Digitization Scope and Experience the Impacts of It On

8/3/2019 Digitization Scope and Experience the Impacts of It On

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DIGITIZATION SCOPE AND EXPERIENCE:THE IMPACTS OF IT ON

PERFORMANCE IN HEALTHCARE ORGANIZATION

Pankaj Setia

([email protected])

Information Technology Management

Eli Broad College of Management

Michigan State University

And

Monika Setia, Ranjani Krishnan and V. Sambamurthy

Please do not cite or distribute without permission

ABSTRACT

Advances in the use of information technologies (IT) have led to the creation of digitized activity

systems in organizations. Though previous research has assessed the value impacts of specific

technologies or overall IT investments, we propose to examine how patterns in the use of

technologies impact performance outcomes. Specifically, we examine how the degree to whichinformation technologies is used within key activity systems creates value in the clinical and

business systems in the healthcare industry. We offer two constructs to capture the degree of ITuse: digitization scope, which refers to the number of technologies applied toward the

digitization of activity systems, and digitization experience, which refers to the amount of

experience with using information technologies within the activity systems. We propose and testhypotheses about the impacts of digitization scope and experience on performance across the

clinical and business activity systems in hospitals. Utilizing archival data on 292 hospitals in

California, our results demonstrate how the use of IT can have significantly distinct effects onperformance in the clinical and business activities in hospitals. More importantly, our research

points to how constructs related to the use of IT can explain distinct pathways in the impacts of

IT on firm performance, particularly in the healthcare sector.


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Digitization Scope and Experience: Performance impacts in Healthcare Organizations

1

INTRODUCTION

Though prior research suggests that information technology (IT) can enhance operational and

financial performance in firms, the dynamics of these impacts is more complex than was initially

perceived (Barua and Mukhopadhayay, 2000; Tanriverdi, 2006). Recent research has utilized the

theoretical lens of complementarities as way of explaining how and why firms could utilize

information technologies in shaping superior performance (Sambamurthy et al., 2003; Barua and

Mukhopadhyay, 2000). Many empirical studies have examined complementary effects as the

integration of IT applications with specific organizational processes (Pavlou and Sawy, 2006,

Rai et al., 2006, Banker et al., 2006, Ray et al., 2004). Other research has studied

complementarities at the level of the enterprise (Aral and Weill, 2007). However,

complementarities could also be viewed in terms of the integration of information technologies

within a cumulative set of business processes, which are referred to as activity systems (Porter,

2001). Most contemporary firms seek to digitize entire activity systems, spanning customer

relationships, operations, financial management, and human resource management (Kalakota and

Robinson, 2003) through a portfolio of information technologies. Therefore, the performance

effects of IT should also be evaluated not just within specific business processes, but also in the

context of entire activity systems. In their seminal analysis, studying the shift from mass

manufacturing to flexible manufacturing systems, Milgrom and Roberts (1990) argue that

complementarities are also generated in firms due to numerous interactions between multiple

factors. They state,

we use the term complements not only in traditional sense of a relation between pairs of

inputs, but also in a broader sense as a relation among groups of activities. The definingcharacteristic of these groups of complements is that if the levels of any subset of the

activities are increased, then the marginal return to increases in any or all of the remaining

activities rises (p. 514).


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Consistent with their line of analysis, our research examines the performance impacts of

complementarities between portfolios of information technologies (i.e., a group of inter-related

digitized business processes) in firms.

Researchers also acknowledge that the nature and level of use of information technologies

plays a key role in the extent to which their impacts on performance are captured (Devaraj and

Kohli, 2003). The digitization of activity systems refers to the level of use of information

technologies within the activity system. Firms encounter two challenges in digitizing their

activity systems. First, a wide range of information technologies are available for digitization and

firms must explore which of these technologies are appropriate for their digitization efforts.

Digitization scope is defined as the variety of information technologies used in the digitization of

activity systems. Second, firms must also develop deep experience with the specific

technologies so that they can implement the needed complementary systems (e.g., business

process adaptations, rewards and incentives) and assimilate the technologies into their activity

systems. Digitization experience is defined as the amount of experience with using information

technologies within the activity systems. Our research examines the extent to which digitization

scope and experience influence the performance benefits gained from the use of information

technologies.

Our research is specifically conducted in the context of the healthcare sector. As a dominant

sector of the economy, the healthcare industry faces major institutional and regulatory pressures,

such as managed care, increasing numbers of uninsured patients, and continual pressures to

reduce costs and enhance the safety and quality of care. Information technologies are viewed as

one of the levers through which hospitals could enhance their financial and operational viability.

In fact, on April 27, 2004, President Bush signed an Executive Order establishing the position of


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National Coordinator for Health Information Technology, charged with the responsibility for

development, maintenance, and oversight of a strategic plan for nationwide adoption of health

information technologies. Research has found that investments in information technology are

associated with increased financial performance (Menon, Lee, and Eldenburg, 2000) and that

hospitals are investing considerably on business IT systems such as patient billing and credit and

collection systems to help enhance their revenues (Eldenburg and Krishnan, 2007). Other

research has focused on the impacts of two different types of information technologies in

hospitals: business and clinical technologies (Cezar, Menon, Yaylacicegi, 2007). Clinical IT

systems such as cardiology information systems, pharmacy management systems, and laboratory

information systems are valuable tools that assist physicians in patient treatment. Physicians

view clinical IT systems as critical factors that drive better quality health outcomes (Robinson

and Luft, 1988). Business IT systems such as costing systems, patient billing, nursing staff

scheduling, and credit collections are critical tools that are used by hospital managers to ensure

smooth administration and drive down costs, while enhancing customer satisfaction with

services. Thus, clinical and business activities represent two distinct activity systems in

healthcare organizations.

Our research examines the impacts of digitization scope and experience within the business

and clinical activity systems on the performance of hospitals. We use data from 292 California

hospitals to reveal that digitization scope alone is not sufficient to increase hospital performance

in their business and clinical activity systems. However, digitization experience shows

significant positive performance effects for business activity systems, whereas both digitization

scope and experience together impact the performance of clinical activity systems.


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The rest of the paper is organized as follows. The next section discusses the theoretical

development and research hypotheses. Next, we describe our data, research methods, and

analyses. Finally, we present our results and discuss their implications.

THEORETICAL BACKGROUND AND RESEARCH MODEL

Organization theory conceptualizes the firm as a set of adaptive routines that evolve with the

exploration of new possibilities and exploitation of old certainties (Schumpeter, 1934;

March, 1991; Eisenhardt and Martin, 2000). According to March (1991), organizational

exploration is associated with experimentation and variation, whereas exploitation is related with

refinement, production, efficiency, implementation, and execution.

Although they have very different impacts on performance, exploration and exploitation are

viewed as complementary, i.e., in the absence of one, the other has no effect (or might even have

adverse effects). For example, exclusive emphasis on exploitation, due to inherent short-term

improvements and the self-reinforcing nature of involved learning, often leads the organization

to ignore newer innovations (Leonard-Burton, 1995; March, 1991). As a consequence, the firm is

trapped in a sub-optimal local maximization strategy and loses its ability to find, evaluate, adopt,

and implement newer innovations (Rosenkopf and Nerkar, 2001). This hurts the firms ability to

thrive in a changing business and technological environment which offers opportunities for

newer adaptations and also threatens the basis for past performance (DAveni, 1994). Further, a

limited and exclusive focus on the exploitation of existing technologies creates an organizational

myopia that limits competencies (Levitt and March, 1988). The concept of exploration and

exploitation has been widely tested in the fields of organizational theory (Holmqvist, 2004; He


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and Wong, 2004), strategy (Winter and Szulanski, 2001), and managerial economics (Ghemawat

and Ricart i Costa, 1993).

Digitization of Activity Systems

As defined earlier, digitization is the level of use of information technologies within the

activity system. We focus on two dimensions of digitization, viz., digitization scope and

experience. Digitization scope refers to the exploration and adoption of a variety of information

technology solutions for the processes within an activity system. Digitization scope varies

according to the ongoing organizational actions in exploring the type of information technology

solutions that might be appropriate for digitizing activity systems, examining their potential

relevance and value, and adopting them for use within the activity systems. Information

technology solutions are developed both by the information systems departments as well as

vendors. As healthcare firms look for information technologies to enhance their performance, a

wide range of information technology solutions are becoming available for digitizing specific

processes and activity systems. Digitization scope is the number of information technology

solutions adopted within an activity system.

The second dimension of digitization is digitization experience. Prior research establishes

that the mere adoption of information systems is not enough (Fichman and Kemerer, 1999). The

adopting organizations must muster knowledge about which specific features of the

technological solution are appropriate (DeSanctis and Poole, 1994), how to mutually adapt the

technological solution and the activity system (Leonard-Barton, 1995), and how to trigger the

needed institutional efforts to routinize the use of the technological solution within the activity

system (Jasperson, Carter, and Zmud, 2005). All of these organizational efforts to exploit the


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capabilities of the technological solution require time and experience. Therefore, digitization

experience is defined as the amount of experience after the adoption of technology solutions

within the activity system.

The Effects of Digitization Scope

Two countervailing arguments are evident about how digitization scope could impact

performance. On one hand, the classical arguments about the benefits of information

technologies suggest that the exploration and adoption of a larger number of information

technology solutions will enhance performance because of their positive impacts on transaction

processing efficiency, decision-making speed and accuracy and organizational intelligence

(Huber, 1990). The ability of IT to enhance the reach and range of firms processes helps

organizations coordinate work across organizational boundaries at a much lower cost (Keen,

1991). Further, information technologies are associated with lower internal and external

coordination costs, and hence digitization should lead to overall lower costs of operations

(Gurbaxani and Whang, 1991). Within the clinical activity systems, greater digitization scope

implies that the hospital has adopted a larger number of clinical applications that cumulatively

would enhance the ability to gather, store, and disseminate clinical information across doctors

and treatment facilities. In addition, the adoption of more clinical applications could also

improve decision-making support by doctors (e.g., adverse medical interactions, prior treatment

history, etc.). Within the business activity system, greater digitization scope implies that

technological solutions to support a wide administrative and patient relationship management

activities (e.g., patient registration, billing, insurance claims) are available. They would benefit

improved efficiency and speed of business activity systems.


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However, the countervailing argument is that digitization scope simply captures the initial

adoption of a large number of information technology solutions. Regardless of the potential

benefits of the technological solutions, their benefits and impacts are not automatic. Prior

research on the assimilation gap demonstrates that there is a significant time lag between the

initial adoption and eventual use of information technologies in the firms activities (Cooper and

Zmud, 1990; Fichman and Kemerer, 1999). Thus, while firms are likely to gain from the

adoption of information technologies, mere adoption does not lead to the realization of their

superior capabilities. Exploration, due to its experimental nature, is known to be uncertain,

unless it is followed with an elongated period of exploitation. Emphasizing the opinion, March

(1991) points out returns from exploration are systematically less certain, more remote in

time, and organizationally more distant from the locus of action and adaptation (p. 73). In

addition, the introduction of new innovations is often disruptive and changes existing work

practices. In the case of a failure to assimilate the innovation, the organization is usually worse

off as it might lose its existing set of successful routines (Mitchell and Singh, 1993). Previously,

this has been documented in the health care organizations for the implementation of enterprise

resource planning (ERP) systems (Dryden, 1998). Therefore, greater experimentation and

exploration with new information systems in healthcare organizations may not be sufficient to

warrant performance improvements.

Taking these arguments into perspective, we propose that digitization scope within the

business or clinical activity systems will not have a significant link with hospital performance.

Therefore, we do not offer an explicit hypothesis.


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The Effects of Digitization Experience

Digitization experience captures the amount of time that a firm has been using any

information technology solution within its activity systems. Prior research has demonstrated that

at least three enabling factors are required in order to enhance the assimilation and use of any

information technology. First, depending upon the nature of the technological solution, users

must make sense of its features and how to apply it in the context of their work (DeSanctis and

Poole, 1994). Users experience significant knowledge barriers in making sense of the

technology and learning how to apply it effectively. With time and experience, they are able to

learn about the features and the effective ways of using them. Second, organizations should

enable assimilation by providing resources in the form of training, management support, or

rewards and incentives. Though these resources are vital, they do not guarantee high levels of

assimilation and use (Orlikowski, et al., 1995). In fact, they motivate users to invest their time

and attention toward making sense of the technology and discovering how to use it effectively.

Therefore, even in the presence of the enabling resources, users need time to develop the needed

experience and competence with the technology solutions. Finally, the effective use of the

technology requires mutual adaptations to the technology features and the work processes to

which it is being applied (Leonard-Barton, 1995). Through a recursive process, organizations

and users discover how to fit the features of the technology to the adapted tasks and

activities so that the technology features are being effectively used. As more time elapses, there

is a higher probability for the mutual adaptation to occur. Purvis, Sambamurthy, and Zmud

(1999) found that greater time since adoption enhances the organizational assimilation and use of

information technologies. Devaraj and Kohli (2003) demonstrated that higher levels of


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assimilation and use are key to the performance impacts of information technologies. Thus,

digitization experience should exhibit significant effects on performance and we propose that:

H1a: Higher levels of digitization experience in the business activity systems will be

significantly associated with the financial performance of hospitals.

H1b: Higher levels of digitization experience in the clinical activity systems will be

significantly associated with the financial performance of hospitals.

Further, we expect that the impacts of digitization experience on performance would vary

between the clinical and business activity systems in hospitals. The nature of clinical and

business activity systems is widely different. Business activity systems tend to be less complex

and more routinized than clinical activity systems. Clinical activity systems refer to the activities

of doctors and nurses in the delivery of medical care. Depending on the nature of the patient

care, different tasks and processes might be invoked in each instance. Many of the activities

might be time sensitive and require quick improvisations, or decisions by the doctors and nurses.

The various sub-processes related to these activities are often interdependent. The complex

clinical activity systems involve coordination across a wider range of processes and hence there

might be limits as to how much digitization experience alone can assist in realizing superior

performance. A well coordinated set of digitized processes would be sine-quo-non for the

realizing performance effects for these complex tasks. In contrast, business activity systems

involve well defined routines that are invoked most of the time in the same way for task

performance (e.g., patient registration, billing, insurance claims, etc.). Thus, digitization

experience alone can enhance the speed, efficiency and cost effectiveness of the performance of

business activities far more than the performance of clinical activities which require inputs from

a wide range of processes.


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each other in enhancing firm performance, and propose that these complementarities within the

business and clinical activity systems will enhance hospital performance:

H2a: Complementarities between digitization scope and experience within business activity

systems will be associated with a positive effect on financial performance of hospitals.

H2b: Complementarities between digitization scope and experience within clinical activity

systems will be associated with a positive effect on the financial performance of hospitals.

However, we also expect that the strength of the links between complementarities and

performance will be different between the clinical and business activity systems. Since they are

more complex, clinical activity systems are composed of a larger number of specific and

interdependent tasks and processes (for e.g., intensive care, radiology, medication management,

operating room, and laboratory). The greater interdependence is compounded by the fact that the

clinical professionals often work in compressed time frames. Therefore, coordination among the

digitized processes is vital. In other words, extended digitization scope will be a more vital

complement to experience in the case of clinical systems compared with business systems. If a

hospital develops digitization experience with a limited number of technology solutions, then the

other processes within the clinical activity system that are not well digitized could impair the

effectiveness of the digitized processes, because of the high levels of interdependence

(Thompson 1967). For example, if laboratory and radiology processes are not as well digitized

and assimilated with the operating room, the effectiveness of digitizing the operating room could

be impaired.

The simpler activities in the business systems call for lesser coordination. Thus,

complementarities between digitization scope and digitization experience will have an ever

greater effect in case of clinical activity systems. Beyond our hypothesized effects (H2a and

H2b), we propose that links between complementarities and performance will be stronger in the

case of clinical activity systems than business activity systems.


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DATA AND METHODS

The data for the research was collected from two different sources HIMSS Analytics and

the Healthcare Quality and Analysis Division of California Office of Statewide Health Planning and

Development (OSHPD). HIMSS collects data on information technology usage via a survey of

hospitals and maintains the data for 27,000 care delivery organizations (CDOs) including 3,989

hospitals through the U.S (Housman et al., 2007; Angst et al., 2007). They group data into two

categories of technologies according to the activity system to which they are applied. Forty

technologies are categorized as business technologies, and forty eight applications are

characterized as belonging to clinical activities (see table 1 for the details of these technologies).

--------------------------------------------

Insert Table 1 about here

--------------------------------------------

To avoid common method bias, data on financial performance of hospitals is obtained from a

different source - Healthcare Quality and Analysis Division of California Office of Statewide

Health Planning and Development (OSHPD). All acute care hospitals licensed by the State of

California are required to submit their annual financial reports to the OSHPD. These reports are

audited before generating the annual dataset. Besides financial information, OSHPD also reports

other data including information on ownership, size, and type of facility that is used in this

research.

Hospital Medicare id was used to merge the two databases together. Our final merged sample

consists of 292 observations for the year 2004.


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Operationalization

Dependent Variable. The hospital performance measure used in this research is net income

per patient day in hospitals. Due to the unique characteristic of healthcare, sales growth and

market share variables might not be appropriate for the study due to the geographic location and

a lack of profit focus that is a characteristic of a large number of hospital organizations. Further,

these variables only capture top line performance. Net income (NI) includes both the top line and

bottom line performance and hence was used to assess the overall value (Vh) for the hospital.

Further, the ratio of net income to patient days is used to remove any bias due to the number of

patients being managed by the hospital.

Independent Variables. Digitization scope and digitization experience were operationalized

through HIMSS data on the number of technological solutions adopted and used by each hospital

and number of years of experience with each of these solutions. The HIMSS database lists a

variety of tasks and processes within the business and clinical activity systems and details a list

of technological solutions for each process within those activity systems (see Table 1). Further,

for each of the hospitals, the database lists the specific technology solutions that they were using

and the year when that solution was initially adopted. We used the count of these technology

solutions as a measure of digitization scope within each activity system. Further, on the basis of

the year of adoption, we computed digitization experience as the number of years of use of each

solution till 2004.

Computation of Digitization Scope: If kih (0,1) indicates whether the information

technology i was adopted by the activity system in the hospital h, then digitization scope is

measured as the ratio:


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Exh = =

M

i 1

kih ( 1/N . ( ==

M

i

N

h 11

kih ) ) where N represents the total number of

hospitals in the sample, and M represents the total population of information technologies

available for the activity system . Since the information technologies include a wide range of

features and functionalities coded into them, Exh measures the extent to which a particular

hospital has explored its technology options to digitize work processes, relative to other

hospitals.

Computation of Digitization Experience: Digitization experience is defined as

Eph = =

M

i 1

Yih. kih ( 1/N . ( ==

M

i

N

h 11

Yih.kih) )

Where Yih represents the experience, or the number of years that a hospital h has used the

information technology i in its activity system . Eph measures the overall experience of the

activity system compared to the average years of experience of an activity system across all

hospitals.

Complementarities (h) Computation: Complementarities are measured as the interaction of Exh

and Ep

i.e. ( =

M

i 1

kih.=

M

i 1

Yih.kih ) ( 1/ 2N .( ==

M

i

N

h 11

Yih.kih) ) ( ( ==

M

i

N

h 11

kih) ) .

The alternate specification involves the assessment of an inverted U-curve (Gupta et al. 2006).

However, that requires digitization scope and digitization experience to be the two ends of a

continuum. Since the two are proposed to be orthogonal (and not continuous) dimensions,

interaction is the valid operationalization to assess the complementarities between them (Gupta

et al. 2006).


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Eighty percent of the hospitals in our sample have more than one year of experience, 50%

have more than 4 years of experience, and around 20% of the hospitals have more than 8 years of

experience with business information systems. For the clinical information systems, more than

half of the hospitals have between 1-1/2 years and 3 years of digitization experience. The

average size of the hospital measured as the mean number of beds staffed is 196. A majority of

hospitals (91%) are general hospitals with the remaining 9% being childrens, psychiatric, or

other specialty types. Sixty one percent of the hospitals are non-governmental not-for-profit and

the rest 39% are either owned by investors, city/county, or district. The descriptive statistics of

all variables used in the study are reported in table 2.

--------------------------------------------


--------------------------------------------

Control Variables

Since our focus in this research is to analyze the financial performance of hospitals, we

control for other healthcare related factors that might impact performance. Past research has

found that a hospitals financial performance is likely to be influenced by size, type, and

ownership. The number of staffed beds was thus used as measure to control for the size. A

dummy variable was used to control for the type of hospital, which took the value of 1 if the

hospital was a specialty hospital and zero otherwise. We used three dummy variables to control

for the three ownership types - government, non-profit and for profit. The government dummy

took the value of 1 if the hospital was a government hospital and zero otherwise. The nonprofit

and for-profit dummies were coded in a similar manner. We dropped the government dummy

from the empirical modes to prevent singularity problems. In addition, we also controlled for

product mix by including the proportion of revenue from Medicare patients and Medicaid

patients. We controlled for asset intensity by including the ratio of patient revenue to total assets


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as a control. Because the regulatory and competitive environment faced by hospitals differs

across states, our sample consists of hospitals only from the state of California.

Empirical Model

We use the hospital as the unit of analysis. After ensuring that the data did not violate the

regression assumptions, we estimated the following regression model using net income per

patient as the dependent variable:

Vh = b0h + bxbh Exbh + bpbhEpbh + bxch Exch + bpch Epch + bbhbh + bchch + b7hZh + h (1)

Where:Vh (NetIncPt)_ = Net income per patient for hospital (h).

Exb(Expr_BusIT)= Ratio of the number of business information technology applications installedin a hospital (h) to average installed for all sample hospitals (digitization scope in business

activity systems)

Exc (Expr_ClnIT)= Ratio of the number of clinical information technology applications installed

in a hospital (h) to average installed for all sample hospitals (digitization scope in clinical

activity systems)

Epb (Explt_BusIT)= Ratio of the number of years of experience of hospital (h) with business IT

applications to average experience across all hospitals in sample (digitization experience in

business activity systems).

Epc (Explt_ClnIT)= Ratio of the number of years of experience of hospital (h) with clinical IT

applications to average experience across all hospitals in sample (digitization experience inclinical activity systems).

b (ComBnBe)= Complementarities in business activity systems, defined as the multiplicative

product ofExb andEpb

c (ComCnCe)= Complementarities in clinical activity systems, defined as the multiplicative

product ofExc andEpc

Zh = Vector of other factors related to income of a hospital including bed staffing level,

ownership type and type of care provided by the hospital, proportion of traditional and managedcare Medicare revenues, and assets per patient for each hospital (h).

Coefficients bxbh, bpbh, bxch, bpch, bbh, and bch represent estimated effects of the explanatory variablesand h is the random error term.


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Complementarities Estimation

To compare the relative impacts of complementarities vis--vis the effects of digitization

scope and digitization experience, we formulated two ratios -Relative Synergistic Impact Ratio

(RSIR), and Cross System Synergistic Ratio (CSSR). These ratios are assessed to interpret

synergistic effects that have not been explored before in the context of healthcare IT.

Let Exh , Eph represent the extent of digitization scope and experience, respectively, by a

hospital h in its activity system , and h represents the degree of interaction between the two

for the activity system where (b,c), where b represents business activity system, and c

stands for clinical activity system. Thus, using the standard notation the value for hospital h,

Vh= f(Exbh, Epbh, Exch, Epch,bh, ch) is a function of two pairs of independent variables Exh and

Eph, and their joint synergistic effect h which is often conceptualized as the relative impact of

one variable in the presence of the other, or px

2

EE V (Milgrom and Roberts 1990, 1995,

Siggelkow 2002). Recently, this formulation of synergies as second order cross partial derivative

has been emphasized to be an important conceptualization that distinguishes complementarities

effect from alignment, fit and other interaction effects (Tanriverdi and Lee 2008). Our treatment

of synergies follows this notation throughout the rest of the paper.

In this research, we conduct several empirical tests to assess and compare the impact on

value of synergies in business and clinical activity systems. These empirical evaluations related

to px

2

EE V are important to improve organizational decision making which relies on the

knowledge of these interactions. Faulty managerial decisions are often a result of the

misinterpretations of these interactions effects (Siggelkow 2002). Three types of empirical

assessments of these synergistic effects are presented to interpret the results.


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greater impact on value than the direct impact of digitization scope and experience. Two types of

RSIR ratios are pertinent to each of this activity system - RSIRx and RSIRp, while the former

are related to the effects of digitization scope, the latter pertain to the effects of digitization

experience. As an illustration, the test of relative impacts in the case of business activity

systems, involves the evaluation:

i. whether RSIRxb = (pbxb

2

EE V ) (

xbE V ) 1, and

ii. whether RSIRpb = ( pbxb

2

EE V ) (

pbE V )1.

A similar evaluation is done in the case of clinical activity systems.

Combined with the first assessment, test of RSIR offers valuable managerial information

related to interactive effects. For example, px

2

EE V >0, and RSIRj >1, j (x, p), and

(b, c), imply that the nature of interaction is complementary, and necessitates a greater

managerial attention because the incremental returns to direct effects are limited and less than the

synergistic effects. px

2

EE V 1, on the other hand, implies that though the

interaction effect is still stronger than the direct effect due to the substitutive nature of the

interaction the impacts on performance is not as much in case the interaction effects are not

recognized by managers (Siggelkow 2002). Similarly, other combinations offer unique insights

that can be leveraged for optimal management of organizational complements.

Finally, the last empirical assessment is related to the comparison of synergies between

digitization scope and experience effects across the two activity systems. This relative

assessment of the synergies impacts is done by evaluating the Cross System Synergistic Ratio


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(CSSRij) defined as the ratio of synergies across the two activity systems i and j, i j. In this

study, the ratio measures the relative impact of synergies in the clinical activity systems as

compared with those in the business activity systems, and is given as CSSRbc =

(pbxb

2

EE V ) (

pcxc

2

EE V ) . A value greater than 1 for the ratio will imply that

the clinical synergies between digitization scope and experience have greater impact than

corresponding synergies in the business systems, whereas a value less than 1 will imply the

greater impact of synergies between digitization scope and experience of clinical activity

systems.

RESULTS

Table 3 contains the results of estimating equation 1. The adjusted R2 of the regression is

14%. The results for business technology systems indicate that digitization scope (Ex), with the

business IT systems does not lead to superior performance. Similarly, higher digitization scope

of clinical IT (Exc) is not associated with performance. Recall that we had not offered

hypotheses about the effects of digitization scope.

A different pattern of results emerges for digitization experience. The results in Table 3

indicate a positive and significant coefficient on digitization experience within business activity

systems. This result is consistent with H1a and indicates that experience with business IT (Epb)

yields a positive payoff to the hospital. H2b predicted that greater digitization experience within

clinical systems (Epc) will be associated with a significant positive effect on the financial

performance of hospitals. However, the results in Table 3 indicate that digitization experience

with clinical IT is negatively associated with performance. These results do not support

hypothesis H2b.


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Next we did the three empirical tests proposed for complementarities. The first test was

related to the assessment whether px

2

EE V is greater than, equal to, or less than zero.

pbxb

2

EE V is found to be insignificantly different from zero (Table 3) and hence H2a, which

predicted that complementarities between digitization scope and experience exploitation within

business systems will be associated with a positive effect on financial performance of hospitals,

is not supported. This result suggests that for business activity systems, digitization experience

itself is sufficient. The results for clinical activity systems however indicate thatpcxc

2

EE V

>0 i.e. it is positive and significant. This indicates that synergies between digitization scope and

experience within clinical systems are associated with a positive effect on the financial

performance of hospitals, as predicted by H2b. The net overall effects of digitization experience

(the total of direct and complementary effects with digitization scope) are positive, suggesting

that joint exploration and exploitation are essential to realize superior performance from more

complex clinical information systems. To summarize, the first assessment of complementarities

establishes positive interaction between digitization scope and digitization experience effects

within clinical systems, but finds these to be independent in the case of business systems. The

results of the hypotheses tests are summarized in Table 4.

--------------------------------------------

Insert Tables 3 and 4 about here

--------------------------------------------

The second empirical test of complementarities was focused at the assessment ofRelative

Synergistic Impact Ratios - RSIRx and RSIRp. In the case of business activity systems, RSIRpb

is less than one indicating that returns from joint synergistic interaction are greater than


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digitization experience alone (see Table 5). RSIRxb, on the other hand is undefined and hence this

study is not able to accomplish the relative assessment of complementarities with the digitization

scope of business IT applications since the digitization scope and complementary effects are both

found to be insignificant. Both RSIRxc andRSIRpc are found to be greater than 1 (Table 5) thus

indicating that returns from joint synergistic interaction are greater compared to the direct impact

of digitization scope and experience within the clinical activity systems. These results indicate

that the effects are different across the two activity systems. More complex clinical activity

systems have greater synergistic impacts as compared to the independent impact of digitization

scope (Exc) and experience (Epc) effects, whereas for the business activity systems, digitization

experience (Epb) effects are greater than the synergistic impacts.

Finally, we assessed the cross system complementarities effects using the ratio CSSRbc which

is found to be less than 1 (see Table 5). This indicates that the between the two activity systems,

the comparative impacts of synergistic interaction is greater for clinical systems as compared

with that for the business systems.

--------------------------------------------

Insert Tables 5 about here

--------------------------------------------

Sensitivity and Robustness analysis

We tested the various assumptions for regression analysis and statistical testing before doing

the analysis. The data was found to be normal and Breusch-Pagan test for heteroskedasticity and

the Linktest for specification errors ruled out any threat to our results due to violation of these

regression assumptions. Further, we tested the robustness of the results to the violation of

distributional assumptions by estimating a non-linear regression. In this regression, the

dependent variable was transformed by taking the square root of the dependent variable (net


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23

income per patient) in the original parametric model in equation 1. Because the dependent

variable had negative values for some of the observations, the largest negative value was added

to the DV before taking the square root. In addition, the number 1 was added over and above the

addition of the biggest negative value because square roots behave differently for numbers

between 0 and 0.99 (the square root of the number decreases as the number itself increases) as

compared with the numbers that are greater than or equal to 1 (the square root of the number

increases as the number itself increases). The addition of number 1 makes all observations

greater than or equal to 1. Results are robust to the transformation of the dependent variable and

are reported in table 6.

--------------------------------------------


--------------------------------------------

We also re-estimated the results using absolute definitions for digitization scope and

experience. That is, digitization scope was defined as the number of information systems

adopted, while experience was defined as the sum of years of experience with information

systems. The results were qualitatively unchanged.

Test for Endogeniety. It is possible that firms which have more resources because of better

financial performance are also more likely to invest in clinical and business IT. That is, net

income and digitization scope of business and clinical activity systems may be simultaneously

determined. To rule out this possibility, we tested the robustness of our results using the two-

stage least squares (2SLS) technique and compared our OLS results to 2SLS (Greene 2000). In

the first stage of the 2SLS, we used the likely endogenous variable (number of business or

clinical IT) as the dependent variable and all the other exogenous variables as independent

variables. We extracted the predicted values of the endogenous variables (number of business or


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24

clinical IT) and used these predicted values as instrumental variables in equation 1, in the second

stage. The results from the 2SLS were similar to those reported in Table 3, which indicates that

our results are not likely to be influenced by endogeneity concerns.

Finally, although we had included the proportion of revenue from Medicare and Medicaid to

control for the patient mix, we also examined the robustness of our results to the inclusion of the

case method index as an additional control variable. The case mix index is a measure of the

average severity of illness of patients treated in the hospital. There was no change in the results

after the inclusion of the case-mix index.

DISCUSSION

Organizations face relatively long periods of incremental change punctuated by changes

driven by technology, competitors, regulatory events, or other significant changes in political and

economic conditions (Tushman and OReilly, 1996). Newer information technology solutions

are developed with functionality to monitor, manage, and incorporate these changes. This

research studies the dynamics of introducing these information technology solutions into a firms

digital activity system. To the best of our knowledge, the research is the first to empirically test

the performance effects of exploration and exploitation of information technologies and their

synergistic effects at the level of an activity system. Further, there is a paucity of research that

examines the role of IT in adding value in healthcare organizations. This research, answers the

calls from the national health IT leadership panel to bring in the theories and concepts from other

disciplines to study the role of IT on hospital performance. (Lewin Group, 2005)

Our empirical analyses use data from 292 California hospitals to examine the effect of

digitization scope and experience and their synergies in clinical and business activity systems.

Our results indicate that in the case of business activity systems, digitization scope does not


26/39


25

impact performance, but digitization experience has a significant positive impact on

performance. In addition, the interaction between digitization scope and experience does not

yield positive effects, contrary to the proposed argument in the current literature that exploration

and exploitation are always complementary.

However, a different pattern of results emerge when we examine the effects of digitizing

clinical activity systems. Digitization scope does not yield positive benefits and digitization

experience results in a negative impact on profits. Thus, our results indicate that in the case of

clinical activity systems, digitization experience alone is not sufficient and in fact has negative

impacts. This suggests that limited digitization of the parts of the clinical activity system

hampers the performance of the doctors and nursing staff as they have to coordinate work across

manual and digital systems. However, the interaction between the two has positive effects on

profits indicating that digitization scope and experience are both needed for performance

improvements in the clinical systems. These results are consistent with those of Cezar et al.

(2007), who use data from Washington hospitals and find that expenditures on clinical IT

(similar to our scope variable) do not have either an immediate or a lagged positive impact on

organizational performance.

The research is not without limitations. While we use the exploration and exploitation

paradigm to assess the complementarities within the activity system, it cannot be claimed that

our measures fully capture the complete diversity of the two constructs. Many other

organizational dynamics may influence exploration and exploitation effects within the

organization. Researchers in the field of organizational theory (Holmqvist, 2004; He and Wong,

2004), strategy (Winter and Szulanski, 2001) and managerial economics (Ghemawat and Ricart i

Costa, 1993) have highlighted the differences in the firms structure, processes, strategies, and


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26

culture that are associated with exploration and exploitation. While exploration is usually

characterized by loosely coupled organic structures, and autonomy and chaos, exploitation often

involves tightly coupled mechanistic structures, and controlled and rigid routines (Eisenhardt and

Martin, 1998; Burns and Stalker, 1961). Indeed exploration and exploitation are complex

constructs with multiple dimensionsand their definition and connotation has been a subject of

wide debate (Gupta et al. 2006). We use proxies because of the nature of our secondary data.

While secondary data offers objectivity in measurement, it does so at the expense of the richness

that can be captured in more detailed inquiry using survey instrument. We believe that our

method of using digitization scope as a proxy to measure exploration and experience as a proxy

to measure exploitation is appropriate in the case of digitization of activity systems, and has been

extensively used in prior research (for example, Rothaermel and Deeds, 2004; Katila and Ahuja,

2002).

In spite of these limitations, our results shed interesting insights on the digitization of two

important activity systems, clinical and business, and suggest that future research is warranted in

this setting. Our research also makes important contributions to the literature on complementary

effects of IT. While synergistic interactions are often proposed to be essential for realizing

performance impacts of IT systems, our empirical findings indicate that the significance of these

impacts is contingent to the context of the study. Our results suggest that synergistic interactions

between exploration and exploitation of IT are more likely to materialize in the case of complex

activities such as clinical activities. On the other hand, in the case of relatively simpler business

IT systems, exploitation is sufficient to produce higher returns. To provide better control, we

restricted our study to the hospital industry. Future research could examine whether these results

hold in other industries.


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27

Empirical estimation of complementarities has added to the complementarities estimation in

three ways. The first test has established a test for differentiation of synergistic interaction to be

substitutive, complementary or independent in the independent variables. Second, test has

developed the concept ofRelative Synergistic Impact Ratios (RSIR), whichhelps determine the

impact of synergistic interaction relative to the direct impacts. Finally, the relative impact of

synergies across business units can be assessed using the proposed Cross System Synergistic

Ratio (CSSR). This three pronged approach for the assessment of complementarities offers first

structured way for empirical assessment which have gained increased traction from researchers.

Our systematic testing of these effects will help establish a framework that will guide future

empirical assessment of complementarities.

Our research has focused on interactions within the business and clinical information

systems. Hospitals are currently exploring IT systems that integrate both clinical and business

modules to provide support to clinical as well as business functions (Vernon, 2005, Serb, 2006).

Future research could examine more complex interactions such as those across business and

clinical systems and explore the pattern of results that emerge when these systems are integrated.

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Table 1: Clinical and Business Technologies included in the Analyses

Clinical Software Applications Business Software Applications

Category Application Category Application

Intensive

Care Ambulatory Clinical Accounts Accounts Payable


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Ambulatory EMR Cost Accounting

Ambulatory Laboratory General Ledger

Ambulatory PACS Billing/Insurance Contract ManagementAmbulatory Pharmacy Credit/CollectionsAmbulatory Radiology Eligibility

Cardiology Cardiology - Cath Lab Encoder

Cardiology - CT

(Computerized Tomography) Patient BillingCardiology - Echocardiology Premium/Insurance Billing

Cardiology - Intravascular

Ultrasound Financing

Data Warehousing/Mining -

FinancialCardiology - Nuclear

Cardiology Financial Modeling

Cardiology InformationSystem

Forms &

Documents

Document Management -Business Office

Home Health Home Health Clinical

Document Management -

HIM

Intensive

Care

Intensive Care/Critical Care(ICU)

Document Management -Human Resources

Intensive Care/Medical

Surgical

Electronic Forms - Business

Office

Laboratory Anatomical Pathology Electronic Forms HIM

Laboratory Information

Systems

Electronic Forms Human

Resources

Microbiology Home Health Home Health Administrative

Medication

Management

Electronic Medication

Administration Record

Patient

Information ADT/RegistrationOutpatient Pharmacy Abstracting

Pharmacy Management

System Case Mix Management

Operating

Room

Operating Room (Surgery) -

Peri-Operative Patient Scheduling

Operating Room (Surgery) -Post-Operative Operations Blood Bank

Operating Room (Surgery) -

Pre-Operative Scheduling Medical Staff Credentialing


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Table 1 (continued): Clinical and Business Technologies included in the Analyses

Patient

Medical

Information

Computerized PractitionerOrder Entry (CPOE) Nurse Staffing/Scheduling

Data Warehousing/Mining

Clinical OR SchedulingDictation Staff Scheduling

Dictation with Speech

RecognitionEmployee

Information Personnel Management

Enterprise EMR Time and Attendance

Enterprise Master Person Index

(EMPI) Benefits Administration

In-house Transcription Hospital Supplies RFID - Supply Tracking

Nursing Documentation Materials Management

Outsourced Transcription

Physician Documentation Other Clinical Data Repository

PatientSurveillance Chart Deficiency Enterprise Resource Planning

Chart Tracking/Locator Executive Information Systems

Clinical Decision Support Interface Engine

RFID - Patient Tracking Practice Management

Radiology Radiology - Angiography

Order Entry (Includes Order

communications)

Radiology - CR (Computed

Radiography)

Outcomes and Quality

Management

Radiology - CT (Computerized

Tomography) Budgeting

Radiology - DF (Digital

Fluoroscopy) Business IntelligenceRadiology - DigitalMammography

Electronic Data Interchange(EDI) - Clearing House Vendor

Radiology - DR (Digital

Radiography) Payroll

Radiology - MRI (Magnetic

Resonance Imaging)

Radiology - Nuclear Medicine

Radiology US (Ultrasound)

Radiology Information System

Telemedicine - Radiology

Others

Emergency Department

Information Systems (EDIS)Medical

Terminology/Controlled

Medical Vocabulary

Nurse Acuity

Obstetrical Systems (Labor &

Delivery)

Respiratory Care Information

Systems


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Table 2 : Descriptive Statistics for Selected Variables (n=292)

Variable Description Mean Std. Dev. Min Max

Expr_BusIT (Exb)Relative exploration of a hospital with

business Information Systems 1 0.25 .25 2.46

Explt_BusIT(Epb)

Relative exploitation of business

Information Systems by the hospital 1 0.34 .13 3.02

Expr_ClnIT(Exc)

Relative exploration of a hospital with

clinical Information Systems 1 0.75 0 2.66

Explt_ClnIT(Epc)

Relative exploitation of clinical

Information Systems by the hospital 1 0.66 0 2.77

No of Business IT

Number of information systems in business

domain 16.28 4.04 4.00 40.00

No of Clinical IT

Number of information systems in clinical

domain 15.93 5.39 2.00 48.00

Experience with

Business IT

Experience (In years) with business

information systems 54.30 40.64 0.00 144.33

Experience with

Clinical IT

Experience (in years) with clinical

information systems 14.18 9.30 0.00 39.25

Bed_Stf Number of staffed beds 196.74 139.14 2.00 875.00

AsstCtrl Total Assets Per Patient Days 2957.71 2458.20 136.65 23242.50

McrtCntr Net Patient Revenue from Medicare 2.65 2.76 -0.49 23.63

McltCntr Net Patient Revenue from Medicaid 1.96 5.05 -2.07 57.78


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Table 3: Regression Results for dependent variable Net income per patient

Variable Description

Standardized

Coefficient Standard Error t-statistics

Business Information Systems

Expr_BusIT(Exb)

Digitization scope of the

hospital within the business

activity system. 0.13 2275.24 0.90

Explt_BusIT(Epb

)

Digitization experience of the

hospital within the businessactivity system. 0.73 1926.35 2.00*

ComBnBe (b)

Complementarities measured as

interaction between

Expr_BusIT and Explt_BusIT -0.68 1573.30 -1.74

Clinical Information Systems

Expr_ClnIT(Exc)

Digitization scope of the

hospital within the clinical

activity system. -0.21 1902.92 -1.23

Explt_ClnIT(Epc)

Digitization experience of the

hospital within the clinical

activity system. -0.52 1543.47 -2.02*

ComCnCe(c)

Complementarities measured as

interaction between

Expr_ClnIT and Explt_ClnIT 0.58 1573.30 1.98*

Control Variables

Bed_Stf Number of Staffed beds -0.23 2.83 -2.33*

AsstCtrl Total Assets Per Patient Days 0.07 0.09 1.18

McrtCntr

Net Patient Revenue from

Medicare 0.22 129.92 2.41*

McltCntr

Net Patient Revenue from

Medicaid 0.35 57.17 4.75**

NPProfit_DumDummy Variable for non-profithospitals -0.21 760.52 -2.23*

FPProfit_Dum

Dummy Variable for for-profit

hospitals -0.13 853.54 -1.39

Type_Care

Type of Care Provided by

Hospital 0.11 356.10 1.94Adjusted R2 of the Regression

model (F value, and P-value in

0.15 (F=4.94, p


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36

parentheses)

* Coefficients significant at p


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37

TABLE 5: Results of Complementary Estimation

Estimated

Statistic Measure Value Test Result

Synergistic

Business

Interaction pbxb

2

EE V

Not

significantly

different from

Zero =0

Di

exp

tec

Synergistic

Clinical

Interaction pcxc

2

EE V

0.58 >0

Di

exp

tec

com

RSIRxb

(pbxb

2

EE V ) (

xbE V )

Un Defined Inconclusive

Re

dig

syn

bu

no

RSIRpb

(pbxb

2

EE V ) (

pbE V )

0 1

Co

cli

tha

eff

RSIRpc

(pccb

2

EE V ) (

pcE V )

1.12 >1

Co

cli

tha

exp

CSSRbc

(pbxb

2

EE V ) (

pcxc

2

EE V )

0


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** Coefficients significant at p