffusion

Theory will be developed oriented to the process of IT innovation adoption in state-

owned health care organisations. Rogers’ (1995) work will be used as the basis for

this framework.

Any business organisation has limited capacity for adopting innovations by investing

in new activities and must apply processes for evaluation and decision-making

(Parker, Benson, & Trainor, 1988). This decision making process can be complex as

resources are limited whilst the design and selection of technical systems is a complex

social process (Parker et al., 1989; Hogbin & Thomas, 1994; Mansell, 1996; Mansell

& Silverstone, 1996a; Mansell & Silverstone, 1996b). Innovation Diffusion Theory

looks at this social process and the role of technology in adoption decision making.

LITERATURE REVIEW

Page 17

To review innovation and change it is necessary to look at how organisations accept

new ideas and absorb them into their everyday being. The basic theories in this area

come from the innovation diffusion literature and these are then supplemented by

work on change, the politics of change and the social impacts of change.

About innovation diffusion Innovation diffusion is a special type of communication. The communication is about

a new idea and therefore brings with it some uncertainty. Diffusion leads to social

change in which the structure and function of a social system are altered (Rogers,

1995). Rogers notes that the rate of diffusion of most innovations, even with obvious

benefits, is slow. He continues by explaining that diffusion is a process through

which an innovation is communicated by certain channels over time among members

of a social system. Most people depend upon a subjective evaluation of an innovation

conveyed to them by individuals like themselves who have previously adopted the

innovation. As decision-making about the acceptance of an innovation is a social

process, innovation decisions are only partly judged on economic grounds, leading to

outcomes that show non-rationalist factors. For example, innovation can confer

status, which is sometimes a major influence. This prestige-conferring ability has

been shown to lead to over-adoption (Scannel, 1971).

Rogers (1995) gives five main steps in the innovation decision process (shown below

in Figure 2-1 The Innovation Process), being:

1. Knowledge, influenced by socioeconomic character, personalities and

communication patterns;

2. Persuasion, influenced by the innovation’s advantages, compatibility,

complexity, trialability and observability;

3. Decision;

4. Implementation;

5. Confirmation.


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18

The basis of the diffusion process is the modelling and imitation by adopters of their

near-peers' experiences with the adoption.

Innovation research looks at both the social groups involved in diffusion as well as the

nature of the innovations. An example, telemedicine, which began in the 1920s with

radio-based consultations to passenger liners, is still being adopted very slowly due to

social and technical reasons (Moore, 1999). Most innovation research has been done

on the social side. In this research project, consideration will be given to both the

social view and the technology view to assess each of their impacts. The following

sections review the literature for the social processes of innovation in organisations

followed by the findings about the impact of the nature of the technology innovation.

Innovation in organisations Organisational innovation is much more complex than individual innovation. Rogers

(1995) identifies a number of factors that affect innovation in organisations. These

include:

• Size: larger organisations tend to be more innovative. This is thought to be

because they have more resources and spare capacity to trial things, as well as

more sophisticated organisation structure and higher technical expertise within

the staff;

Figure 2-1 The Innovation Process, adapted from Rogers (1995)

1. Know ledge 2. Persuasion 3. Decision 4. Implementat ion 5. Confirmation

Adoption Rejection

LITERATURE REVIEW

Page 19

• Technical expertise: it has also been found that technical experts are more

likely to adopt radical innovations than the average decision-maker is;

• Openness of structure: this relates positively to speed of diffusion;

• Formalisation of organisation and process: this has a negative impact;

• LEADER CHARACTERISTICS WITH THE LEADER’S ATTITUDE TO INNOVATION HAVING A STRONG INFLUENCE;

• Internal organisational structure; and

• External characteristics of the organisation. These are depicted Figure 2-2 below which shows the variables and their impact

either positive or negative on the level of innovativeness.


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Figure 2-2 Variables of Organisational Innovativeness, from Rogers (1995)

browna2

This image is not available online. Please consult the hardcopy thesis available from the QUT Library.

LITERATURE REVIEW

Page 21

A number of studies have confirmed the usefulness of Rogers’ model. One study

found the innovativeness of local health departments in California related to their size,

based on staff and budget; the size of the city served and its cosmopolitan-ness; and

the accreditation and prestige of the health director among his or her peers. However,

the overall size of the community and the size of health department appeared to be the

main drivers (Mytinger, 1968). A further study, an evaluation of the adoption of

medical technology in 25 hospitals in the Mid-West USA, found decisions were based

on the perceived attributes of the innovation and the hospital organizational

environment (Meyer & Goes, 1988).

Diffusion and the nature of the innovation Rogers (1995) presents 5 characteristics of the technology that influences its rate of

adoption:

1. The relative advantage of the innovation over existing technologies;

2. The compatibility of the innovation with the current environment, including

compatibility with values, beliefs and past experiences;

3. The complexity of the innovation;

4. The trialability of the innovation which is the ability to test out an innovation

before fully committing; and

5. The observability of the innovation, which is the ability to see the innovation

elsewhere.

These five characteristics should be measured by the perceptions held by the adopters.

Figure 2-3 below, represents all of Rogers’ (1995) factors combined.


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2.2.2. Critique of the Theory Rogers (1962; 1995) work on innovation appears as a mainstream foundation in

innovation research. Roger’s version of Innovation Diffusion Theory has been used

as a framework in health (Mytinger, 1968; Awad, Engelhardt, Coleman, & Rogers,

1984; England, Stewart, & Walker, 2000; Titler & Everett, 2001; Wieringa, Denig, de

Graeff, & Vos, 2001; Dooks, 2001; Berwick, 2003; Barth & Hansel Sherloick, 2003;

Pronk et al., 2003); IT (Bayer & Melone, 1989; 1994; Van Akkeren & Cavaye, 1999;

Lyytinen, 2001; Mustonen-Ollila & Lyytinen, 2003; Al-Gahtani, 2003), health IT

(England et al., 2000; England, 2001; Ash et al., 2001; England & Stewart, 2003;

Gosling et al., 2003; Gladwin et al., 2003) and for other organisations and

technologies (Lundblad, 2003, Gladwin et al., 2003)

Figure 2-3 Innovation Adoption Influences, derived from Rogers (1995)

LITERATURE REVIEW

Page 23

This general acceptance of Roger’s work sits across a vast domain of innovation

research and the criticism of it tends to be concerned with specific details or its

applicability to certain unique situations (Lundblad, 2003). As a general, overarching

theory Roger’s work, itself based upon over 1000 other innovation studies, is well

accepted. Authors have commented on assumptions within Rogers’ model, which is

inherently linear. Linear models of innovation diffusion are being challenged

requiring innovation theory to appreciate that living systems are dynamic, non-linear

and inherently unstable (Mansell, 1996). Others also find that technological

improvement in organisations is not a smooth process but follows a "lumpy or

episodic pattern.” The initial introduction of a new technology appears to be

followed by a "burst of adaptive activity" which is often short-lived before, new

technologies are taken for granted and stability follows (Tyre & Orlikowski, 1993).

This pattern does not match the usual “S” curve distribution of uptake shown by

innovation diffusion theory.

Others find that most innovation research and frameworks include a pro-innovation

bias related to an “efficient-choice” perspective underlying Innovation Diffusion

Theory which claims organisations independently and rationally adopt innovations

(Abrahamson, 1991). Abrahamson identifies other factors such as forced adoption

and fads/fashions. Recognising the validity of Abrahamson’s position, other factors

will be captured in the design for this current research project, in particular the forced

adoption perspective will be investigated through the environmental/policy part of the

framework, and the fashion and fad parts through interviews about influences on IT

investment decision making.

One area of concern to this study is the impact of the external environment on

innovation, which is not highlighted in Rogers work. However, researchers have

previously commented on the impact of society upon health delivery (Hibbard, Jewett,

& Legnini, 1997; Bailit, 1997; Chassin, 1998; Sisk, 1998). Therefore, due to the

profile and political nature of Australian health care the impact of societal

expectations on innovation is a topic to be investigated through this research. Porter

(1980) provides comprehensive models for assessing the impact of environment upon

strategy. These include assessments of strengths versus weaknesses, opportunities


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versus threats and leader attitudes as determinants of strategy with competitive

pressures being applied by suppliers, competitors, funders, clients and legislators.

However, in their entirety, these are too sophisticated for the current level of

theoretical understanding of innovation in health care. Therefore, aspects of Porters’

work will be integrated into the conceptual framework thereby enhancing Rogers’

theory. Rogers’ and Porter both include leader attitudes and these have already been

incorporated into the framework. The aspects that will be incorporated from Porters’

work is those areas which exert pressure on the organisation. Porter names these

determinants of competition, however this label does not really apply to government

owned health care, however, the pressures exerted are likely to be real and need to be

explored. Therefore, the framework will include policy/environmental pressures from

stakeholders including suppliers, funders and government.

Additional theoretical perspectives Additional innovation diffusion theoretical perspectives have been proposed.

Examples include:

Tzokas & Saren (1992) review the work of Robertson and Gatignon (1986). They

find that new product development and marketing activities by suppliers should be

taken into account in innovation research. However, although this is presented as a

new direction in innovation research, it appears instead to be a restatement and

refinement of the role of the change agent and the technology factors of innovation

adoption as identified by Rogers.

McDade, Oliva et al (2002) find that organisational size is a key determinant of

innovativeness when looking at the adoption of high-technology products. This aligns

with Rogers’ assertions of the drivers of organisational innovativeness. However,

McDade, Oliva et al modify this by assessing radicalness of innovation and overall

organisational preferences. They find that this leads to a mismatch between an

organisation’s expressed preferences and actual purchases. They believe that this

shows that adoption is often a process of compromise that recognises that greater

complexity of compared with individuals. This seems to offer a valid extension to

Rogers’ higher-level views.

LITERATURE REVIEW

Page 25

Innovation Summary & Derived Framework Rogers’ models of technological and organisational attributes that drive diffusion

rates provide a framework for structuring ideas and research about the significant

variables. The view of environmental/policy impact on innovation is less clear.

Overall, Rogers’ work provides a comprehensive model that is compatible with the

other innovation studies examined. Therefore, the following table summarises the

main factors of Rogers’ model:

Technological Attributes Of

Innovation Rate

Organisational Attributes of

Innovation Rate

Relative advantage over

existing technologies

Size

Compatibility Technical expertise

Complexity Openness of structure

Trialability Formalisation of organisation

Observability Leader characteristics

Internal organisational structure

External characteristics of the

organisation

These factors form the fundamental framework of this research and will be assessed

through the research to gain a view about IT and health care and the features of each

that determine their diffusion rate. However, in addition, this study will also explore

societal expectations and the policy environment.

In addition, Rogers’ model has been integrated with some of Porter’s competitive

forces to form the “Organisational Innovation Framework” shown in Figure 2-4

below. Porters’ competitive forces, in the form of supplier involvement, funders, and

government have been incorporated as the Environmental/Policy arm of the

framework. This framework provides a simplified and conceptual model of the major

factors in their works. This will be used as the structure against which the remainder

of the literature review and the research study itself will be carried out.

Table 2-1 Summary of determinants of innovation rate

Page 26

Figure 2-4 Theoretical Framework

Technology

Organisation

Relative Value Compatibility Complexity Trialability Observability

Leader Attitude

Formalisation

Interconnectedness

Openness

Size

Slack

OrganisationalDemand/Blockage

TechnologyAttractiveness

Attitudeto

IT adoption

LeaderActions

Complexity

Environment/Policy

Society'sExpectations

Freedom toAct

Adopt / DeferReject

Centralisation

LITERATURE REVIEW

Page 27

2.3. The Features of Health Organisations2 The nature of an organisation is significant to its adoption of innovations (Rogers,

1995). The conceptual framework has identified a range of attributes that are

significant to organisational innovativeness. These factors (indicated in Figure 2.4,

above) are leader characteristics, centralisation, complexity, formalisation,

interconnectedness, organisational slack, size and external openness. Studies that

specifically assess these attributes in health care organisations have not been

identified nor does there appear to be any work comparing the innovation

characteristics of health to other industries. However, there is a large body of

literature describing aspects of each innovation factor. Therefore, this section of the

literature review will discuss the literature about the organisational factors with an

emphasis on health whilst using banking as a contrast. This review will follow the

structure of the theoretical framework, which will then be used to synthesise and

summarise the information before drawing conclusions about the likely innovation

characteristics of the health industry (England et al., 2000). These conclusions will

then assist this research in several ways, including, acting as basis for triangulation of

the findings in Studies One and Two, and informing the interview process in Study

One.

Of concern to this project is the predominance of US studies compared to those from

other locations. In particular, research literature on organisational issues and

innovation in Australian and New Zealand health is sparse. There is adequate factual

and statistical documentation on the structure, size and performance of Australia’s

health industry (AIHW, 1995; AIHW, 1998a; AIHW, 1998b; Stewart & England,

2002) however, organisational theory and research is scarce. Therefore, in this

review, US literature has been used when no Australian/New Zealand version is

available. Where there are reasonable issues in interpretation or generalisation caused

by this, it will be indicated and discussed.

2 The article England et al (2000) relates to this section and is provided in the appendices. This article provides an analysis of health’s organizational profile in regards to innovation and draws conclusions relevant to this thesis.


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2.3.1. Leader Characteristics Leader characteristics in Rogers’ (1995) model relate to the beliefs the key managers

hold towards innovation. When management is innovative, the organisation tends to

follow; when management is not innovative neither is their organisation. Therefore,

the attitudes of the executives towards IT led change are likely to be significant to the

way they embrace or resist such changes.

An Australian study provides a thorough insight into the attitudes of the key staff in

the health care sector (Degeling, Kennedy, Hill, Carnegie, & Holt, 1998). It described

the professional sub-cultures of medical clinicians, medical managers, nurse

clinicians, nurse managers and lay managers and found some distinct cultures within

these professional groups.

Staff with a medical background were relatively unaware of the power differentials in

health yet readily accepted and used the power available to them in the existing health

structure. It also appeared that medical trained staff believed these power differentials

were “natural, necessary and rightful.” In contrast, nursing related staff were aware

of their subordinate position within health organisations but did not accept that such a

power differential was necessary. On another dimension, clinical staff members view

their work as a vocation with emphasis on its experiential and social nature. This

contrasts with management staff members who see their work as part of a career and

view their work as instrumental and calculative.

This study describes aspects of the overall culture in Australian health organisations

though a full analysis is out of its scope. However, the cultural aspects of Australian

health described above may be significant in the approach to IT adoption. These will

be analysed through the leader characteristics, centralisation, formalisation,

interconnectedness and external openness aspects of this study, allowing some

assessments of the culture and its impact to be made.

The above findings lead to interesting conclusions when combined with Zuboff's

(1988) view of the changes caused by IT in organisations. Zuboff conducted a highly

regarded sociological study of the impact of IT on organisations and their workers.

To explain her beliefs, Zuboff defines a new word, “informating.” This word refers to

LITERATURE REVIEW

Page 29

the way that the application of IT to a traditional process can also generate significant

management and measurement information. She finds that much of the impact of IT

comes from changes such as informating and the tendency to structure work into

process. This seems to clash with the findings about the cultures of the medical

clinicians and medical managers whilst supporting the lay managers. Medical

clinicians and medical managers beliefs would appear to clash with the idea of

centrally-controlled systems coordinating practice and imposing processes, although

their culture would seem to support stand-alone, closely-controlled systems within the

clinicians’ control. Nurse clinicians and nurse managers would appear to be culturally

aligned with process-oriented systems that lead to greater teamwork. In addition, the

existing power structure, readily accepted by medical staff and challenged by nursing

staff, is likely to be affected. These types of impacts and the resulting tensions have

been found to exist elsewhere and it has been observed that such significant levels of

change facilitated by IT are often resisted (Mansell, 1996). This sets a scene in which

the adopting managers may perceive a political and cultural environment in which IT

is difficult to implement.

A further facet of leadership characteristics is evidenced by an organisation’s culture.

The impact of culture on IT use and success has been investigated for many years

(Mason & Mitroff, 1973; Ein-Dor & Segev, 1978; Ein-Dor & Segev, 1982). Early

thinking suggested that only innovative, risk-taking firms would make aggressive use

of IT (MacMillian, 1982; Segars, Grover, & Kettinger, 1994) however, Grover,

Segars and Durand (1994) assessed the impact of culture upon IT usage and one

finding was that education levels, government structures and resources were key

determinants of organisational practice compared with sociological dimensions. It is

likely that senior banking and senior health managers will all be highly educated, and

as the focus of this study is Australia, government structures will be constant.

Therefore, the cultural element most likely to be identified in this research will be the

availability of resources. This was addressed in the conceptual framework through

measurements of size and organisational slack. Grover, Segars and Durand (1994)

also found that the use of IT as an operational tool rather than a strategic resource

seemed to be directly related to the cultural dimensions of uncertainty avoidance.


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Other writers have looked at the specific aspects of culture in health care. Australian

health is characterised by complexity, technology, multiple agendas, and diverse

funding arrangements (Braithwaite, Lazarus, Vining, & Soar, 1995). In addition,

medical training teaches doctors to be the only important decision-makers, not part of

a team. Doctors are expected to know the right answers to all questions of clinical

care and to work without error. Clinicians therefore challenge support systems such

as practice guidelines calling them "cookbook medicine" (Chassin, 1998; Degeling et

al., 1998).

Deluca and Enmark Cagan (1996) see health organisations’ culture influencing IT

through two major influences. First is the attitude to historic investments can be

either flexible or inflexible. With a flexible attitude, the organisation considers IT

investment as largely expendable. Those with an inflexible attitude believe that the

organisation must preserve its IT investment. Secondly, they see that management

disposition to risk is a key factor. Using these two influences, they therefore find four

categories of organisation, see Figure 2-5.

Figure 2-5 IT Strategic Disposition Model (DeLuca & Enmark Cagan, 1996)

browna2

This model is not available online. Please consult the hardcopy thesis available from the QUT Library.

LITERATURE REVIEW

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Therefore, management’s attitude to risk is again highlighted as a cultural factor that

will influence IT adoption. It has been found that when firms view the levels of

investment and associated risks involved with IT projects as unacceptable then IT is

relegated to a supporting role within the organisation's plan (Vitale, 1986).

Most of the literature identified considers IT in commercial enterprises with a

competitive orientation and profit goals. However, not-for-profit organisations have

significant differences (Herzlinger, 1996). Non-profit organisations lack basic

accountability mechanisms of other businesses. The not-for-profit status of

government health care organisations can be expected to have an impact on the

leaders’ view of innovation. If the measurement and accountability processes are

different, then it seems reasonable to expect different behaviours.

The governance structure and processes surrounding IT are also key indications of the

values held by the key leaders in the organisation. Governance is important to ensure

that the multiple stakeholders across the organisation are fairly represented and the

diverse needs balanced (Boar, 1997). No literature has been identified looking at the

governance structures used in Australian health care IT, however, this can be readily

researched by addressing topics such as centralisation and formalisation.

A final area being reviewed concerning the leaders’ values is their involvement with

IT in their organisations and area that has been well covered by the literature. A

common set of views is the need for management’s involvement in IT ( Davenport,

Hammer, & Metsisto, 1989; Reponen, 1994; Zhao, 1995; Gates & Hemingway, 1999,

Grindley, 1999; Sauter, 1999). Having established the importance of senior

management’s involvement with IT, a number of writers then comment about the

impact of senior managers. One study finds that autocratic management may inhibit

the development of more innovative and market-oriented IT applications. In the U.S.,

a more competitive culture and lower acknowledgement of differences in

organisational-power or status, means more managers are involved in IT selection and

development, which influences the portfolio of applications developed resulting in

market impact versus firm impact (Grover, Segars, & Durand, 1994). Whether health

management is more autocratic than those in higher IT adopting Australian

organisations has not been.


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2.3.2. Centralisation Centralisation is the measure of how tightly an organisation is controlled by its core

management. Decentralised organisations tend to be more innovative than centralised

ones (Rogers 1995). Mansell (1996) argues that the selection of advanced

technologies is made through the exercise of power, though one of the questions this

raises for this research is who has the power and who is exercising it? Whether this

decision-making power in the health sector is centrally controlled or decentralized is

not apparent in the literature.

Whilst no formal measurements of the centralization or decentralization of health

management have been identified, Braithwaite et al (1995) using Australian health as

their frame of reference, contend that clinicians currently place their own agenda and

that of their profession over the needs of their customers. They reinforce this by

commenting on a lack of strategic thinking amongst clinicians and health executives

and by talking about the disunity of purpose in hospitals. They state that hospitals are

professional bureaucracies with a dual hierarchy. The hospital hierarchy has a

managerial one, which is a hierarchical pyramid divided into divisions, such as

medicine, nursing and administration. The second hierarchy is within the clinicians’

professional groups. The management hierarchy looks after the business activities of

the hospital; the professional hierarchy looks after the management of the patient.

They explore how the two hierarchies have conflicting objectives, such as cost

efficiency and economy of scale versus quality of care and the use of state-of-the-art

technology. The allegiance of doctors to their hospital tends to be low, and doctors

tend to identify more with their profession and even their specialty sub-group. This is

different from most organisations that have a single management, power and authority

hierarchy. When combined with the work on professional sub-cultures (Degeling et

al., 1998), it becomes clear that making decisions and managing change in hospitals is

a difficult process. Therefore, one may assume that whilst there may be a formal, and

perhaps centralised, management hierarchy, in effect hospitals offer a fragmented,

‘tribal’ structure. The impact of this disunity of purpose and health’s complex power

structure would appear to be an important factor in decisions about innovation

adoption. Applying Rogers’ findings of the positive correlation between

LITERATURE REVIEW

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decentralisation and innovativeness, one may conclude that cross-organisational,

enterprise-wide innovations will move slowly in health, whilst smaller innovations,

within the domain of a sub-culture should flourish.

2.3.3. Complexity As noted in Rogers’ (1995) work, the more complex an organisation is then the more

innovative it tends to be. This is thought to be due to increased complexity leading to

increased demands for new ways of doing things.

One study notes that few organisations today are as complex as a modern hospital.

This complexity is claimed to have passed the stage where even the most brilliant

executive can keep the complete business model in mind (Degeling et al., 1998). In

addition, the Australian health care industry has a complicated organisation at a

national level (AIHW, 1995; AIHW, 1998a; Stewart et al., 2002). This complexity is

at an economic level, with a dual public/private structure; at an organisational level,

with multiple states having differing structural implementations; and at a funding

level where Medicare, Commonwealth and State structures add significant

complexity. Overall, it is reasonable to assert that Australia’s public health care

industry is complex. Using Rogers’ analytic framework, it would therefore be

expected that hospitals are highly innovative.

2.3.4. Formalisation Formalisation is the measure of how rigidly an organisation is controlled by formal

rules and procedures. Rogers’ (1995) notes that increasing formalisation leads to

reducing levels of innovation.

Another indication of the formality of an organisation can be seen through its internal

quality management systems. It is claimed, in the US at least, that health care has far

poorer levels of quality than is found in other industries (Wennberg, Barnes, &

Zubkoff, 1982; Chassin, 1998; Kohn, Corrigan, & Donaldson, 2000; Institute of

Medicine Committee on Quality of Health Care in America, 2001). Health has

developed systems and processes that rely on people performing with a level of

perfection that is not possible whilst other industries have devised systems and


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34

processes that compensate for human frailties (Chassin, 1998). This suggests,

therefore, that health is less formal in its approach to internal processes than other

industries.

One study compares the formality of health with that of aviation (Helmreich, 1997),

noting that society expects more formality and less creativity from other professions

that manage our personal safety. For example, pilots are trained to use checklists,

operate as team members and are trained to avoid human error.

Applying Rogers’ models to the views about the lack of formal processes of Chassin

and Helmreich provides an indication that health should have greater levels of

innovation than other industries. However, it needs to be assessed at what level this

lack of formalisation occurs. The above examples all look at clinical processes and

clinical freedom. This research needs to identify the level of formalisation around IT

management processes before any conclusions can be reached.

2.3.5. Interconnectedness Interconnectedness is a measure of the way the organisation communicates within its

internal groupings. Highly interconnected organisations tend to be more innovative

(Rogers 1995).

As noted earlier, Braithwaite et al’s (1995) comment about the disunity of purpose

within Australian hospitals suggests that in many ways hospitals have low levels of

interconnectedness. Others find that health is an open organisation with collegial

patterns of control rather than a rigid hierarchy (Martin, 1987). Most doctors attend

collegial management meetings but are not invited to formal management meetings

and seem to be outside the planning process. Doctors also appear to be outside the

health system value structure that is aiming for cost-effectiveness in the face of

constrained resources. The study also notes that physicians are perceived to value

their professional role more than their societal role within the health organisation.

Overall, it is concluded that this professional approach has inhibited the ability to

achieve cross-boundary solutions (Braithwaite et al., 1995).

LITERATURE REVIEW

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The literature therefore points to a low level of interconnectedness within hospitals,

with barriers between professional groups and particularly between the doctors and

the formal management. This would be expected to lead to lower levels of

innovation, especially those that are enterprise-wide.

2.3.6. Organisational Slack Organisational slack is the measure of the amount of spare resources available in an

organisation. Higher levels of slack lead to higher levels of innovation, as it is then

possible to allocate resources for innovation (Rogers 1995). No literature has been

identified showing the level of slack in health or other industry sectors.

2.3.7. Size Rogers’ model (1995) shows a positive link between organisation size and

innovativeness. Size has also been found to be an important determinant in IT usage

(Clemons & Row, 1991; Segars et al., 1994). Government budgets show that by any

standards, health organisations tend to be large (AIHW, 1995; AIHW, 1998a), so this

should be an enabling factor to innovativeness.

2.3.8. External Characteristics of Organisation (Openness) Rogers’ model (1995) finds that the more open an organisation is to the outside world

the more innovative it is, in part due to its exposure to a wider range of ideas.

No literature has been identified that specifically comments on the openness of health

to outside environments. However, a number of studies, including Australian

research, have found that health professionals move within collegial groupings

(Martin, 1987; Braithwaite et al., 1995; Degeling et al., 1998). It could therefore be

expected that the health professionals are relatively poorly connected with the

environment outside of their collegial group. This would lead to lower levels of

innovativeness, particularly concerning new ideas diffusing from other economic

sectors.


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36

2.3.9. Conclusions about Health Organisations Rogers’ (1995) theory on the factors influencing the rate of diffusion of innovations is

being used as the framework for assimilating all the organisational literature. In

support of the use of Rogers’ models, it was found in an earlier study that software

adoption could not be divorced from organisational context (Sillince & Frost, 1994).

Table 2-2, following, restates Rogers’ (1995) theory on the factors influencing the rate

of diffusion of innovations.

Organisational

Factor

Impact

Leader characteristics

The willingness of the leaders to innovate directly determines

the rate at which the organisation innovates.

Centralisation The degree to which power is vested in a few individuals is

negatively associated to innovativeness

Complexity The level of knowledge and expertise held by members of the

organisation. Complexity encourages innovation.

Formalisation The degree to which an organisation emphasises rules and

procedures impacts on innovativeness. High formalisation

stifles innovation.

Interconnectedness The degree by which units in the social system are linked by

interpersonal networks. High interconnectedness leads to high

innovation.

Organisational slack Spare capacity within the organisation allows time and

resources to trial new ideas

Size Larger organisations tend to be more innovative

External characteristics

of the organisation

The openness of the organisation’s systems to the outside

increases innovativeness.

This review leads to the conclusions regarding these factors in regards to health

organisations shown in Table 2-3, below

Table 2-2 Rogers’ organisational factors

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Organisation

Factor

Features identified Impact

Leader

characteristics

No reviews of leaders and leadership style of health organisations were

identified.

Senior management’s involvement in IT projects is seen as essential for

their success (Reponen, 1994; Gates et al., 1999).

74% of senior executives are positive about IT (Grindley, 1999)

Executives tend to focus on the cost of IT rather than the benefits

(Schwartz, 1992).

Medical managers believe in power structures, are independent, believe in

two lines of responsibility for administrative and clinical issues and deny

institutional shortcomings as explanations of practice variation (Degeling

et al., 1998).

Lay managers prefer a formal organisation, believe in a single line of

responsibility and want transparent systems of accountability (Degeling et

al., 1998).

Leaders may feel threatened by IT as they are not able to keep up with the

technology and changes it creates (Mansell et al., 1996b;.Schneider &

The innovativeness of managers in health and other

organisations is not understood. This needs to be

investigated.

The disunity of purpose found in health creates a

special challenge for health organisations. Physicians

have not been quick to adopt IT and maintain strong

leadership positions in health.

The involvement of health executives in IT and IT

executives in the health business plan are unknown.

Table 2-3 Organisational factors derived from the literature


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Bowen, 1995)

Medical training teaches doctors to be self-reliant, not work as team

members and reject support systems (Chassin, 1998).

Managers in non-profit organisations lack the basic accountability

mechanisms of self-interested owners, competitors and profit measures

(Herzlinger, 1994; Herzlinger, 1996).

Autocratic management stifles the innovativeness of lower managers

(Grover et al., 1994).

IT management needs to be involved in corporate strategy to eliminate the

disappointment in IT’s performance (Parker et al., 1988; Hogbin et al.,

1994).

Centralisation The literature does not specifically review the centralisation of power

within health organisations.

There are findings of disunity of purpose, strong sub-cultures and the role

of doctors outside of the management (Braithwaite et al., 1995; Degeling

et al., 1998).

Decentralisation encourages innovation. However, in

health this appears to be a complex issue to

determine. Management appear to believe in

centralised control however, the clinical groups

appear to have their own structures and

responsibilities. Where the actual control lies is

unclear.

Complexity Hospitals are complex, use high technology, suffer from disunity of This finding should point to a high level of

LITERATURE REVIEW

Page 39

purpose and have complex funding arrangements (Braithwaite et al.,

1995).

organisational innovativeness.

Formalisation No specific documents were identified commenting on the level of

formality in health organisations compared with other organisations.

Formalisation has a negative impact on innovativeness.

Health lacks the formal quality systems found in other industries

(Chassin, 1998).

IT needs formal measurements, management and definition to perform

well (Allen, 1987; Boar, 1994; Strassmann, 1996; Weill and Broadbent.,

1998).

The comparison of this factor to other industries is

unknown and needs further research.

Interconnectedness Disunity of purpose leads to silos and a fragmented organisation

(Braithwaite et al., 1995).

Clinicians and medical managers tend to believe in the separation of

clinical and administrative responsibilities (Degeling et al., 1998).

Nurses tend to believe in team-based approaches (Degeling et al., 1998)

Physicians value their professional role more than their societal role in the

health organisation (Martin, 1987).

Doctors tend not to be invited to management meetings and appear to be

outside the planning process as well as the value structure that aims for

The literature points to a very fragmented

organisation with “disunity of purpose” and

conflicting styles, cultures and agendas amongst the

participants. This will lower the level of

innovativeness.


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cost-effectiveness (Martin, 1987).

Organisational

slack

No literature was identified showing the level of internal slack in health or

other organisations.

No conclusions can be drawn about the impact on

innovativeness.

Size Large organisations tend to be more innovative (Rogers, 1995)

The level of innovativeness of U.S. health departments was found to be

related to the size of the community supported and their staff and budget

levels (Mytinger, 1968.

The effectiveness of IS is linked to the quantity and quality of

technological resources (Keen, 1991)

Health organisations tend to be large, especially state

health departments. Therefore, these organisations

should be more innovative than smaller

organisations.

External

characteristics of

the organisation

External pressures, via funding, is forcing health to review the way it

looks at service delivery (Gold, 1999).

Non-profit organisations do not receive the same external guidance that

for profit organisations receive from shareholders and competitors

(Herzlinger, 1994).

Many strategic uses of IT are driven by the need to keep up with

competitors (Reponen, 1994)

Health professionals appear to align more with their profession than the

health care organisation that employs them (Braithwaite et al., 1995).

The interconnectedness of health with external

organisations is not clearly recorded. Certainly,

health workers are well connected within their

professions, however, if according to Braithwaite et

al (1995) this is more of an internal organisation

connection than an external one so may not be a

strong source of new ideas.

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2.4. The Features of Health IT The focus of this study is the adoption of IT in health. The conceptual framework has

identified the attributes of significance to technology’s diffusion. These factors are

relative value, complexity, compatibility, observability and trialability. Studies

assessing these attributes across health IT have not been identified. However, there is

health informatics literature that describes the factors. Therefore, this section will

review the literature about IT factors with an emphasis on health, using banking as a

contrast. This review will follow the structure of the theoretical framework, which

will then be used to synthesise and summarise the information and to draw

conclusions about the likely innovation characteristics of health information

technology (England et al., 2000).

2.4.1. Relative Advantage The relative advantage of an innovation is the degree to which it delivers benefits

compared to the status quo. Innovations with a high relative advantage compared to

the current environment diffuse more rapidly (Rogers, 1995).

There is a broad range of literature about IT’s relative advantage; however, few

studies have performed sophisticated cost-benefits analyses of hospital technology,

including IT (Braithwaite et al., 1995). Therefore, the literature will be reviewed on a

wider scope to show economic benefits, productivity analysis and the general value of

IT. The literature on IT benefits is divided, one school of thought claiming little or no

value returned from IT investments, the other school claiming significant value.

(a) The case for the low value of IT Those claiming little or no value assert that computer spending and business

performance are unrelated or spending produces a low level of return (Bowen, 1986;

Loveman, 1994).

Strassmann (1997b) concludes that it is not how much an organisation spends on IT

but how the technology is used that makes the difference, coining the term


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information productivity to describe the benefits delivered by IT versus the

expenditure made. At a macro-economic level, there is a whole body of argument

around IT productivity. Nobel Prize winning economist Robert Solow (1987) said

"We see computers everywhere but not in the productivity statistics.” This is a comment on the assertion that national productivity statistics do not show the

benefits of computers. This lack of obvious link between economic productivity

measures and IT spending is labelled “The Productivity Paradox.” Evidence of the

existence of the productivity paradox was found in the measurement that over 40% of

all U.S. capital investment goes into IT yet few firms have gained major productivity

improvements (Davenport & Short, 1990; Sichel, 1997). A number of studies and

writers have noted that the real value of IT is only released with significant effort and

when organisations reengineer their processes and downsize (Vitale, 1986; Davenport

et al., 1990; Schwartz, 1992; Cortada, 1997; Strassmann, 1997a; Thorp, 1998)

Studies that assessed health IT showed that managers are unable to measure the value

of their IT and regard it to be an unknown quantity (CSC, 1998; Kimball-Baker, 1998;

CSC, 1999). Further studies find executives are disappointed with IT and a view that

IT has failed to save money or add to competitive advantage (Gupta & Collins, 1997;

Grindley, 1999).

(b) The case for the high value of IT The productivity paradox has been subjected to review and challenge (Brynjolfsson,

1994; Brynjolfsson et al., 1996a; Brynjolfsson & Yang, 1996b). Research has found a

range of results. More recently, researchers have found productivity improvements

and other significant measures such as economic growth, yet consensus on IT

productivity is yet to be reached. It has been questioned whether better measurements

would show IT making a higher contribution (Nolan & Croson, 1995) as in one study

only 44% of companies could successfully measure IT’s contribution to the bottom

line (A.T.Kearney Inc, 1997).

LITERATURE REVIEW

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Weill (1992) found that IT productivity could be broken down by type of use.

Investments in basic transaction processing (such as payroll) were found to yield

significant returns whilst he could not identify returns from strategic or informational

systems. However, there appears to be agreement that that many of the benefits of IT,

such as customer service, quality and range of offerings, are qualitative and intangible

(Licht & Moch, 1997; Sichel, 1997). These gains, therefore, are not picked up by

output statistics, meaning that many of the gains of computers are not measured (van

Nievelt, 1993; Brynjolfsson, 1994).

A number of studies concluded that initial gains in competitive performance were

sufficient and sustainable enough to justify the corporate resources necessary for

planning, developing and implementing innovative IT (Segars et al., 1994; Hogbin et

al., 1994; Whaling, 1996; Thorp, 1998; Weill et al., 1998). This positive view of IT

was supported in a study that found 74% of senior executives were positive about the

return on IT investments (A.T.Kearney Inc, 1997). Writing specifically about health

IT, a number of authors identify benefits from IT in health, though these appear to be

speculative rather than actual. DeLuca and Enmark Cagan (1996) gave typical returns

on investment, see figure 2-6, below (DeLuca et al., 1996).

Figure 2-6 Typical Returns on IT Investment in Health (DeLuca et al., 1996)

browna2

This image is not available online. Please consult the hardcopy thesis available from the QUT Library.


Page 44

Van Bemmel et al (1997) noted that benefits of health IT may be:

1. Non-quantifiable:

a. More complete and more accurate data;

b. Improved accessibility of data (i.e., data is easy to obtain);

c. Data becomes accessible for management purposes (i.e. the data has

more utility for management); and

d. Easier selection of cases for medical education and research.

2. Quantifiable benefits with non-monetary measurements:

a. Reduced time to produce results eliminating the need for urgent

reports;

b. Automated appointment systems may reduce waiting times for patients

and allow the combination of several appointments;

c. Automated nursing systems may reduce the time needed for data

recording; and

d. Digital imaging in radiology may reduce the time between ordering

and action being taken on the results.

3. Quantifiable, monetary benefits:

a. The reduction of stocks and reduction of loss of perishable goods; and

b. Faster invoicing and reduction of accounts.

More specifically, Handler (1998b) identified the benefits expected from a

computerised patient record as:

• Improved access and efficiency;

• Improved documentation;

• Improved clinical practice;

• Improved clinical science;

• Improved security;

• Reduced resource use;

• Decreased malpractice cases and insurance costs; and

• Improved bottom line.

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Another team wrote about clinical decision-support systems and gave the following

benefits:

• Improved patient care;

• Reduced costs;

• Dissemination of expert knowledge;

• Management of clinical complexity;

• Monitoring clinical details;

• Management of administrative complexity;

• Education of students & residents; and

• Support for clinical research

(Perreault & Metzger, 1999).

Gold (1999) summarises all of the above, claiming computers allowed better

communication of patient information thereby enhancing health delivery and

efficiency leading to the better use of information, the reduction in duplicate services

and the avoidance of adverse outcomes.

Studies of the strategic or competitive use of IT are just emerging. These studies

provide extensive evidence of the critical role of IT in corporate strategy (Segars et

al., 1994). At an industry level, strategic IT can change the very nature of the industry,

its products, offerings or services, for example McKesson’s Economist systems

(Clemons et al., 1988), ATMs (Brady, 1986), airline reservation systems (Doll, 1989)

and point of sale systems (Brady, 1986). However, for strategic IT initiatives to

succeed, the resources required to succeed must be able to be leveraged by IT

(Clemons et al., 1991). When IT can be leveraged, researchers have claimed

significant results (Mayne, 1986).

(c) Conclusions about the value of IT Much of the debate has focused on the quality of data used to evaluate IT’s value.

Whilst some studies found IT yielding significant gains, other studies continue to

question the real value of IT (Brynjolfsson et al., 1996a; Strassmann, 1997a).

This overall section on the relative advantage of IT leads to one main conclusion: that

the relative advantage of IT is a complex and uncertain topic. Whilst, on balance, it


Page 46

appears that IT does deliver benefit it is by no means certain, nor easy to predict or

measure, the level of that benefit. When factored with the types of use for IT, the

range of returns documented and the levels of risk, it may be assumed that

management has an uncertain belief about the relative value of IT. This is especially

true in health care where it has been noted that a lack of published studies exists. The

expected impact of relative advantage on the rate of IT diffusion is therefore likely to

be neutral.

The significance of the above discussion to this research is the way decision-makers

look at their IT expenditure. Do they believe there is a direct connection between

spending and benefit or do they understand that business alignment activities, such as

re-engineering, will be required to get a payback?

2.4.2. Complexity The more complex an innovation is, the slower it will diffuse (Rogers, 1995) and the

more difficult an idea is, the more likely it is to be “killed” (White, 1996). This

section will look at a number of IT-related issues to draw conclusions about its level

of complexity. This study has remained focussed on the complexity as it appears to

the organisation’s leaders rather than the technical complexity faced by the IT staff.

However, there can be little doubt that IT is a complex set of technologies requiring

specialist skills.

One of the major topics in the IT literature is the planning required for IT to ensure it

meets the goals of the organisation it supports. Reponen (1994) suggested the

objective of IT policy and planning is:

"Information management strategy is a long-term precept for directing, implementing and supervising information management.”

Much of the early research on IT planning has focused on frameworks for identifying

opportunities, creating managerial awareness and positioning the firm with respect to

its technological abilities and competitive opportunities (Earl, 1986; Earl, 1988;

Sillince et al., 1994; Boar, 1994; Boar, 1997). It is readily apparent that the planning

LITERATURE REVIEW

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and strategic use of IT is a highly complex and specialised process, yet it was

observed that senior managers often have only an intuitive understanding of the power

and potential of IS (Gupta et al., 1997).

The link between business needs and IT strategy has been one of the major concerns

of IT managers for many years (Kriebel, 1968; King, 1978; Pyburn, 1983; Parker et

al., 1988; Parker et al., 1989; Reponen, 1994; Hogbin et al., 1994; Boar, 1994; Zhao,

1995; Boar, 1997; CSC, 1998; CSC, 1999). Several writers noted that this linkage is

rarely achieved (McClean & Soden, 1977; Earl, 1986; Reponen, 1994).

This linkage was commonly referred to as alignment and was seen as a major factor in

the success and value of IT to an organisation Boar (1994).

Miller (1999) takes the complexity of alignment one-step further. He introduces a

factor called synchronisation. This is defined as keeping change-enabling

technologies and the pace of change synchronised to produce positive business

results. Therefore, for this study the need for alignment adds to the complexity of the

technology.

The conclusion that can be drawn from these comments about planning and alignment

is that IT requires sophisticated management skills, techniques and involvement if it is

to be relevant to the organisation. This is a strong indicator that the successful

adoption of IT is a complex process.

Benefits realisation is another significant area within the literature about IT

management. IT is implemented in the expectation of achieving benefits. Studies

showed that benefits are by no means automatic and that benefit realisation was a

continuing process (Thorp, 1998). A number of writers state that for benefits to be

realised, organisational change, such as reengineering is required (Davenport et al.,

1990; DeLuca et al., 1996; Strassmann, 1997b; Thorp, 1998).

These writers show that the achievement of benefits is not an automatic or simple

process and requires skills and effort. This can be expected to impact on the


Page 48

perception of the complexity technology providing another indication that IT is a

complex technology to manage in an organisational context.

Several writers address the critical success factors required for IT projects, again

reinforcing the complexity of IT (Keen, 1991; Meyer et al., 1992; Pollalis & Frieze,

1993; Davenport, 1994). These critical success factors include many sophisticated,

complex organisational concepts, again reinforcing the perception of IT’s complexity.

A major complexity related message from the literature is that IT is difficult to use

(Lucas, Weill, & Cox, 1993; Stewart, 1995; Clegg et al., 1996; van Bemmel &

Musen, 1997; SCO, 1997; Strassmann, 1997a). IT projects often encounter general

problems that can be divided into three categories:

1. The system does not perform as expected;

2. The system is ready later than expected; and

3. The system is more expensive than expected.

In addition, as with many specialised disciplines, there is a language and terminology

barrier surrounding IT and a shortage of suitable skilled people (Smits, van der Poel,

& Ribbers, 1997; Carr, Miller, & O'Brien, 1998), confirmed by a study on IT

adoption that found computer innovations are slowed or blocked due to the lack of

technical knowledge available (Attewell, 1992).

Considering the whole issue of complexity, it can be concluded that as well as the

technology itself being complex, the environment within which it is used is also

complex. This above section on IT complexity has shown that the management,

planning, alignment, achievement of benefits and use of IT is complex. What has not

been reviewed is the complexity at the bits-and-bytes level of IT and the range of

specialist skills required to run the day-to-day technical operations of an IT

department. Based on the conceptual model this can be expected to lead to slower

innovation diffusion.

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2.4.3. Compatibility Compatibility is the measure of how well an innovation fits and supports the current

organisation and its environment (Rogers, 1995). Compatibility occurs at many levels

from technical fit through to cultural alignment. This section will look at IT issues

relating to compatibility.

A factor in the compatibility of IT with organisations is its acceptance level. In health

organisations there are a number of specific issues about the adoption of IT

innovations that directly influence the compatibility of the technology.

First, about the acceptance of innovations in general, it was found that innovation

creates uncertainty that leads to resistance. (Gerwin, 1988). Secondly, a number of

IT-related factors magnify the political strife caused by innovation within

organisations through changes in power structures, divisions between supporters and

detractors of IT, poor relations between IT departments and users and the implicit

social and cultural aspects built into the software that may conflict with the status quo

(Zuboff 1988, Friedman, 1989; Quintas, 1996; Mansell et al., 1996a; Thorp, 1998).

Research has found that acceptance of IT is strongest in finance, insurance and real

estate, with the number of employees in “head office” type staff positions being a key

driver (Whaling, 1996; Sichel, 1997; Strassmann, 1997a). In contrast to the banking

industries level of acceptance, Schwartz (1992) noted that advanced IT is a major

topic in many organisations but there is a gap between talk and action due to the

failure of too many IT projects to have immediate tangible payoffs. This confirms the

tension IT causes due to low compatibility with the need for rapid returns and also

shows that the relative advantage issues identified earlier in this chapter are having an

impact upon the acceptance of IT.

Shera (1983) reviewed another challenge to the acceptance of IT; its ability to produce

information overload and stifle creative thinking. He parodies the Rhyme of the

Ancient Mariner…

"Data, data everywhere - and not a thought to think!"


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Reviewing the acceptance of IT in health, authors wrote of the increasing acceptance

and belief in the value of health IS. A major motivation is the concept that the

availability of data about the patient and improved knowledge about the options for

diagnosis and treatment will lead to better and more economical outcomes (Nash &

Coker, 1998; Marietti, 1998; Hammond, Pollard, & Straube, 1998; Conte, 1999). However, as a counterpoint to this acceptance of IT, Shortliffe (1998) noted that the

rate of change in the health care environment has been so rapid that health

administrators have not been able to cope with the change in IT and the human and

organisational sides of health have remained relatively stagnant (Rind et al., 1993;

Detmer & Friedman, 1994; Dick et al., 1997; Sands, Rind, Vieira, & Safran, 1998;

Shortliffe, 1998; Johnston, Leung, Wong, Ho, & Fielding, 2002).

Continuing this theme of acceptance, one team wrote of their experiences in a Korean

health centre with the implementation of a health management information system,

finding it a positive experience. They also reported that patients' satisfaction levels

about services provided increased (Chae et al., 1994). Others reported positively on

IT in health. They found the benefit of direct order entry by doctors to be compelling

at the Latter Day Saints hospital in Salt Lake City (DeLuca et al., 1996).

Miller & Schwyn (1999) carried out an analysis of the perceptions of the IT

department held by the stakeholders at a children's hospital, finding a range of poor

perceptions. This poor perception has been confirmed in other studies (Heeks,

Mundy, & Salazar, 1999).

It appears, therefore, that the health industry accepts computers at a conceptual level,

even in the clinical setting, though with reservations. These reservations relate to the

theoretical potential of IT versus the actual available systems. In addition, a major

barrier to compatibility the social and organisational framework and evolution is

lagging causing the uptake and exploitation of IT to lag.

Analysis of the pattern of use of IT in health is another means of understanding the

compatibility of IT with health care. A study of 470 physicians in academic medical

centres found that most frequent use of computers was for academic rather than

LITERATURE REVIEW

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clinical work. The respondents believed that computers are slightly beneficial to

health care whilst self-education and access to current information are the most

beneficial uses. The physicians saw functions such as clinical order and data entry as

much less desirable uses. Clinical order entry is the process of requesting tests,

treatments or other patient-centric services. This survey is to be treated cautiously

though, due to a low response rate from some of the institutions taking part (Detmer et

al., 1994). It is interesting to note that in contrast, DeLuca & Enmark Cagan (1996)

found that the benefits of order entry by doctors were compelling.

Other writers believed that currently computers in health care are predominantly used

to provide facts about the patient in an organised and timely manner or for the support

of managed care functions such as patient profiling (Cerne, 1995; Clayton &

Hripcsak, 1995).

Physicians are a significant population in the health sector and physician resistance

was seen as a barrier to IT acceptance by a number of writers (Nash et al., 1998;

Marietti, 1999; Conte, 1999). As noted by Degeling et al (1998), the culture of the

physician community creates challenges for the adoption of IT (Berkowitz, 1998;

Degeling et al., 1998). Research has found that those physicians with formal

informatics training or higher levels of education reported that computers would be

more beneficial to health care than to those physicians with no informatics training or

older less educated ones (Detmer et al., 1994; Johnston et al., 2002) Some believed

that health care providers’ lack of willingness to use sophisticated decision support

systems was a barrier to the implementation of improved systems. They viewed

changing this as a major step (DeJesus, 1999; Overhage, Tierney, & McDonald, 1999;

Teich, 1999).

One study founds that physicians had a positive attitude towards the paper-based

record but remained to be convinced that a computer-based record is superior. It also

found that nurses and therapists had a more positive attitude towards computer-based

systems than physicians (Dumont, van der Loo, van Merode, & Tange, 1998).


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It appears that, overall, physicians resist their own use of computers in the clinical

setting. This would appear to be a significant negative impact on the adoption of IT in

health care.

This section on compatibility has raised a number of issues across a broad range of

topics. Firstly, it appears that linking the organisation and IT is not an easy task. The

acceptance of IT within industries also challenges the level of acceptance. Some

industries, particularly banking, have a high level of acceptance, yet health appears to

face social and technical challenges to IT in the clinical setting. In particular doctors’

attitudes to clinical IT seem to show a low level of compatibility. In part, this is due

to the low availability of suitable systems and also due to the slow evolution of the

culture and social aspects of health management. In conclusion, it appears that IT

compatibility in clinical health care is currently at a low level.

2.4.4. Observability Observability is the ability to see an innovation in use in a similar environment.

Higher levels of observability lead to more rapid adoption of an innovation (Rogers

1995). The maturity of the market, the availability of products, the record of

accomplishment and the installed user base all indicate the level of observability of

health IT. A number of writers have commented on the lack of suitable IT in the

clinical domain. This included medical record systems (Shortliffe, 1998),

computerised patient records (Dick et al., 1997; Handler, 1998a; Handler, 1998b) and

clinical decision support (Perreault et al., 1999). Others note the lack of observable IT

and identify barriers to improvement (Clayton, Sideli, & Sengupta, 1992; Clayton et

al., 1995).

It appears, therefore, that the market for clinical IS is still perceived as immature

having significant barriers to be addressed. There are few complete systems

implemented and observable. This finding will lead to slower adoption of clinical IT.

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2.4.5. Trialability No literature has been specifically identified showing the trialability of health IT.

However, the previous discussions about the difficult of achieving alignment, the risks

of project failure, the low acceptance by doctors, the inability to observe successful

clinical systems and the complexity of projects may reasonably lead to the assumption

that IT projects are not easy to trial. This would therefore make the adoption of IT

innovations slower.

2.4.6. Conclusions Regarding Health IT Rogers’ factors affecting the rate of diffusion of a technology and their impacts are

show in Table 2-4, below.

Technology Factor Impact

Complexity

Lower complexity leads to faster diffusion.

Compatibility High compatibility of the technology with the current

environment leads to faster diffusion.

Observability The ability to observe the technology elsewhere speeds up

diffusion.

Relative Advantage The greater the relative advantage of the new technology over

the old the greater the rate of diffusion.

Trialability The ability to trial the technology on a limited scale increases

the rate of diffusion.

This review leads to the conclusions about these factors regarding health IT shown in

Table 2-5, below

Table 2-4 Summary of Rogers’ technology factors


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Technology

Factor

Features identified Impact

Complexity No generally accepted methods for the financial and economic

management of IT, or the measurement of benefits (Strassmann, 1997a;

Thorp, 1998).

Gaining benefits from IT requires sophisticated management,

organisational changes and significant technical skills (Davenport et al.,

1990; Bullen, 1995; Strassmann, 1997a; Thorp, 1998; Weill et al., 1998).

IT creates disruptive change in organisations (Zuboff, 1988).

High levels of technical skills are required to run major IT projects and

the technology is surrounded by jargon, (Keen, 1991; Smits et al., 1997;

Weill et al., 1998).

Advances in health IT requires changes in the way the health sector

works and additional research, (Handler, 1998a; Perreault et al., 1999).

All the literature points to the high complexity of IT.

None of it appears to suggest health IT is low

complexity.

This would slow down diffusion.

Compatibility Managers have come to expect IT as part of their support structure

(Cortada, 1997).

IT requires organisational and individual change (Zuboff, 1988;

This body of literature suggests that there is

compatibility between IT use and health organisations

but this compatibility is mostly in the

Table 2-5 Technology factors derived from the literature

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Davenport et al., 1990).

Change threatens individuals and organisations (Rosegger, 1991;

Schneider et al., 1995; Kotha, 1998).

Society’s expectations of service levels and speed of service are rising

(McKenna, 1997).

IT enables new ways of doing things assisting desired reforms (Zuboff,

1988).

Some IT initiatives have no clear need (Lucas et al., 1993).

Physicians find computers useful for administration support and self-

development (Detmer et al., 1994).

Physicians find computers difficult for clinical work (Detmer et al.,

1994).

The health IT market is not yet mature and solutions are not yet ideal

(Handler, 1998a; Handler, 1998b).

IT departments within organisations are often poorly aligned and

culturally different (Allen, 1987).

IT tends to integrate processes and break down barriers in organisations.

To do this requires a common vision. Health has multiple management

structures and differing internal goals (Braithwaite et al., 1995).

Strategic IS requires the necessary supporting infrastructure to be in

managerial/administration area. Clinical areas remain

less compatible. This would cause a slow uptake of

clinically oriented IT.


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place (Weill et al., 1998). Whether health has this infrastructure is

unclear.

Observability IT is in common use across most industries and is now a major focus of

capital investment (Minoli, 1994; Quinn & Baily, 1994; DeLuca et al.,

1996; Thorp, 1998).

The literature says executives, in general, are positive about the use of IT

but also contradicts this by saying executives are disappointed by IT

(Grindley, 1999).

IT has mostly been applied to data related work. Knowledge systems are

less prevalent (Cortada, 1997).

IT departments are not well regarded by the organisations they serve

(Allen, 1987).

The effects of IT have been too difficult to measure (Brynjolfsson et al.,

1996b; Strassmann, 1997a).

Major gains have been seen in industries such as banking but little data

has been published showing the returns (Whaling, 1996).

Health organisations do not know the return they gain from IT (CSC,

1999).

Health IT projects are viewed as problematic (Stewart, 1995; van

The literature delivers a divergent view on how

observable the successful application of IT is.

Certainly, a vast amount of IT is being implemented

but both successes and failures are visible. The

successes appear to be most visible in banking and

general administration areas. Health projects,

particularly those addressing clinical areas, are less

common and are generally seen as problematic. The

literature suggests a middle range value for the impact

of observability on the diffusion rate in health.

LITERATURE REVIEW

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Bemmel et al., 1997; Heeks et al., 1999).

A number of health IT projects have been studied and found to be

beneficial. These projects, however, are often research or trial projects

(Chae et al., 1994; DeLuca et al., 1996; Sands et al., 1998; Halamka et

al., 1998).

Clinical health IS are still rarities (Handler, 1998a; Handler, 1998b;

Duncan, 1999; Perreault et al., 1999).

Relative

Advantage

IT spending and business performance are unrelated (Strassmann,

1997a).

Productivity gains from IT remain a topic of debate. Measurement

techniques and available data give no clear answer (Brynjolfsson & Hitt,

1995; Brynjolfsson et al., 1996a; Brynjolfsson et al., 1996b; Cortada,

1997; Strassmann, 1997a).

Health managers do not know the return they gain from IT (CSC, 1999).

Basic transaction processing systems and IT investments used to reduce

costs show good returns (Weill et al., 1998).

Strategic IT initiatives are hard to quantify and have the highest level of

failure (Weill et al., 1998).

IT changes organisations causing political reactions that may be

The relative advantage of IT appears to be clear for

basic transaction systems and cost-saving systems.

Therefore, these should be well diffused. More

complex systems, particularly strategic systems, have

far more doubts about their advantages. Techniques

for measuring advantage are not well developed.

Health shows hope in the benefits to be achieved from

strategic IT, such as clinical systems but real barriers

remain in the ability of the technology to deliver that

benefit.


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unpredictable (Zuboff, 1988).

A large number of IT projects fail to achieve their objectives (Stewart,

1995).

Benefits are not automatic, their realisation requires organisational

change (Davenport et al., 1990).

There is increasing belief in the value of IT in health care (Chae et al.,

1994; DeLuca et al., 1996; Sands et al., 1998; Conte, 1999).

The benefit of IT in the clinical setting remains in doubt. Issues, such as

how to replace paper medical records with a computer, have not been

addressed successfully (Dick et al., 1997; Sands et al., 1998; Shortliffe,

1998; Dumont et al., 1998; Perreault et al., 1999).

This variable of diffusion rate would seem to point to

faster uptake of basic systems and slow uptake of

clinical systems.

Trialability IT causes non-linear innovation rather than evolution (Zuboff, 1988).

Benefits require organisational change to achieve (Davenport et al.,

1990; Strassmann, 1997a; Thorp, 1998; Lillrank & Holopainen, 1998).

Organisations need complex infrastructures to implement IT enabled

organisations and strategic IT (Nolan et al., 1995; Weill et al., 1998).

The literature does not clearly address the issues of the

trialability of IT in health. However, the degree of

change IT causes, the cross-organisational co-

ordination required, the infrastructure that must be in

place and the costs involved all imply that major,

strategic systems are exceedingly difficult to trial. This

could be expected to slow down the adoption of major

systems.

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2.5. Environment/Policy The conceptual framework has defined environment/policy as one of the areas that

may have an effect upon the adoption of IT innovations in organisations. The

Australian health sector has been an environment of change and reform for many

years since the introduction of Medicare (Stewart & England., 2002)3. To permit

analysis, the framework has mapped the policy factors of interest as being the

pressures exerted by suppliers (including employees and their associations), buyers,

legislators and clients. This section on environment/policy will therefore review

literature applicable to each of these to establish the current knowledge base of these

factors.

Buyers/Legislators In some industries, buyers have strong influence whilst in others such as monopolies,

the buyer has weak influence. In the Australian and New Zealand context, the buyers

of health services are the governments that fund the health system, though they in turn

are pressured by the electorate, which is the consumer of health services. In line with

this, Meyer, Silow-Carroll et al (1993) note that, in part, health-spending patterns are

driven by factors outside of the control of the policy makers including: variability in

practice patterns and utilisation rates; the availability and desire for expensive high

technology medical care; aging population; behaviour and lifestyle; environmental

conditions; and emerging public health threats.

This implies that the buyers have challenges as to the amounts they spend on health

care and pressures on the amount of control they can exert. Pressures from the

consumer for high-technology health care means that the buyer is under pressure to

exert influence for populist, rather than rationalist, policies for the health care

purchasing (Meyer et al., 1988; Chassin, 1998). Therefore it appears that the

consumer/buyer influence on health policy makers’ may be significant.

Suppliers One of the major suppliers of services to the health industry is the medical profession

and its various professional sub-groupings. A major driver of health expenditure has

been found to be the number of physicians able and willing to integrate new 3 An overview of the reform of Australian health has been written and published in the U.S. text “Health care Reform Around the World”, (Stewart & England., 2002)


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technologies into their practices (Meyer et al., 1993). Chassin (1998) provides similar

comments about this leading to over-adoption of advanced technologies, claiming this

is exacerbated by a push from suppliers who seek to gain returns on their investments.

It therefore appears that health supplier groups are able to exert significant pressure

on the health system. This pressure allows the suppliers to achieve their own goals

whilst maybe not addressing those best for the health care organisation. The supplier

group most likely to be of interest in this study is the clinicians.

2.5.1. Consumer Expectations Society has a broad range of expectations that influence the strategies that

organisations adopt. Health, being such an important issue to most people, possibly

faces more expectations than most other sectors. This section will review some of

these, whilst this study will ask managers about their perceptions of the pressures

society puts upon them. One area of pressure comes from an increasing consumer

oriented view of health. Society continues to demand increasingly improved services

with reduced waiting times (McKenna, 1997). This consumerism movement is likely

to pressure health care delivery with demands for improved service, reduced waiting

times and greater accuracy.

Examining the pressures faced by policy-makers, Meyer et al (1993) believe that the

goal of health policy-makers should be the provision of better health rather than

offering more health care. Supporting Meyer et al's argument, Fuchs (1986)

provocatively claims that

"Those who advocate ever more physicians, nurses, hospitals and the like are either mistaken or have in mind objectives other than the improvement of the health of the population.”

What is not explored are the views held by the policy-makers themselves. Are the

policy-makers already trying to follow this health improvement path? If not, what

constraints are they operating under?

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It has been noted that the computerisation of health care delivery may generate

additional social pressure. Inequities may result with those who are wealthier and

computer-literate getting better care than those with no access to computers (Kassirer

J.P, 1995; Mansell, 1996; Gold, 1999).

Therefore, it seems likely that social and political factors may exert pressures that

drive IT decision-making. Part of this research therefore needs to identify whether

societal and political pressures are influencing decision-makers’ decisions and how

these expectations affect them.

2.5.2. Conclusions about Environmental/Policy Factors The specific issues within the environment/policy arena that will influence health IT

innovation have not been clearly defined. However, factors that appear to exert

pressure have been identified and need further investigation. Therefore, this research

project will undertake some exploratory research to identify relevant topics of interest

for future research.

2.6. Level of IT Adoption in Health care The diffusion level of IT is one of the central concepts of this research project. It is

frequently asserted that health care lags behind other industries in its application of IT

(Shortliffe, 1998; CHIC, 2000), yet little research exits that proves this. Therefore,

this research project needs to measure the IT adoption level in Australian and New

Zealand government health care. Whilst many studies assess the impact of IT usage

(Bailey & Pearson, 1983; McFarlan, 1984; Ives & Learmonth, 1984; Johnston &

Vitale, 1988; Segars et al., 1994; Grover et al., 1994) few have formally assessed the

level of adoption, rather many comment on expenditure levels.

It has been identified that on average, companies across all industries spend 5% of

their revenue on IT and information systems (IS) capital expenditure representing

40% of total capital expenditure (Minoli, 1994) and in the U.S. some health care

organisations have IT capital budgets exceeding 50% of total capital budget (DeLuca

et al., 1996) (Quinn et al., 1994).


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At a macro-economic level the U.S. devotes the highest proportion of Gross Domestic

Product (GDP) to IT (approximately 2.8%) whilst Japan, with its historically high

level of productivity growth, spends only 1.4% of GDP (Strassmann, 1997a).

Limited data are available on health care itself. However, senior academics have

commented on the low rate of health IT adoption (Shortliffe, 1998) whilst the

Australian health industry body the Collaborative Health Informatics Centre (CHIC)

estimated that Australian hospitals spend 1.5% of revenue on IT.

The multinational computer company CSC’s annual study of IT issues provides some

tangible evidence of the phenomenon investigated by this study (CSC, 1998; CSC,

1999). CSC found that health care organisations spent on average 2.5% of revenues

on IT; however, these figures are distorted as CSC’s survey included life sciences

organisations in the health care data. The life sciences organisations included

pharmaceutical and biotechnology organisations that had far higher levels of IT

expenditure than hospitals and other care providers. The study also found that the rate

of growth of the IT budget is 20% per annum in life sciences but only 7% in health

providers and payers.

In an economic review of adoption, it was found that the service industries with the

greatest adoption levels of IT are finance, insurance, and real estate (Sichel, 1997).

Looking at an industry with high acceptance of IT, Whaling (1996) reviews IT

innovation in U.S. banks and found great acceptance of IT and its capabilities. He

reported that as much as 10% of the banks’ discretionary funds are being spent on

technologies to redesign the processing of transactions further. This included

workflow software, imaging technology and the outsourcing of IT. However,

showing a lower level of acceptance by the public, Schneider & Bowen (1995) noted

that IT-led services, such as the early use of bank teller machines, have been slow to

be adopted due to the technology's ability to be threatening to people and make them

feel stupid.

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Analysis of IT use in Health care A limited number of studies have analysed usage of IT in health. Examples of these

are examined below.

Kim & Michelman (1990) looked at ability of IT to assist the gain of competitive

advantage in U.S. hospitals. They examined successful IT use within and outside

health, reviewed literature and examined field experiences in health. They found that

health’s systems are isolated and independent. They believe that this has been driven

largely by environmental influences and lacks coherent policy within organisations.

This is supported, in part, investigations into HMO’s IT investments (Wholey,

Padman, Hamer, & Schwartz, 2000).

A Canadian study found that health IT has high-to-moderate functional sophistication,

low technological sophistication and even lower integration levels (Paré & Sicotte,

2001). They recommend that future investments go towards integration rather than

the development of bedside technologies. However, this study was limited as it failed

to consider either management culture or methods; rather, its focus was purely on

technology factors.

In single speciality physicians’ groups in the U.S., it was found that IT expenditure

correlates with operating margin (Smith, Bullers Jr, & Piland N, 2000). Whilst a

further study found that the health social structure shapes the use of computing in

health. Rather than being a rationally designed technical system, health IT systems

are a result of professional-managerial and intra-professional conflict (Dent, 1996).

2.7. Innovation Diffusion Research Recent publications have looked at innovation diffusion from a number of

perspectives. The majority of the health and IT innovation research identified

included significant qualitative methods. This seems to be a reflection of the

relatively underdeveloped theoretical base for innovation in health and its underlying

social base. As McFarland (1979) said nearly 25 years ago, health needs its own form

of organisational theory. The literature shows that such a theory does not yet exist,


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with researchers continuing to develop their own models and understanding of health

organisations. This project will contribute to this theoretical base.

The following sections will review some of these in the areas of health innovation, IT

innovation, and health IT innovation.

2.7.1. Health Innovation Diffusion Research One of the factors underlying many health studies is the claim that hospitals are

different to other organisations. McFarland (1979) presented a detailed case study of

innovation in one U.S. hospital during the mid 1970s. He concluded that hospital

organisations are unique requiring their own organisational theory. When McFarland

conducted his case study, the use of computing was at a basic level and therefore he

provided very little review of this field.

Berwick (2003) applied Innovation Diffusion Theory as a means of examining the

slow rate of innovations in health. He provided a number of examples and identifies

that the perceptions of the innovation, the characteristics of the individuals adopting

the change and context and managerial factors within the organisation as the three

main influences. These findings mirror Rogers’ factors and those adopted as the

theoretical framework for this research project.

Another study finds significant positive relationships between top hospital managers'

innovation intentions and risk propensity, self-efficacy, perceived organisational

strategy, perceived information processing capability, and perceived resource

availability (Tabak & Barr, 1999). This study again reinforces Rogers’ overview that

claims these leadership characteristics contribute to the level of innovativeness of an

organisation.

Koch, Lam et al (1996) provide a study of the acceptance of innovations in hospitals.

They proposed a multi-stage model showing three distinct phases in the innovation

process:

1. Knowledge Awareness,

2. Evaluation-Choice, and

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3. Adoption-Implementation.

As a further conclusion, this study found that the Chief Executive Officer's support or

otherwise was highly influential on the outcome of the innovation proposal. The

findings of this study closely reflect Rogers’ work and the theoretical framework for

this research project.

2.7.2. IT Innovation Research There have been many studies on IT innovation diffusion. A large proportion of these

have looked at the adoption of specific technologies or methodologies (Tyre et al.,

1993; Orlikowski, 1993; Karahanna, Straub, & Chervany, 1999; Mustonen-Ollila et

al., 2003; Al-Gahtani, 2003)

Several studies identified barriers to adoption of IT. For example, in a study of 1200

Californian travel agents, Dougan (2003) found that the lack of information about the

relative advantage of computerised reservation systems is the best explanation for the

identified pattern of suboptimal adoption.

Kirveennummi and Hirvo (1998) developed a framework for analysing barriers to IT-

related change in organisations. They suggested barriers were structural, managerial,

user barriers, technical, and combination barriers. They applied their framework to

understand the reasons for project failure and develop a set of project critical success

factors. However, this project did not apply Innovation Diffusion Theory; if it had, it

would have discovered that the proposed framework equated with the organisational

and technical issues already identified.

2.7.3. Health IT Innovation Research A limited number of researchers have recently started investigating the effect of

health IT innovation. One such study tried to identify when a health organisation is

ready to adopt clinical IT (Snyder-Halpern, 2001). However, this only reported the

development of these indicators. Validation and actual use were yet to occur.


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Using a series of case studies, Sobol et al (1999) found barriers to IT adoption in

health grouped as:

• Knowledge problems (lack of knowledge about technology, fear of unknown,

uncertainties relating to cost and return),

• Approval problems,

• Design problems ( database difficulties facility design), and

• Implementation problems (equipment compatibility, training, regulatory and

legal, short Chief Information Officer tenure).

In an Australian example, Southon et al (1999) reviewed the failure of a major patient

management system implementation in NSW. They found that organisational issues

were key to IT success. The key issues in the failure were the:

• misfit between strategy and structure (central decision making that could not

force its decisions to be accepted in distributed areas), and

• internal organisational tensions.

Contributing factors were also:

• uncertainty of benefits, and

• the difficulty of coordinating large projects across health organisations.

Confirming one of the factors in Rogers’ model, it was found in the U.S. that

nationally affiliated HMOs adopted IT better than the other HMOs. This was found

to be due to economies of scale and the need to manage a more complex business

environment (Wholey et al., 2000).

2.8. Conclusions

2.8.1. Gaps and Weaknesses in the Literature A number of omissions and weaknesses are apparent in the literature:

1. Some of the core questions about the nature of health organisations compared

to other organisations are not considered. It has been difficult to comment on

internal organisational attributes such as complexity, formalisation,

interconnectedness and slack.

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2. The literature in IT investment and usage has a heavy emphasis on banking

and finance. Little analytical work has been done on health IT.

3. There is a heavy focus on competitive advantage, competition and profits as

drivers of IT projects and investment. Very little has been written about the

use of IT in government and not-for-profit organisations.

4. The literature on IT usage appears to be lacking in academic rigour. Much of

the literature is anecdotal or advises on techniques. Little is published using

rigorous qualitative and quantitative methods or detailed studies. What

research there is seems to have an emphasis on qualitative methods,

suggesting the formative theoretical basis of much of this work.

5. No studies comparing IT adoption across industries were found.

6. Most of the recommended methods lack any studies proving the effectiveness

of the methods. Evidence-based health care is a developing trend; evidence-

based management is not.

7. Much of the management literature recommends best practice but the

development of the practice is not justified.

8. McFarland (1979), writing in the mid-1970s, identified the need for a

specialised theory on hospital organisations. This need remains unanswered.

2.8.2. Commentary & Relevance for this Study This research project aims to develop theory whilst addressing some of the identified

gaps in the literature. In addition, it aims to address some of the barriers that are

preventing IT from delivering greater benefit to health care organisations. In

particular, issues raised in the literature review which this project investigates are:

• Aspects of health organisations that contribute to the IT adoption phenomenon;

• Health executives’ expectations and beliefs about IT’s relative value, complexity,

compatibility, observability and trialability;


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• IT adoption levels in health in comparison to banking;

• Social and environmental pressures, if any, that impact upon health executives’

decision making; and

• The relevance of the proposed theoretical framework and any possible

enhancements relating to IT in healthcare.

This chapter started with the construction of a theoretical framework, as shown in

Figure 2-4 Theoretical Framework. The remaining sections have reviewed current

literature based upon the framework and summarised past research against this model.

The body of relevant literature was vast, yet the answer to the research question

remains hidden. Previous works have highlighted the issues and given some of the

answers, however it is now time to properly address the research question and

phenomenon under review. The remainder of this project will now use the model to

conduct research. This will provide insight into the validity of the framework and any

derived refinements, as well as confirm or challenge the conclusions drawn in this

chapter. Hence, the remainder of this research project aims to uncover the answers,

link back to previous work and show a new way forward.

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3. Methods

The aim of research is the discovery of the equations which subsist between the elements of phenomena.

Ernst Mach (1838–1916) Popular Scientific Lectures


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3.1. Overview This research aimed to answer the question:


To develop the understanding of state health’s IT adoption further required

comparison with another industry as a baseline. Common perception was that state

health is a slow adopter, therefore it was appropriate to use a high adopter as the

baseline to maximise the contrast. Therefore, this research investigated both the state

health and banking industries. Banking was chosen for a number of reasons

including:

1. Its apparent relative high IT adoption level (Whaling, 1996; Smits et al.,

1997), offering a significant contrast to state health use;

2. Its similarity to state health in its reliance on confidential data about a large

number of clients; and

3. For the pragmatic reason that there are readily identifiable organisations to

study.

This project was non-experimental, comparing findings between a group of state

health industry subjects and a group of banking subjects. The project design was

retrospective and cross-sectional to assess the influences that led to the status quo

whilst keeping the size and duration of the project constrained.

A fundamental feature of this research was the focused, specialised nature of the

population of interest. The reasons for selecting such a focused population will be

addressed later in this chapter. However, the identified population had only a

maximum of six state health organisations of interest in Australia (government state

health agencies at state or territory level with complexity to require sophisticated IT

investments), two health organisations in New Zealand (based upon advanced clinical

computing initiatives) and four national banks. As noted earlier, the key decision-

maker analysis identified a small number of actual decision makers, fewer than 20 in

the government state health sector. These are very senior managers who are generally

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difficult to access and then for only limited time. Such executives exist in an

environment that is politically charged and highly competitive. The research design

therefore considered this specialist population, its limited access, and its constraints

on open information sharing. This required applying a method able to reach these

subjects, gain meaningful input in a short time and allow comprehensive analysis

yielding in-depth insight of this small population.

The literature review was the foundation of this study. The start of the literature

review built a theoretical framework using well-established literature from Innovation

Diffusion Theory. The latter parts of the review applied Innovation Diffusion Theory

to available literature about health organisations, health IT technology and the

environment/policy areas (England et al., 2000) 4. As identified in the literature

review, there was only a fledgling theoretical base underlying the organisational

dynamics of health and health IT and very few clear pointers about the relevant

environmental/policy factors to consider. This research project, therefore, needed to

develop and test a theoretical framework for state health IT adoption whilst building

initial views about environmental/policy factors.

In view of these attributes of the research project, an emphasis on qualitative and

descriptive methods was appropriate. However, to advance the theory to a greater

degree the project design needed to support movement towards a more structured

approach with a more formalised outcome resulting. This was not possible with a

purely qualitative approach so this project was designed with two differing studies,

allowing triangulation and different research paradigms to be applied.

Study One was qualitative and applied open interviews with top-level managers

responsible for allocating funds to IT; its objectives were to verify and enhance the

conceptual framework developed in the literature review, gain insights into the

research question and to develop an understanding of the role of environmental/policy

factors. Study Two was survey based and sought the perceptions of the senior IT

managers about the way they applied IT, and senior managers about their perceptions 4 This analysis has been published in a peer-reviewed journal as: England, I. W. R., Stewart, D., &

Walker, S. (2000). IT Adoption in Health care: When Organisations and Technology Collide.

Australian Health Review, 23, 176-185. A copy is in the appendices of this document.


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of IT’s role in their organisation. This provided quantitative, though still descriptive,

measures of the roles that the various contributing factors play, and their relative

effects in state health and banking. Measures included the dependent variable, being

IT adoption levels, and three independent factors: environment/policy, organisation

and technology.

3.2. Ethics This research project involves interviewing and surveying of human subjects. Ethical

issues were therefore considered and ethics approval sought, and gained, from the

QUT Ethics Committee.

The main issues raised by this research were that the managers who were the research

subjects could reveal topics which, when published, cause embarrassment to their

organisation or lead to personal criticism from their superiors. The project presented

no obvious risks to the University.

To mitigate the risks, participants and their organisations were kept anonymous and

no personal or background information on the participants was collected, it being

unnecessary under the theoretical framework used. The research was written and

presented in such a manner that the identification of the participants or their

organisations cannot be deduced.

Interview subjects were sent a consent notice at the time appointments were

confirmed and briefed about consent at the start of the interviews. The written

surveys contained a consent briefing on page one.

3.3. Study One – Executive Interviews Study One is a qualitative study of the attitudes and beliefs about IT innovation

adoption held by the key decision makers in regards to state health IT investment.

Study One builds upon the literature review and introduces the flexibility required to

research the impact of policy and environment. The design of this initial study is

described in the following sections.

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3.3.1. Study One – Theory Revisited As described in Chapter Two, the conceptual framework for this research is based

upon Innovation Diffusion Theory (Rogers, 1995) with extensions. The theory has

been used in health to understand many phenomena including the uptake of public

health promotion activities and the investment in high-technology equipment such as

CT scanners and cardiac surgery units (Scannel, 1971). Previous studies have

validated the application of the constructs within this theory as a method for

examining health innovations (Meyer et al., 1988).

The method used in Study One was based upon proven interview techniques (Maykut

& Morehouse, 1994; Rubin & Rubin, 1995; Kvale, 1996; Silverman, 1997) with

analysis comprising of condensation and categorization using a form of constant

comparative analysis (Glaser & Strauss, 1967; Strauss & Corbin, 1990; Kvale, 1996).

This approach is founded upon Symbolic Interactionist theory, such that it is assumed

that humans act towards people and their environment based on the meanings these

have for them (Blumer, 1969). As such, this theory predicts that the executives would

share information based upon the meaning they have developed in regards to IT and

IT adoption whilst filtering this meaning based upon their experiences with

interviews, research and sharing of information.

3.3.2. Study One - Target Population The population of interest was government-owned state health organisations in

Australia and New Zealand large enough to have made implementation attempts at

clinical IS. Study One’s aim was to validate the conceptual framework and research

policy influences in state health care. Therefore, it was only necessary to research the

state health industry in the first study.

The population has been defined this way to focus on the state health organisations

that make the vast majority of IT adoption decisions in Australia and New Zealand.

The state health population was deliberately oriented to remove those at the lowest

end of IT adoption by imposing the criteria of clinical systems. This ensured a focus

on those organisations that are making real attempts at IT adoption, facing barriers

and have therefore developed an understanding of the underlying issues. This offered

more insight than picking the entire population that includes health entities with little


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or no interest in IT. In particular, there was a focus on government owned state health

due to the size and sophistication of these organisations, their complexity, their central

role in implementing major innovation and reform and the homogenous nature of their

policy environment. Private hospitals are diverse, the majority of them being smaller

than 100 beds, though a few large sophisticated ones exist (AIHW, 1998a). The small

private hospitals tend to rely upon IT as a means of automating billing rather than as a

core business enabler and generally do not have dedicated IT management and staff.

Therefore, including private hospitals and hospitals that have not faced clinical

systems issues in this research would have answered the wrong question. Rather than

finding the reason for low adoption patterns, the survey may have found the reason for

no adoption or identified a different set of organisational or environmental issues.

Review of the research question allowed determination of the population that could

provide the answer, which in turn informed the appropriate research methods.

Examination of the question showed that key to obtaining an answer was a process

that gained insight into the perceptions, beliefs and decision-making processes of

managers who actually allocate resources and procure enterprise-wide IT systems.

The definitive way to assess this was to gather information directly from the decision-

making managers themselves. Key decision-maker analysis and discussions with

state health managers showed that decisions to allocate resources to major IT

investments are made at very senior levels within state-owned health organisations,

typically at deputy director-general (or similar) level. This, therefore, implied that a

very small population determined the pattern of state health IT adoption in Australia

and New Zealand. To be relevant, this research had to use methods that directly

assessed the opinions and behaviours of this focused population. Effective research of

this population, its perceptions, beliefs and actions required techniques that gave

depth and richness; this directed the research towards qualitative methods and strong

use of triangulation to ensure accuracy of findings. Indirect methods or other large

population methods that would yield data that could be manipulated statistically were

felt to be less accurate in assessing the beliefs of the decision makers. Such wider

scale surveys would require research of people not directly involved in the decision-

making process and therefore may not have provided a correct assessment, merely an

assessment of the opinions held about the decision-making process and opinions

concerning the actions of the decision makers. This would be akin to a waiter in a

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restaurant asking other diners what they think you would like to order. The waiter

may get the right answer sometimes, but frequently will not. Therefore, such broader

methods were felt to be unreliable and inappropriate.

Therefore, for Study One, the target population was the most senior operational

management with responsibility for allocating the IT budgets and approving IT

expenditures. In Australian States where there was no centrally controlled state health

system, the executive at the major tertiary hospital was selected. Due to the targeted

population of interest, there were six suitable state health organisations in Australia

(QLD Health, NSW Health, Alfred in Victoria, SA Health Commission, WA Health,

NT Health), and two from New Zealand (A+, Auckland’s Crown Health Enterprise

and Capital Coast Health, Wellington’s Crown Health Enterprise). As noted at the

start of this chapter, gaining accurate answers about decision-making processes

required direct access to these decision makers, hence the selection of this population.

3.3.3. Study One - Interviews Design Study One was a qualitative process based around interviews and using condensation

and categorisation processes to identify meanings. Study One interviewed state health

executives from Australia and New Zealand responsible for determining the IT

budgets of state health services operating tertiary hospitals. The independent not-for-

profit industry development organisation, the Collaborative Informatics Centre

(CHIC), was used to provide the names and contact details of the appropriate

executives meeting these criteria. These were usually the executive managers that are

above the Chief Information Officer (CIO) or equivalent. Invitations were sent to all

leaders meeting the selection criteria and interviews were scheduled with all leaders

accepting the invitation. An endorsement from CHIC was attached. Follow-up

telephone calls were made about 1 week after the letters were sent to book

appointments. In cases where the nominated executive delegated the interview to the

CIO, these were rejected to retain the consistency of the sample and that organisation

was omitted from this research. To protect the confidentiality of the subjects within

such a small population, their names, positions and employer must remain

confidential.


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The interviews were conducted either face-to-face, or via telephone and took between

45minutes and one hour to complete. They were designed to gather preliminary

information about the policy and environmental factors that affect IT adoption and

explored the organisation and IT variables. The semi-structured approach was used to

ensure the subjects covered the necessary breadth of topics if not spontaneously raised

by the subject, whilst allowing sufficient freedom for new concepts to emerge (Strauss

et al., 1990; Rubin et al., 1995; Kvale, 1996). The three interviews were kept as open

and free as possible with a small number of open-ended questions used by the

interviewer as prompts. All interviews were taped with two recorders to ensure

clarity of recording and transcribed by a professional typist for accuracy.

3.3.4. Study One – Analysis & Data Management The quality of the data collected was rigorously protected. The researcher reviewed

the transcripts and recordings to verify correctness. A continuous comparison using

axial and longitudinal coding was used to structure the transcripts and identify

meanings (Glaser et al., 1967; Strauss, 1987; Berg, 1989; Strauss et al., 1990; Maykut

et al., 1994; Rubin et al., 1995; Kvale, 1996; Silverman, 1997). These processes were

facilitated by the research tool “NVivo” which allowed the coding work to be

conducted directly upon the original transcripts. Identified topics were allocated a

brief, coded description and related comments were given the same coding. NVivo

supported this process through its on-line text coding processes. Then the topic codes

were grouped into related hierarchies of “nodes” to create a model of the information

within the interviews. This technique ensured no re-entry or changes to the transcripts

and meant that coding was performed using original statements. These codes and

resulting model were then assessed for their fit with the theoretical framework.

This entire capture and analysis process protected the data quality, content and intent

from end-to-end. Quotes from the interviews are used in this report in a verbatim

form, with only details that could identify the subject, the organisation or the

employer being altered into a generic form. Where quotes were unclear or seemed

unintelligible, reference was made back to the original recordings. During the coding,

some quotes were used to support and clarify several coded meanings. In the

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presentation of this report some quotes will be used once where they clearly, and

strongly, contribute to multiple meanings.

As the aim of this first study was to test the fit of the theoretical framework to the

claimed phenomenon of slow IT adoption in state health, the coding, where

appropriate, brought the nodes together under the major constructs of this theory.

However, topics not readily fitting Innovation Diffusion Theory were separately

identified and coded as appropriate, making it clear what extra factors exist. This

analysis is presented in Chapter 4 as a “mind-map” diagram followed by a section of

narrative describing each “node”, its meaning and the significant discussion. In

addition, the enhanced theoretical framework resulting from this analysis is presented

in Chapter 4. This enhanced framework was therefore used as the basis for Study

Two.

3.3.5. Study One - Reliability, Validity Validity is the concept that the study measures what it claims to measure. Reliability

is the concept that the research’s findings can be reproduced

This research was been designed to provide reasonable levels of validity and

reliability considering the weak theoretical basis, small population sizes and available

resources. The design aimed to apply the well-established Innovation Diffusion

Theory to the weaker domain of state health IT diffusion

The Study One interviews were used both to test the conceptual framework and also

to expand it for later use in Study Two. Validity was enhanced by interviewing until

saturation was achieved, tentative theory development and triangulation against the

second half of the literature review. Comparison of the Study One outcomes with the

literature review outcomes provided a level of confidence in the validity of the

outcomes. Overall validation was protected in a manner defined by Kvale (1996) and

shown in Table 3-1, below.


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Stage Validation Approach

Thematising The literature review built its theoretical framework on the well-

established foundation of Innovation Diffusion Theory. This was

brought into context by applying literature from the field of health

management and IT to gain preliminary. This led to the development

of a conceptual framework.

Designing This ensured the approach built upon the conceptual framework in an

ethical way with no harmful consequences real or perceived to the

participants.

Interviewing Ensured a quality interview with careful recording and checking of the

subjects’ statements for correct meaning. The interview design, open

format with prompts, assisted the capture of the major thoughts held by

the subjects, enhancing completeness.

Transcribing Transcribed the interviews verbatim with only changes made for

confidentiality.

Analysing Interpreted using well-established coding methods using the assistance

and consistency of a proven tool – NVivo. Aligned back to the

conceptual framework for reasonableness and unexpected findings.

A sample of non-identifiable interview text and its coding is included

as Appendix D as an example.

Validating Use Study Two and the Conceptual Framework as alternative views for

triangulation. Discuss Study One findings with peers and industry

leaders for reasonableness.

Reporting In this document, fully reported the findings with no selective editing.

Reliability was promoted through the techniques noted above that assisted the

accuracy of data capture, transcription and analysis. This reduced the opportunities

for the subjects’ words to be altered or lost.

Table 3-1 Study One Validation Approach

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3.4. Study Two – Survey-Based Research Study Two followed after the analysis and model building in Study One were

complete. The enhanced conceptual framework deriving from Study One was used as

the basis for this second study.

3.4.1. Study Two - Research Directions Study Two aimed to take the enhanced conceptual framework and test it against the

“real world” as a method of answering the research question. As a reminder, the

research question was:


The research question and the enhanced conceptual framework enabled Phase Two to

be a quantitative, descriptive stage gaining opinions from a wider range of managers

about a range of subsidiary questions that would help answer the research question.

These subsidiary questions were:

Q1: Is there a difference in IT adoption between state health and banking?

Q2: Are IT issues significant to the adoption patterns in state health?

Q3: Are Organisational issues significant to the adoption patterns in state health?

Q4: Are Environment/Policy issues significant to the adoption patterns in state

health?

Each of these questions has a number of measurable factors derived from the

conceptual framework as shown in Table 3-2.


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Detailed Question Factors Or Measures

Is there a difference in IT adoption? Ratio of IT spending to revenue

IT expenditure per staff member

IT maturity

Are IT issues significant? Relative value

Compatibility

Complexity

Trialability

Observability

Are organisation issues significant? Leader characteristics

Centralisation

Formalisation

Interconnectedness

Slack

Size

External openness

Are environment/policy Issues

significant?

Measures of society’s influence and

expectations

Relationship between Questions The elements of each detailed question are unique and independent apart from leader

characteristics which appear in both environmental/policy and organisational

measures. However, all of the questions are oriented to the impact on maturity;

therefore, the phenomenon of interest may be caused by a combination of some or all

of the factors. The initial conceptual framework shows these factors acting

independently, however, as the research evolves this will be refined and

interdependencies identified.

Table 3-2 Subsidiary Elements to Detailed Questions

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3.4.2. Study Two - Design Study Two was a survey-based study oriented towards the enhanced theoretical

framework and output of Study One. Study Two sought measurements of perceptions

from senior state health managers about their organisations and IT services through

surveys. As noted, the theoretical basis for research into state health organisation

remains weak, it was therefore necessary to work with the perceptions of managers,

rather than specific, and yet unknown, objective measures of the organisation. Due to

the need to obtain a reasonable response rate, the wide range of questions being asked

and the low likelihood of senior executives responding to a large survey, two smaller,

focussed survey instruments were used. One survey measured both the dependent

factors (IT adoption measures) and the IT-related measures of the independent factors.

This “IT-Survey” was sent to senior IT managers. The second survey was designed to

measure the independent factors of policy (derived from Study One) and organisation.

This “Organisation-Survey” was sent to senior operational managers who utilise IT

services.

Sampling Strategies & Sample Sizes As with Study One, the population of interest remains all government-owned state

health organisations in Australia and New Zealand large enough to have made

implementation attempts at clinical IS. However, unlike Study One, the aim in Study

Two was to understand unique attributes of state health in comparison to another

industry. Therefore, the population in Study Two also included the major national

banks covering the same geographic area as the state health population. In a similar

way that the state health population included a requirement for an interest in clinical

information systems, the banking population was required to have an active e-

commerce strategy (eg Internet banking, telephone banking and a nationwide teller-

machine network). The requirements for participating organisations to have an active

IT strategy and a common geographic spread reduced differences in social and

economic environments whilst ensuring each group held a progressive vision of IT.

As noted for Study One, the population has been defined this way to focus on the state

health organisations that make the vast majority of IT adoption decisions in Australia

and New Zealand. Issues surrounding other health organisations, such as private


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hospitals, remain the same as for Study One. Similar issues arise with the banks,

where smaller regional banks, building societies and credit unions were excluded to

ensure a more homogeneous population. Therefore, as for Study One, there are a total

of six suitable state health organisations in Australia (QLD Health, NSW Health,

Alfred in Victoria5, SA Health Commission, WA Health, NT Health) and two in New

Zealand, (Auckland & Wellington). For the banking sector, the four major,

nationwide banks were the population of interest (ANZ, Commonwealth, NAB, and

Westpac).

Study Two required data to be gathered about policy, organisational and technical

issues. In this instance, the data about these factors did not have to be supplied by the

decision maker, though for consistency they needed to be supplied by a manager of

similar influence and with a similar “world view” of the organisation and its

environment. Therefore, it was decided, having already contacted the managers who

made IT decisions, it would be better to target a different group of managers. This

was done in part to avoid a low response rate that may have resulted and also to get a

different view of the issues and therefore different data, rather than potentially ending

up with the same data supplied in Study One but in a quantitative form. This led to

the identification of a sample of senior state health managers to receive the surveys.

The IT Surveys were sent to the most senior IT manager in each state health or

banking organisation (usually known as the Chief Information Officer or CIO). The

Organisation Surveys were sent to senior line managers who did not have purchasing

or decision making responsibility for IT.

The requirement to focus on senior, influential managers also led to a small

population. In addition, due to the small size of this specialised population there is

limited ability to apply any statistical techniques; therefore, the surveys must be

designed to offer the maximum opportunity for descriptive analysis and broad

comparisons.

5 The Alfred hospital was chosen in preference to the Victorian State Health Department due to the nature of the organisations. The Department of Health acts as a policy body and has delegated operational decisions, such as the procurement and deployment of IT, to the health networks. The Alfred was chosen as one of Victoria’s most complex health networks and therefore most like the other states’ health departments.

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For similar reasons to those presented with Study One, any approach to create a larger

sample or population is likely to reduce the accuracy of the data. Therefore, it was

decided to attempt to obtain a census from these senior managers, enabling

presentation of the data from this population without additional statistical issues.

Although the CIO was the target of this study, the IT Survey was mailed to multiple

senior IT managers within each organisation of interest, aiming to ensure at least one

response per organisation. The managers receiving the survey were either the CIO

(the prime target) or up to three of the CIO’s direct reports. Where multiple responses

from one organisation were received, the complete one supplied by the most senior IT

manager was used, and if the responders were of equivalent levels, the one whose job

was most responsible for the achievement of business (as distinct from technical)

objectives was used to ensure a business orientation to the responses. A second mail-

out was made some 6 weeks following the initial mail-out to non-responding

organisations to increase the response rate.

The Organisation Survey was mailed to a range of senior non-IT executives, without

direct, line responsibility for IT but similar influence as the decision-makers

interviewed in Study One. The aim was to gain the perceptions of the most senior

customer of the IT departments. This typically meant sending the document to

Deputy Directors General through to Regional Managers. Where multiple responses

from one organisation were received, the one supplied by the most senior manager

was used, in an attempt to restrict the surveyed opinions to those of the most senior

executives only. A second mail-out was made some 6 weeks following the initial

mail-out to non-responding organisations to increase the response rate.

3.4.3. Study Two – Measurement Techniques

Dependent Variables The dependent variable in this study is the level of IT adoption in an organisation.

The level of IT adoption cannot be easily determined, as IT is a complex technology

applied in many ways. The desired measurement cannot be binary (i.e. adopted or

not) as with some adoption measures due to the varying range and sophistication of


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potential IT uses. This research, therefore, sought to identify the degree of adoption,

which forms a continuous scale from zero upwards.

There appear to be common approaches to measuring IT adoption levels. The first

approach uses measures of expenditure in relation to some other measure of the

organisation’s size (Strassmann, 1997a); the second approach uses features of the use

of IT in the organisation to judge the maturity of adoption (Nolan, 1979). In addition,

this project has developed a third measure by assessing IT usage levels by the

workforce. The following paragraphs will review each of these methods, beginning

with IT expense-related methods.

Expense-Related Adoption Measures The most common expenditure-related measure of IT adoption is the ratio of IT

expense to revenue (Strassmann, 1997a). The strength of this measure is its ease of

calculation and the ready availability of the required data. However, it is also a very

limited measure that ignores the decision-making and strategic context therefore

making for oversimplified comparisons. Alternate expense-related measures are the

IT expenditure per employee and number of IT employees as a ratio of total

employees.

Using the definition of IT applied in this research project, the cost of IT comprises

hardware, consumables, software, personnel, housing and overhead (van Bemmel et

al., 1997). However, assessing the true and complete cost of IT is a challenge as

funding is often dispersed with funds being supplied from different budgets, or hidden

through consultancies, office supplies, service contracts and other areas (Strassmann,

1997a; Weill et al., 1998). Therefore, to improve the accuracy of the expenditure-

related measures a range of related measures were applied giving a broader view of

adoption. Measures used were:

• Ratio of IT expense versus total revenue. Expected results were in the range

of 0.3% to 15% (CSC, 1999).

• The ratio of IT staff to total staffing. This is a continuous variable with a valid

range of 0% to 100%. There was no literature to support an expected range.

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• The dollars of IT expense per staff member. This is also a continuous

variable, and whilst expected values were less certain, early indications were

that they would be between $500 and $25,000 per head per annum.

Despite the simplicity of expenditure-related measures, the issues arising required a

range of adoption measures to achieve a triangulated view of IT uptake. Therefore

maturity- related and usage-related measures were also applied.

Maturity-Related Adoption Measures IT theory identifies that as organisations increase their adoption of IT they grow in

their maturity of its use (Nolan, 1979). Due to this linkage between maturity and

adoption, maturity can therefore be used as a further indicator of adoption. This

concept has been validated by others (Lyytinen, 2001). A number of models exist

showing how this IT maturity develops (Nolan, 1979; Foote, 1992, Paulk, Curtis,

Chrissis, & Weber, 1993). Study techniques and instruments assessing maturity and

technical sophistication have also been developed (Friedman & Wyatt, 1997;

Gronlund & Crouch, 1997; Paré et al., 2001; Paulk, Goldenson, & White, 2002).

These have been applied successfully in health IT assessments (Sillince et al, 1994;

Gronlund et al., 1997).

For this research project Nolan’s concept of maturity was used as a basic premise. As

described in the section on data collection, below, a survey instrument was designed

to assess IT maturity. Likert scales with seven boxes were used as the measurement

scale for most of the questions. The phrasing of the questions sought the manager’s

level of agreement with a statement ranging from “totally disagree” to “totally agree.”

These scales were used to derive an average value for each category and an average

value for the total maturity questions, which is taken as the “Maturity Index.”

Usage-Related Adoption Measures As a third measure of IT adoption, this research developed a simple usage indicator

which was used to identify general IT usage levels and e-mail usage levels within the

organisation. This indicator was intended as a third triangulation measure in concert

with the previous two types of measures.


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Independent variables The design of Study Two assumed that the theoretical framework and Innovation

Diffusion Theory could be used to analyse the phenomenon of IT adoption in state

health. The theoretical framework identified three major groups of independent

influencers being organisational factors, technology (IT) factors and policy factors. Measurement of organisational issues was through leader characteristics,

organisational centralisation, organisational formalisation, interconnectedness,

external openness, size, complexity and slack (Rogers, 1995). The Study Two survey

included questions to determine the perceptions of the management about their

organisation, its culture and practices allowing assessment of the factors defined in the

theoretical framework. The detailed design of the survey and its questions are

documented later in this chapter.

IT factor measurements are based around the significant attributes of technology as

defined by Rogers (1995). The survey investigated each of these areas, namely,

relative advantage compatibility, ease of use, trialability and observability.

The environmental/policy influences were loosely defined. Measurement of these

environmental/policy influences was focussed on their influence on innovation

adoption rather than absolute measurements. Therefore, due to the exploratory nature

of this area of the research, and the desire to develop the conceptual framework

further, general topics were explored assessing the importance of government, unions,

clients and public opinion as factors on IT investment. These were general questions

embedded within the organisational survey, seeking to establish whether policy

should be an area of more detailed study in the future. These questions sought the

level of agreement with a range of policy-related statements.

3.4.4. Study Two – Data Collection Survey Two was designed to collect its data through two surveys, the IT-Survey

assessing IT issues being completed by IT management, and the Organisation-Survey

assessing organisational and policy issues completed by senior business management.

There were two very slightly different versions of each instrument, one set using

“banking language” and the other using “health language.” The questions, however,

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remained the same. This was done to ensure face validity of the survey instruments

(see the appendices for examples of all four resulting survey instruments). The

questionnaires were designed for self administration, mailed to the subjects with a

stamped, self-addressed envelope to enable their return. The surveys were coded to

allow identification of the organisation and responding person.

To assess the level of IT adoption, it was intended to create a survey using Nolan’s

(1979) theoretical base reoriented with current best-practice guidelines. However, a

review of the literature found that the UK’s National Health Service Executive had

previously developed a survey instrument to assess the maturity of a hospital’s IT in

preparation for initiating clinical IT projects (Gronlund et al., 1997). This survey

instrument, being close to the requirements of this project, was therefore modified to

fit the needs of this project and this modified version pre-tested with senior

professionals involved in state health IT.

The pre-test was carried out with two IT professionals not directly part of the survey

population but with enough experience to confirm the validity of the survey

instrument. The participants were the IT managers for major state health districts,

giving them a similar view as the Chief Information Officers being surveyed, yet

outside of the survey populations. In addition, the survey was reviewed by the senior

IT planner with Australia’s largest health IT supplier, chosen to give a differing but

related view of the survey design, and maybe highlighting issues missed by the IT

managers. The pre-test required the testers to explain their understanding of each

question to an interviewer, identify any ambiguities or areas lacking clarity. The

survey was revised following this pilot. As an example of this revision, the heading

Vendor Effectiveness was used to replace Innovation Factors as it was felt to be less

confusing and give a better indication of the section’s intent to discover perceptions of

technology, which the reviewers felt was led and supplied by vendors. The resulting

survey had 50 questions and provided input to this research in two ways. First, the IT

questionnaire captured data about the level of IT adoption, the dependent measures of

this research. Secondly, the IT questionnaire assessed factors innate to the

technology, supporting calculation of the independent measures of this study. Each

topic was surveyed with a number of questions to ensure different facets were

investigated. Apart from the usage and general sections, the survey captured


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responses on a 7-point scale from 0 to 6 giving the level of agreement with a range of

statements, where 0 represented “totally disagree” and 6 represented “totally agree.”

The IT Survey addressed the following major topics:

• Vision, Direction & Strategy (9 Questions) to address the IT planning

processes, the linkage to business planning and measurement and feedback

processes.

• Culture (9 Questions) to address cost allocations, involvement of non-IT staff,

attitude to organisational change and degree of centralisation.

• Communications (4 questions) to investigate how well the IT function

communicates its plans, projects and procedures.

• Standards (8 questions) to address areas in which standards and process are

implemented, especially those relating to procurement, benefit realisation and

change management.

• Usage (Technical Infrastructure)(2 questions) to ask for the percentage of

staff and regularity of use of computer systems in general, and e-mail

specifically.

• Vendor Effectiveness (5 questions) to measure the IT departments’ beliefs in

the value, quality and relevance of their technology using Rogers’ 5

technology factors.

• Information Resource (8 questions) to determining the organisations’ attitudes

to information as a resource.

• General Information and Statistics (5 questions), measuring IT budgets in

dollars and number of staff based on full-time equivalents.

A similar development and pre-testing regime as used with the IT Survey was applied

to the Organisation Survey development using hospital mangers as the pre-test

subjects. There was a critical requirement to keep this questionnaire brief to ensure a

reasonable response rate from the senior managers, therefore the resulting

Organisation Survey instrument comprised 28 questions derived from the theoretical

framework. The questions assessed issues about organisations, as determined from

Innovation Diffusion Theory, and policy issues raised as a topic of interest during the

literature review. As with the IT Survey, Likert scales with ranges from 0 (strongly

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disagree) through 6 (strongly agree) were used to gather responses. The resulting

survey design was as follows:

• IT Business Value (10 Questions) to assess a range of issues around IT’s value

including productivity, resource usage, quality and payback.

• Organisation Structure (8 questions) to assess the centralisation, formalisation

and interconnectedness of the organisation’s structure.

• Size(one question under Influences heading, the rest under Size)(5 questions)

to assess the size of the organisation and the slack (spare capacity) within it.

• Policy (under the Influences heading on the survey instrument)(4 questions) to

assess basic information on stakeholder influence.

• External Openness (under the Influences heading on the survey instrument) (5

questions) to assess the willingness to gain ideas from external sources.

The mapping and calculations applied to these surveys to derive specific innovation

attributes are shown later in this chapter.

Once returned, the surveys were keyed into an IT Survey spreadsheet or an

Organisation Survey spreadsheet. The remaining processes of Data Cleaning, Quality

Management, Data Management and Analysis are described in later sections of this

chapter.

3.4.5. Study Two - Output Scales & Derived values The following section describes the way in which data were extracted from the survey

responses and the calculations applied to produce the independent and dependent

variables for this research.

Independent Variables – Vendor Effectiveness (Technology) Five technology factors were gathered directly in the IT Surveys. These were Rogers’

(1995) main factors of:

1. Compatibility

2. Relative value

3. Complexity


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4. Observability

5. Trialability

The result are five scores per organisation, one per factor (between 0 and 6), and an

average technology rating for each organisation (between 0 and 6). These are

described in Table 3-3, below. In addition, the average for each industry was

calculated.

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Factor Measurement Commentary

Compatibility

A score between 0-6 where 0

shows no fit between available

IT and the needs of the business

and 6 shows a high fit.

No weighting applied.

Relative

Value


shows low belief in the value of

IT and 6 shows a positive belief.


Complexity


indicates a belief that IT is not at

all complex to put into use and 6

indicates a belief in high

complexity.

No weighting applied. In most of

this research, this scale is reversed

to ensure that high results indicate

pro-innovation.

Observability


indicates no suitable IT is

observable in other

organisations and 6 indicates

ready observability.


Trialability


indicates a belief that IT cannot

be trailed and 6 shows a belief

that IT is easily trialled


Table 3-3 Technology Measurements Described


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Independent Variables - Organisational Table 3-4 Deriving the Organisation Variables, below, describes the major

organisational factors derived from the survey questions and the process of obtaining

them.


Leader Characteristics

A score that describes the

view of the leadership’s

attitude to business

innovation and IT. This

ranges between 0-6 where 0

shows poor attitudes to IT

adoption and 6 shows very

positive attitudes.

Derived as a simple average of

all the leader questions in the

Organisation Survey. No

weighting applied. Fourteen

individual questions were

averaged to derive this value.

The questions come from the

Organisational survey within the

sections of size (2 questions),

openness (2 questions) and IT

value (10 questions).

The mapping of the questions is

shown in Figure 3-1.

Centralisation

An indicator of the level of

centralisation of the

organisation. A score

between 0-6 where 0 shows

low centralisation and 6

shows very high

centralisation.


all the 4 centralisation questions

contained within the

Organisational Structure section

of the Organisation Survey. No

weighting applied. In this

analysis and presentation this

scale is reversed to ensure that

high results indicate pro-

innovation.

Table 3-4 Deriving the Organisation Variables

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Size

Fiscal measures of revenue

and IT expenditure in

dollars

Derived directly from the

questions in the Size category in

the Organisation Survey. No

transformation applied, size

measurements, such as revenue

were used as direct comparators.

Complexity

A score between 0 and 6

where 0 indicates a view

that the organisation is not

at all complex and 6

indicates a view that the

organisation is complex

Directly transcribed from

question 3 of the Size category in

the Organisation Survey, see

Figure 3-1 for details.

Slack

A score between 0-6 where

0 indicates no slack, and 6

indicates plenty of slack


the slack question, Question 1

within the Size category in the

Organisation Survey. No

weighting applied.

Formalisation


0 indicates a very informal

organisation and 6 indicates

a highly formal organisation


the two formalisation questions

(Q 5 and 6) within the

Organisation Structure category

in the Organisation Survey. No

weighting applied. In most of the

analysis and presentation this

scale is reversed to ensure that

high results indicate pro-

innovation.


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Interconnectedness


0 shows a very poor internal

connectedness within the

organisation and 6 shows

strong internal connections


the two interconnectedness

questions (Q 7 and 8) within the

Organisation Structure category


weighting applied.

External Openness


0 shows very poor external

connections with the

organisation and 6 show

strong external connections


the 3 openness questions within

the Openness/Influences category


weighting applied. See Figure 3-

1 for details.

Independent Measure - Policy This measure is a score between 0 and 6 calculated as the simple average of the four

policy questions within the Policy/Influences category of the Organisation Survey.

No weighting was applied. In each case the scales mean 0 – strongly disagree with

the statement, through to 6, strongly agree with the statement.

Dependent Variable - Adoption Three types of measure were used to assess adoption:

1. Assessment of Maturity

2. Expenditure-related measures

3. Usage Measures

The interpretation of each of these measures is described in this section.

The maturity assessment was calculated from the IT Survey responses and is shown in

the following table, Table 3-5 and in Figure 3-1, below.

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Scoring Maturity Factor Calculation

Low High

Vision

The average of all 9

questions in the Vision

and Strategy category of

the IT Survey.

0, poor vision for IT

usage

6, strong vision

for IT usage

Culture


questions in the IT

Culture category of the

IT Survey.

1, poor IT

management culture

6, strong IT

management

culture

Communications


questions in the IT

communications

category of the IT

Survey.

0, poor

communications

between the IT

department and the

organisation

6, strong

communication

between the IT

department and

the organisation

Standards


questions in the IT

Standards category of

the IT Survey.

0, poor application

of expected

standards to IT use

6, excellent

application of IT

standards

Information

Resource


questions in the

Information Resource

category of the IT

Survey.

0, poor attitude to

the role of

information in the

organisation

6, strong view of

the strategic value

and management

of information

Overall maturity

Index

The average of all the

scores for the sub-

measures of maturity,

i.e. vision, culture,

communications,

standards and

information with each

sub-measure having an

equal weighting.

0, low IT maturity 6, high IT

maturity

Table 3-5 Maturity Factors Described


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Expenditure-related measures were derived from the IT Survey’s questions about

revenue, IT expenditure, staff levels and IT staff levels. Calculations were performed

to derive IT expenditure as a percentage of revenue, IT expenditure per employee and

IT staff as a percentage of total staff. These ratios were calculated directly from the

supplied data.

The usage-related measures were assessed from the frequency of use questions in the

IT Survey. To derive a score from these usage ratings, a weighted average was used.

The weights were as follows:

Usage Weighting

Not at all 0

Once a month 1

Once a week 2

Daily 3

All the day 4

A score for IT usage was calculated summing across the categories weighted by

percentage of users within that category. The higher the resulting value, the greater

the level of IT use within the organisation. The same assessment using the same

weightings was made for e-mail usage giving another measurement of IT usage.

These weightings were arbitrarily allocated, there being no method identified in the

literature. This approach seemed simple and at least gives a quantitative indication of

overall IT usage adoption.

The following figure provides a diagrammatic summary of the process used to convert

the surveys into data:

Table 3-6 Usage Weightings

METHODS

Figure 3-1 Factor calculation map


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Inherent weighting in this approach The aim of this data analysis approach is to be neutral among all factors. There is no

research evidence or subjective views to support any other approach. Innovation

diffusion theory gives a view that some factors may be more important than others,

but in the absence of defensible hypothesised relationships, this research sought to use

the data as collected and reveal the relative contributions of the factors.

3.4.6. Study Two - Data Cleaning The data gathered in each of the surveys were reviewed for completeness. Surveys

with incomplete Likert scale data were rejected. Responses with incomplete or

unreasonable numeric data (eg revenue or IT expenditure) were verified against

publicly available documents, such as annual reports. Reasonableness was

determined by simple ratios between the supplied figures. If the percentage of IT

expenditure against revenue were above 10% in state health or 15% in banking, both

revenue and IT expenditure would have been checked. If a value was missing or

found to be inaccurate after being checked, the publicly available report number was

substituted; otherwise, the survey response was used.

3.4.7. Study Two - Data Quality & Data Management All surveys were keyed into a spreadsheet specially set up for the capture of the data.

After data entry, the survey was proof-read against the spreadsheet by two separate

people to ensure accuracy. A unique identifier was placed on each survey and

recorded in the spreadsheet to ensure it could not be entered twice. Collation of the

data from Study Two was done through an Excel spreadsheet to derive the initial

scores and averages required for statistical analysis. This spreadsheet was then loaded

directly into SPSS for analysis to avoid any data entry errors.

3.4.8. Study Two - Analysis With such a small population and sample, statistical power was limited and complex

analysis would have little meaning. However, the small population had the advantage

that a census could be achieved, making statistical measures, such as standard errors

and p-values irrelevant. Therefore, the analysis consists of simple descriptive

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statistics (average, minimum, maximum and standard deviation), Spearman and

Kendal’s Tau-b correlations, graphical presentations of the data and discussion of

relative strength of perceptions.

3.4.9. Study Two - Validity & Reliability

The validity of Study Two was addressed in a number of ways:

1. Testing the survey instruments in advance with subjects similar to the sample

to ensure they were intelligible and appropriate.

2. Assessing the various factors through multiple questions addressing the same

or similar concepts.

3. Gathering a census, avoiding statistical issues related to inference.

Reliability has been facilitated through the design of the questionnaires with multiple

questions assessing the majority of factors and the dependent measure – IT adoption –

being assessed by three totally separate approaches.

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4. Study One - Executive Interviews

The people who live in the past must yield to the people who live in the future. Otherwise the world would begin to turn the other way round.

Arnold Bennett (1867–1931) British novelist.

STUDY ONE – EXECUTIVE INTERVIEWS

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4.1. The Interviews Described

4.1.1. Patterns of acceptances Invitations were sent to the executive responsible for the IT budget in four states and

one territory, one CEO of a Victorian Health Network6, and two New Zealand Health

Board CEOs. Of the eight invited five responded, two of these delegated the response

to their IT managers. As the IT managers were outside the desired population these

interviews were declined. Therefore, 3 interviews were undertaken.

The subjects were all senior, experienced state health managers, each with at least 20-

year careers in the state health industry. One was a doctor turned administrator,

another a nurse turned administrator and the final one a career administrator. Each

was between 40 and 60 years of age and had been in their current roles for at least 18

months.

4.1.2. Features of the Interviews Each interview took between 45minutes and 1 hour. The participants showed strong

interest and good knowledge about the purchasing processes and reasons for investing

in IT. They were equally clear about the reasons for not investing in IT. Each

participant talked freely and with minimal prompting and encouragement. Although

the interviewer had a checklist to ensure areas of specific interest were addressed,

these were hardly required due to the enthusiastic and expansive nature of the

participants’ discussions. There appeared to be no evasion of topics or obvious hiding

of information.

The executives demonstrated good awareness and understanding of the current state

of IT. The overall attitudes displayed by the executives were analytical and appeared

objective. Each of the leaders was experienced in IT adoption decisions within their

organisation. They spoke with conviction, knowledge and authority. It appeared that

saturation had been achieved as the third interview raised no information that had not

been raised in the previous two interviews.

6 As before, a hospital was chosen in preference to the Victorian State Health Department due to the nature of the organisations. The Department of Health acts as a policy body and has delegated operational decisions, such as the procurement and deployment of IT to the health networks.


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4.2. Coding described The following section describes the coding derived from the interview data. The

overall coding “tree” is shown in Figure 4.1 on the following page. The meanings

applied to the codes and the significant meanings discovered within the interviews are

explained in the later sections of the chapter, following the structure of the coding

tree.

4.2.1. The Coding Process As noted in Chapter 3, coding was conducted using the NVivo analysis software.

Identified topics were allocated a brief, coded description and related texts were given

the same coding. The coding began with low-level concepts being identified prior to

axial coding. The axial coding took into account the a priori categories provided by

Innovation Diffusion Theory. Where these categories fit completely and

appropriately, they were used as the highest level in the tree, however, where a factor

did not obviously belong to an a priori category, new nodes were created.

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ORGANISATIONAL FACTORS

TECHNOLOGY FACTORS

SOCIAL FACTORS

TYPE OF DECISION

Interview Coding22/11/2003 - v6

Slack Low slack

Leader Characteristics

Leader as an enhancer

Core competence

Support from IT mgmt

Good view of IT

View IT as change agent

Leader as a barrier

Skeptisism re benefits Imprved understanding of benefits

Undefined need

Poor view of IT Poor support from IT industry

Political decision making Political influence

Risk averse

Low acceptanceLow clinical acceptance

Improving clinical acceptance

Leader funding in big bites

Other priorities

think there are some major barriers

Expectations Business benefit expected for invest Expectation of high benefits

Size

Size as a barrier Fragmented

Size as an enhancer Large org

Centralisation

Centralisation as a barrier Centralised

Centralisation as an enhancer

Complexity of organisation

Organisation Complexity as a barrier High complexity of org confused needs

Organisation Complexity as an enhanc

Formalisation High formalisation

Openness Not open to outside ideas

Interconectedness

Observability Low observability

Relative Advantage

Benefits as a barrier

Unclear benefits

Low benefits

Poor view of IT

Benefits as an enhancer Good view of IT

Business benefit expected for invest

Trialability

Trialability as a barrier Low trialability

Trialability as an enhancer

Compatability

Undefined need

Poor support from IT industry

Poor compatability

Low clinical acceptance

Low acceptance

Complexity of technology

Technical Complexity as a barrier

High complexity confused needs

Technicaly complex

Technical Complexity as an enhancer High technical complexity

Social barrier

Social involvement

Social acceptance

Social disinterest

Political decision makingPolitical influence

Central decision

What happens when a new IT project isinitiated

Concensus decision

Decentralised

Incremental budgetting

Investment allocation

Figure 4-1 The Coding Tree


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4.3. Organisational factors

Within Innovation Diffusion Theory, Rogers (1995) promotes the significant factors

for an organisation’s level of innovation as being7:

• Slack

• Leader Characteristics

• Size

• Centralisation

• Complexity

• Formalisation

• Interconnectedness

• External Openness

The following sections present the topics raised in the interviews as they fit to Rogers’

factors followed by analysis and review of the meanings presented.

4.3.1. Slack Slack is Rogers’ measure of the spare resource available within an organisation.

Higher levels of slack encourage innovation.

Findings Regarding Slack The interviews identified that state health organisations have few, if any, surplus

resources or capacity. This is termed low slack. For example, one subject

commented:

“There is always a demand, far bigger demand for capital than the capital we’ve got available of which IT would be a portion of. I can think back to my first year when I was chief executive at[a health organisation] I think I had $36 million capital requirements which people considered priority one and I only had $6 million that we were going to invest in capital.”

At no time in the interviews did the subjects suggest that they had spare resources. 7 See the appendices for the published paper that gives an analysis that examines these variables and their role in health (England et al., 2000)


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Slack Discussed The leaders were clear that their organisations were stretched achieving current

outputs with available resources. In addition, they commented that each year the

demand for capital expenditure significantly exceeded available funds. This is a

strong indication that the state health organisations have low slack. Based upon the

theory (Rogers, 1995) this will act to slow down the organisations' ability to innovate.

Therefore, it seems likely that that current resourcing levels are inhibiting the uptake

of IT within state health organisations.

4.3.2. Leader Characteristics Rogers identifies leader characteristics as a key influencer on innovativeness. This

section presents the comments by the subjects that demonstrate their beliefs and

behaviours towards IT innovation. These have been grouped as background

characteristics, enhancers and barriers.

Findings Regarding Leader Characteristics

Leadership background It became apparent that state health leadership is a complicated role and possibly quite

different to leadership in other industries. These differences seem to relate to the lack

of unity of purpose (Braithwaite et al., 1995), tribalism (England et al., 2000) and

barriers between professions as well as the strong professional alignment of the

clinicians. In particular, the leaders believe that clinicians do not take an “enterprise

wide” view of health, nor necessarily a positivist or rationalist approach to

investment. This appears to constrain the leaders’ ability to make optimal economic

decisions, requiring them to consider political/social factors. As one subject

commented:

“[Health] is not a single industry either... …There is still a craft mentality if you like, almost Masonic in its manifestation in many of the clinical specialties and a lot of that is supported by the fact that what they are doing is to significant degree an art form anyway and not so much a science. There is not a lot of evidence base to what is done. So, you know, they do actually tend to be at odds with each other. You’re never going to get every one to agree that the next best thing in your IT world to implement is the radiology system.


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Somebody who is in something else is going to say, ‘No, implement the chunks in my area.’ ”

In addition, the leaders seem to believe that the demand for IT within state health is

potentially limitless and must therefore be subject to rigorous cost control. For

instance:

“It seems to me that you could keep investing in IT forever and a day and it still wouldn’t be able to meet everybody’s needs, there will always be something hanging out there that you could add next.”

The subjects clearly expressed their expectations of IT. Each of them had clear

requirements that IT projects must meet to be eligible for adoption. The first

expectation was that IT must deliver a business benefit to receive continued capital

funding and also that when competing for resources the benefit must be more

significant than other alternatives.

“ …[if we] built our business cases correctly and based our capital decisions on sound business and the benefit is still there then we will keep increasing the expenditure in those areas if they are still driving the same [return] compared with the alternatives.”

“Well it comes back to what reward they’re going to produce for the organisation. In health there is always a challenge of Buildings vs. IT vs. Staff Development and a whole range of issues that you would call capital related. The reality is that you have to look at where you can get the most gain for the resources.”

It also appears that the level of funding is not necessarily restricted; rather that

funding can be applied should suitable benefits be evident. It was unclear, however, if

this funding was at the expense of other initiatives, which would seem to be so,

considering the low slack identified.

“Well I think it is a matter looking at our business and certainly identifying businesses and looking at whether their opportunities can support the business. And, if there is a viable response we will fund. There is no barrier with a lack of funding to put in IT.”

However, against this background of unlimited demand for IT, perhaps combined

with enthusiastic, uncritical views of IT’s benefit from those seeking funding, the


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leaders retain their expectation that IT projects must return benefits to receive

funding. It is also clear that the leaders do not automatically assume that IT is a

worthwhile investment. Rather they approach IT adoption and investment on a

rational, case-by-case basis.

“Everyone tells us that there is an opportunity. We don’t know that until we test the market. We test the market and see what it can offer, then we develop a business case on the basis of the partnership between the market and our own organisation. Which in turn will lead into a funding service if we can deal with the rates of value to the organisation.”

Leader as an enhancer The leaders demonstrated some positive beliefs and actions that enhance the adoption

of IT within health care, though in reality these appear to be luke-warm endorsements

and fail to recognise any compelling return on investment. Firstly, they expressed the

importance of IT to them by expressing the belief that IT is now a core competence

required by health organisations.

“… I think IT is now a core competence for everybody.”

They also recognised the faith and demand within areas of the health organisation for

increased funding of IT.

“…if you talk to IT people they will say it is insufficient…”

In addition, IT is seen as change agent, having a core role in the implementation of

any future changes being implemented in health. They expressed the belief that IT

would empower and facilitate change by making information and knowledge

available. However, this did not recognise the ability of IT to be the change itself,

rather they saw IT having an indirect role:

“It is the key to change; absolutely the key to change. The technology’s there to support the information and the information is the knowledge that will create change. And this is an industry that needs really good information to create change.”


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In a more limited sense, some of the leaders expressed a good view of IT’s outcomes

for their organisation. However, the following quote, the main one supporting the

positive outcomes of IT, gives credit to the collection and structuring of data enabled

by IT:

“I think that probably in [our state] we are doing well. We have been lucky; we have made some decisions along the way. We have a unique patient identifier effectively in fact. A 25-year period or something we’ve got the best data history by a long way and probably in the world, I think ours is ranked as one of the top 5 collections in the world.”

Leader as a barrier The leaders spent considerable time discussing problems and challenges surrounding

IT. These attitudes combine to give the impression that the leaders hold negative

views about increased IT adoption. Many of these issues relate to the technology and

are presented later on in this chapter.

The major theme expressed by all subjects is a relatively poor view of IT, its

achievements, its fit within their organisations and the performance of IT vendors.

When asked how IT had benefited the organisation, one leader responded:

“Probably poorly actually. I believe that we don’t have IT being led by a strong information culture or recognition of strategic importance of information.”

The above shows that one issue is the way health uses information. After all, if

information is not a key resource then innovations that improve its use will not be

valued. Again, in other comments the negative view of IT, the way it is used and its

acceptance within the organisation was identified:

“I would suggest that if you polled enough people you would get either a neutral to negative position on IT and I think that because I don’t believe that our systems are being used to assist our decision making to the extent that they could.”

“IT will be viewed depending on how user friendly the system is or whether it meets requirements and there are variable thoughts on that.”


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“I mean everybody loves email and all that, but it doesn’t really help your decision making faster. If any thing it slows it all down, because of the ‘Because I can send information to you syndrome I will’. Well, if it is relevant or not is not the question. But, because I can send a 500-page report to 60 people at the click of 2 extra buttons I tend to do it. The fact that those 60 people then have to spend time working out whether they are going to read it or not doesn’t factor in my decision making. You know email is one that I personally am not comfortable with totally, because I don’t think it is a system that we have worked out how to run. But it is there, just not helping our decision making really.”

The poor perception of IT is not only due to the role of information in health, nor the

appropriateness of IT’s use. Rather, the leaders have luke-warm to negative opinions

of the true clinical value of current IT:

“… things like adverse drug reaction type set-ups and some more clinical processes are mapped into our computers. So the computers are helping us with some of our final decision making and that perhaps drives quality. That is an area that we are just on the verge of implementing I reckon. I think most of our systems at the moment just help us manage patient information, they don’t actually guide the decision.”

The strongest criticism, however, is reserved for the IT suppliers, their performance,

their products, their cultures and their behaviours. The leaders expressed their views

strongly, with passion and anger. They cited many, many examples. A representative

range of these quotes is given below:

“I am a bit of a cynic. I think the IT industry they had a few good big lunches on health and everybody else. I think the IT industry is still a stand out industry in the world at the moment in as much as they pay themselves a hell of a lot more than anybody else, they drink a lot more booze and food at lunch and do things with a lot more glitz and glamour, and for an industry that probably hasn’t produced the goods yet to my satisfaction I think that is inappropriate. Quiet frankly, it amazes me that an average or a good IT person in the private sector can earn more than the [top health executive]. When there is just absolutely no comparability of the responsibility taken, that just staggers me.”

“I think the IT industry by and large has enjoyed health and everybody else as their customers, and they charge like wounded bulls and if people did real analysis of what they are getting I am not sure that the benefits are there in every case.”


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“Half the [IT] products we buy we only implement half the functionality because they are so incredibly complex that it takes you 4 years to work out what those elements actually do, and by then it is time to put the new version in. I think we haven’t worked out as an industry, and that is both the users and other people, properly saying what they want and the IT industry building it. I don’t think we have actually worked out what we want. You’ve only got to think of your own desk top and the amount of information you get when you get a copy of Windows I mean it is 800 megabytes long or something now, and its got infinite details in it that I know somehow somewhere allow the most exquisite hand crafting of my desk top environment, but it is not practical, it is not useful.”

“Where the issue is, is in the clinical areas, because nobody has produced products which mirror the way people work in the clinical area. Alternatively, there is a need to modify the work practices. There has been no justification to date of how IT can actually create better work practices to produce a dividend. So it’s this barrier of how clinicians work and it doesn’t mean to say they can’t change but we still haven’t had a solution which mirrors a good outcome for both clinicians and computers.”

“…2 years ago a number of us went and had a look at the 3 major vendors’ best six sites and we saw nothing that gave us any impression that they were successfully addressing the challenge.”

“The bottom line is we’re not prepared to take risks having had so much experience with vendors who don’t perform. We’re not prepared to take those risks of saying’ have I got the answer for you’, when we are the ones who pay and the vendors just make money.”

“A lot of people have been burnt in health and IT associated with outsourcing or co-sourcing and costs of an effective system and things like that. I think that has forced people to stand back a bit.”

“Well it is interesting, I sometimes think they, in some areas they do it well and in some areas they don’t do it very well at all. It is understanding the requirements and working together…But certainly many people perceive IT has been there to take money out of the industry.”

After expressing these emotions, the leaders provided additional facts about their

belief in the poor performance of the IT industry.

“…the way the IT world has behaved, in terms of trying to drive the agenda and therefore perhaps if you like, I think the information aspects have been almost subordinated to technology at times. You know, “this is the only product you can buy so this is what you get and you have to structure your business this way if you want to use this product”, that sort of thing.”


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“…if we don’t get stuff a whole heap more automatic, rather than adding more and more burdening in terms of having people pumping information into the computers, then I don’t think we are going to reap the potential benefits. I think they are all there and some of what we have had to do has been the homework, I accept all that and groundwork. But I think we have done more ground work and not enough reward reaping across the whole IT system so far, and pumped a lot of information into the system and I don’t believe that we are using it enough yet.”

“I am very much into appropriateness of technology and that is really a big thing of mine. And I think the IT world is the worst in terms of that at the moment, although I think that it can get there and maybe if it does then all the work we’ve done will just be seen to have been good foundation work.”

“The challenge is that we haven’t had solutions to most problems.”

“In other words we’ve got a very diverse IT industry each trying to look at market share, but none of them producing anything that mirrors the needs of consumers. I have tracked this throughout the world in many countries, Europe, America, and here, and there is dissatisfaction wherever you go unless you have a home-grown portion which can be very expensive.”

The inability or difficulty of measuring the value of IT remains a barrier to its

enthusiastic acceptance. This means that many decisions are based upon the value of

IT as a facilitator of change, rather than the technology’s innate value:

“Now I think that is one of the main issues for health, the lack of ability to demonstrate the impact of IT.”

“… the Board asked me to go back over the business cases to whether the $26 million had paid its way. No one had been able to demonstrate it, and I said to them unless you set clear criteria at the beginning as to how it is going to pay its way it is very hard to measure during the process. Most people would argue here that you can’t measure it. I would have some challenge to that, sometimes it is difficult because there is 2 arguments, one is sometimes it is difficult to be precise cause it is a contributor, and other cases I actually have seen it bring a return. But for me it is part of a change process.”

Reinforcing this, whilst the leaders remain sceptical, they also perceive limited

acceptance of IT across their organisation:


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“I mean to actually get to where we need to be we must to do a significant technology change again, but the world is not ready for that just yet but we have to start planning for it.”

More specifically, they feel there is low acceptance by clinicians, for a number of

reasons, including ease of use, appropriateness and political factors. They voiced this

perception extensively:

“As soon as somebody puts in a true clinical decision making support tool it’s actually going to challenge their autonomy in patient management, and I am not sure anybody is arguing about that either, except maybe some of the clinicians…”

“If you talk to clinical people they see the level of investment in IT and health in [location] has been exorbitant.”

“If I look at capital costs prior to my coming here, they spent about [$XX] million on a new system. Now, that was considered relatively high relative to some of the sector, not all the sector, and certainly not other tertiary institutions. But for staff who were seeking basic capital equipment, like surgical equipment to undertake procedures, and these procedures were being cancelled, this was quite horrific.”

However, the leaders also perceive that attitudes may be changing in clinical areas by

allowing improved quality of service, if not tangible benefits:

“…I think they are comfortable with the technology and I think they are now becoming more aware of how the systems might actually help them in their true core business practice.”

“…the quality of service that [our computerised call centre] allows is just stunning. You can be confident that everybody is getting constant information, and providing the information that they’ve been getting has been vetted by enough people, you can then argue that you are behaving in the safest possible way that current medical science allows you to pursue. We do that, say, in the call centre, but we haven’t got to that level yet within the main IT system.”

“I think the clinicians are now ready for it, I don’t believe the clinicians have been overly keen on using some of the systems. I think a lot of the clinicians are interested in computers because they are interested in technology, but I think they are much more happy with it keeping out of their core business.”


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“I think now there are people who are understanding that they can use the computers in their core business to some significant advantage.”

“So, I think the mood is now changing and people are prepared to move forward on some of that.”

A further barrier to adopting IT is found in the leaders’ belief that the need for

additional IT is unclear or undefined. With an unclear view of what is required, it is

unlikely that managers are willing to commit scarce resources.

“What we don’t have, and God knows when we’ll get it, is patient blood pressures and temperature charts. Once again, we could determine the relevance of them and determine whether or not anybody actually wants them or what do we just deal with discharge summaries and case notes electronically and say that forms the map of a record. I don’t know.”

“One of the reasons for lack of ownership, and maybe what’s perceived as lack of investment, is the ability to demonstrate, or for people to understand, what is good information and what will really measure up, because we’ve been cramped in with that for years. What are the key indicators of good performance? You know what I mean? What should we be measuring?”

“Information therefore is exceptionally important to us; I think we need to more clearly articulate information’s role and its importance and if we did that IT would be recognised as a pretty good enabler.”

“I don’t feel yet that we have moved to the point were we’ve made the information the clear and predominant driver.”

“I think we haven’t worked out as an industry, and that is both the users and other people, properly saying what they want and the IT industry building it. I don’t think we have actually worked out what we want…”

Another significant characteristic demonstrated by the leaders is their risk-averse

nature. Being risk averse is likely to lead to a low willingness to adopt innovations.

This risk aversion shows up in their absolute belief that patient service must not be

compromised. When asked about the willingness to trial innovations they

commented:

“Well I don’t mind it as long as there is not impediment to service delivery in the process.”


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“So I think there are some major barriers in health because of the ultimate outcome being the most poor of the lot. I mean, if the banks stuff up they might stuff up at the point of view of just financial side of it, if health doesn’t do a good enough job it affects people.”

The risk aversion relating to IT is reinforced by the leaders’ perceptions of their

previous IT projects, which appear to have led to a resistance to further innovation.

As a final theme, it was clear that the leaders had other priorities for investment of

time, energy and resources. Some of these priorities are ensuring that the basic

provision of health services continues and that safety and quality is maintained.

Against these basic needs, IT is a low priority, perhaps even a luxury:

“Basically then there is a rigorous process by which we prioritise towards criteria. They have to demonstrate how they relate to the criteria that relates. The first thing that gets it is safety. In areas like safety and quality and I mean quality, safety this isn’t about going from a Mini to a Rolls Royce, this is about let’s keep them running basically, let’s be able to undertake the surgical procedures.”

“So, safety and things that will bring an additional revenue therefore pay for itself and things like that. And when you really get to the top end of the scale, we just don’t get to invest in, that’s really around the some of the elevated stuff, or stuff that won’t bring a financial return but would enhance quality.”

“So basically, my feeling is, my judgment for the last 12 months is they haven’t even been able to cover all of the safety issues.”

“So there is a series of criteria every case is looked at against these and then there is a capital list that is done for the next 12 months and basically I also keep a contingency. I have a records group that actually looks through the clinical, and corporate people who actually assess all of that, meanwhile there is also an IT strategy and behind it is where our priorities are and what we are going to invest in. And then we just accordingly prioritise that and cut cloth according to what we were doing overall as an organisation. At the end of the day that gets thrown in the pot with the rest of the capital requirements.”

Leader Characteristics Discussed The attitudes of an organisation's leadership are claimed to be a major determinant of

organisational innovativeness (Rogers, 1995). Therefore, the attitudes and beliefs


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displayed by the leaders in Study One are of great significance to understand the

likely IT innovation behaviour of the organisations. Nearly every facet of the

interviews demonstrates the subjects’ attitudes, not only those topics specifically

relating to their beliefs. Rather, as posited by symbolic interactionism, their expressed

opinions about technical issues, organisational issues or social issues represent their

beliefs and create the social reality within which they operate.

Overall, the leaders demonstrated thinking that is likely to act as a significant barrier

to their increased adoption of IT. In a very limited way, they exhibited positive

attitudes to IT, such as by recognising it as a core skill for their organisations and that

IT can have a key role as an enabler of change. However, this tended to be their view

looking forward. The actual experiences and comments of the executives were,

overall, critical and negative.

The negative attitudes the leaders displayed far outweighed the limited optimistic

views. They expressed disappointment with current achievements. A number of

areas stood out as concerns that can be grouped as follows.

1) the nature of health organisations;

2) the value of IT;

3) the achievements of IT; and

4) their personal values and priorities.

The leaders’ beliefs about the nature of health organisations Firstly, the leaders believe they are operating in a challenging environment made up

of many factions and interest groups. They feel that unified, enterprise-wide

decisions are not going to be easily achieved as each group operates to its own

paradigm. This belief must act to stifle their willingness to invest in fundamental

enterprise-wide IT solutions, as they cannot expect to gain support and commitment

from a significant proportion of their organisation. This contrasts with most other

enterprises that have managed to implement and benefit from basic, core, enterprise-

wide IT that have facilitated enhanced organisational performance. In addition, this

belief about the factional nature of the organisation combined with their experience of


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the political behaviours of groups appears to lead them to decisions that disperse

resources across many groups rather than centralised the resources on a few major

initiatives.

Finding 1: The leaders’ perception that health organisations are factional leads to a belief that enterprise-wide projects are difficult to achieve. This acts as a barrier to increased IT adoption.

The leaders’ beliefs about the value of IT The health leaders seem to operate in a vacuum about the value of IT and were

generally sceptical about the benefits delivered by IT. They held realistic commercial

expectations that benefits would exceed costs but were unable to get measurements,

accurate feedback or a real sense of the contribution IT was making. The consensus,

which was largely based upon intuition, was that at best IT pays its way, but more

likely, that the business case is not strong. However, this is in conflict with the

leaders’ beliefs about IT investment that requires investments to show a sound return.

Finding 2: Leaders expect IT to make a strong return of investment yet have no factual basis for assessing this return. This acts as a barrier to increased IT adoption.

Adding emotional fervour to this feeling are the actions of the IT suppliers. The

leaders expressed, in direct, clear and strong terms, their mistrust of IT suppliers.

They believe that the IT industry has had large profits from health whilst delivering

little value. The social and sales actions of the IT supplier community seems to have

caught the leaders’ attention and inspires a degree of cynicism and mistrust.

Finding 3: The behaviours of IT vendors are not compatible with the culture of the health leaders. This conflict is likely to make IT investments poorly regarded in comparison to other capital expenditure. This acts as a barrier to increased IT adoption.

The leaders’ beliefs about the achievements of IT Secondly, the executives were critical of the degree to which their IT departments and

IT suppliers actually achieve their intended objectives. The leaders base this belief

upon a number of observations including IT’s poor support of the work practices


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found in health. This was particularly an issue for clinical areas where they perceive a

poor fit for IT in clinical settings and a corresponding resistance to IT from clinicians.

The executives all stated that IT is not currently compatible with clinical

environments and remains poorly accepted by clinicians.

Finding 4: The leaders’ experiences of IT projects, reinforced by similar experiences of other staff, make the leaders reluctant to invest in IT. This acts as a barrier to increased IT adoption.

The leaders’ values and priorities The leaders, whilst sounding confident, strong and decisive also demonstrated a risk-

averse nature, especially where patient service or public scrutiny is involved. Earlier

studies have concluded that a low risk-taking attitude will reduce an executive’s

willingness to innovate (Tabak et al., 1999). Combining this with the leaders’

perception of the lack of observable or trialable IT solutions creates a significant

barrier to the adoption of IT by this group.

Finding 5: The leaders demonstrate a risk-adverse nature, yet perceive significant risk in IT projects. This acts as a barrier to increased IT adoption.

In addition, the leaders demonstrated that they had many other priorities against which

IT had to compete. They all emphasised a rational economic approach to investment

evaluation, whilst noting the political nature of the final decision. This is further

strengthened by the findings of low slack, above. In addition, the leaders showed a

primary concern for patient safety and increasing income streams. With the uncertain

contribution of IT to these – or any other factors – then IT is not a high priority

investment.

Finding 6: The leaders face considerable demand for scare resources, and due to the uncertainty of IT performance, give it a low priority. This acts as a barrier to increased IT adoption.

4.3.3. Size Another factor in innovation is the size of the organisation. Larger organisations tend

to be more innovative due to increased need and increased capacity to innovate.


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Findings about Size As noted in the framework, organisational size is a factor in the innovativeness of the

organisation. The leaders shared their perspective on the size of health organisations:

“…it is not a single industry either I mean it is all very well to talk about an industry but you go and try and talk to the GPs and ask them what they think of the hospital doctors and ask the hospital doctors what they think of GPs and it is not all bouquets and roses. Basically they, the people who do radiotherapy, reckon that the cancer cutters are idiots and got it wrong, and all there is still a craft mentality if you like almost sort of Masonic in its manifestation in many of the clinical specialties…”

Therefore, it appears, as identified in the literature review, that although health is an

enormous industry, it does not behave in that manner, rather acting in the manner of a

tenuous alliance of professional or departmental groups. This makes many

behaviours more in-line with those of a small organisation.

Size Discussed Health organisations are certainly amongst the larger organisations in most cities,

however they do not act as large organisations. Previous research has identified that

health organisations act in a fragmented manner, working along professional and

departmental lines (Braithwaite et al., 1995; Degeling et al., 1998). This study

confirms this with several comments made about the internal political decision-

making and the power of professional groups. This finding also relates to that of

interconnectedness, where the "tribal" nature of health organisations contributes to

poor internal communications. This study therefore concludes that despite the

apparent large size of health organisations, they continue to act as an alliance of small

organisations. Size positively correlates with innovativeness (Rogers, 1995),

therefore health's granular organisation can be expected to result in a level of

innovativeness below that of similarly sized organisations.

Finding 7: Despite the large size of health organisations, the low level of interconnectedness and strong clustering of employees into professional groups creates the effect of health being many virtual small organisations. This acts as a barrier to increased IT adoption.


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4.3.4. Centralisation Centralisation of management and control is an important factor in an organisation’s

innovativeness. Increasing centralisation reduces innovativeness due to the lack of

ability of most of the workforce to take decisions and adopt innovations.

Findings about Centralisation The leaders talked about their IT strategy and expenditure being centrally controlled.

“…we do actually have core central systems which are applied across a group of hospitals and in fact the way that’s done at the moment is the rural hospitals have one particular common central system and metro have another one.”

“…systems are centrally operated and managed…”

“Now that core stuff, the actual capital investment, the implementation, the management of the network and the management of all the services and the data centre and all that is centrally funded. We buy it from the [central government] perspective and say it is here you may now go ahead and use it. We try to get the hospitals to use it in a similar format…”

“[IT management and control is] very central, well in [my health organisation] we only have one solution we don’t allow any other opportunities for people to do their own thing.”

“At the moment it is centrally managed.”

“…I basically believe it should be driven by an integrated strategy…”

It appears from all of the subjects that IT is a centrally controlled function.

Centralisation Discussed Increasing centralisation reduces innovativeness. The interviews revealed a

dichotomy within health, though a clear resolution for this research. It is apparent that

health is a mixture of central and decentralised control. This aligns with the granular

nature of the health industry. Clinical and patient-centric functions devolve to the

individual clinical teams whilst control of corporate functions, such as finance and IT,

is central and tight. Each leader gave a clear indication that decision-making and

funding for IT are centralised with little or no room for local initiatives. Perhaps this


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dichotomy contributes to the difficulty of making IT innovations. In general,

clinicians, being part of a politically astute and well organised profession, are well

connected, able to wield power, and are accustomed to making most of the decisions

that relate to their work. Yet clinical IT would be a centrally controlled project

causing major changes for the various professional and departmental groups, a

significant and obvious change to the status quo of clinical decision-making. This

may create a backlash of resistance to change that is not purely based upon technical

and economic issues. Therefore, whilst the centralisation of IT is likely to hinder IT

adoption, the dichotomy of health’s control may also magnify this effect through

political or power issues.

Finding 8: Health’s multiple power structures ensure clinical freedom within the larger enterprise but conflict with the centralised approach to IT implementation. This is most clearly seen in clinical areas where enterprise-wide IT adoption remains slow.

4.3.5. Complexity of organisation Complexity is the measure of how complex an organisation is. More complex

organisations tend to have greater needs for innovation and greater skills to facilitate

new technologies.

Findings about Complexity The leaders presented few direct statements about the complexity of their industry.

However, it was implicit in many statements where they talked about how difficult

their industry was to work in. Rather than viewing what they did as complex (it

seemed to be a given that health is technically complex) they saw their environment

as complex and difficult – with politics, professions and factions. However, one

comment directly showed the highly complex nature of health:

“… a lot of industries deal with a range of products and it might be 2 products or 200 products, when we deal with a multitude of variety. I mean, if you look we could deal with up to 2000 to 3000 different products and services. That is more complex, and that is one of the things that I have found is the complexity of our requirements usually tends to be significantly more and that is why I keep working to say lets bring it back as simple as possible, cause we can get swamped by the complexity.”


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Complexity Discussed Innovation Diffusion Theory asserts that complex organisations tend to be more

innovative than less complex ones. The causal factors behind this are thought to be

that complexity leads to an increase in solution-generating behaviour and the presence

of higher skilled staff. The leaders clearly believe that theirs is a highly complex

industry.

Finding 9: The health leaders believe health to be a highly complex environment. This should lead to increased innovativeness.

4.3.6. Formalisation Formalisation is the measure of how standardised and procedure driven an

organisation is. High formalisation stifles innovation.

Findings about Formalisation All leaders indicated a formal approach to the allocation of investments in IT:

“Now management of that has actually occurred largely in the past by getting a group of those hospital representatives together if you like and have them effectively vote on where we were moving forward with the IT systems. So they would have say in the decisions but the accountability of the decisions still sat with me as the budget holder. Now in our latest development we’ve actually tried to transfer the budget over to an aggregation of those end-users and say ‘Right-o you’ve now got the budget which will guarantee to you that you’ve got the say as to what we are doing.’ And that’s how we run the core and central systems.”

“From my point of view you still go through a normal process, so if there is a solution the funding will be forthcoming, that is my experience.”

“…by having a rigorous capital expenditure process, whereby all of the areas submit their requirements, their prioritised requirements. And also, you’ve got your capital plan with some things that you’ve just got to replace; do you know what I mean? I would like to see five-year plans out and some services have got them and some haven’t.”

They also described a formal process for the management and control of IT selection,

operation and architecture.


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Formalisation Discussed This research provided few indicators of the level of formalisation in health, though

strongly indicated very formal processes for the procurement of IT. Each leader

readily described formal processes for gaining consensus, defining business cases,

selecting IT solutions and implementing them. They also indicated rigid formality

about local flexibility for IT solutions, with centrally defined systems being the norm.

This will degrade the rate at which health adopts IT innovations.

Finding 10: The health leaders support a formal, controlled approach to IT acquisition. This should lead to reduced innovativeness.

4.3.7. Interconnectedness Interconnectedness is a measure of the ease and frequency of internal

communications. More highly interconnected organisations innovate more easily.

Findings about Interconnectedness The leaders provided little direct comment about interconnectedness, though they

frequently commented upon the fragmented, political and competitive nature of health

organisations. The leaders, therefore, seemed to view health as a very political,

fragmented organisation with groupings that compete against each other. This quote

included earlier in this chapter was the clearest statement of this view:

“…it is not a single industry either I mean it is all very well to talk about an industry but you go and try and talk to the GPs and ask them what they think of what they think about the hospital doctors and ask the hospital doctors what they think of GPs and it is not all bouquets and roses. Basically they, the people who do radiotherapy, reckon that the cancer cutters are idiots and got it wrong, and all there is still a craft mentality if you like almost sort of Masonic in its manifestation in many of the clinical specialties…”

Interconnectedness Discussed Whilst no specific answers were given about interconnectedness, the interviews

provided a significant amount of information about the political and fragmented

nature of health professionals posited in the literature (Braithwaite et al., 1995;

Degeling et al., 1998). Consensus exists that health is not a single industry, fails to


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demonstrate common purpose, and that the groups within health competed and

disrespected each other. By implication, this would show a low level of

interconnectedness and therefore act as a barrier to innovation, especially of

enterprise-wide initiatives such as IT. This supports finding 7.

4.3.8. Openness Openness is the measure of how well the organisation listens to, and learns from, its

external environment. Organisations with greater openness tend to innovate more

readily.

Findings about Openness The leaders gave little indication of learning about IT from other industries, though

they frequently visited other health organisations. They did indicate that clinicians,

within and outside of the health care organisation, have a tendency to operate in silos

and preserve their independence. When asking about their influences and sources of

ideas the leaders provided answers within their own industry or the health IT industry,

never sources from other arenas. Prompts from the interviewer in this area met with

responses about the difference of health to other industries.

“I haven’t been looking specifically, but I listen. Dave Garet(President of the major US health informatics society) is a regular visitor here, and if he or some one similar comes and tells me they’ve found the ideal product then we will probably jump for joy.”

“Ring up all the GPs and say would you give us your practice details and we will put them on the computer and they say what do you want that information for and why?”

Openness Discussed The leaders demonstrated little knowledge of IT solutions and IT management

practices in other industries. They discussed issues within their peer group in other

health organisations but maintained a relatively closed view to other approaches to IT.

Finding 11: The low level of external openness about IT in health contributes to the barriers to IT innovation.


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4.3.9. Organisation factors summarised It is apparent from the interviews that health organisations are difficult entities in

which to adopt IT innovations. The interviews gave a clear picture of the mainly

negative state of the innovation factors within health organisations in relation to IT.

Whether this is a truly accurate picture is not a fundamental concern, as, at the very

least, it indicates a negative attitude among the leaders regarding IT and its

applicability to their organisations. This alone will have a major impact on how

rapidly IT innovations diffuse in health. If, instead, it is accurate that nearly every

innovation factor is degraded with respect to IT, then the phenomenon of slow IT

adoption in health can reasonably be expected to occur.

4.4. Technology factors Within Innovation Diffusion Theory, Rogers promotes the significant factors for a

technology’s level of innovation as being: relative value, complexity, compatibility,

observability and trialability. The following analysis will examine the leaders’ views

about these constructs. These technology findings are, by their very nature,

subjective, representing the leaders’ beliefs rather than objective measures. However,

it is these beliefs, not the objective facts, which underpin the leaders’ behaviours;

therefore these beliefs are the important concepts that must be understood.

4.4.1. Relative Value Relative value is the measure of how much benefit an innovation delivers relative to

the current method. Innovations with greater perceived relative value tend to diffuse

at a greater rate.

Findings about Relative Value There was considerable scepticism about the relative advantage of IT investment.

This took the form of showing that the leaders doubt that IT is the most rational

economic investment that they can make. For instance, the following quotes show a

belief that whilst compelling arguments can be made about IT’s benefits, achievement

and delivery of these benefits has rarely been achieved:


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“…I would also put it to you whilst the IT people are probably very good at showing you a business case with all sorts of savings, I am pretty sceptical as to how often they actually follow through and achieve the savings.”

“I think every time somebody has introduced a new step of desktop computing we’ve not said “ok lets reap the benefit” and what we have actually done is immediately ploughed any benefits straight back into more computer, more software, more data and I don’t know yet that that is doing us any good. And just the simple fact that every time somebody builds a bigger chip with a bigger memory a new lot of software comes out and I don’t know how much more the new lot of software, that actually consumes that amount of memory or hard disk space, I am not sure how that in real usability terms has actually improved things. For me, one of the most important issues is that software and use of computers has not achieved a level of sort of intuitive interaction that is going to be needed to be truly useful. If we don’t get voice recognition maybe even simple things like, proximity detectors so you can know when a doctor’s standing next to a certain patient’s bed so when he starts talking it records that against the particular patient. Those sorts of issues, if we don’t get stuff a whole heap more automatic, rather than adding more and more burden in terms of having people pumping information into the computers, then I don’t think we are going to reap the potential benefits.”

More specifically, the leaders have either little confidence that IT can make a return

on investment, through to outright disbelief in the value of IT in the clinical setting.

These beliefs are backed by layers of experience, beliefs and facts, such as the speed

of technical obsolescence, or doubts about full recognition of IT cost. In fact, as noted

below, rather than being a benefit, IT is seen as a “necessary evil”:

“... if you are looking generally in health, then there is not a lot of return on investment in straight dollars terms.”

“Well I have doubts whether it pays its way in the reality of the world. I mean given the investment that [health organisation] has with 16,000 plus computers with devices plus all the infrastructure, the redundancy is so quick. I mean I was at a building site the other day where they were saying we are putting in all this cabling and yet people clearly indicated to be redundant in five years due to radio frequency. You know you’ve got to ask the question, when do you make your investment? Because of the redundancy factors, but from my point of view I’ve got some cynicisms that IT actually pays its way, but I suppose it’s become a necessary evil because you’ve got to do the work. These days, with the cost of labour, IT is probably fairly equivalent in some of these circumstances but I don’t think we still appreciate the full cost of it.”

“Now I think that is one of the main issues for health; the lack of ability to demonstrate the impact of IT.”


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“…you’ve got to look at it from a perspective of how it can add value strategically and there is a lot of doubt whether it does.”

When compared to other investment opportunities, IT is not favoured:

“…we’ve derived a benefit for what they offer but I think they aren’t as good as some of the benefits that a few things that we’re looking at in the clinical field might derive for us.”

In fact, although IT’s potential is recognised, it is doubted that health uses information

well enough to properly benefit from IT investments:

“Information therefore is exceptionally important to us. I think we need to more clearly articulate information’s role and its importance and if we did then IT would be recognised as a pretty good enabler.”

“…I believe that we don’t have the IT being led by a strong information culture or recognition of strategic importance of information, and therefore instead of IT being seen as something that is an enabling tool to support the information aspects of your business; and health is very much a knowledge business.”

More specifically, in clinical areas, there is great doubt about whether any value

exists, or even whether the systems work:

“We are talking about clinical service delivery. I am sure the banks wouldn’t invest in any of the services that are clinical, given the evidence we have that they don’t work.”

And the view of the IT industry and its ability to perform remains poor:

“And they just haven’t produced the goods yet.”

Having noted all the doubts about relative value, some acknowledgement is made of

value being derived from the data within IT:

“…there is a lot more usage of information to look at survival rates and treatment of intervention and all the others and that is really where the major benefit is.”


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Relative Value Discussed Innovation Diffusion Theory posits that the major influence on the rate of adoption of

a new technology is its relative advantage over the current technology. The

interviews with the health executives clearly showed their uncertainty or disbelief in

the value of IT. Whilst it may be possible to produce data and studies that disprove

this, it is important to remember that the perceptions of the decision makers count in a

decision-making process and, as described by Symbolic Interactionism, the executives

will act based on the meaning IT represents to them (Blumer, 1969). In addition, the

section above, about leader characteristics, shows a clear view that the relative value

of IT, especially in the clinical setting, is low.

Overall, this perception of poor or no value is very similar to that reported in many

previous works in other countries (Strassmann, 1997a; Thorp, 1998; CSC, 1998; CSC,

1999). When combined with the expectation the leaders hold, that IT must make a

suitable return on investment, it is likely that this perception of relative value is

hindering the uptake of IT innovation.

Finding 12: The value of IT solutions has not been properly measured and articulated. This is a barrier to adoption.

4.4.2. Complexity of technology The complexity of a technology is a measure of the difficulty with which it is applied

to an organisation. Low complexity encourages more rapid adoption.

Findings about Complexity The leaders indicated that IT is a complex innovation to adopt in many ways. This

complexity included not only the technology itself but its impact upon people, social

situations and business processes:

“Well the issue is its got to be covering all the bases as far as what is the interest of various stakeholders. If you are making a significant IT investment in health, which is particularly affecting patient care, you’ve got to accommodate privacy, you’ve got to accommodate what does it mean for the consumer, what does it mean for the provider, what is Big Brother’s approach


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to information data base and all of those sorts of things need to be addressed.”

“…because if we become unstuck, we are going to come unstuck on the information and the fundamentals around the information: the ownership, the access, the privacy, the confidentiality. None of which have anything to do with technology until you actually know what it is that you want and technology will probably offer you 25 different ways of doing it. But because the technology is that developed, but if you don’t actually start the social debate, form opinions, etc, so that it is all right to keep people’s medical records centrally and distribute them away to doctors. If you don’t get over that hurdle then don’t waste your time with the universally available electronic patient records.”

“..a lot of it is culture change and things like that. It is not so much that the technology that we’ve got is wrong.”

“I think there has been quite a lot of inexperience related to IT and what’s required, but I think that is really improving. The level of lack of criteria and lack of specification about what was required to what we actually do now; I think definitely there are some issues about understanding what is required.”

One view expressed is the continuing complexity and obsolescence of technology:

“I mean, to actually get to where we need to do a significant technology change again, but the world is not ready for that just yet but we have to start planning for it.”

More specifically the complexity of projects adopting IT was noted:

“…the first implementation of certain systems, we may have had to buy 1000 PCs and install them across the system.”

Complexity Discussed The complexity of IT remains a challenge to the health executives who find the

technology, its implementation, the politics surrounding it and the staffing to be

complex and a disincentive to act. This complexity combines with the leaders’ other

perceptions of relative value and risk to make new IT adoptions appear a difficult

proposition.


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Finding 13: The complexity of IT projects and the organisational issues they cause make it difficult for executives to support IT investment. This is a barrier to adoption.

4.4.3. Compatibility Compatibility is the measure of how well an innovation fits into the existing

organisations. Compatibility covers a range of dimensions including technical,

cultural, processes and social. Highly compatible innovations diffuse more readily

than lower compatibility innovations.

Findings about Compatibility The managers raised many questions about the compatibility of current IT with their

organisations. Initially e-mail, one of the most common applications of IT, came

under scrutiny. The following quote, though used previously, gives a clear view of

this:

“I mean everybody loves email and all that but it doesn’t really help your decision making factor if any thing it slows it all down, because of the ‘Because I can send information to you syndrome, I will.’ ”

In a more general sense, the leaders questioned the compatibility of IT with health.

Again, due to the clarity and relevance, repeating earlier quotes shows this:

“In other words we’ve got a very diverse IT industry each trying to look at market share, but none of them producing anything that mirrors the needs of consumers. I have tracked this through out the world in many countries, Europe, America and here and there is dissatisfaction wherever you go unless you have a home-grown portion which can be very expensive.”

“…a number of us went and had a look at the 3 major vendors’ best six sites and we saw nothing that gave us any impression that we were successfully addressing the challenge.”

“The challenge is that we haven’t had solutions to most problems.”

“…there was no perfect solution.”

This compatibility issue is most noticeable in clinical areas:


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“Certainly I think that the systems have been more financially focused with insufficient clinical focus, and I think that that is what will drive us, and I think that there has been a real distance between IT and clinical which needs to work together…”

Compatibility Discussed The leaders’ attitudes have already been identified that the compatibility of IT is poor

in the clinical setting. In part, the executives attributed this to poorly defined needs

whilst they also felt the IT industry had done a poor job at meeting their needs.

The compatibility issue, in many ways, blended with the complexity and relative

value issues to create a single impression of risk versus return. The leaders found that

IT did not fit their organisations well, especially in clinical areas. The combination of

these factors created a perception that IT was not yet justified and would not be

subject to increasing investment levels.

Finding 14: The senior managers do not believe that suitable IT solutions exist to meet their functional and process needs, especially in clinical areas. This is a barrier to adoption.

Finding 15: The low perceived relative value, the low perceived compatibility and the perceived high levels of complexity combine in the leaders’ minds to create a sense of high risk and low return. This acts as a barrier to adoption.

4.4.4. Observability Observability is the measure of whether it is possible to see the proposed innovation

in use in other places. Innovations that are readily observable to the adopter tend to

diffuse more rapidly than non-observable innovations.

Findings about Observability Several statements expressed doubt in the ability to see suitable IT in use. Many of

these statements have been noted previously. In addition, the leaders doubted that

desired outcomes were possible:


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“I mean, for us, we need to get all that technology with that interface into our system and I am not sure how doable that is any way.”

“…if he or some one similar comes and tells me they’ve found the ideal product then we will probably jump for joy.”

“Where the issue is, is in the clinical areas, because nobody has produced products which mirror the way people work in the clinical area.”

“…2 years ago a number of us went and had a look at the 3 major vendors’ best six sites and we saw nothing that gave us any impression that they were successfully addressing the challenge.”

More specifically, whilst the leaders have not observed suitable IT solutions, they

have observed failures:

“We are talking about clinical service delivery. I am sure the banks wouldn’t invest in any of the services that are clinical, given the evidence we have that they don’t work.”

“A lot of people have been burnt in health and IT associated with outsourcing or co-sourcing and costs of an effective system and things like that. I think that has forced people to stand back a bit.”

“… having had so much experience with vendors who don’t perform.”

Observability Discussed Underlying the previous findings is a lack of tangible evidence that IT is addressing

the needs of health organisations. This is particularly apparent in clinical areas. The

leaders clearly identified both the lack of credible sites where they could see the

technologies in operation and a history of failed projects.

Finding 16: The leaders do not believe they can see the IT they need operating anywhere in the world. This is a barrier to adoption.

4.4.5. Trialability Trialability is the measure of how readily an innovation can be trialled before

committing to whole scale adoption. Innovations that are simple to trial are easier to

adopt.


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Findings about Trialability The leaders discussed about their experiences implementing new IT. One of the

major themes is the complexity of implementation, which, in effect, makes trialling

too difficult:

“When we set up our call centre we ran with a set of software that comes out of the U.S. via the UK, and you are actually setting up decision making protocols. Getting the doctors to sit down and actually review those protocols and modify them for local conditions and all the rest, was a horrendously difficult process.”

As noted in the following quote, it takes so long to implement systems that they are obsolete when installed, this makes it impractical to trial them prior to adoption due to the effort required.

“Half the [IT] products we buy we only implement half the functionality because they are so incredibly complex that it takes you 4 years to work out what those elements actually do, and by then it is time to put the new version in. I think we haven’t worked out as an industry, and that is both the users and other people, properly saying what they want and the IT industry building it. I don’t think we have actually worked out what we want. You’ve only got to think of your own desk top and the amount of information you get when you get a copy of Windows I mean it is 800 megabytes long or something now, and its got infinite details in it that I know somehow somewhere allow the most exquisite hand crafting of my desk top environment, but it is not practical, it is not useful.”

Trialability Discussed The ability to trial IT innovations was closely aligned with the observability of them.

In general the interviews showed that significant IT innovations were not in evidence,

therefore trialling was not an option. However, comments on complexity also implied

that trial implementations were too difficult to consider so did not reduce the risk of

technology adoption.

Finding 17: IT solutions are too large and complex to trial. This is a barrier to adoption.

4.4.6. Technology conclusions Overall, the executives expressed views that will cause the slow adoption of IT. They

expressed clearly their lack of conviction that IT delivers significant benefits,


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especially in the clinical environment. They also were very clear that they could not

see the systems they wanted in operation anywhere else.

The concepts of complexity and compatibility were very closely linked for the

executives. They saw these as indicating the likelihood that they could succeed and

draw value from any IT investment. As such, these factors seemed key in the

assessment of risk and had a significant impact on the perception of the worth of any

relative value, or rather, whether the risk of achieving any value was high.

Observability and trialability also merged in the leaders’ minds, and they were certain

that no suitable systems existed. This shaping of the way the factors relate led to

further refinement of the theoretical framework, identified later on in this chapter.

4.5. Social Factors (Environmental / Policy Factors) One of the areas to be explored in this research was the effect of policy-level

pressures or social factors on the innovation process. Although not addressed by

Rogers, it seems reasonable to question the impact that community concerns, lobby

groups, political interests, labour unions, legislation or media attention has on the

executives decision making. These factors may influence the decisions or act as

constraints preventing decisions. For instance, does the risk of media interest in IT

investments influence the executives?

Findings about Social Factors When asked about policy, social or political considerations the leaders were clear that

they did have an effect on their decision-making:

“…their involvement is helping upset some of our agendas, so we are pretty keen on using our consumer groups and we’re into citizens’ juries and all sorts of things and picking people at random off the electoral roll for certain major health events.”

“… [the public] is wanting to get involved and government are having to involve them because the rationing aspect of health is becoming far more prominent and so to deal with that rationing we can’t avoid it we actually have to get the public involved.”


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This shows that any decisions involving widespread use of IT, especially where it is

used to manage patient information, is going to require scrutiny from public groups,

consumer groups and other citizen engagement approaches. The public reaction and

scrutiny of projects also means that errors around IT are magnified and publicised:

“…when I walked in here there had been a huge [IT project] situation here and I walked in I had to redefend us again, and basically it was all around IT. Now, if we had spent that money on medical equipment and god knows what else, I mean there has been loads of money spent on facility redesign that hasn’t attracted any comment at all.”

One key issue around IT in health is privacy:

“…it’s going to be an interesting one at the moment with the whole HealthConnect issue and the last time that was really put to society in 1987 for the Australia card and we all know what the answer was: ‘bugger off!’ Now, a bit like my argument before about how now people are beginning to become more comfortable with systems and know that if really somebody wanted to they could probably synthesize the data that they want or you can give it to them and make sure it is accurate. I think society is moving in that area and I think the HealthConnect issue if it is properly managed has a pretty good chance of getting up. But you know equally mismanaged it will be scuttled, so society is very pivotal in setting some of those expectations and the fact now that we’ve got much clearer privacy legislation and a much better understanding through debate and argument in the community, of what their privacy is and how they can help protect it.”

“So community want to get involved in the process of community decision making, community are now much more aware of their own privacy and other issues and are becoming more vocal on that. However, I also think that through becoming better informed they are now less inclined to say no. Categorically I think now there are better informed people and we can actually get some significant degree of informed consent.”

However, apart from prominent failures, and privacy concerns there was a general

view that society lacked interested in health’s use of IT, which also suggested the

leaders hold beliefs that IT is a relatively small part of their concerns:

“Look somebody could try and make a meal of it, but it is at a low percentage and provided there wasn’t some sort of fraud or naughtiness to actually get the media interested I don’t think that they would particularly care.”


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“I think in terms of hype they are getting more realistic and IT is sort of below 5 % [of our expenditure].”

“The point being I think it just disappears into the noise of this sort of distribution of funds around the traps.”

“None of that constrains us. I think that most people would be ignorant to what is happening. There would only be a few of the significant stake-holder groups, such as associations of Mental Health etc.”

The leaders also explored the political environment and constraints within which they

had to make decisions. The political pressures they face come from both the “big

picture” politics of State and Federal governments, as well as the intra-organisational

politics between the professions or other interest groups:

“We live in a political environment and in fact recently we were after some specific sort of new greenfield initiative. So, in that arrangement there was enough money to put a significant chunk into PACs and buy a PET machine and a few other things. At that level it definitely becomes a political decision, because medicine is as much an art form, well it is more of an art form if you believe half of what you read than it is a science, and so its really balancing up those competing demands, and it’s probably got to do with who is doing the screaming and why they’re screaming.”

“Exceptionally difficult; I mean the medical industry has got to be one of the hardest industries to work with.”

“I mean it is an industry where we don’t have, you know, worker bees, and everybody is intelligent. They all have an opinion. Most of them are actually interested and therefore most of them have an opinion. And many of them are exceptionally well politically connected or whatever else, and that is the big P and the little p and so getting any consensus on anything in the health industry is bloody hard.”

“Well political pressure makes no pressure at all to invest. IT is not something that, from a political point of view, is too much on the top of their list, because it is something that is fairly intangible. The history with most IT projects have a down side to them. So, I mean from my point of view, they’re not something that is warm and fuzzy politically, but at the same time I think governments want to see an investment in IT within the State.”

“Basically there is sort of a feeling that you shouldn’t overspend on IS, so I do think that has an impact on what people do.”


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Social Factors Discussed The leaders gave ambiguous messages about the social factors. They certainly

identified the public expectation of consultation on major IT and information

management initiatives. They also identified the relatively low impact of intra-and

extra-organisational political factors on their IT decision making, almost believing it

to be uninteresting to political interest groups. Therefore, having noted that the social

factors exist, they seem to discount them as a major influence upon what they do.

This conflicts with their stated views that they cannot make large expenditure on IT

without review, and that any errors will be subject to public scrutiny.

This seems to be an area in which the leaders themselves are unclear, or unwilling to

face their constraints. The leaders have previously been identified as risk-averse and

have also admitted that large expenditure or mistakes will receive public and media

scrutiny. It would seem likely that, whether the leaders acknowledge it or not, they

are in fact constrained by public visibility of their decisions and risk of error.

Finding 18: Public interest and scrutiny of IT investments acts as a barrier to the leaders taking decisions.

4.6. Type of decision The final main area of discussion examined the decision-making process used to

invest resources and adopt IT innovations. Simple, centralised decision making tends

to happen more quickly than extensive, consultatative, democratic-style decision-

making.

Findings about Type of Decision When discussing whether decisions were made centrally or de-centrally, the leaders

explored both facets leaning heavily towards central decision-making. Regarding

central decision-making, they supported the concept, but backed up by broader

consensus gathering:

“Well I think actually a few people have got to use their authority to make a decision but they certainly have to consult widely or it just doesn’t happen.”


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Again, a variation of central decision-making and control with broader consultation

was discussed. Though it is important to note that the selection is decentralised,

whilst the project control and specification remains centralised:

“I don’t believe [head office] alone should pick a system. What you have to do is sell by working on the information requirements, these are the requirements that need to satisfy the organisation, the service and the individual orders and so on requirements. Then you can say ‘Right, that is what we need to achieve, now we’ll get a system that delivers to that.’ Get a small group of clinical and corporate people and get them to look at as to how that system meets those criteria. You can’t have 500 people through a service. It’s the concept of buying what we need to get that information we need to. You know that information is important to us, because often that step has been left completely then saying, ‘Well alright, we will get you a user-friendly system.’”

Some decentralised aspects are noted, though these seem to relate to the actual

implementation of the technology, once the adoption decision has been made:

“But the hospitals themselves still then do a hell of a lot on their own from there. How they then distribute the systems within the hospital, where the terminals are, what they are, how they support them, their own help desks etc or certain categories of help desk, local area network, all those issues are managed at an independent hospital level.”

Type of decision Discussed The decision-making processes the leaders used for IT investment selection were all

centrally oriented with controlled consensus/consultative processes. This combines

with centralised funding and management models, and formal processes to act as a

restraint to innovation, supporting earlier findings.

4.7. Summary The interviews with the leaders were open and free flowing. Each of the executives,

as expected, provided relatively short appointments, requiring the discussions to stay

on-track and preventing opportunities to explore peripheral issues. As intended, it

was apparent that each of the interview subjects was the major decision maker about

IT expenditure and budget allocations. In addition, it appeared that by the end of the

third interview saturation had been reached, with no new concepts emerging in the

final interview.


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The NVivo analysis tool became commercially available at the start of this research

project. It was used as the main tool for storing interviews and unstructured data,

performing analysis, coding, review and model building. This tool greatly facilitated

the process, providing ready access to all data, coding and nodes in an easy to

understand, colour coded manner. Its use provided significant productivity and

flexibility improvements.

4.7.1. The Findings Summarised Each factor within Innovation Diffusion Theory has been examined through these

interviews as well as the proposed factor of environmental/policy issues. Only the

factor of complexity is readily seen as enhancing IT innovation adoption, the

remaining factors all appearing to hinder the adoption process. These findings are

summarised in Figure 4-2 Summary of Findings After Study One, below, which is an

enhanced version of the original framework, showing where enhancers and barriers

were identified.

Overall, it can be concluded that the decision maker is faced with many factors that

act as a barrier to increased adoption of IT in health.

First, the health organisation is unusual. Whilst all staff, departments and functions

share common goals – the treatment of illness and disease – they do so without the

sense of being a single team. This is reflected in multiple power-structures,

professional allegiances and a lack of an organisation-wide view. This deters

enterprise-wide IT innovation by influencing a number of Rogers’ factors. Such an

organisation creates the appearance of low slack (whether true or not) through internal

competition, ensures a need for high formalisation to function effectively (never mind

efficiently), requires centralisation of many functions to avoid fragmentation and

increase the required complexity of the organisation.

Secondly, the position that IT has achieved in health is poor. It has failed to

demonstrate its value, and remains incapable of supporting clinical work, the core

business of a health care organisation. In addition, the culture and behaviour of IT


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vendors has done little to assist health managers to feel trusting and respectful towards

IT.

Thirdly, health remains under scrutiny of the public and politicians. This makes

managers reticent, consciously or not, to expose themselves to review and criticism.

These three factors influence the health leaders. Considering that health leaders

already have a risk-adverse nature, the organisational barriers towards IT innovation,

the challenges presented by the technology itself and the risks of public scrutiny

ensure that the health leaders hold a poor view of increased IT adoption.

Therefore, Study One has found evidence that causal factors exist which may lead to

the phenomenon of low IT adoption in health care. However, the exact level of this

adoption, and its comparison to other industries is yet to be established.

Figure 4-2 Summary of Findings After Study One


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4.7.2. Implications for the Framework Whilst the flow of discussion was not structured around this study’s conceptual

framework, the analysis readily fitted the discussion to this framework and provided

support for the relevance of the framework and its application, in the enhanced form,

to Study Two.

The coding made apparent how closely linked many of the concepts within the

framework were. Single comments could be coded against multiple nodes as they

provided support to multiple concepts. For instance, complexity and relative value

were closely linked, observability and trialability were linked, and leader attitudes

were heavily influenced by the whole range of technical attributes. This research

allowed the theoretical framework to be enhanced as it appears that the leaders’

attitudes about adopting IT are influenced by their opinions of the technology, in

particular the value of IT and the availability of suitable solutions. The belief in the

difficulty of using IT and its perceived current poor fit to clinical environments

heavily influences their belief about the value of IT. Therefore, the framework will be

modified to show compatibility and complexity influencing relative value, which in

turn influences the leaders’ attitudes. In addition, the leaders seem to blur the

concepts of trialability and observability; however, they were quite clear that they

could not observe suitable IT systems that addressed their needs. So again, the

framework is adjusted to show observability and trialability combining to influence

the leader. It also appears that organisational attributes of interconnectedness and

slack are directly influencing their willingness to advance IT projects. These two

variables seem to create a belief that there are too many conflicting demands yet

insufficient resources to satisfy the needs of every “tribe.”

These observations provide evidence for a refined model which is shown below, and

it is this model that will form the basis for the discussion of Study Two of this

research. Study Two was conducted further examine health’s IT adoption, review and

improve the framework to a greater degree and provide an assessment of the strength

and influence of the factors examined in Study One.

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Figure 4-3 Revised Conceptual Framework

5. Study Two - Management Surveys

Knowledge is the only meaningful resource today. The traditional ‘factors of production’ – land (i.e. natural resources), labour and capital – have not

disappeared. But they have become secondary. Peter F. Drucker 1909 - : Management Academic


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5.1. Study Two – Survey-Based Research

5.1.1. Introduction The findings from the survey of the health and banking sectors are used to give a

deeper description of the factors investigated, to assist in further understanding and

comparison and to guide future research work.

5.1.2. Response Profile The Australian health industry provided a broad range of responses, whilst no

responses were received from New Zealand. This had minor impact upon the

research project allowing for a census of all Australian states to be prepared as a

benefit of the reduced scope, population and resulting generalisability. In addition,

this reduced population provided a better alignment with the banking population

which was only Australian.

Due to confidentiality and commercial sensitivity, only 2 of the 4 major banks

provided responses for the IT-Survey and a different combination of 2 banks for the

Organisation-survey. The responders were all from National, Westpac, ANZ, and

Commonwealth banks. As expected, this meant that rather than a statistical

comparison being possible between the industries, a quantitative, descriptive approach

was adopted.

The response profile for each survey was:

Organisational Surveys

• 42 sent out

• 15 returned

• 8 usable (6 health, 2 banking)

The six usable health surveys were all from Australian sites, giving a census of

Australian state health. The two banks, based upon publicly available information,

seem to be representative of the others within the major four. Fundamental

characteristics of the major banks are shown in table 5-1, below, giving an indication

of the relative homogeneity of the population.

STUDY TWO – MANAGEMENT SURVEYS

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IT Surveys

• 21 sent out

• 12 returned

• 8 usable (6 health, 2 banking)

The six usable health surveys were all from Australian sites, giving a census of

Australian state health. The two banks, based upon publicly available information,

seem to be representative of the others within the major four.


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Organisation Annual

Revenue

Main Activities IT-facilitated services

National Australia Bank $10,501 M Business and Personal Financial Services

Wealth Management;

Capital Markets;

Corporate Finance;

Foreign Exchange;

Money Market;

Financial risk management and project and structured finance activities;

Securities Services (for funds and funds managers);

Cards and Payments;

International Trade and Business Finance;

Asset Finance and Fleet Management;

National Australia Investment Capital Ltd (venture capital).

Key strategies are:

Deliver solutions that help meet customers' complete financial needs;

Build and sustain a high performance culture;

Build trusted relationships with all stakeholders;

Build and manage our portfolio of businesses for strong and sustainable Total

Shareholder Return; and

Create and leverage strategic assets and capabilities for competitive advantage.

ATM

EFT-POS

Internet Banking

Internet Share Trading

Front-Office Systems

Back-Office Systems

Foreign Exchange Dealing Systems

Payment & Settlement Systems

Table 5-1 Characteristics of the major Australian banks


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Organisation Annual

Revenue


Total assets of $397 billion.

Over $73 billion in assets under management and administration;

$311 billion in funds under custody and investment administration; and

7.8 million banking customers and more than 2.8 million wealth management customers.

Commonwealth $14,225 M Financial markets activities,

Corporate finance,

Securities underwriting, trading and distribution,

Payments and transaction services and equities

Personal Banking,

Commonwealth Financial Services,

Business Banking,

Institutional Banking,

Home Owner's Insurance (Commonwealth Connect Insurance Limited),

Funds Management,

Southern Cross Community Fund,

Telephone Banking,

Stockbroking (Commonwealth Securities Limited),

Internet Banking,

Mobile Bankers

For half year ended 31 December 2002, statutory net profit after tax was $622 million. Net

ATM

EFT-POS

Internet Banking



Back-Office Systems




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Organisation Annual

Revenue


profit after tax on a cash basis was $1,208 million for the half year ended 31 December

2002.

The Group has in excess of A$333 billion in assets held and funds under management.

ANZ Banking

Corporation

$13,023 M Consumer and Small Business Banking Products,

Mortgages,

Funds Management, Insurance,

Personal e-Commerce,

Cards (Telstra Visa and Telstra Qantas) and Distribution.

Announced a strategic alliance with E*TRADE Australia to provide on-line broking

services to customers

Business Banking,

ANZ Investment Bank,

ANZ Asset Finance,

Asset Management and

Corporate e-Commerce.

ATM

EFT-POS

Internet Banking


Back-Office Systems



Westpac $13,010 M Provides banking services to consumers and small & medium sized businesses.

Also provides funds management, unit trusts, superannuation and insurance services and

products.

Cash management,

Trade finance,

ATM

EFT-POS

Internet Banking



Back-Office Systems


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Organisation Annual

Revenue


Corporate Advisory

Capital Raising.

Lending,

Deposit Taking,

Payment Services,

Investment Portfolio Management Advice,

Unit Trust & Superannuation Management,

Nominee and Custodian Facilities,

Insurance Services,

Consumer Finance,

Leasing,

Factoring,

General Finance,

Foreign Exchange Dealing,

Money Market Services




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The surveys were returned in a well-completed state. The usable surveys were all

complete, apart from one health organisation omitting its annual revenue. This

omitted figure was readily obtained from published reports and used to complete the

surveys (AIHW, 1998a). One survey had an erroneous revenue figure, by a factor of

10, which was obvious when comparing revenues between health organisations and

calculating the ratios of IT expenditure versus revenue. Again, publicly available

reports were used to obtain a more appropriate revenue figure.

5.1.3. IT Maturity The dependent variable for this study is the level of IT adoption. This study assessed

IT adoption by measuring the IT maturity, expense-related factors and usage-related

factors. These three sets of measures, combined, give a view of the IT adoption levels

of the responding organisations.

As described in Chapter 3, the Maturity Index is derived as the simple average of the

scores for each maturity factor.8 The maturity factors being calculated as the average

of each of their applicable questions. By using the average of the factors to calculate

the index, it avoids any unintentional weighting between factors due to them having

differing numbers of questions.

Maturity Findings The average total maturity index and supporting factors, by industry are shown in

Table 5-2, below:

8 Analysis of IT maturity between banking and health, based upon these data, has been published in a peer-reviewed journal (England et al., 2003). This paper is included in the appendices.


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Factor/Index Health

(n = 6)

Banking

(n = 2) X SD± Min Max X SD± Min Max

Vision 3.78 0.47 3.11 4.33 4.56 0.00 4.56 4.56

Culture 2.74 1.14 0.78 3.78 5.23 0.48 4.89 5.56

Communications 4.25 0.74 3.00 5.00 4.62 0.18 4.50 4.75

Standards 3.65 0.57 2.75 4.38 4.63 0.18 4.5 4.75

Information 4.19 0.40 3.88 4.75 4.50 0.00 4.50 4.50

Maturity Index 3.59 0.42 2.97 4.37

4.50 0.19 4.18 4.64

It is worth noting the differing measurements between the industries. In general, the

health industry provided neutral responses, on average, whereas the banks provided

positive responses. It is also interesting to note that on three of the five measures the

health and banking response ranges do not overlap, with banking being notably more

positive than the responses from health.

As further indicators of maturity, measurements were made of IT usage (including

email usage), IT expenditure as a percentage of revenue, IT expenditure per head and

IT staff as a percentage of total headcount. As described in Chapter 3, the IT usage

scores are a number derived by totalling the proportion of staff at differing IT usage

levels multiplied by a weighting for each usage level. These yielded the results shown

in Table 5-3:

Table 5-2 Maturity Factors by Industry


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Maturity Measure Health

(n = 6)

Banking

(n = 2)

X SD± Min Max X SD± Min Max

IT Expense vs.

Revenue 2.63% 1.50% 0.70% 4.09% 7.63% 4.10% 4.73% 10.53%

IT $ per Head $2,500 $1,880 $686 $5,710 $18,600 $1720 $17,400 $19,800

IT Staff vs.

All Staff 0.82% 0.37% 0.39% 1.43% 5.45% 2.14% 3.93% 6.96%

IT Usage 224 85.0 140 330 380 14.1 370 390

E-mail Usage 224 85.0 140 330

195 63.6 150 240

In a similar trend noted from the Maturity Index results, banking shows a higher

rating on every measure apart from e-mail usage, and again, the ranges are distinct,

with health failing to reach even the minimum levels in banking.

Whilst e-mail usage is higher in health than in banking the significance of this finding

is unclear. E-mail may be acting as a substitute in health care for the more

sophisticated enterprise-wide IT found in banking. Further research into this is

needed. E-mail may be a valid indicator of maturity, however, it is equally possible

that, e-mail usage may not be a good indicator of maturity, and in fact it may act in an

inverse way as an indicator of low maturity.

Overall, it appears that by every maturity factor but one and the maturity index

assessed in Study Two, banking is a more advanced user of IT than health. Despite

the small population size, some variability across institutions and within each industry

was evident. However, the pattern shown in the following graph does suggest that

there is a difference between the two industries with banking having a greater

maturity than health.

Table 5-3 Other Maturity Measures by Industry


Page 153

Maturity & Contributing Variables by Industry

0

1

2

3

4

5

6

Vision

Culture

Commun

icatio

ns

Standa

rds

Inform

ation

Maturity

Inde

x

health

banking

The most marked differences are seen in the area defined as “culture.” These factors

are discussed in Table 5-4 Maturity differences between industries below.

Figure 5-1 Maturity and Variables By Industry

Page 154

Topic Health Banking Description of response patterns

Vision Direction

Strategy

3.77 4.56 The banking organisations appear to implement more rigorous planning processes and link them more closely

to the organisation’s strategic plan. The banks’ IT departments also introduce “feedback loops” ensuring they

assess the perception of their performance amongst the user communities and measure the effectiveness of

their suppliers.

Culture 2.74 5.23 The banks have a culture that aligns IT more closely with the business. One of the most significant areas of

difference appears to be the way that the banks allocate costs for IT back to business units and involve

business unit management in IT decisions. Health appears to do this less readily, retaining a centralised

funding model and seeking lower levels of clinician involvement.

Communications 4.25 4.62 A similar pattern of response, though banking appears to include more IT awareness as a part of staff

induction.

Standards 3.64 4.62 Basic technical standards appear to be equally implemented across both health and banking. However,

banking implements a wider range of standards than health, particularly in the areas of organisational change

and benefits realisation. This observation ties in with the difference in culture, where the banking IT staff

seem to take a stronger view of their role in supporting the business.

The information

resource

4.19 4.50 Both industries view information as a business asset to be managed wisely and made available where needed.

Table 5-4 Maturity differences between industries


Page 155

Building upon this comparison are the other maturity measurements such as IT

expenditure versus revenue, proportion of IT staff and frequency of usage of IT by

general staff. Again, a trend emerges that the banking sector has more mature usage,

procedures and practices than health. This is also reflected in increased expenditure

levels and increased usage with increasing maturity, as predicted by Nolan (1979).

These other maturity measurements show that the traditional measure of IT

expenditure as a proportion of revenue is 2-to-3 times higher in banking than health

(7.63% vs. 2.63%, respectively), again showing a significantly higher adoption level

in banking than health. The ratio identified for banking is very similar to that

identified for the finance sector identified in the literature review (7.63% vs. 7%,

respectively) (Weill et al., 1998). This adds credence to the validity of this finding.

The IT expenditure per employee shows a seven-fold increase in banking compared to

health ($18,600 vs. $2,500 per employee, respectively), whilst the ratio of IT staff to

all staffing is also 7 times higher in banking than health, again reinforcing banking’s

higher use of IT per staff member (5.45% vs. 0.82% of all staff, respectively). Whilst

these support the view of a different adoption level, it is through these measures that

the differences in the strategic nature of banking and health industries are most

apparent, in that health is a hands-on service where staff members are required to

interact with patients, whereas banking requires far fewer staff to deliver client

service and orients itself around the efficient handling of computerised transactions.

Of note, however, is one measure in which health is slightly ahead of banking, that of

e-mail usage (224 vs. 195, respectively). It is hard to attribute any significance to this

finding though, email being a generic tool that is readily implemented in most

organisations, therefore requiring little maturity. Health may be a greater e-mail user

as a “band-aid” to address weaknesses in other systems and processes, or to overcome

the poor levels of interconnectedness identified in Study One. Alternatively, this

phenomenon may be a result of health care’s distributed nature, with many

departments spread over large areas.

Maturity Measures Compared With a complete set of all of the maturity measures from each returned survey, it was

possible to carry out bivariate analyses, showing the two-way correlations between


Page 156

measures. This analysis was performed using only health subjects to avoid any cross-

industry issues. Kendall’s Tau_b analysis was used as there was a mixture of ordinal

and nominal data with a small data set (Polit & Hungler, 1999). This analysis

supports further development and critique of the maturity measures.

Correlations - Health Only

1.000 .467 .000 .333 .333 -.067. .188 1.000 .348 .348 .851

6 6 6 6 6 61.000 .552 .333 .333 -.067

. .126 .348 .348 .8516 6 6 6 6

1.000 .138 .138 .138. .702 .702 .702

6 6 6 61.000 1.000** .600

. .005 .0916 6 6

** 1.000 .600. .091

6 61.000

.6

Correlation CoefficientSig. (2-tailed)NCorrelation CoefficientSig. (2-tailed)NCorrelation CoefficientSig. (2-tailed)NCorrelation CoefficientSig. (2-tailed)NCorrelation CoefficientSig. (2-tailed)NCorrelation CoefficientSig. (2-tailed)N

Maturity

Usage

E-mail usage

IT exp/revenue

IT exp/staff

IT staff/Total staff

Kendall's tau_bMaturity Usage E-mail usage IT exp/revenue IT exp/staff

IT staff/Totalstaff

Correlation is significant at the .01 level (2-tailed).**.

The majority of these differing maturity measures show positive correlations to each

other; therefore to a greater or lesser extent there seems to be some association

between them. The main patterns that can be observed are:

• IT exp/revenue is perfectly correlated with IT expenditure per staff member

and moderately correlated with the ration of IT staff against total staff,

• E-mail usage has only weak correlations with the factors apart from overall

usage with which it has a moderate correlation; and

• The Maturity Index moderately correlates with usage, IT exp/revenue and IT

exp/staff, though shows a very slight negative correlation with the ratio of IT

staff to total staff. This observation aligns with Nolan’s model, where

increased maturity leads to increased expenditure in a non-linear manner, and

Table 5-5 Maturity Measures Correlated


Page 157

may suggest that low maturity IT organisations make up for their lower

maturity by using extra staff.

Applying Factor Analysis to the maturity measures shows there are two sub-measures

of maturity being assessed. The factor analysis is shown below in tables 5-6, 5-7 and

figure 5-2.: Table 5-6 shows that 80% of the variance can be explained by the first

two factors, with 95% being explained by the first three factors.

Initial Eigenvalues Extraction Sums of Squared Loadings

Component Total % of Variance Cumulative % Total % of Variance Cumulative %

1 2.66 44.30 44.30 2.66 44.30 44.30

2 2.17 36.24 80.54 2.17 36.24 80.54

3 .85 14.15 94.69

4 .22 3.71 98.40

5 .10 1.60 100.00

6 .00 .00 100.00

Component 1 2

IT expense / revenue .88 .41

IT expenditure / staff .97 -0.2

IT staff / total staff .80 -.40

Usage -.41 .85

E-mail usage .10 .85

Maturity .37 .63

Extraction method: Principal Component Analysis.

Table 5-7, above gives a view of how the maturity measures load the two major

factors. Factor 1 has been named “Resource Commitment” as it relates to dollars and

staff committed to IT. It is interesting to note the primary role of the IT expenditure

Table 5-6 Component Variance

Table 5-7 Maturity measure component analysis


Page 158

per staff member as this is the measure proposed by Strassmann (1997a). Factor 2 has

been named “Pervasiveness” as it is oriented to the level of use and maturity of in the

organisation.

Resource Commitment

1.0.50.0-.5-1.0

Per

vasi

vene

ss

1.0

.5

0.0

-.5

-1.0

maturity

e-mail usageusage

it staff /total staff

it exp/staff

it exp/revenue

Figure 5-2 shows the individual measures in a scatter chart against the two factors.

The factor analysis was conducted “as is” as well as being rotated using varying

models. Rotation provided no clearer analysis than the unrotated analysis, which has

therefore been presented.

Based upon this analysis the scores for factor 1 and factor 2 have been generated and

used in later analysis as the indicators of IT adoption.

The Maturity Index, while part of the IT Pervasiveness measure also approaches the

IT Resource Commitment component as shown on the graph, Figure 5-2, above. This

adds some weight to the use of Maturity Index as a generic IT adoption indicator,

though it also suggests that it requires some “tuning” to make it relevant to each

component.

Figure 5-2 Factors within maturity measures


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Of interest, though unknown worth, is the scatter chart of the scores calculated for

factors 1 and 2, see Figure 5-3, below. This shows an “S” shaped curve when

Pervasiveness is used as the independent variable and Resource Commitment the

dependent variable. The outlier, with low pervasiveness and high resource

commitment is probably due to the special circumstances of the organisation it

represents. This is an organisation with a relatively small health service covering

extremely large geographic distances and a sparse population. It is understandable

that overheads, such as telecommunications costs, would demand an unusually high

level of resources. When writing in 1979, Nolan found an “S” shaped relationship

between maturity and expenditure. Whether the “S” curve found in this study is a true

indication of Nolan’s findings or mere coincidence cannot be assessed with such a

small data set, however, it is a tantalising linkage back to earlier theory.

Resource Commitment vs Pervasiveness

Pervasiveness

1.51.0.50.0-.5-1.0-1.5

Res

ourc

e C

omm

itmen

t

2.0

1.5

1.0

.5

0.0

-.5

-1.0

-1.5

Figure 5-3 Scatter graph of the two adoption factors


Page 160

Maturity Summary9 The objective of analysing maturity levels of IT adoption was to discover if the often

asserted slow adoption of IT in health was fact or legend. This research indicates that

it is fact. As noted earlier in Chapter 3, above, previous studies using Nolan's stages

model found it applied to health and that those organisations that moved through the

stages most quickly were the most successful and those that slowed or stalled were the

least successful (Meyer et al., 1992). This research aligns with earlier research by

suggesting that health organisations seem to be in a lower maturity state than the

banking organisations with respect to IT adoption and use.

Finding 19: It appears from the analysis that, at least in Australia, IT in health lags behind that in banking in nearly all facets. It appears that health implements less sophisticated management practices, has poorer attitudes towards IT and applies fewer resources.

Some health executives assert that health makes as good use of IT as any other

industry (England, 2001). This view must now be challenged. The health industry

seems to make poorer use of IT, and in fact has no better or worse experience with IT

return on investment than the banking industry.

The analysis of the differing measures of adoption suggests that adoption occurs

across a number of dimensions, with two major themes being the commitment of

resources and the pervasiveness of the technology. It seems there is some validity to

the concept of the Maturity Index, usage and traditional revenue based measures. It is

noteworthy that e-mail usage correlates poorly if at all with the other measures

evaluated.

5.1.4. Organisation The organisation factors are measures of the significant determinants of organisational

innovativeness as derived from the conceptual framework. The organisation factors

were derived from the Organisation Survey as described in Chapter 3. This section

will review the data from the health care and banking organisational surveys to

9 An earlier form of this analysis of IT maturity in health has been published in a peer-reviewed journal (England et al., 2003). A copy of the paper is included in the appendices.


Page 161

understand the nature of these factors in health and identify any noteworthy

characteristics.

Organisation Findings

Factor/Index Health

(n = 6)

Banking

(n = 2)


Leader Attitude 4.05 0.68 3.21 5.14 4.89 0.30 4.64 5.07

Centralisation 2.29 1.60 0.00 2.29 1.00 0.71 0.50 1.50

Formalisation 1.75 1.44 0.50 4.50 1.00 1.41 0.00 2.00

Interconnectedness 3.17 1.83 0.00 5.00 5.00 0.00 5.00 5.00

Slack 3.17 1.83 0.00 5.00 5.00 0.00 5.00 5.00

Complexity 4.67 0.82 4.00 6.00 4.00 1.41 3.00 5.00

External Links 4.17 0.93 3.00 5.00 4.75 0.35 4.50 5.00

Organisation Index 3.32 0.67 2.23 4.12

3.66 0.04 3.59 3.72

In these results 0 indicates a perception that is strongly detrimental to an

organisation’s innovativeness, whilst 6 indicates a perception that is strongly pro-

innovation. Work has not been performed to calibrate these scales and thereby

understand their sensitivity, so comparison will be used as the means of analysis and

discussion. According to the theoretical framework, centralisation and formalisation

correlate negatively to innovation, therefore to calculate the organisation score inverse

coding was used for these measures.

Both health and banking show a neutral to slightly positive score for the Organisation

Index, however the individual factors show more variation and lead to a focus on

individual factors. There are differing patterns between the two industries with health

10 Centralisation and Formalisation correlate negatively with IT adoption. Therefore the Likert Scales for questions related to these two factors were reversed so that a higher score would consistently reflect a propensity to greater IT adoption throughout all factors.

Table 5-8 Organisational factors by industry10 -


Page 162

showing moderate levels of variability across organisations. The banks gave a

consistent view of interconnectedness and slack whilst the health organisations

covered the whole range in their answers.

The organisational variables yield a much less clear picture than that gained of IT

maturity. When all the variables are presented in a manner that correlates with

increasing innovativeness a mixed pattern emerges, with neither industry standing

apart from the other. The following graph, 5-2 describes the pattern of drivers of

innovation.

The variables in which banking scores better than health are of greatest interest;

however, it is also important to understand the areas in which these data do not match

the theoretical framework. Table 5-9 Analysis of organisational innovation drivers,

below, examines these drivers.

Figure 5-4 Graph of Organisation Innovation Drivers

Organisation Drivers of Innovation

By Industry

Mean External Links

Mean Complexity

Mean Slack

Mean Interconnectedn

Mean Formalisation

Mean Centralisation

Mean Leader Attitude

6

5

4

3

2

1

0

Industry

banking

health

Page 163

Organisational

Innovation

Driver

Health

Score

Banking

Score

Analysis

Leader Attitude 4.05 4.89 The banking managers have a more positive perception of IT than their health counterparts do.

Centralisation 2.29 1.00 This result shows that health has a higher centralisation score (i.e. less centralised due to the reverse coding) which is

expected to lead to a more innovative approach to IT.

Formalisation 1.75 1.00 This variable also uses reverse coding; therefore a score of one shows high formalisation. Both health and banking

show high levels of formalisation, a result that matches public perception.

Interconnection 3.17 5.00 Banking shows considerably higher levels of interconnectedness than health.

Slack 3.17 5.00 Banking reports the presence of greater slack within the organisation, which contributes to some freedom to innovate.

Complexity 4.67 4.00 Health appears to be a more complex organisation than banking. However, the difference is slight allowing no real

confidence to be held in the significance of this finding.

External Links 4.17 4.75 Banking appears to be somewhat better at learning from external sources, but again responses were close.

Table 5-9 Analysis of organisational innovation drivers


Page 164

The value of the leader characteristic, not surprisingly after the emotions expressed in

Study One, shows that health leaders are less positive about IT innovation than their

banking peers. In absolute terms the difference in measured leader attitude shows

banking being around 20% greater than health. This difference seems to give an

indication that leader attitudes have an impact, and this is predicted by the framework

and supported by Rogers (1995). Additional research is required to calibrate the

sensitivity of this measure.

This finding of a less positive leader attitude therefore provides additional support for

Findings 2, 3 and 4 which find that the health leaders do not have proof of the value

of IT, find the behaviour of IT vendors inappropriate and have poor experiences with

past IT projects.

Health exhibits a more highly decentralised structure than banking. Considering the

similarity of individual branches within a bank compared to the differences seen

within departments of a health organisation, this finding is intuitively correct. The

size of the relative difference (2.29 for banking versus 1.00 for health) suggests that

this factor is worthy of further research. One explanation as to why health is more

decentralised yet exhibits lower IT innovation may come from the way health

achieves its decentralisation. As noted earlier, health operates as a series of disparate

groups (Degeling et al., 1998; England et al., 2000; England, 2001), in effect being

many small organisations with a degree of co-operation. If this is the cause of the

increased decentralisation in health, then it is likely to be a dysfunctional form of

decentralisation (perhaps even a form of isolation) rather than one designed to

encourage freethinking, empowerment and innovation. Two alternative conclusions

are also possible: one that health is generally more decentralised than banking, but

control of IT projects has a centralised form. Certainly, this is supported by Study

One. The second alternative conclusion is that health’s level of IT innovation would

be even worse than measured here if it were not such a decentralised organisation.

This driver therefore needs further research to understand the sensitivity of its

influence on IT innovation in health.


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Finding 20: Further investigation of the distributed nature of health organisations is needed to understand its nature and positive or negative influences on the adoption of IT.

Formalisation, which is an inversely coded variable, is found to be high in both

banking and health (1.00 vs. 1.75 respectively). This level of formalisation will act as

an inhibitor to both organisations. However, as both industries are ones that people

expect to be reliable and accurate, then such levels of formalisation are to be

expected. The level of difference is not likely to be meaningful between the two

industries, especially considering the high level of formalisation exhibited by both.

The values obtained for interconnectedness are considerably different between the

industries, with banking being 2/3rds greater than health (5.00 versus 3.17). As noted

earlier, health operates in a granular nature with poor communication across and up-

and-down the organisation. This result reflects that fragmented nature, mirrors the

work of the other researchers (Braithwaite et al., 1995) and may contribute to health’s

lower level of IT innovativeness. It has been observed that in an organisation, the

acceptance of end-user communities and the establishment of an organisation-wide

vision for innovation is required (Meyer et al., 1992). Gaining such acceptance and

creating such a vision will be harder in a poorly interconnected health organisation.

This supports the previous Findings 1, 6 and 7. With this degree of difference,

supported by previous studies, it is reasonable to assume that interconnectedness has

an influence on the lower rate of innovation in health IT.

Slack is reported as being greater in banking than health. This difference (5.00 in

banking and 3.17 in health) appears to be of a level that makes it worth investigating

further, and certainly supports the health leaders’ contentions in Study One of facing

too many demands for capital expenditure compared to the available resources. This

reinforces Finding 6 from Study One. However, this finding goes against some

widely held perceptions that commercial organisations are “lean and mean” and that

government organisations are inefficient. This finding needs to be researched more

deeply for two obvious reasons: firstly that the granular structure of health may be

inefficient and competitive, leading to the appearance of limited slack; secondly, that

the questioning in this study may have been naïve - after all, what politically-aware


Page 166

senior manager in a government entity is going to readily admit to having too many

resources? The potential outcome of such a claim could be disastrous.

The complexity measure is higher in health by around 15% (4.00 for banking versus

4.67 for health), and certainly this is intuitively correct when the range of health

services, specialities and geographic access is compared with that of banking. In

addition, the granular nature of health adds further to the complexity, potentially

giving some support to Finding 1. This finding should increase the level of

innovativeness in health over banking. More surprisingly, the two industries record

relatively similar values for complexity, yet to an outsider health, with its complexity

of services, departments, professions, equipment and specialities, appears far more

complex to the casual observer. This result is likely due to the nature of the survey

instrument which assesses the perceptions of the subjects, not the objective reality.

Banking demonstrates more openness to external ideas and communication than

health (4.75 vs. 4.17) though both show a reasonable degree of openness to external

ideas. The lower openness, even if slight, fits in with the literature that found that

health workers tend to congregate and learn from their professional groupings. This

could lead to a greater level of innovativeness in banking, providing further, if

limited, reinforcement to Finding 8.

Organisation Summary The findings of Study Two are partially in line with Study One and the conceptual

framework. However, Study 2 indicates that health shows measures for

centralisation, formalisation and complexity that are not as predicted by the

framework, nor Innovation Diffusion Theory. This research is unable to determine

whether these are important differences, or result from experimental bias. In addition,

the research is unable to determine whether these pro-innovation forces are valid, but

counteracted by stronger innovation barriers such as leader attitude or slack.

However, even if the results are significant it may well be that they are caused by

health’s unusual organisational make-up. The lack of unity of purpose and granular

framework make simple measurements of formality, centralisation and complexity

insufficient to gain a proper appreciation of the organisation’s make-up.


Page 167

It is apparent from these findings that whilst a start has been made on the role of

health’s organisation on managerial decision-making that there is much more that can

be learned.

5.1.5. Technology Technology factors are the third major domain defined in the conceptual framework.

These factors assess the attributes of technology that contribute to its speed of

adoption and consist of relative value, compatibility, complexity, observability and

trialability. The technology variables were derived from both the IT Survey as

described in Chapter 3.

Technology Findings

Factor / Index Health

(n = 6)

Banking

(n = 2)


Relative Value 2.78 1.20 0.00 4.00 2.50 0.71 2.00 3.00

Compatibility 2.56 1.01 0.00 3.00 3.00 1.41 2.00 4.00

Complexity 2.79 0.97 1.00 4.00 2.50 2.12 1.00 4.00

Observability 3.33 1.23 2.00 5.00 5.00 0.00 5.00 5.00

Trialability 3.33 1.58 1.00 6.00

5.00 0.00 5.00 5.00

Technology Index 3.33 0.39 3.00 3.80 3.70 0.14 3.60 3.80

Table 5-10 shows the values obtained for the technology factors in both health and

banking for IT. In these results 0 indicates a perception that is strongly detrimental to

innovation adoption, whilst 6 indicates a perception that is strongly pro-innovation

11 According to the framework, complexity correlates negatively to innovation, therefore to ensure

high scores are always pro-innovation, the Likert Scales for the questions making up this element have

used inverse coding.

Table 5-10 Overall technology variables by industry with inverse coding for complexity11


Page 168

adoption. Work has not been performed to calibrate these scales and thereby

understand their sensitivity, so direct comparison will be used as the means of

analysis and discussion.

It is interesting to note that health has generally been neutral to adverse perceptions of

IT’s innovation factors whereas banking is generally neutral to positive in its views.

This seems to align with the finding of banking’s greater adoption of IT. Also notable

is the wide ranges within the health scores from minimum to maximum whilst

banking is far more consistent, however in both industries compatibility and

complexity showed wide ranges, perhaps indicating confusion in this area or

ambiguity in the survey instrument.

The results from the technology questions were provided by the senior IT managers

and are summarised in Figure 5-5 Graph of technology factors, below. This self-

reporting study contains questions about the value contributed by IT (and hence IT

departments), and hence gives the opportunity for a conflict of interest and even self-

serving results when compared with the leaders’ interviews in Study One and the

Study Two Organisation Surveys. Therefore some conflicting findings are expected.

Figure 5-5 Graph of technology factors


Page 169

One general trend that can be observed is banking’s generally higher results than

health. In all technology factors, apart from relative value, banking has a more

supportive view of IT than health.

The most significant differences occur in the areas of observability and complexity.

Banking certainly believes it can observe relevant systems in use elsewhere and that

they are of reasonable complexity for implementation and use. These results are

examined in more details in Table 5-11, below.

Innovation Diffusion in Australian State Owned Health

Page 170

Technology

Innovation

Driver

Health

Score

Banking

Score

Analysis

Relative

Value

2.78 2.50 Health shows a slightly higher belief in the value contributed by IT than banking. This difference

is small between the two industries and may have little or no impact.

Compatibility 2.56 3.00 Banking perceives that its IT systems are a better fit to the needs, culture and processes of their

organisations than health does. Banking shows a score some 17% higher than that recorded by

health which should have a positive impact upon the rate of technology adoption in banking.

Complexity 2.79 2.50 The two industries perceive their IT solutions to be relatively similar in complexity.

Observability 3.33 5.00 Banking shows a strong result for the observability of banking IT, some 50% greater than that in

health. This can be expected to have a positive influence on banking’s rate of IT adoption.

Trialability 3.33 5.00 The health IT managers are less convinced that they could trial suitable IT than their banking

peers. Banking reports a value 50% greater than that of health. This can be expected to have a

positive influence on banking’s rate of IT adoption.

Table 5-11 Technology Factors Analysed


Page 171

Surprisingly, despite the passionate opinions of the health leaders, the IT Survey

found that IT managers in health perceive the value of IT to be slightly greater than

their counterparts in banking. These opinions are provided despite the health leaders’

clear statements in Study One that they had no reliable measurements of IT value and

that they mistrusted the business cases presented by their IT management. If the

leaders are correct, this result may have no analytical foundation for its accuracy;

rather it may rely solely upon the subjective views of the IT managers. Considering

the different levels of IT adoption in health and banking, it is possible that health IT

managers are more easily able to perceive the value contributed by IT compared to

pre-IT days than their banking peers who work in organisations that have been

saturated with IT for a number of years. Alternatively, it may be valid that health

does gain marginally greater value from its IT than banking, however, evidence of the

relative adoption levels make this scenario hard to accept.

The two industries perceive their IT solutions to be relatively similar in their fit to the

industry, its culture and business needs, though there is a slightly greater level of

compatibility reported in banking (3.00 vs. 2.56, respectively). Depending upon the

sensitivity of this measure, this difference may be significant or inconsequential,

however the health leaders in Study One perceived that there was no compatible IT in

use or available, especially for clinical processes. It seems likely that this measure is

at least providing an indication of a difference, supporting Study One. Again, this

may be a case of self-serving responses minimising the difference, after all, what

reasonable IT manager will willingly admit that they are providing a service

incompatible with the needs of their employer? This topic will be revisited when

reviewing observability and trialability.

The measure of IT complexity between health and banking is very similar (2.50

versus 2.79 respectively). This is no surprise considering the rate at which

organisations update their technology and speed at which IT becomes obsolescent. It

is likely that most large organisations will use very similar technology (eg, software

from Microsoft, and Oracle, TCP/IP-based networks, server computing based upon

open systems), therefore this is a reasonable finding.


Page 172

In terms of observability, the health IT managers were less convinced that they could

observe suitable IT in use elsewhere than their banking colleagues (3.33 for health

versus 5.00 for banking). The banking score suggests that banking IT managers

believe there are observable IT solutions that meet their needs to quite a high degree,

whereas health’s result shows less conviction about the available solutions. This

health result provides support to the health leaders’ views that no suitable solutions

exist. However, it also challenges the health IT managers’ own views that they are

providing better relative value than banking. If banking can observe good IT in use

but health cannot (banking records an observability score 50% above that of health),

then it seems a paradox that health considers itself to be delivering higher value than

banking.

The trialability scores show the exact same pattern to those of observability, with

banking being 50% higher than health (5.00 vs. 3.33 respectively). This lends limited

support to the conclusion following Study One that observability and trialability were

similar concepts in the minds of managers. This result also reflects the health leaders’

views that no suitable solutions exist and the complexity of technology and

organisation prevents trials. Again, the banking managers are reasonably convinced

that they can trial the IT solutions they wish to adopt. This again raises the issue of

conflicting scores between health’s IT managers’ beliefs that they deliver greater

relative value than banking IT. If health genuinely believes the systems it needs

cannot be either observed or trialled, then it is difficult to see how they can be

delivering greater value. Drawing a more tenuous conclusion, it can even be

suggested that these results show that health’s IT managers are claiming good work

performance for themselves, yet blaming the outside IT vendors for their problems.

Technology Summary Overall, the scores for the technology results are similar between industries. Maybe

this is due to common thinking and methods between IT managers. After all, IT

managers tend to move between organisations whilst retaining their IT role, whereas

health leaders tend to have stayed within health care. Therefore this may provide a

more common view of IT issues expressed by the IT managers However, there is a

distinct pattern that shows that health can neither observe nor trial the desired


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solutions. This reflects the claims of the leaders in Study One and supports findings

11, 13 and 14

The health IT managers believe that they deliver more value and their systems are as

readily accepted as those in banking are. This does not reflect the views of the health

leaders and conflicts with their own statements that they cannot observe nor trial

suitable IT systems. The health IT managers’ views may be fact, or may be a case of

the health IT managers wanting to believe their own worth and being out of touch

with their clinical users and executive managers. This is certainly the view expressed

by health leaders. Alternatively, this contradiction may reflect a dichotomy of

thinking, namely that “back-office” systems (such as finance and payroll) are

delivering value whilst “clinical” systems do not. For this to be an appropriate view

the health IT managers would have had to answer some of the survey with a clinical

view and the remainder with a back-office view. Whatever the reality, it is clear that

additional research, using non-subjective measures, will be required to determine this

core issue.

The role of IT vendors was also a focus of this research. Based upon the leaders’

views about their own IT departments, and the conflicts in the IT managers’ answers,

future research needs to look at the efficacy of in-house IT departments as a

contributor to health’s IT adoption pattern. To do this, objective measures of IT

efficacy will be required.

5.1.6. Environmental/Policy Due to the lack of a framework or theoretical basis for the environmental/policy

influences, the questions developed were broad ranging with no attempt to create a

single construct. Rather they were an attempt to identify areas of interest and

establish a framework or model to allow future research.

Environmental/Policy Findings As there is no framework for understanding the environmental/policy factor it is

inappropriate to aggregate or process these data in any way beyond averaging for each


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question by industry. The questions and industry average results are presented in

Table 5-12 below.

Policy Question Health

(n = 6)

Banking

(n = 2)


IFL6 Government & political factors 4.83 1.17 3.00 6.00 2.50 0.71 2.00 3.00

IFL7 Public opinion and media 3.33 1.03 2.00 5.00 3.50 2.12 2.00 5.00

IFL8 Clients’ needs and opinions 3.17 2.32 0.00 6.00 5.50 0.71 5.00 6.00

IFL9 Labour relations and industrial concerns

3.33 0.82 2.00 4.00

2.5 0.71 2.00 3.00

These results show the perception the managers who answered the Organisation

Survey hold about the influence of external factors. The data show different patterns

between health and banking. Health is generally neutral on all questions apart from

the influence of government, whereas banking is slightly negative on all issues apart

from the needs of clients, which at 5.5 indicates a keen awareness of client needs.

Health exhibits a broad range of responses to each question suggesting a wide range

of perceptions throughout the industry about the influence of policy factors.

The industry results are also presented in the following graph, Figure 5-6.

Table 5-12 Policy Questions


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Environment/Policy Questions

By Industry

Mean IFL9Mean IFL8Mean IFL7Mean IFL6

6.0

5.5

5.0

4.5

4.0

3.5

3.0

2.5

2.0

INDUSTRY

b

h

Question IFL6 asked about the level of influence that government and political factors

had on IT strategy. This was perceived to be nearly twice the influence in health than

in banking (4.83 vs. 2.50 respectively). Question IFL7 asked about the influence of

public opinion and the media upon IT strategy. Both health and banking reported

similar moderate responses (3.33 vs. 3.50, respectively) suggesting an ambivalent

view in both industries. IFL8 shows a significantly stronger response in banking

compared with health (5.50 vs. 3.17). This question seeks the degree that clients’

needs and opinions influence the IT strategy. Finally, IFL9 seeks the level of

influence that labour relations has on IT. Again health provides a middle-of-the-road

value, whilst banking provides a more assertive view that labour relations are not

much of a factor (health 3.33 vs. banking 2.50).

Due to the exploratory nature of the environmental/policy questions, the questions

were diverse and certainly not measuring a single factor. Environmental/policy issues

were claimed to be of low significance by the leaders in Study One. However, these

Study Two responses have some extreme values, implying an awareness and

Figure 5-6 Graph of Policy Variables

banking

health


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consideration in some areas that is greater than acknowledged, even though the

majority are neutral.

Interestingly, though of unknown importance, is the magnitude of differences in

responses to IFL6 and IFL8. If these responses are correct, then it appears that health

receives environmental/policy pressures from within, namely its leadership, whereas

the banks are substantially influenced by the customers. This pattern is one that was

contemplated in the discussion in Chapter 2 comparing not-for-profit organisations’

behaviours and drivers to for-profit organisations.

There appears to be little influence from the media or public opinion, with the health

sector being reasonably more concerned about industrial relations than the banking

sector. This may reflect the levels of unionisation or governments’ desire to be seen

as a fair employer. If it is an indicator of unionisation, this may be important, as this

has been found to be an adverse influence on innovation (Koeller, 1996; Morris &

Donn, 1997; Fitzgerald & Sterling, 1999; Link & Siegel, 2002).

Policy Summary The policy questions have identified some potentially large differences in attitudes

between health and banking. The importance of these is perceived to be great by the

respondents who gave strong positive responses to some of the questions. The

significance of these influences and the influence they cause are, at this time,

unknown and should be the subject of future research.

Finding 21: There are large differences in the environmental/policy influences on health and banking which justify future research.

5.2. Implications for the Framework As a census obtained from health care for both the dependent and independent

variables, it was possible to carry out bivariate analyses showing the correlations

between various factors within the enhanced framework. This allowed further

development and critique of the framework. To perform this testing of the model

required the omission of the banking data as there is no basis for assuming that health


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and banking can be described by the same model due to the fundamentally different

nature of the industries. In addition, it is not appropriate to examine a banking

framework due to only 50% of the population of four having supplied data which

would have raised statistical concerns.

Considering the nature of the data, Kendall’s Tau_b correlations have been calculated

for those relationships implied by the model whilst others have been investigated to

identify any further underlying relationships. Kendall’s’ Tau_b is the most relevant

method for calculating correlations on small sets of ordinal data, and as the

Spearman’s correlation analysis showed no different patterns, only the Kendall’s

correlation results have been shown (Polit & Hungler, 1999).

Maturity has now been replaced within the model by the two factors Resource

Commitment and Pervasiveness. Additional values have been calculated for the

higher level constructs within the framework by summing the individual factors as

described in the following list:

• Innovation Opportunity = Size + Complexity + Openness;

• Willingness to Invest = Complexity + Compatibility + Relative

Value;

• Compat + Complex = Compatibility + Complexity;

• Is It Possible = Trialability + Observability;

• Ability to Implement = Interconnectedness + Formalisation +

Centralisation + Slack; and

• Unity of Purpose = Interconnectedness + Formalisation +

Centralisation.

There is considerable overlap between the factors used within these constructs; this is

due to the exploratory nature of this research and the attempt to develop the

theoretical framework. Analysis of a variety of combinations of factors aimed to

uncover those higher-order constructs that were most relevant and influential.

Correlations of maturity measures, the above constructs and the factors of Relative

Value and Leader Attitude are shown in Table 5-13, below, and have also been added

to the annotated framework diagram, Figure 5-7.


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Table 5-13 Correlations within the framework

Correlations

1.000 .067 -.072 .430 .730 -.552 -.276 -.276 -.183 .467 . .851 .845 .260 .064 .126 .444 .444 .643 .188 6 6 6 6 6 6 6 6 6 6

1.000 .215 .430 .365 -.276 .138 .414 .183 .067 . .559 .260 .355 .444 .702 .251 .643 .851 6 6 6 6 6 6 6 6 6

1.000 .277 .000 -.296 .371 .074 .392 .501 . .485 1.000 .428 .322 .843 .340 .173

6 6 6 6 6 6 6 6 1.000 .589 -.802 * -.178 -.356 .589 .430

. .171 .039 .647 .360 .171 .260 6 6 6 6 6 6 6

1.000 -.756 -.189 -.189 -.250 .548 . .060 .639 .639 .576 .165 6 6 6 6 6 6

* 1.000 .071 .357 -.189 -.690 . .846 .330 .639 .056 6 6 6 6 6

1.000 .714 -.094 .276 . .052 .814 .444 6 6 6 6

1.000 -.283 .000 . .481 1.000 6 6 6

1.000 .000 . 1.000 6 6

1.000 . 6

Correlation Coefficient Sig. (2-tailed) N Correlation Coefficient Sig. (2-tailed) N Correlation Coefficient Sig. (2-tailed) N Correlation Coefficient Sig. (2-tailed) N Correlation Coefficient Sig. (2-tailed) N Correlation Coefficient Sig. (2-tailed) N Correlation Coefficient Sig. (2-tailed) N Correlation Coefficient Sig. (2-tailed) N Correlation Coefficient Sig. (2-tailed) N Correlation Coefficient Sig. (2-tailed) N

Resource Commitment

Pervasiveness

Innovation Opportunity

Willingness

Compat+Complex

Is it possible

Ability to implement

Unity of purpose

Relative value

Leader attitude

Kendall's tau_b Resource

Commitment Pervasiveness

s Innovation Opportunity Willingness

Compat+ C mplex Is it possible

Ability to implement

Unity of purpose Relative value Leader attitude

Correlation is significant at the .05 level (2-tailed). *.


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Figure 5-7 Annotated Conceptual Framework with Correlations


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The conceptual framework developed to this point was based upon a number of

factors influencing the leader to create a Leader Attitude towards adopting IT. The

assumption being that this attitude would then lead to adoption behaviours. Therefore

it was expected that the individual factors would correlate to greater or lesser degree

with Leader Attitude and the measures of IT adoption.

The correlation between Leader Attitude and “Is it Possible” is -0.69, a moderate

correlation though, surprisingly, in a negative direction. One way of making sense of

this is to view this correlation as showing that the leaders who hold the most positive

views of IT’s role in health are also the most sceptical about whether IT’s potential is

currently being achieved, whilst those with lower expectations are finding that IT is

meeting their needs. This immediately suggests that there will be issues around the

centrality of the Leader Attitude; after all, if the leaders with the highest expectations

are the most sceptical, it is unlikely that it will be those with high attitudes that will

have strong adoption patterns.

Of real surprise was the lack of any correlation between Relative Value and Leader

Attitude. Rogers (1995) suggested that the dominant technology factor was Relative

Value. This lack of correlation does not support Rogers. However, Study One

showed the Health Leaders held uncertain views of IT’s value, if they held a view at

all. It may be this confusion that results in the lack of any correlation.

Innovation Opportunity correlates moderately with Leader Attitude (0.50) which

suggests that the organisational attributes which make an organisation receptive to

innovation (Size, Openness, and Complexity) are of some importance in the

innovation behaviour within health care organisations.

Ability to implement correlates with Leader Attitude to a low level (0.28) which

suggests that the leaders have some awareness of their organisation’s ability to accept

innovations and implement them successfully.

Within the framework, Leader Attitude was predicted to be a key determinant of the

level of IT innovation. The Leader Attitude correlates against IT Resource

Commitment at 0.47. This indicates that the managers’ opinions are moderately


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important in determining the allocation of dollars and staff to IT. This is a logical

conclusion. Curiously, the Leaders Attitude correlates to only a very low level

against Pervasiveness (0.07). This suggests that whilst the leaders may influence the

allocation of resources they are not major influencers in the use and sophistication of

IT techniques applied. This position may be explained due to the seniority of the

leaders surveyed. At such a senior level, these managers are more likely to be in

resource allocation roles than actual implementation roles. It is more likely that

sophistication and quantity of usage is influenced by the IT management and the

middle managers of the health care organisation.

Having identified some unexpected results at the level of the proposed framework, it

was decided to calculate a wider range of correlations at the level of the individual

measures, composite constructs and the derived maturity factors. It is important to

note, however, that these correlations are based upon the small population for this

research project. Therefore, whilst correlations can be discussed, the actual findings

must be treated with caution and no generalisability should be assumed. Many of

these correlations were weak and inconclusive, whilst a number showed moderate

correlations. Those with notable correlations are shown in Table 5-14, below.

Table 5-14 shows all of the correlations within the framework where:

a) The correlation is with either of the two maturity factors and has a

value greater than 0.5 or less than -0.5; or

b) The correlation between the two factors or constructs is greater than

0.75 or less than -0.75.

These have been deemed to be the correlations of interest as they are most useful in

the development of an improved conceptual framework. Each correlation meeting

these criteria will now be discussed, the implications considered and impact upon the

framework reviewed.


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Factor 1 Factor 2 Correlation Analysis

Compatibility +

Complexity (inverse)

0.73 “Compatibility + Complexity” was chosen as a measure as it indicates the expected

ease of implementation of an IT solution and therefore the level of risk. This result

shows that when the risk is perceived to be low (High Compatibility + High

Complexity (inverse)) then additional resources are committed to IT. However,

looking at the results for Compatibility and Complexity independently casts doubt

upon this combined measure. The two measures individually appear to operate

differently and give better information disaggregated.

Observability -0.50 There is a negative correlation between observability and resource commitment. An

interpretation that makes sense of this result is that organisations which have made

larger IT investments are the most likely to doubt the observability of suitable

systems. An alternate view may be that organisations that cannot see suitable

solutions are willing to invest more to develop them. Neither of these interpretations

was predicted by the framework.

Resource

Commitment

Trialability -0.69 Again, there is a negative correlation between trialability and resource commitment.

Similarly, one interpretation that makes sense of this result is that organisations

which have made larger IT investments are the most likely to doubt the trialability of

Table 5-14 Noteworthy Correlations


Page 183


suitable systems. An alternate view may be that organisations that cannot see trial

solutions are willing to invest more to develop them. Neither of these interpretations

was predicted by the framework.

Centralisation

(inverse coded)

-0.69 Opposed to Rogers’ assertion, this study has found that increased centralisation leads

to greater resource allocation in IT. This could be due to a number of reasons such

as:

a) Decentralised organisations may have decentralised IT budgets and are

therefore underreporting their level of resource commitment; and

b) Centralised IT operations are more effective and able to justify increased

funding.

Compatibility 0.58 As predicted by Rogers, there is a moderate link between one of the maturity factors

and compatibility. This suggests that the more compatible the technology is with the

organisation the more it is used by the staff.

Pervasiveness

Complexity (inverse

coded)

0.55 As predicted by Rogers, there is a moderate link between one of the maturity factors

and complexity. This suggests that the less complex the technology is with the


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organisation the more it is used by the staff.

Willingness Is it Possible -0.80 Willingness, a measure of relative value, compatibility and complexity shows a

strong negative correlation with “Is it Possible”, the combined measure of trialability

and observability. This again reflects the duality of views in regards to IT. The more

those managers believe in the value of IT and its ability to easily fit their

organisation, the less they believe that such technology exists. This split view is

difficult to reconcile – it would seem that if the technology does not exist, then it

cannot be compatible or of low complexity either! This is more evidence of the

issues around IT management and its lack of clarity about its role and level of

achievement.

Compatibility +

Complexity

Is it possible -0.76 This correlation is very similar to the previous observation about Willingness and Is

it Possible, due to the similarity of measures.

Is it Possible Centralisation (inverse

coded)

0.79 There is a strong relationship between centralisation and “Is it Possible.” The less

centralised the organisation the more it perceives that suitable IT systems exist. This

may be a feature of health organisations, that due to the internal tensions, those

organisations that exert less centralised control feel it is easier to make IT projects


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succeed.

Ability to

Implement

External Links 0.89 External links (openness) was determined to be a factor that added to the “innovation

opportunity” measure. It appears that External Links also correlates strongly with the

“ability to implement” measure.

Interconnectedness 0.93 Interconnectedness is a sub-measure of Unity of Purpose and correlates strongly.

Unity of

Purpose

Slack 0.93 Slack correlates strongly with Unity of Purpose though the implication of this is

unclear. It may suggest that “ability to implement” is a more relevant construct than

“unity of purpose.”

Leader Attitude Observability -0.79 A moderate negative correlation exists between the leader attitude and observability.

The more positive the leader is in regards to IT’s potential the less they believe that

they can see appropriate IT in use.


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These correlations are also diagrammed in Figures 5-8 and 5-9, below. Figure 5-8

shows the measures relating to Pervasiveness, and Figure 5-9 shows the measures

correlating with Resource Commitment.

The correlations and diagrams show clearly that the technology measures are the ones

most involved in the Pervasiveness and Resource Allocation processes. The

Organisational measures show their own correlations, but these form a “network” that

is disconnected from the network that influences the IT maturity factors. Of interest,

at this detail, is the contrast with the higher level network diagram, Figure 5-7. In that

higher level diagram organisational factors are seen to be the most influential,

particularly “Innovation Opportunity” against the Leader’s Attitude and the Leader’s

Attitude against Resource Allocation.

Figures 5-8 and 5-9 show the influence of individual measures against the two

maturity factors. As can be seen in Figure 5-8 Complexity and Compatibility have the

largest relationship with the Pervasiveness. This same influence holds true in Figure

5-9. Figure 5-9 shows the measures that influence Resource Commitment. This

reinforces the conclusion that organisations that perceive IT to be complex invest

more resources. In addition Compatibility also appear to be directly influential upon

resource commitment along with the organisational factor of Centralisation. Notably,

Rogers predicts that decentralisation of organisations promotes innovation; however,

remembering the inverse coding of Centralisation, this result shows that increased

centralisation increases innovativeness. This is possible an indicator that due to the

expense and complexity of health IT, organisations that centralise functions such as IT

manage to achieve greater adoption of IT.


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Figure 5-8 Correlations with Pervasiveness


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Figure 5-9 Correlations with Resource Commitment


Page 189

Further exploration of correlations between the factors making up the Maturity Index

and the framework’s constructs was performed. This carried some implication of

“circular logic” by checking the influence of maturity factors against Organisational

and Technological factors and constructs outside of the two derived maturity factors.

However, these correlations were calculated to gain tentative insights into the more

subtle workings of Organisational and Technological factors on the organisations’ IT-

related behaviours. With this caution in mind it was interesting to note the following

correlations:

Resource Commitment with Vision 0.55

Pervasiveness with Vision 0.55

Pervasiveness with Information 0.89

This suggests that the maturity factors in some way relate with the vision for the use

of IT and the organisation’s belief in the strategic value of information. The cause

and effect relationship is undeterminable, however both Vision and Information are

maturity factors that relate to the leaders’ beliefs about the strategic role of both IT

and information. This reinforces the need for a construct in the framework that

considers the leaders’ wider beliefs.

Overall, Study Two gives a more complex and fragmented view of the innovation

process in health care. Study One led to a refinement of the framework whilst the

early analysis of Study Two refined that framework and gave a clearer view of the

two aspects of IT maturity. This final part of the analysis gives a much more detailed

picture, but runs the risk of losing the overall conceptual view. It must also be

remembered that due to the population size, great care must be taken with these

findings. This detailed picture suggests that aspects of the technology have a great

importance in the Resource Commitment and Pervasiveness. In particular, Resource

Commitment seems to be greater when hospital managers perceive that no suitable IT

solution is available. The cause and effect nature of this relationship is not evident;

whether high investment leads to disillusionment, or lack of available solution leads

to extra research and development funding has not been determined. Pervasiveness

seems to be driven by two major domains:

1. the organisation’s centralisation; and

2. the belief in the compatibility and ease of use of IT.


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In addition, the Leader Attitude correlates moderately well with Resource

Commitment (0.47) and to almost no degree directly with Pervasiveness (0.67). This

suggests that the leaders initiate IT projects by allocating resources yet the eventual

use of the technology is determined by the willingness of other layers of the

organisation to put it into effect.

This final set of findings has not been formally identified as findings in the summary

of this project. The population and data are too tentative to make such a confident

statement. Rather, this final analysis gives a clue for future research directions and

helps with the refinement of the final conceptual model.

Study Two has found interesting patterns and assisted the refinement of the

framework. Assembling this information with that of Study One gives a more

complete view of the health IT adoption phenomenon. The following chapter will

synthesise Studies One and Two leading to a basis for an improved framework and

policy recommendations for the future of health IT.

6. Conclusions

Weep not that the world changes – did it keep a stable, changeless state, ‘twere cause indeed to weep

William Cullen Bryant (1794 – 1878) American Poet


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6.1. Developing the Conceptual Framework The conceptual framework was initially developed in Chapter 2 as part of the

literature review. This was then enhanced in Chapter 4 using qualitative interviews to

refine the conceptual model. Finally, Chapter 5 analysed the way that aspects of the

model correlate with derived maturity factors and each other.

Balancing the subjective views of Study One with the more analytical results of Study

Two has identified the following major categories within the framework:

IT Resource Commitment is one of the two maturity factors being used. This relates

to the allocation of financial or human resources to IT projects. As such, resource

commitment is most closely aligned to the senior managers’ decisions to adopt IT

innovations.

Pervasiveness is the other of the two maturity measures. Pervasiveness relates to the

quantity and quality of usage of an IT innovation. This includes the number of users,

the frequency of their use and the implementation of best-practice IT methods.

Pervasiveness is more aligned with the organisational decision to diffuse an

innovation internally following the manager’s prior decision to adopt.

Readiness to Implement, a category that combines the development level of the

technology (using Rogers’ categories of Trialability and Observability) with the

freedom of action created by the organisation’s level of centralisation (Rogers’

category of centralisation, where lower centralisation encourages innovation). This

takes the leaders’ perceptions about the readiness of technology and combines it with

the organisation’s freedom to adopt creating a hybrid organisational/technological

category. The implication behind this category is that more centralised organisations

require more highly developed technology for adoption to occur, whilst decentralised

organisations can innovate with less mature technology. As such “Readiness to

Implement” acts as an indicator of the readiness of a specific innovation in a specific

organisation. Readiness to Implement acts as an influence upon the IT Resource

Commitment maturity factor.


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Ability to Implement looks at the organisational attributes necessary for the spread of

an innovation across the breadth and depth of the organisation. This includes

Interconnectedness, Slack and Formalisation that create the resourcing, procedural

and communication support for the diffusion of the innovation to occur following the

decision to adopt. This links with the Pervasiveness factor of IT maturity.

Innovation Opportunity looks at the organisational attributes that create the need for

the innovation that is to diffuse. This comprises the factors that create knowledge

about and demand for innovation such as Size, Openness and Complexity

(organisational). Innovation Opportunity aligns with Pervasiveness.

Willingness to Use is a domain that looks how well the innovation fits in with the

organisation. This comprises the measures of Complexity (technical) and

Compatibility which give an assessment of how easy the technology is to learn and

implement as well as how well it fits the current organisation. Less complex

technology that is highly compatible will be more rapidly spread across the

organisation, which is shown from its linkage to the Pervasiveness maturity factor.

Strategic Disposition is a domain that looks at the leadership’s view about IT,

information and its strategic place in the organisation. Whilst Information and Vision

were originally gathered as indicators of maturity, they also show the attitudes of the

leadership in relation to IT. This broadens the understanding of the Leader’s Attitude

as currently gathered by the survey instruments. Rogers states that Leader Attitude is

the major influencer on Innovation Adoption behaviour yet the survey Leader Attitude

proved to be only a moderate indicator of Resource Commitment and no indicator of

Pervasiveness. This broader Strategic Disposition domain aligns more fully with both

Resource Commitment lending support to its use as an indicator of manager’s

willingness to make Innovation Adoption decisions, and Pervasiveness showing how

the manager’s disposition supports the need for diffusion of the innovation.

The final model is shown in Figure 6-1, below.

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6.2. Summarising Studies One & Two This project has taken a broad approach to understanding a new area of research.

Study One, due to its qualitative approach, gave a good understanding of the views of

the Leaders in regards to IT innovation. These views were remeasured in Study Two

in a more tangible form. By combining these two studies the following points have

been discovered.

Senior state health leaders:

• are influential in the initial innovation adoption decision;

• influence the diffusion of the innovation across the organisation through the

creation of a vision for IT and Information within their organisation;

• lack confidence in the IT solutions available to them;

• lead very complex and fragmented organisations with little unity of purpose;

• face many conflicting demands for resources;

• find IT vendors act inappropriately;

• do not believe there is a compelling business case for IT investment; and

• do not believe that effective clinical IT exists.

IT managers:

• believe that they cannot see suitable IT solutions in use;

• believe that their current systems are compatible with state health’s needs;

• believe they are delivering reasonable value; and

• seem to be out-of-step with their senior executives’ view of IT.

State health Organisations:

• are fragmented, with multiple agendas;

• those that are least fragmented and most open to the outside world more

readily innovate with IT;

• have very limited spare resources;

• have risk averse leaders; and

• are influenced by politics and unions.


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These findings are merely the start; they must be used develop to lessons and

recommendations that will improve the state of IT in state health. The following

sections take the initial steps in this direction aiming to learn from this research and

develop a future policy and research agenda that can advance the use of IT in state

health.

6.3. Studies One & Two Compared & Contrasted Studies 1 and 2 have addressed the similar issues from different perspectives. Study

One used open interviews of the state health leaders who approve IT expenditure.

Study Two used surveys of the opinions held by the IT managers in state health and

banking and other senior managers who are IT consumers in state health and banking.

Considering the differing approaches and respondents between the two phases, the

findings about state health’s IT adoption attributes have a great deal of similarity.

Study One gave a real sense of the passions and specific views of the respondents and

yielded a very detailed and colourful view of the leaders’ attitudes, beliefs and issues.

It provided a deep understanding of the leaders’ difficulties in matching cost to risk

and return. From Study One it was a straight-forward task to refine the conceptual

framework.

Study Two provided a less clear view of the issues but a more robust view of the

outcomes. Whilst the survey responses largely agreed with the Study One analysis,

there was a noticeable difference between studies concerning the value of IT. The

Study One leaders were quite clear that IT value had not been established in state

health, yet the state health IT managers in Study Two seemed to perceive the value as

greater than in the high-adopting banking industry. As discussed in Chapter 5, this

may be due to a number of reasons including: the IT managers overestimating their

value, the IT managers not fully understanding the business perspective of the state

health leaders, or the ability of the state health IT managers to see the initial impact

their systems have on the business compared to banking where systems are in their

second or third generation.

CONCLUSIONS

Page 197

Study Two reflects a much more fragmented view of the issues caused by the

relatively low number of questions that could be asked of such a senior management

population. Whilst Study Two gives a more analytical and structured view, providing

a basis for future questions, it lacks the details and vibrancy, even passion, of Study

One. However, due to the focussed nature of Study Two it has been possible to refine

the framework with a sense of underlying reality rather than merely perception.

When combined, Studies One and Two tell a more comprehensive story. The depth

of the leaders’ comments adds richness and understanding to the Study Two surveys,

whilst the surveys start to quantify and identify the problem areas in more detail.

However, neither phase has given definite answers nor identified cause and effect.

The aim of this research, as an initial investigation in the area, was exploratory.

Further detailed studies are now required to identify problem areas and the proposed

resolutions.

The following table lists the findings and shows the way the studies support these:


Page 198

6.3.1. Summary of Findings This table summarises the findings in both Studies One and Two. It is considered that Study Two supports the findings if the Organisational or

Technology measures, when compared with Banking, indicate that state health care faces an issue to a greater extent than banking.

Framework

Category

Finding Description Barrier

or

Enhancer

Links to

Literature Review

Identified

in

Stage 1

Supported

By

Stage 2

Organisational

Leader

Interconnectedness

1 The leaders’ perception that state

health organisations are factional leads

to a belief that enterprise-wide

projects are difficult to achieve. This

acts as a barrier to increased IT

adoption.

Barrier Anticipated through the

lack of unity of purpose

and cultural topics in

Section 2.3.1 above

� �

Table 6-1 Summary of Findings

CONCLUSIONS

Page 199

Framework

Category


or

Enhancer

Links to

Literature Review

Identified

in

Stage 1

Supported

By

Stage 2

Organisational

Leader

2 Leaders expect IT to make a strong

return of investment yet have no

factual basis for assessing this return.

This acts as a barrier to increased IT

adoption.

Barrier Anticipated in Section

2.4.2 above.

� Not directly

identified

Organisational

Leader

Technology

Compatibility

3 The behaviours of IT vendors are not

compatible with the culture of the

leaders. This conflict is likely to make

IT investments poorly regarded in

comparison to other capital

expenditure. This acts as a barrier to

increased IT adoption.

Barrier Not identified in the

literature.

� �

Organisational

Leader

4 The leaders’ experiences of IT

projects, reinforced by similar

Barrier Not identified in the

literature.

� �


Page 200

Framework

Category


or

Enhancer

Links to

Literature Review

Identified

in

Stage 1

Supported

By

Stage 2

Technology

Observability

experiences of other staff, make the

leaders reluctant to invest in IT. This


adoption

Organisational

Leader

Technology

Observability

Trialability

5 The leaders demonstrate a risk-

adverse nature, yet perceive

significant risk in IT projects. This


adoption.

Barrier Risk orientation identified

as a key factor - Section

2.3.1. Attitude to IT not

strongly identified, though

suspected – Section 2.4.1.

� Not directly identified

Organisational

Leader

Slack

6 The leaders face considerable demand

for scarce resources, and due to the

uncertainty of IT performance, give it

a low priority. This acts as a barrier to

increased IT adoption.

Barrier Not directly identified. � �

CONCLUSIONS

Page 201

Framework

Category


or

Enhancer

Links to

Literature Review

Identified

in

Stage 1

Supported

By

Stage 2

Organisational

Size

Interconnectedness

7 Despite the large size of state health

organisations, the low level of

interconnectedness and strong

clustering of employees into

professional groups creates the effect

of state health being many virtual

small organisations. This acts as a

barrier to increased IT adoption.

Barrier Not directly identified but

suspected - Section 2.3.2.

� �

Organisational

Centralisation

Interconnectedness

8 Health’s multiple power-structures

ensure clinical freedom within the

larger enterprise but conflict with the

centralised approach to IT

implementation. This is most clearly

seen in clinical areas where enterprise-

wide IT adoption remains slow.

Barrier Not directly identified but

suspected - Section 2.3.2.

� �


Page 202

Framework

Category


or

Enhancer

Links to

Literature Review

Identified

in

Stage 1

Supported

By

Stage 2

Organisational

Complexity

9 The state health leaders believe state

health to be a highly complex

environment. This should lead to

increased innovativeness.

Enhancer Identified in Section 2.3.3. � �

Organisation

Formalisation

10 The state health leaders support a

formal, controlled approach to IT

acquisition. This should lead to

reduced innovativeness.

Barrier Not directly identified for

state health care.

� Not as

formalised

as banking.

Organisational

External Openness

11 The low level of external openness

about IT in state health contributes to

the barrier to IT innovation.

Barrier Suspected to be a barrier

in Section 2.3.8.

� Limited

CONCLUSIONS

Page 203

Framework

Category


or

Enhancer

Links to

Literature Review

Identified

in

Stage 1

Supported

By

Stage 2

Technology

Relative advantage

12 The value of IT solutions has not been

properly measured and articulated.

This is a barrier to adoption

Barrier Identified as an issue in

Section 2.4.1.

� �

Technology

Complexity

Organisational

Interconnectedness

13 The complexity of IT projects and the

organisational issues they cause make

it difficult for executives to support IT

investment. This is a barrier to

adoption.

Barrier Indicated, though not

confirmed, in Sections

2.4.2 and 2.3.3.

� �

Complexity

appears to

correlate

positively

with both

maturity

factors.


Page 204

Framework

Category


or

Enhancer

Links to

Literature Review

Identified

in

Stage 1

Supported

By

Stage 2

Technology

Compatibility

14 The senior managers do not believe

that suitable IT solutions exist to meet

their needs, especially in clinical

areas. This is a barrier to adoption.

Barrier Identified in clinical areas,

Section 2.4.3.

� �

Technology

Compatibility

Complexity

Relative Value

15 The low perceived relative value, the low perceived compatibility and the perceived high levels of complexity combine in the leaders’ minds to create a sense of high risk and low return. This acts as a barrier to adoption.

Barrier Not previously identified. � Not identified in Study Two.

Technology

Compatibility

Observability

16 The leaders do not believe they can

see the IT they need operating

anywhere in the world. This is a

barrier to adoption

Barrier Not previously identified. � �

CONCLUSIONS

Page 205

Framework

Category


or

Enhancer

Links to

Literature Review

Identified

in

Stage 1

Supported

By

Stage 2

Technology

Complexity

Trialability

17 IT solutions are too large and complex

to trial. This is a barrier to adoption.

Barrier Suspected, based upon

Section 2.4.2.

� �

Societal Factors

Public Scrutiny

18 Public interest and scrutiny of IT

investments acts as a barrier to the

state health leaders taking decisions.

Barrier Not previously confirmed. � Seemed to

be not

supported.

Maturity 19 It appears from the analysis that, at

least in Australia, IT in state health

lags behind that in banking in nearly

all facets. It appears that state health

implements less sophisticated

management practices, has poorer

attitudes towards IT and applies fewer

resources.

Not applicable

.

New finding. �


Page 206

Framework

Category


or

Enhancer

Links to

Literature Review

Identified

in

Stage 1

Supported

By

Stage 2

Organisation

Interconnectedness

20 Further investigation of the distributed

nature of state health organisations is

needed to understand its nature and

positive or negative influences on the

adoption of IT.

N/A New finding. �

Environmental / Policy 21 There are large differences in the

environmental/policy influences on

state health and banking which justify

future research.

N/A New finding. �

CONCLUSIONS

Page 207

6.4. Strengths & Limitations

6.4.1. Strengths As indicated in Figure 6-2, below, the research design has a number of strengths.

First, the design is systematic in its approach to researching a previously unaddressed

topic. It moves from well-proven theory to testing in a new domain before applying

actual measurements. Secondly, the design includes two studies with differing

methodologies and different respondents in each organisation. This is designed to

provide multiple views of the same phenomenon to triangulate the findings better.

Thirdly, the application of triangulation continues to the dependent variable which is

measured in three domains with six measures: the most common data found in the

literature (the ration of IT expense to revenue), the alternative proposed by industry

commentators (IT expenditure per employee) and a different view altogether via a

maturity scale. Next, the process has been designed on the principle of parsimony, to

be as simple as possible with the best possible chance of gaining the necessary data

and completing the project in a timely manner. A final strength to the project is its

grounding in the theoretical framework derived from Innovation Diffusion Theory.

Rogers (1995) cites other studies that have confirmed the validity of organisational

and technological factors as the main influences in rates of innovation diffusion.

Figure 6-2 Strengths of this research project

• A strong theoretical base to build upon;

• Accessing information directly from the decision makers;

• An initial, loose unstructured study to gain breadth, depth and

richness in the understanding of the issues and provide an initial

frame of reference in this uncharted area;

• A more structured second study to provide more focussed

understanding and begin to identify points of investigation for future

attention; and

• A strong reliance on triangulation for confidence in validity of

conclusions.


Page 208

6.4.2. Limitations A major limitation of this study is the available population size. This restricts the

generalisability of the findings. However, as noted earlier, since the population of

interest is the senior, policy-making executives of large government organisations,

this limitation may also be seen as one of the inherent strengths of the project. A

focussed and highly relevant population has been identified and saturation was

achieved in Study One while complete representation was achieved in Study Two.

This ensures confidence in the findings.

An inherent limitation in this study is its cross-sectional design and the consequential

temporal displacement between the measurements of the dependent and independent

variables. IT adoption is being measured through current budgets and maturity that

are determined by investment and budget decisions over the previous years, yet the

independent variables are being measured from the current perceptions of managers.

These two may be out-of-step if management or attitudes have recently changed or if

management are about to approve a significant change in budget.

Another limitation comes about from the need to gain co-operation from the

respondents. To ensure a reasonable response rate the survey had to be concise yet

the number of detailed issues raised by the literature review and theoretical

framework was large. This project therefore takes a focus upon the theoretical

framework at the level of Innovation Diffusion Factors. This has the benefit of

keeping the level of detail sought from the surveys in step with the level of confidence

in the theoretical framework. After all, there would be no point surveying the

executives in detail while the framework itself is yet to be tested. This survey has

kept the depth of enquiry consistent with the intended progress in theory development.

In addition, rather than seeking specific measurements, which would require an

excessive investment of time from the participants (resulting in a much reduced

response rate), perceptions were sought. These perceptions are likely to be incorrect

in their exact measures, but should provide sufficiently close results for the nature of

this project.

The finance industry has been selected as a comparison against state health. Whilst

this has been done for reasons detailed above, it is also clear that financial

CONCLUSIONS

Page 209

organisations have many differences to state health organisations not taken into

account in this study. For instance, state health care is a hands-on labour-intensive

industry with services provided by skilled staff to clients, yet banking is inherently a

transaction-processing industry in which services are delivered through standardised

processes. This type of difference is almost certain to have an influence on the ease

with which IT can be applied to banking compared with state health. In addition, the

banking industry maintains high levels of confidentiality about its spending and

investment patterns for competitive reasons. This makes it difficult to ask specific

questions and get any survey response. The limitations are summarised in Figure 6-3,

below.

6.5. Analysing the Research Questions The overall research question is:

“What factors affect the adoption & diffusion of IT in state-owned health organisations and how do the policy, organisation & technology environment influence the rates of adoption/diffusion in state health.”

Figure 6-3 Limitations of this research project

• The lack of previous research on IT diffusion in health gave no

previous base to build upon therefore the research needed to

begin at the top, and gradually build more detail making

specific, detailed findings difficult to achieve;

• The research could only provide vital insights and point to

likely conclusions rather than making statistical statements;

• Temporal displacement between observed adoption and current

management attitudes presents limitations; and

• The research is based solely upon the subjective views of senior

managers; independent objective assessment was not made.


Page 210

Chapter 3 identified four subsidiary research questions to guide the analysis of this

project. These will be addressed in turn followed by the overall research question.

6.5.1. Is There a Difference in IT Adoption between State health and Banking?

The Study Two survey’s analysis of maturity (see Chapter 5) provides evidence that

Banking does have a higher IT adoption level than state health. Triangulation through

assessments of best-practice use, budgets and usage add credence to this finding.

When backed by maturity theories (such as Nolan, 1979), it is reasonable to suggest

that there is a difference between state health and banking, with state health having a

lower level of adoption. Full validation of this recommendation will require a

detailed quantitative study; however, within the limits of this project reasonable

evidence exists for this conclusion.

6.5.2. Are Policy Issues Significant? At this time, this study provides early evidence that suggests policy issues should

remain a subject of interest. The responses in Study Two indicate that there is some

perception of environmental/policy influence on IT adoption, particularly in state

health care where governments and unions appear influential making their attitudes a

topic of interest. Additional research needs to be carried out to build a conceptual

framework for environmental/policy influence and to test it adequately.

6.5.3. Are IT Issues Significant? Study One gives a very strong indication that state health leaders are not increasing IT

funding, as they perceive many shortfalls in IT. Whilst Study Two did not support

Study One in the areas of complexity and relative value it did provide support in the

areas of observability and trialability. It also raised issues about the IT managers’

appreciation of the real business needs. Based upon this triangulation of the full range

of IT issues, and the discrepancies raised between Studies 1 and 2, it is reasonable to

find that IT issues have significance.

CONCLUSIONS

Page 211

6.5.4. Are Organisation Issues Significant? Study One gives a very strong indication that organisational issues provide challenges

to the leaders that require them to allocate resources to other uses, not to IT. In

particular, the disunity of purpose, lack of organisational slack and poor

interconnectedness measured in Study Two reinforced the findings in Study One. In

addition, Study One gave a clear view of the leaders’ attitudes to IT and IT

investment, this being a major influence on IT adoption. Based upon this

triangulation, it is reasonable to believe that organisational issues are significant.

6.6. Summary Response Do environmental/policy, organisational and IT issues contribute to the IT adoption

patterns in state health? Based upon this study’s findings that organisational and IT

issues are an influence on IT adoption and the finding that policy requires additional

research, it is reasonable to answer this fundamental question in the affirmative.

Therefore, the answer to the overall research question is that state health appears to

have a lower rate of IT adoption than banking. IT and organisational issues appear to

exert significant impact on this diffusion pattern whilst environmental/policy factors

appear to have some influence requiring further research.

6.7. Concluding Remarks This project has addressed a frequently observed, yet little investigated, phenomenon -

the apparent slow adoption of IT in state health care. It now seems reasonable to

conclude that, state health in Australia is indeed a slower adopter of IT. However,

probably more alarmingly, not just low adoption of IT is occurring, but also low

acceptance of sound IT practices and planning. This is evidenced by state health’s

lower overall IT maturity than banking.

The analysis and findings have addressed the issues of organisation, technology and

environment/policy, finding they all have an impact upon state health’s IT uptake.

However, if all that this project does is observe and identify the barriers to IT

innovation then only part of the job has been done, and a significant opportunity has


Page 212

been missed. This section will therefore make a “call for action,” seeking to look

beyond the findings and express opinions about the meaning of these findings and the

avenues for beneficial advancement of IT in state health.

First, the organisational aspects appear to be the most significant causes of state

health’s slow uptake. Considering that IT has been successfully applied in nearly

every other field of human endeavour, from art, movie making, music, engineering,

commerce, manufacturing to exploration and navigation, it is hard to believe that with

more than 40 years of IT development the IT industry continually gets it wrong in

state health whilst succeeding elsewhere. This is not to say, of course, that the IT

industry is perfect; rather that the unique aspects of the state health care industry seem

to be highly significant. Within the organisational findings, the political/cultural

makeup of state health care appears to hold a dominant role. The granular

organisation of state health and the political power of the medical profession appear to

combine to make significant changes very difficult. Of course, it has frequently been

observed by academics, politicians and others that health organisations are different.

State health is a professional bureaucracy that have been under pressure to adopt a

managerial framework for the past 30 years, Of significance though, is that this

professional bureaucracy is acting as a barrier to the uptake of IT.

As a further organisational aspect, why has state health remained one of the only

industries to offer individual, tailored services to all clients, avoiding the

standardisation and process models that have delivered quality and productivity across

the world since Henry Ford invented the production line in the 1900s? Is state health

care really that different? Alternatively, maybe the culture and strong political

interests within state health prevent such radical change. If this is so, then IT is not

being used merely as a technology, rather it is being pursued by a managerial agenda

as a change agent, attempting to rein in the power of the professions and transform

state health into a managerial-led paradigm. As such, IT is doomed to low adoption

and failure, as are many of the management fads applied to state health that ignore the

real nature of the organisation and its power structure.

The executives contributing to this survey seemed very aware of the organisational

and cultural constraints they face which result in unacceptable risks in attempting to

CONCLUSIONS

Page 213

make enterprise-wide change. They felt this through strong inter-tribal competition

for resources, poor support for IT initiatives and a clinical culture that does not readily

accept current clinical IT. This feeling of constraint was reinforced by poor

management techniques that have, as yet, failed to show the real value of IT in state

health care. Yet such techniques have existed for several years (Kaplan & Norton,

1996). Balanced Scorecards offer a way of assessing all-round performance and

support for corporate strategies whilst health economics has offered approaches, such

as cost-utility, cost-minimisation, cost-effectiveness and cost-benefit, to assess the

effectiveness, efficiency and equity of allocating resources to IT (Canadian Medical

Association, 1984a; 1984b). The failure to apply these and truly measure IT’s impact

is more of a failure to think strategically about IT and its contribution to the

organisation’s goals than it is a failure of measurement. Without a clear

understanding of IT’s intended value, measurement is a futile exercise.

To move forward on the organisational front requires a changed paradigm from state

health managers and an alternative view of IT’s value. IT must no longer be a tool for

automation, streamlining, process control and “informating.” Rather IT must be

repositioned as a productivity tool for the use of clinicians and led by clinicians’

needs. In this case, the corporate agenda needs to come second, though by enabling

clinical computing, the corporate managerial needs can be met, too. Only then, can IT

avoid being the victim of a change agenda and take a positive role as a facilitator of

state health care delivery. In addition, the managers need to reassess the way they

plan the alignment of IT with their strategies. Following such a strategy-led planning

approach it will become much clearer about the role IT is taking in the achievement of

the overall state health strategy (Kaplan & Norton, 2001). IT is rarely, if ever, going

to achieve goals itself, rather it is a support function and an intangible asset.

However, by understanding the enabling power of IT and its contribution to other

parts of the organisation achieving their goals, then IT’s role and value will become

much clearer.

As for the information technology aspects, it is apparent that IT vendors and staff are

their own worst enemies. The vendors have shown insensitivity to the people and

organisations of state health care. They are perceived as overpaid and self-indulgent

with no focus on delivery of outcomes that matter to state health management and


Page 214

staff. To move forward, the IT vendors need to review the way they present

themselves to their clients, but more importantly, they need to gain a firmer

understanding of the value they can contribute. An IT system supplied with a benefit

assessment methodology and benefits realisation assistance should be highly attractive

to state health managers. This would require a greater degree of external openness to

be demonstrated by the state health organisation through a willingness to engage more

closely with the IT supplier.

The state health IT departments have also done themselves few favours. They appear

to have different views about what is value compared with their executives. However,

as IT’s role is to support the business, then this differing viewpoint is by necessity a

sign of a failure to be aligned with the organisation. The state health managers need a

much better understanding of the state health business, its needs and their executives’

expectations. The IT managers need an open dialogue with their leadership, and they

need to understand more honestly their own performance and the gap between it and

the executives’ expectations. In addition, the IT managers have a direct and personal

interest in showing the value of IT investments. Individually, or collectively, they

should identify and apply a method for measuring the contribution of IT. This will

lead to a better understanding of the value IT is contributing and give clearer direction

about ways of improving. Moving on from this, the research also found the following

disconcerting contradiction in the IT managers’ beliefs: how can this professional

group claim that there are no suitable systems in existence, yet also claim they are

delivering compatible, valuable systems? This creates a credibility gap and several

interpretations are possible. Maybe:

• the IT managers are adopting a passive, blaming stance, saying that they are

doing a good job but its not their fault because the outside world does not have

the systems they need;

• it is their way of saying that they are as good as anyone else, because nobody

else has shown a better way; or

• they are reporting that at a technical/tactical level they are spending money

wisely but implicitly indicating that they have no concept of the business

meaning of value.

CONCLUSIONS

Page 215

If it is true that appropriate solutions for Australian state health care are not

observable, then pro-active, results-oriented state health and IT managers should be

doing something about it. The state health IT managers should be creating a national

improvement agenda, working with academia and government innovation

programmes to develop appropriate IT solutions. The state health managers should be

lobbying for a research framework and funding.

Having identified the issues, what should be done? There are a number of relatively

simple ways forward, but they require commitment and support from high-level

policy makers. First, it is imperative that the value of state health IT is measured and

an approach for the assessment of future projects developed. This can be achieved by

implementing existing managerial and economic frameworks, educating managers in

their utility and validity, putting them into effect and building a data base of

benchmarks.

Next, the state health IT research and development agenda needs refocussing. The

agenda needs to accept that enterprise-wide state health systems should not be

designed in the same hierarchical manner as most other business systems. A solid

understanding of the state health culture and society needs to be developed and this

should form the bedrock for a future model of state health systems development.

Such systems will need to capture the unique and individual needs of each

professional or departmental group, offer each group control and ownership, yet be

able to integrate into a larger technical framework to meet the needs of patients and

managers. Traditional IT research and development begins by defining processes and

data models. A new methodology will be required in state health, one that begins

with sociological research, such as social constructivist studies, to understand the true

nature of the environment in which the system is to be implemented.

As a further initiative, a new way has to be found of working with clinicians and

meeting their needs from IT. Again, a basic understanding of the daily life of a

clinician is absent from the traditional IT design methodology. Ethnographic studies

may uncover the reality of a clinician’s day and point to better ways that IT can assist

them. In addition, such attempts to assist clinicians must be approached in a way that


Page 216

supports the clinician and their patients whilst avoiding undue impacts upon the

power and social structures of state health.

Proposing new approaches to value measurement, overall system design and the

development of clinical systems is the easy part. Turning this into reality is much

harder. What is being proposed here is a significant new direction. Its realisation will

require co-ordination between state health, academia and the IT industry tied together

in a common framework. Seed funding will be required to develop the overall

framework, new methodologies and provide the coordination, communication and

vision. Funding for the initial framework is most likely to come from government

innovation and research programs as well as from the state health departments

themselves. This will ensure more open access to the resulting intellectual property

and standards. In addition, a program structure will be required to integrate the efforts

and ensure a common framework develops that will lead to tangible, useful IT

systems that can be integrated when implemented. It is now time to develop the

research and development agenda and seek the support required to turn it into reality.

6.7.1. Next steps The application of one major theoretical base has made the project achievable, but has

therefore left other significant areas to be answered whilst creating a completely new

range of questions. These unanswered areas demand attention via future research

projects.

Implications for Future Research There are clear doubts about the compatibility and availability of suitable IT solutions

for state health care. This research has highlighted an existing issue whilst raising the

cultural, power and political perspectives as a potential foundation for the identified

resistance. Detailed work based upon frameworks such as human interactionism, in

which social reality as constructed by the way actors relate to each other (Schwandt,

1997), is required to understand the desired nature of IT in the clinical domain.

Current IT development methodologies take little, or no, account of the cultural and

political environment, assuming that rational process and data models represent the

organisation. This is clearly too rigid an approach, especially in such complex

organisations as state health care. Many researchers are looking for technical

CONCLUSIONS

Page 217

solutions, continuing to build a “better mousetrap,” but understanding human

interactionism and the role of structured interests may yield insights into appropriate

communication and co-ordination frameworks and point the way to solving the fit of

IT for clinicians and the alternate structural groups within state health.

This project has also shown some issues resulting from the nature of state health

organisations, their disunity of purpose and their granular form. As stated by

McFarland (1979) a quarter of a century ago, organisational theory does not fit health

well. Health’s communication, decision, control, influence and political processes are

not as simple as the traditional hierarchical bureaucracies (Weber, 1987) or more

modern organisational forms such as matrices and adhocracies. Health-specific

theories are needed. Social constructivist studies may help understand the way this

organisation type responds to innovations and identify more suitable approaches to

change management and IT systems than those currently used. The monolithic,

enterprise-wide information system appears to be continually rejected by the clinical

interests within health. A better understanding of this social group and its reaction to

information technology is required.

It is also evident that effective clinical information systems have not yet been

developed. Traditional IT research and development identifies processes and data as

the foundations for the design of software. It may now be time to apply more

ethnographic techniques to understand the real activities and needs of clinical

professionals. A rigid process model is unlikely to meet the real needs of clinicians;

however, a deep understanding of clinicians’ real work, rather than espoused work,

may lead to a better understanding of potential IT solutions.

Ultimately, these research directions imply a new direction for state health, IT and

health informatics. A sociological approach backed by appropriate organisational

theory can lead to a new model of IT design, and delivery. This will lead to a package

of management tools (including financial techniques and change management

techniques), software and methodologies. Achievement of these requires a

concentrated and focussed research program performed in co-operation between

academia and the state health industry with government sponsorship. Therefore, the


Page 218

next step in this research project must be the creation of a research agenda and the

identification of funding.

Implications for Health Practice & Policy This research has not suggested that state health is using an inappropriate level of IT.

In fact, it is apparent that due to the uncertain value of health IT and the myriad other

demands for resources, current IT resource levels may be all that is justified.

However, this does not assist the achievement of the potential gains that IT can

deliver to the state health industry. This project has raised a great many additional

questions that need to be addressed to enable the state health care to benefit from IT’s

potential. Important questions include:

• What is the effectiveness and efficiency of state health IT departments

compared to those in other industries? This study has raised issues about how

well these departments are aligned with the rest of the state health enterprise,

and how well the IT professionals understand the needs of the state health

industry. Improved performance in state health IT departments is likely to

lead to greater benefits, better solutions and better business support.

• What is the real value of IT in state health care? No reliable analytical method

has been applied to quantify value. However, several methods are available

and need to be applied with the full support of state health executives.

Whether state health can benefit further from IT remains an open question. Looking

at other industries, and imagining the uses IT could be applied to in state health,

suggests that the opportunities are limitless. Lifetime electronic records, remote

delivery and monitoring, optimisation of resources across health districts, booking and

scheduling in a manner offered by the travel industry and decision support are all

potential achievements awaiting the right environment. However, at this time the

management science and the sophistication of IT management are not at a level that

allows these innovations to proceed freely.

Therefore, the best way for state health IT to proceed is to focus on management and

social issues in preference to the ever-seductive technology. Research and

development funds should be allocated, as a priority, to benefits analysis methods and

CONCLUSIONS

Page 219

a deep and rich understanding of clinical behaviours and work. Deeper knowledge in

all of these areas will alleviate the major barriers to increased IT adoption.

Summary of recommendations At the conclusion of this project, the following points summarise the major findings

and recommendations:

IT Vendors:

• need to become more culturally sensitive to the needs of the state health

industry;

• need to develop solutions that move away from a single enterprise-wide view;

and

• need to develop a solution that provides an enterprise-wide framework, yet

offers departmental flexibility. This may appeal to the needs of both executive

and clinical staff.

State health’s IT Managers:

• need to develop a better understanding of their business from the executives’

and clinicians’ points of view;

• need to apply this new understanding to the development of measures to show

the efficacy and value of current IT projects then apply this to future projects;

and

• need to critically review the performance of their departments and ensure it

aligns strategically with the state health organisations.

State health Leaders:

• need to define their real needs from IT;

• need to be willing to deal with the politics and factions when the time is right

to make fundamental investments in systems; and

• need to be prepared to spend considerably more on IT, when the vendors and

IT departments have done their “homework.”

This concludes the description of this research study. Looking back at the project, its

aims implementation and outcomes leads to the question “Was it worthwhile?” The


Page 220

answer is a resounding “Yes,” this project has achieved its aims, opened doors for

future research, posited a new theoretical framework and allowed the development of

a pragmatic research and development agenda. As an outcome of this project it is

now:

• empirically assessed that state health IT adoption faces issues, at least when

compared to banking;

• determined that health IT adoption requires special emphasis on both the

adoption decision (Resource Allocation) and the diffusion process

(Pervasiveness);

• apparent which organisational and technical factors influence adoption and

diffusion of state health IT; and

• possible to apply a framework to enhance the evaluation of future state health

IT projects assisting in their design and optimisation for improved uptake.

These outcomes and their value, along with the policy recommendations, must now

stand on their own. They have been researched, documented, reviewed and now

published. It is time for them to face their own Innovation Diffusion process. If these

outcomes have good relative value, strong compatibility, low complexity, support

trialability and are observable, then maybe, just maybe, these ideas will grow wings,

spread and lead to changes in the way state health IT is delivered.

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8. Appendices

1. Publications

Australian Health Review (23)

HIC 2001

Stewart (2002)

Australian Health Review (26)

2. Project Documents

Study Two IT Survey Health Version

Study Two IT Survey Banking Version

Study Two Organisation Survey Health Version

Study Two Organisation Survey Banking Version

Study Two Invitation Letters

Study One Semi-Structured Interview Prompts

Study One Interview Coding Sample

Page 248

8.1. Publications

browna2

The following articles are not available online. Please consult the hardcopy thesis available from the QUT Library. Australian Health Review (23) HIC 2001 Stewart (2002) Australian Health Review (26)

8.2. Project Documents

8.2.1. Health IT Survey

Centre For Public Health Research

Health care IT Adoption Survey

Thank you for your participation in this survey. Should you have any questions about this survey please contact the principal investigator, Mr Ian England on 0438 005807. Ethics Statement The identity of participants and their organisations is to be kept confidential and will not be identifiable from any published reports. Your participation in this study is voluntary and you are free to withdraw at any time. If you have any concerns about the ethical conduct of the research, you can contact the Secretary, University Human Research Ethics Committee on 3864 2902.

Instructions

• Please respond to every question in the survey. • The majority of questions present a statement about your organisation and your level

of agreement or disagreement. Please mark the box closest to your opinion with an X, like this:

• Some of the answers require a numeric response. Please write these in the space

provided. • When you have completed the survey, please post it in the enclosed envelope.

Thank you once again for your assistance

X

Vision, Direction & Strategy

1 Your organisation has a fully developed information management & Technology strategy

2 Business and IT plans are reviewed regularly within your organisation

3 The business planning and IT planning processes are closely linked

4 Your organisation’s information requirements have been reviewed and documented

5 Quality criteria are used and enforced in external supply contracts

6 Service level agreements are agreed and formalised with user departments

7 User departments think the IT department provides the best possible service

8 The IT department formally assesses how it is perceived by its users

9 Your organisation’s IT department generally provides a lower cost service than that in other similar organisations

QUT CENTRE FOR PUBLIC HEALTH RESEARCH

IT Adoption Survey

Culture

1 IT costs are allocated back to the business units using them

2 IT funding is linked to achievement of performance objectives

3 Clinicians are involved in IT budget planning

4 Spending on IT outside of the IT department is accounted for

5 IT budget and cost recovery is linked to benefits realisation

6 The cost of organisational change and development associated with IT projects is included in the IT project costs

7 Patients’ views are considered when making IT decisions

8 Clinician’s views are considered when making IT decisions

9 IT is organised and managed centrally

Communications

1 Staff are generally aware of IT projects within your organisation

2 IT projects are considered centrally in an enterprise-wide manner

3 Staff across your organisation are aware of the IT strategy

4 IT awareness (including security, confidentiality, data protection) is included in the staff induction program

Standards

1 A standard IT procurement methodology is used

2 A standard benefits management methodology is used

3 A standard change management methodology is used

4. A standard organisational development methodology is used

5 The IT procurement methodology is effective

6 The benefits management methodology is effective

7 The change management methodology is effective

8 The organisational development methodology is effective


IT Adoption Survey

Technical Infrastructure

1 What percentage of the staff use computers (any type) as part of their work?

Not at all _____ % Once a Month ______% Once a week ______% Daily ______% Most of the day ______%

2 What percentage of the staff use e-mail?


Vendor Effectiveness

1 IT vendors new offerings provide clear, and compelling value

2 Vendors software and hardware offerings are well aligned with your organisation’s business needs

3 New software and hardware offerings are easy to implement and use

4 IT is possible to observe new IT offerings in use in similar organisations prior to adopting them

5 It is possible to trial new IT offerings on a limited scale before implementing them on an organisation-wide basis

The Information Resource

1 The staff perceive clinical data to be of high quality

2 The staff perceive administrative patient data to be of high quality

3 The staff perceive management data to be of high quality

4 Most staff groups wish to improve data quality

5 Most staff desire to obtain and share more information

6 Staff are aware of the potential and availability of information systems and sources

7 Information flows well across departmental boundaries

8 Information flows well up and down the organisation

General Information & Statistics

1 Your organisation’s number of full time equivalent staff

2 Your organisation’s number of IT staff

3 Your organisation’s total annual revenue

4 Total IT recurrent budget per annum

5 Total IT capital budget per annum

Thank you for completing this survey. Please return it to QUT in the attached envelope.

8.2.2. Banking IT Survey

IT Adoption Survey

Thank you for your participation in this survey. Should you have any questions about this survey please contact the principal investigator, Mr Ian England on 0438 005807. Ethics Statement The identity of participants and their organisations is to be kept confidential and will not be identifiable from any published reports. Your participation in this study is voluntary and you are free to withdraw at any time. If you have any concerns about the ethical conduct of the research you can contact the Secretary, University Human Research Ethics Committee on 3864 2902.

Instructions

• Please respond to every question in the survey. • The majority of questions present a statement about your organisation and your level

of agreement or disagreement. Please mark the box closest to your opinion with an X, like this:

• Some of the answers require a numeric response. Please write these in the space

provided. • When you have completed the survey, please post it in the enclosed envelope.


X

Vision, Direction & Strategy

1 Your organisation has a fully developed information management & Technology strategy

2 Business and IT plans are reviewed regularly within your organisation

3 The business planning and IT planning processes are closely linked

4 Your organisation’s information requirements have been reviewed and documented

5 Quality criteria are used and enforced in external supply contracts

6 Service level agreements are agreed and formalised with user departments

7 User departments think the IT department provides the best possible service

8 The IT department formally assesses how it is perceived by its users

9 Your organisation’s IT department generally provides a lower cost service than that in other similar organisations

Culture

1 IT costs are allocated back to the business units using them


IT Adoption Survey

2 IT funding is linked to achievement of performance objectives

3 Line mangers are involved in IT budget planning

4 Spending on IT outside of the IT department is accounted for

5 IT budget and cost recovery is linked to benefits realisation

6 The cost of organisational change and development associated with IT projects is included in the IT project costs

7 Customers’ views are considered when making IT decisions

8 Line managers’ views are considered when making IT decisions

9 IT is organised centrally

Communications

1 Staff are generally aware of IT projects within your organisation

2 IT projects are considered centrally in an enterprise-wide manner

3 Staff across your organisation are aware of the IT strategy

4 IT awareness (including security, confidentiality, data protection) is included in the staff induction program

Standards

1 A standard IT procurement methodology is used

2 A standard benefits management methodology is used

3 A standard change management methodology is used

4. A standard organisational development methodology is used

5 The IT procurement methodology is effective

6 The benefits management methodology is effective

7 The change management methodology is effective

8 The organisational development methodology is effective


IT Adoption Survey

Technical Infrastructure

1 What percentage of the staff use computers (any type) as part of their work?


2 What percentage of the staff use e-mail?


Vendor Effectiveness

1 IT vendors new offerings provide clear, and compelling value

2 Vendors software and hardware offerings are well aligned with the organisation’s business needs

3 New software and hardware offerings are easy to implement and use

4 IT is possible to observe new IT offerings in use in similar organisations prior to adopting them

5 It is possible to trial new IT offerings on a limited scale before implementing them on an organisation-wide basis

The Information Resource

1 The staff perceive customer related data to be of high quality

2 The staff perceive administrative data to be of high quality

3 The staff perceive management data to be of high quality

4 Most staff groups wish to improve data quality

5 Most staff desire to obtain and share more information

6 Staff are aware of the potential and availability of information systems and sources

7 Information flows well across departmental boundaries

8 Information flows well up and down the organisation

General Information & Statistics

1 Your organisation’s number of full time equivalent staff

2 Your organisation’s number of IT staff

3 Your organisation’s total annual revenue

4 Total IT recurrent budget per annum

5 Total IT capital budget per annum


8.2.3. Health Organisation Survey


Health care IT Adoption Survey

Thank you for your participation in this survey. Health care organisations face unique challenges in the adoption of information Technology. This survey aims to measure organisational attributes of health organisations that may impact the way innovations are adopted. It also measures health organisations’ experiences of dealing with IT vendors. Ethics Statement The identity of participants and their organisations is to be kept confidential and will not be identifiable from any published reports. Your participation in this study is voluntary and you are free to withdraw at any time. If you have any concerns about the ethical conduct of the research you can contact the Secretary, University Human Research Ethics Committee on 3864 2902. Should you have any questions about this survey please contact the principal investigator, Mr Ian England on 0438 005807.

Instructions

• Please respond to every question in the survey. • The majority of questions present a statement about your organisation and your level of agreement or

disagreement. Please mark the box closest to your opinion with an X, like this:

• Some of the answers require a numeric response. Please write these in the space provided. • When you have completed the survey, please post it in the enclosed envelope.


X

IT Business Value

1 IT improves internal communication & coordination

2 IT strengthens the strategic plan

3 IT improves management decision making

4 IT streamlines business processes

5 IT improves your organisation’s throughput and service volumes

6 IT improves labour productivity

7 IT enhances the utilisation of equipment and facilities

8 IT reduces the cost of your organisation’s services

9 IT improves the quality of services delivered

10 IT investments have a good payback

Organisation Structure

1 Your organisation has a centralised structure

2 Your organisation makes decisions centrally

3 Financial control is centralised and delegations are minimised

4 IT is funded from a central budget

5 Your organisation has formal quality management and processes

6 Your organisation is managed and operated through clear policies and procedures

7 Communication flows easily up and down your organisation

8 Communication flows easily across your organisation


IT Adoption Survey

Size

1 Your organisation has spare capacity and is not stretched by current workloads

2 Your organisation’s IT skills are of the highest standard

2a Your organisation takes risks trialling innovations

3 Your organisation and industry is one of the most technically, and operationally complex

4 Your organisation’s annual revenue is: $

Influences

1 Your organisation learns from others in the same industry

2 Your organisation learns from others in other industries

3 IT vendors understand your business needs

4 IT vendors bring you useful ideas

5 IT vendors are valuable business partners to you

6 Government and political factors influence your IT strategy

7 Public opinion and the media influence your IT strategy

8 Your clients’ needs and opinions influence your IT strategy

9 Labour relations or industrial concerns influence your IT strategy


8.2.4. Banking Organisation Survey


IT Adoption Survey

Thank you for your participation in this survey. Organisations face unique challenges in the adoption of information Technology. This survey aims to measure organisational attributes of organisations that may impact the way innovations are adopted. It also measures health organisations’ experiences of dealing with IT vendors. Ethics Statement The identity of participants and their organisations is to be kept confidential and will not be identifiable from any published reports. Your participation in this study is voluntary and you are free to withdraw at any time. If you have any concerns about the ethical conduct of the research you can contact the Secretary, University Human Research Ethics Committee on 3864 2902. Should you have any questions about this survey please contact the principal investigator, Mr Ian England on 0438 005807.

Instructions

• Please respond to every question in the survey. • The majority of questions present a statement about your organisation and your level of agreement or

disagreement. Please mark the box closest to your opinion with an X, like this:

• Some of the answers require a numeric response. Please write these in the space provided. • When you have completed the survey, please post it in the enclosed envelope.


X


IT Adoption Survey

IT Business Value

1 IT improves internal communication & coordination

2 IT strengthens the strategic plan

3 IT improves management decision making

4 IT streamlines business processes

5 IT improves your organisation’s throughput and service volumes

6 IT improves labour productivity

7 IT enhances the utilisation of equipment and facilities

8 IT reduces the cost of your organisation’s services

9 IT improves the quality of services delivered

10 IT investments have a good payback

Organisation Structure

1 Your organisation has a centralised structure

2 Your organisation makes decisions centrally

3 Financial control is centralised and delegations are minimised

4 IT is funded from a central budget

5 Your organisation has formal quality management and processes

6 Your organisation is managed and operated through clear policies and procedures

7 Communication flows easily up and down your organisation

8 Communication flows easily across your organisation

Size

1 Your organisation has spare capacity and is not stretched by current workloads

2 Your organisation’s IT skills are of the highest standard

2a Your organisation takes risks trialling innovations

3 Your organisation and industry is one of the most technically, and operationally complex

4 Your organisation’s annual revenue is: $

Influences

1 Your organisation learns from others in the same industry

2 Your organisation learns from others in other industries

3 IT vendors understand your business needs

4 IT vendors bring you useful ideas

5 IT vendors are valuable business partners to you

6 Government and political factors influence your IT strategy

7 Public opinion and the media influence your IT strategy

8 Your clients’ needs and opinions influence your IT strategy

9 Labour relations or industrial concerns influence your IT strategy


Page 291

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Page 297

8.2.10. Semi-Structured Interview Prompts

Innovation Adoption Phase 1 Qualitative Interviews

Interview Questions, Major Themes & Prompts

Theme 1 Tell me about the way IT is managed…

a. Is IT managed centrally or de-centrally

b. Does the budget reflect this?

c. How is the annual capital budget determined?

d. How is the annual IT budget determined?

Theme 2 What happens when a new IT project is proposed?

a. What justification process is required for new IT projects?

b. Are decisions centralised and based on authority

c. Do major new IT systems require broad consensus?

d. Does society or political pressure affect you IT investment program?

Theme 3 How is IT viewed in the organisation?

a. Is it a core competence

e. Is it a strength or weakness

f. Does IT change the organisation’s strategy or create new opportunities?

g. Do you believe IT’s main contribution is strategic, or in the areas of cost reduction- or somewhere else altogether…

h. Does IT pay its way?

i. Do your views match the organisation in general

j. Does IT address threats in the external market?

Theme 4 How well does the IT industry support your organisation?

a. Are the available IT solutions compatible with your organisation?

k. Do they meet your needs

l. Do they work

m. Are they available

n. Can you see them elsewhere

o. Can you trial them

p. How do you feel about adopting new IT innovations in this organisation?

q. Enthusiastic, cautious?

Page 298

8.2.11. Interview Coding Sample

Project: Health IT Innovation

DOCUMENT CODING REPORT

Document: XX Interview

Created: 20/02/2001 - 4:45:51 PM

Modified: 21/06/2003 - 5:33:02 PM

Nodes in Set: All Nodes

Node 1 of 88 (1 2 1 5) /Organisational factors/Leader Characteristics/Leader

as an enhancer/risk averse

Passage 1 of 4 Section 0, Para 24, 92 chars.

24: Well I don’t mind it as long as there is not impediment to service delivery in the

process.


34: we only have one solution we don’t allow any other opportunities for people to do

their own thing.

35:


38: The bottom line is we’re not prepared to take risks having had so much

experience with vendors who don’t perform we’re not prepared to take those risks of

saying have I got the answer for you. When we are the ones who pay and the vendors

just make money.

39:


63: So I think there are some major barriers in health because of the ultimate outcome

being the most poor of the lot I mean if the banks stuff up they might stuff up at the

point of view of just financial side of it, if health doesn’t do a good enough job it

affects people.

Page 299

Node 2 of 88 (1 2 2 1) /Organisational factors/Leader Characteristics/Leader

as a barrier/scepticism re benefits


12: But if you are looking generally in health than there is not a lot of return on

investment in straight dollars terms.


30: every one tells us that there is an opportunity we don’t know that until we test the

market, we test the market and see what it can offer, then we develop a business case

on the basis on the partnership between the market and our own organisation. Which

in turn will lead into a funding service if we can deal with the rates of value to the

organisation.


67: We are talking about clinical service delivery I am sure the banks wouldn’t invest

in any of the services that are clinical given the evidence we have that they don’t

work.


71: Well I have doubts whether it pays its way in the reality of the world I mean given

the investment that XXX Health has with XXX plus computers with devices plus all

the infrastructure the redundancy is so quick, I mean I was at a building site the other

day when they were saying we are putting in all this cabling and yet people clearly

indicated pure redundant in five years due to radio frequency. You know you’ve got

to ask the question, when do you make your investment? Because of the redundancy

factors, but from my point of view I’ve got some cynicism’s that IT actually pays its

way, but I suppose it become a necessary evil because you’ve got to do the work these

days with the cost of labour IT is probably fairly equivocal in some of these

circumstances but I don’t think we still appreciate the full cost of it.