Technological innovations: a framework for communicating diffusion effects

Technological innovations: a framework forcommunicating diffusion effects

Franklin J. Carter Jr.a,1, Thani Jambulingama, Vipul K. Guptaa,*, Nancy Meloneb

aErivan K. Haub School of Business, St. Joseph's University, 5600 City Ave, Philadelphia, PA 19131, USAbJohn F. Donahue Graduate School of Business, Duquesne University, 600 Forbes Avenue, Pittsburgh, PA 15282, USA

Received 21 June 1999; accepted 23 July 2000

Abstract

The paper investigates the impact of the institutional aspects of the innovation±adoption process on the success of its

implementation. More speci®cally, we concentrate on the adoption of ®ve information technologies using a data set from the

aerospace and defense industries. We investigate such factors as advocacy, breadth of support, time of adoption, and intra-

organizational communications. Several hypotheses are formulated and empirically tested. We ®nd that advocacy by middle

management does not have a positive effect on the success of implementation. # 2001 Elsevier Science B.V. All rights

reserved.

Keywords: Diffusion of information technologies; Aerospace industry; Software engineering; Technological innovations; Target organization

group; Management advocacy; Communication mechanisms

1. Introduction

The diffusion of an innovation is conceived as the

process by which knowledge of an innovation spreads

throughout a population, eventually to be adopted or

not adopted by a decision-making unit in the organi-

zation [29]. The degree of acceptance and the rate at

which this process takes place is contingent upon the

characteristics of the innovation, networks used to

communicate the information about the innovation,

characteristics of those who adopt the innovation, and

the actions and characteristics of the agents of change.

This concept of innovation diffusion has been applied

to innovations ranging from new ideas to new machine

[3,30,32].

In the last few years, understanding the diffusion of

information technologies (ITs) has been important to

both practitioners and researchers. Nilakanta and Sca-

mell [25], for example, deal with the effects of com-

munications on the diffusion of data base design tools.

Grover et al. [15] addressed the issue of IT diffusion

and organizational productivity as perceived by senior

information systems (IS) executives. A study by Lai

and Guynes [20] investigated the adoption behavior

between IT adopters and nonadopters at the organiza-

tional level. The IS research community started focus-

ing on diffusion of innovation research in mid-1980s

and Prescott and Conger [26] summarized this stream

of research from the mid-1980s to the mid-1990s. In

spite of the substantial number of studies and reviews,

the IS innovation literature remains underdeveloped

due to the complex and context-sensitive nature of the

Information & Management 38 (2001) 277±287

* Corresponding author. Tel.: �1-610-660-1622;

fax: �1-610-660-1229.

E-mail addresses: [email protected] (F.J. Carter Jr.),

[email protected] (T. Jambulingam), [email protected] (V.K. Gupta).1 Tel.: �1-610-660-1463.

0378-7206/01/$ ± see front matter # 2001 Elsevier Science B.V. All rights reserved.

PII: S 0 3 7 8 - 7 2 0 6 ( 0 0 ) 0 0 0 6 5 - 3

phenomenon. It appears that there can be no single all

encompassing theory of IT innovation, as the ever

changing nature of IT will keep `the whole' beyond

our grasp. Few will argue, however, that IT innovation

cannot be understood without careful attention to the

personal, organizational, technological, and environ-

mental context within which it takes place.

This paper focuses on three issues. First, we look at

the properties of ITs that affect adoption. A framework

is presented in which ITs are characterized in terms of

level of abstraction and of target user. We consider

innovations that are either predominately methodolo-

gical or tool-based. Tool-based innovations are more

concrete and typically require a front-end ®nancial

commitment on the part of the adopting organization.

Methodologies are primarily abstract; while they do

not have to be `purchased,' they often require ®rms to

devote substantial resources to learning how to use the

innovation in order for the adoption to be successful.

In addition, ITs can be described in terms of their

target user in the organization. We studied innovations

that are targeted either to administrative levels in the

organization or to technical staff. We examined both

methodological and tool-based innovations with

respect to their compatibility with innovation advo-

cacy and their effect on the speed and probability of

the adoption.

The second issue is the process by which IT diffu-

sion occurs. Diffusion of innovations has been char-

acterized as a three-stage process involving initiation,

adoption, and implementation. In this study, we con-

centrate on adoption and implementation, looking at

the factors that affect each stage as well as the con-

nection between stages.

Third, we investigate the effects of various types of

communication on the adoption of IT. Communica-

tions are examined with respect to the differential

effectiveness of distinct types of mechanisms, which

are characterized on two levels: organizational

resources required for use and the formalism of their

use. For example, developing training programs

requires relatively high resources. Ad hoc consultation

is an informal mechanism. We also examine commo-

nalties in communication effectiveness across the

adoption process and multiple ITs.

This paper focuses speci®cally on software engi-

neering innovations. Software engineering is the

technological and managerial discipline concerned

with the systematic production and maintenance of

software products developed and modi®ed on

time, according to speci®cation, and within cost esti-

mates [11,27]. Software engineering innovations

may be primarily methodological (e.g. step-wise

re®nement, data hiding) or tool-based (e.g. program

design languages). In some instances, they are a

combination of both. A large portion of an organiza-

tion's software budget is often devoted to maintaining

and developing of systems containing routines similar

to code developed for other systems. For this reason,

innovations that facilitate reusability and mainte-

nance, or which speed development time or help

control costs, are potentially valuable. Although this

potential value is well known, diffusion of software

engineering tools and methods is often slow and

imperfect [28].

2. Research framework

Adoption of technology proceeds as follows:

1. Initiation: The stage during which the adopting unit

acquires information about the innovation and

goes through an approval process for using the

innovation.

2. Adoption: Developing capabilities for using the

innovation, such as training and/or hiring person-

nel, or physically acquiring the innovation.

3. Implementation: Using the innovation in produc-

tion for any complete software development

projects.

The level of abstraction of a particular innovation is

expected to affect the diffusion process. It has been

suggested that intangible innovations, such as new

software development philosophies, because they are

more abstract with less observable outcomes, are

adopted more slowly than more concrete innovations,

such as hardware-based ones. Those in IT also tend to

have large, unobservable, components: Methodolo-

gies, in particular.

An important characteristic is the innovation's tar-

get organization group (TOG). Some are primarily

targeted at technical staff while others are at admin-

istrative staff. The TOG for an IT innovation could be

individual software engineers (SEs): to provide soft-

ware reusability, facilitate the software production

278 F.J. Carter Jr. et al. / Information & Management 38 (2001) 277±287

process, and increase the quality and standardization

of the software.

Previous studies of innovation transfer [18,24,35]

have stressed the top management championship as a

precursor to the successful introduction of innovation:

the higher the level of advocacy, the more likely it will

be successfully adopted [10,12,14,17]. Champions

from other organizational levels, however, also have

a role in diffusion [22].

Earlier research often failed to address the possible

impact of ìntermediate-level' advocacy on the adop-

tion of innovations. An exception is a study by Daft

[8], who suggests that top-management sets an overall

goal of organizational responsiveness to innovation,

while lower organizational members then champion

innovations consistent with their own area of exper-

tise. Informal communication networks tend to be

used extensively to promote innovations. It is easier

for intermediate-level members to use them.

It is useful to look at the possible effects of ìnter-

mediate-level' advocacy by considering whether a

potential advocate may have different effects on adop-

tion depending on the target group. This leads to the

following hypotheses:

H1a: Middle management primary advocacy will

have a signi®cant positive impact on the adoption of

innovations with an administrative TOG.

H1b: Technical staff primary advocacy will have a

signi®cant, positive impact on adoption of innovations

with a software engineering TOG.

Research on opinion leadership provides evidence

that opinion leaders are generally fairly close in out-

look and social class to the population they lead.

Howell and Higgins [16] suggest why this principle

may apply to advocacy as well; interaction with

similar people leads to building coalitions of support

for the innovation among peers and others in the

organization.

Other research, however, suggests an alternative

hypothesis. In a study of the adoption of technological

versus administrative innovations by hospitals, char-

acteristics of the chief of medicine and hospital

administrator were analyzed with respect to the adop-

tion of innovations [19]. It was assumed that the chief

of medicine would be more likely to champion tech-

nological innovations and the hospital administrator

would be more likely to support administrative inno-

vations. The authors hypothesized, however, that the

advocacy of an innovation was associated with

broader involvement in the hospital and would be

positively associated with adoption. This hypothesis

was somewhat supported in the case of chief of

medicine's involvement with administrative activities.

Finally, Daft suggests that technological innovations

supported by administrative personnel and adminis-

trative innovations supported by technical staff will

tend to be òut of synchronization with perceived

needs and are less likely to be acceptable'. Based

on the above, we suggest the following hypotheses:

H1c: Advocacy by an inconsistent level will have a

signi®cant negative impact on adoption.

H1d: Top management primary advocacy will have

a signi®cant positive, impact on adoption.

Innovations that require large capital commitments

may have to be adopted in a top-down fashion, with

the championship of top management. Smaller scale

tangible and intangible innovations or those where a

high degree of learning is necessary seem to have

greater potential for a bottom-up adoption in which

there is broad-based support for the innovation, rather

than single primary advocate.

Organizations that experience dif®culty in adopting

an innovation during an early stage of the process may

hesitate to continue. For example, it is dif®cult to

install a tool or train personnel to use a methodology

then the probability of implementation may be

reduced or slowed [21]. Different actions may in¯u-

ence the diffusion process at different stages, in part

because requirements vary [34]. We propose, then, the

following hypotheses:

H2a: The smoothness of the process during the

adoption stage of the diffusion will affect the prob-

ability and timing of implementation.

H2b: The earlier that an innovation is adopted, the

earlier will it pass through the implementation stage

and the greater the probability it will be implemented.

Information moves from a source informed about

the innovation, through such channels, such as tech-

nical journals, or interpersonal channels such as ven-

dors, consultants or electronic bulletin boards, to an

individual or organization. The importance of using

various communication channels has been studied

[1,4,9,13,30]. Few researchers, however, have expli-

citly studied the timing of the communication [5].

Research has shown that a successful innova-

tion process is often characterized by extensive

F.J. Carter Jr. et al. / Information & Management 38 (2001) 277±287 279

communication. Transition support mechanisms differ

from communications in various ways. First they tend

to be more proactive. Next, they usually require at

least some commitment of ®nancial resources, which

may be substantial. Also, the intent of providing

transition support is to facilitate adoption, and clearly

this is not always the intent of communication, indeed

negative communication is possible.

We identi®ed several types of representative transi-

tional support mechanisms through interviews with

software engineering experts. Mechanism's can gen-

erally be characterized as either low resource level,

requiring relatively little commitment of resources, or

high resource level, personal mechanisms. We, there-

fore, state the following hypothesis:

H3a: There is a signi®cant difference in the use of

high resource communication mechanisms between

adopters and nonadopters.

Informal mechanisms are unstructured or loose.

Examples include: providing written documentation

about the technology and articles about the technology

from technical or scholarly journals as well as provid-

ing pre-packaged technical information. Formal tran-

sition mechanisms, on the other hand, may re¯ect a

more organized approach. This leads to an additional

hypothesis as follows:

H3b: There is a signi®cant difference in the use of

formal communication mechanisms between adopters

and nonadopters.

Low commitment, personal, transition mechanisms

include site visits to other organizations using the

technology and sending personnel to seminars or

conferences. Either internal or external personnel,

offsite or onsite, can provide high commitment sup-

port. External, high commitment mechanisms are

training by outside personnel and assistance in the

form of expert consultation at the vendor's or devel-

oper's facilities. Internal, high commitment mechan-

isms considered are training prepared by in-house

personnel, providing on-site ad hoc consultation and

on-site regular consultation. Training programs gen-

erally represent the most formal mechanisms. Regular

forms of consultation are also relatively formal.

Bayer and Melone [6] have a more complete dis-

cussion of an adaptation of the diffusion framework.

3. Empirical study

3.1. ITs as innovations

The IT innovations examined here were selected as

examples of the innovation types. They are software

cost models (SCM), complexity metrics (CM), struc-

tured programming (SP), and program design

language (PDL). SCM are estimation tools for devel-

opment projects. CM are algorithms that can be used

to estimate the complexity of software code. SP is a

methodology used to modularize software code. PDLs

are tools that assist a SE in translating a system design

into executable code.

As shown in Fig. 1, the ®ve innovations were chosen

with different levels of abstraction and TOG. SP and

PDLs are targeted to individual SEs, SCM and CM are

administrative aids, PDL and SCM are tool-based, and

SP and CM are primarily methodologies.

A set of communications mechanisms were chosen

to vary by resource needed (high or low), and structure

type (formal or informal). Low response communica-

tions depend on whether they are personal or mass-

communication mechanisms. Fig. 2 shows where each

®ts into the framework.

Fig. 1. Characteristics of the innovation.


Low response mass-communications mechanisms

selected for study are (1) providing written documen-

tation about the innovation or articles about the inno-

vation from technical or scholarly journals and (2)

providing pre-packaged technical information. These

can be distinguished in terms of degree of formalism;

written documentation and articles are relatively infor-

mal mechanisms.

Low response personal-communication mechan-

isms are (1) going on site visits to organizations where

the innovation is used and (2) sending personnel to

seminars or conferences. Site visits are typically less

formal. Nationally recognized experts are often in¯u-

ential option leaders in the adoption process. Inter-

personal interaction with these experts often takes

place at seminars and conferences. High response

communications include (1) training by outside per-

sonnel, (2) training by in-house personnel, (3) on-site

regular consultation, and (4) on-site ad hoc consulta-

tion. Of these mechanisms, ad hoc consultation is an

informal mechanism. Training programs generally

represent the most formal mechanism.

3.2. The population

Participants in the research program were indivi-

duals responding for major software developers and

consultants for the government. They knew about their

organization's adoption, postponement, or rejection of

the innovations. A screening criterion for including an

organization (or unit) for a particular IT innovation

was whether the ®rm had gone through initiation.

Most diffusion studies contain a pro-innovation bias

which is magni®ed by the grouping together of all

potential adopters.

Participants were informed of the study though a

letter sent by the two principle investigators to

National Security Industrial Association (NSIA)

members, who represent major defense contractors

in the US as well as small consulting ®rms and

developers. In the solicitation letter, the study was

described and individuals were asked to identify peo-

ple who had knowledge of the adopt, reject or post-

pone decisions about any of ®ve software engineering

innovations. The initial contacts returned a form indi-

cating who would participate in their unit, and for

which innovations. In some cases, the participant was

the addressee; in most cases, they were other people.

Each business unit was permitted only one participant

for each innovation; thus, a business unit could have a

maximum of ®ve participants. In most cases, a single

participant was knowledgeable about more than one

innovation.

3.3. The survey instrument

Data were collected using structured survey instru-

ments administered over the telephone. The 19-page

questionnaires included a broad range of issues related

to the adoption of innovations. Although different

survey instruments were developed for each innova-

tion, they shared a subset of questions.

Fig. 2. Communications types.


The survey asked questions requiring closed-form

responses. For example, participants were asked about

the extent to which they had used the transition

mechanisms at a particular stage of adoption. They

responded using a 7-point scale representing a range

from `1' (not at all) to `7' (very much). Similarly,

participants were asked to supply dates when these

activities were started.

It is well-known that phone surveys impose some

problems [23]. In our case, data were collected retro-

spectively, that is, after the event. For analysis pur-

poses, the data were assumed to approximate

longitudinal data. Causal implications of the research

cannot, however, be strongly asserted.

There were 230 interviews completed: 41 for com-

plexity metrics; 60 for program design languages; 61

for software cost models and 68 for structural pro-

gramming. The percentage of respondents passing

through each stage, for each innovation, is shown in

Fig. 3.

Primary advocacy for adopting the innovations

came from the following organizational levels: top

management 11.5%; middle management 36.5%;

technical staff 35.5% and broad-based support

16.5%. In approximately 95% of the cases, respon-

dents selected at least one level of primary advocate.

Characteristics of the innovation that have

received empirical support include relative advantage,

compatibility, and perceived complexity of the inno-

vation [33]. Beliefs about advantages and disadvan-

tages of the innovations were used as control variables.

Also, because use of ITs studied here are often

partially controlled be government mandates, we

included questions about the in¯uence of such

mandates.

3.4. Methodology

For each stage, two adoption dependent measures

were considered: movement and timing. Movement

was operationalized by the fact whether the organiza-

tion began a stage; e.g. for a production project.

Timing was measured as the year that the stage begins.

Using PROBIT models, we examine the effects of

IT properties and extensive use of communication

mechanisms on movement into the stages. To examine

the diffusion process for production, we also included

adoption history variables, such as ease and timing.

A manager trying to allocate resources ef®ciently

might reasonably ask whether resources allocated at

one stage have delayed impacts (e.g. increasing the

probability of successfully executing later stages). For

example, are certain forms of communication more

effective at accelerating adoption if provided at one

stage rather than another? We address questions of this

nature using èvent-history' models2 [2].

The empirical evidence of diffusion research

strongly supports the assumption of an S-shaped

curve and there are right-censored data3. That is,

some organizations have not yet passed through the

adoption stage. An analysis which excludes these

observations from the estimated model would be

biased. A commonly used methodology for the ana-

lysis of longitudinal data where `censoring' can pro-

duce bias and loss of information is event history

analysis.

Typical mathematical diffusion theory models spe-

cify to some degree the rate at which innovations are

adopted. The function describing the cumulative rate

of adoption generally is an S-shaped curve. Estimation

of these S-shaped curves depends upon their speci®-

cation. If the adoption function is speci®ed explicitly,

parametric methods (e.g. maximum likelihood) can be

used for estimation and inference. If not speci®ed,

parametrically weaker statistical methods must be

used. The Cox proportional hazards model [7] is an

example of such a method. The Cox model allows for

right-censoring data. The nonparametric survivor

function permits a wide-range of adoption curves,

including an S-shaped curve. We, therefore, use pro-

portional hazard models to examine the in¯uence of IT

properties and communication mechanisms on timing

of adoption for the two stages. For timing of imple-

mentation, we also included adoption history vari-

ables.

2 Event-history analysis, also known by a variety of other names

including survival analysis, lifetime analysis, failure-time analysis,

reliability analysis, or hazard-rate analysis, is a class of models and

methods for dealing with situations in which the dependent variable

is categorical and the data are censored.3 Statisticians refer to data as `censored' if there is an outcome

but it occurs after the collection of data is discontinued. Use of

regression models rather that the more appropriate event-history

models in these situations can lead to seriously biased estimators.


3.5. Results

3.5.1. Properties which affect adoption

The probability of reaching the adoption stage, was

found to be a function of several factors: mandates,

perceived technical complexity and problems with the

innovation, and an innovation-speci®c variable (see

Table 1). Organizations that have developed capabil-

ities for using these innovations are more likely to bid

on contracts mandating use of the IT (MANDNT) in

the next year. They also, however, perceive the inno-

vation as being more technically complex than orga-

nizations which have not yet developed capabilities

(XCOMPLEX). Adopting organizations are less

likely to have `wait and see' attitude about technical

problems with the IT (TECHPROB). Technical pro-

blems are associated with lowering perceptions of

bene®ts of the innovation. In addition, organizations

are less likely to have developed capabilities for using

methods-based ITs, especially complexity metrics

(METHOD).

Table 2 shows the results of estimating Cox propor-

tional hazards models for timing of the adoption stage.

The output is evaluated with the score test of the

standard null hypothesis that all coef®cients are equal

to their zero start values. In all cases shown, the Chi-

squared statistic has a p-value of <0.001. (see Stein-

berg and Colla [31] for additional details about the

Score test).

Overall, organizations which bid on contracts man-

dating use of the innovation (MANDATE) develop

capabilities for using the IT earlier. Thus, perceived

technical problems and lack of economic advantages

(NOBENEFIT) are associated with later adoption.

Fig. 3. Percentage of organizations that have passed through each stage of diffusion.

Table 1

Probit analysis for adoption (develop capabilities)a

Parameter Non-adopt

(mean for

D � 0)

Adopt

(mean for

D � 0)

Estimate S.E.

Constant 1.00 1.00 1.12 0.41

MANDNXT 2.78 5.62 0.31 0.05

XCOMPLEX 3.66 2.78 ÿ0.14 0.07

TECHPROB 3.50 2.10 ±0.29 0.07

METHOD 0.60 0.32 ÿ0.53 0.23

a Log-likelihood � ÿ78:06; ÿ2 times log-likelihood ratio (Chi-

squared� � 89:12 (4 d.f.).

Table 2

Hazards model estimation for timing of adoptiona

Covariate Covariate

means

Estimate S.E. T-statistic

MANDATE 1.37 ÿ0.83 0.17 ÿ4.90

NOBENEFIT ÿ0.13 ÿ0.25 0.09 ÿ2.80

INHS-TRAIN-PA 3.60 0.08 0.03 2.62

PREPAK-PA-SE 1.72 0.12 0.03 3.70

a Log-likelihood � ÿ868:32.


Table 3 shows results of the analysis of the orga-

nizations' probability of reaching the implementation

stage. Organizations which were more likely to bid on

contract mandating use of the IT (MANDNXT) had

higher probability of reaching implementation. When

there was a belief that the innovation interfered with

current work (INTERFERE), the organization had a

lower probability of using the IT for production.

Interference with on-going projects is a type of reduc-

tion of perceived bene®ts. The CM innovations were

less likely to be implemented.

If all organizations are used in the analysis, we can

gain some additional insights into factors affecting

adoption. Perceived technical problems or lack of

economic bene®ts and technical staff resistance are

signi®cant in¯uences.

The positive in¯uence of middle management advo-

cacy on adoption of a wide range of ITs is not what

was expected. However, H1c is partially supported by

negative effect of middle management on Ada adop-

tion. Results of additional analyses show that organi-

zations with broad-based advocacy for the innovation

are more likely to develop capabilities (p < 0:1) and

implement the innovation (p < 0:001). There is no

support for H1d.

Table 4 shows the analysis of organization's timing

reaching the implementation stage. The results show

that innovations targeted to individual SEs tend to be

used in production earlier. The likelihood of bidding in

the next year on contracts mandating use of the IT

(MANDNXT) is associated with earlier implementa-

tion. The effort an organization must expend in using

an innovation impacts production use (EFFORT). The

less perceived effort involved in using the IT in a

production environment, the earlier the production use

of the IT.

We found that more observable innovations will not

be adopted more rapidly. More observable ITs were as

software cost models, and PDLs. Innovations targeted

to administrators (CM and SCM) were adopted sig-

ni®cantly later (p < 0:001) at both adoption and

implementation. Ada adoption was earlier for the

adoption stage (P < 0:001).

We did ®nd that organizations with broad-based

support for adoption develop capabilities earlier (p <0:01) and go through production earlier (p < 0:001).

H1a and H1b were not supported. Middle manage-

ment advocacy was hypothesized as having a positive

impact on adoption of administrative innovations.

In fact, there was a signi®cant negative impact on

timing of adoption (p < 0:01) and implementation

(p < 0:05). Technical staff advocacy had no signi®-

cant impact on timing for innovations targeted to SEs.

3.5.2. Process by which diffusion occurs

We hypothesized that early stages have an effect

on later stages. The process examined in this study

are smoothness and timing. Support was found for

H2b. Organizations that developed capabilities

earlier (TIME-DC) had a higher probability of moving

into the implementation stage. The time an organiza-

tion enters implementation is also highly dependent

on the timing (TIME-DC) and ease (EASE-DC)

of developing capabilities for using the innovation.

Earlier and smoother development of capabilities

is associated with earlier implementation of the

innovation. This provides some support for H2aand H2b.

Table 3

Binary probit analysis implementationa

Parameter Non-adopt

(mean for

D � 0)

Adopt

(mean for

D � 1)

Estimate S.E.

Constant 1.00 1.00 2.48 0.66

MANDNXT 4.83 5.70 0.11 0.06

INTERFERE 0.47 ÿ0.15 ÿ0.33 0.12

TIME-DC 12.37 11.02 ÿ0.13 0.03

CM 0.16 0.09 ÿ1.28 0.38

a Log-likelihood � ÿ87:48; ÿ2 times log-likelihood ratio (Chi-

squared� � ÿ58:37 (6 d.f.).

Table 4

Hazards model for timing of implementationa

Covariate

means

Estimate S.E. T-statistic

TIME-DC 12.20 ÿ0.15 0.020 ÿ7.47

SE 0.45 0.52 0.178 2.95

EASE-DC 3.91 0.15 0.053 2.92

MANDNXT 5.62 0.11 0.046 2.53

EFFORT 3.12 0.14 0.066 2.24

ADHOC-SE ÿ0.01 ÿ0.26 0.122 ÿ2.18

a Log-likelihood � ÿ732:48.


3.5.3. Effects of communications on adoption

Overall, effects of communication mechanism use

on diffusion were mixed. There was some support for

H3b. When there is extensive use of training prepared

by in-house personnel during initiation (INHS-

TRAIN-PA), organizations develop capabilities for

using these innovations earlier. Also, during initiation,

use of prepackaged technical information (formal)

was associated with earlier implementation of Ada

(PREPAK-ADA). Ad hoc consultation at the organi-

zation's site (informal) is associated with later imple-

mentation of ITs targeted to individual SEs (ADHOC-

SE). However, there were no clear insights.

T-test results provide some support for H3a and

H3b. Using the categorization of communication, use

of communication mechanisms were aggregated to

combine high resource and formal mechanisms, low

resource and formal mechanisms, high resource and

informal mechanisms, and low resource and low

mechanisms. T-test analyses were performed using

development of capabilities (adoption) and production

use (implementation) as grouping variables in separate

analysis. Differences were signi®cant and in the

expected direction for high resource, formal mechan-

isms (p < 0:01) for both stages. Organizations which

passed through adoption and implementation made

more extensive use of these communications. Use of

low resource, formal communications was also more

extensive for organizations which developed capabil-

ities (p < 0:01). Based on these results, there is stron-

ger support for H3b, the effect of formalism of

communication mechanisms on adoption than for

H3a.

Additional proportional hazards analyses, using the

same communication mechanisms, also provide sup-

port for H3b. Using only the grouped variables as

covariates, both high and low commitment, formal

mechanisms had a signi®cant, positive association

with timing of developing capabilities (p � 0:001).

Only high commitment, formal mechanisms had a

signi®cant effect on timing of implementation

(p < 0:01).

4. Conclusion

This paper examined the adoption process of ®ve IT

innovations. These were because they varied in the

level of abstraction and the target organizational group

of innovation.

Organizations' adoption behavior was empirically

examined as a multi-stage process. Participants

responded to questions about the organization's adop-

tion decisions, the adoption process, communication

mechanisms used to facilitate adoption, and beliefs

about the innovations.

Several implications can be derived from the

results. First an organization develops capabilities

for using an innovation and the timing of that process

if the participants consider that there are advantages in

the innovation; beliefs are important to a successful

adoption process.

Top management advocacy generally had little

effect on adoption and that successful adoption of

these innovations can often be characterized as a

bottom-up, rather than top-down, process. However,

innovations often do not require large initial capital

outlays; they require highly professional `people

resources'.

Primary advocacy is also important. However,

depending on the stage and measure of adoption,

results were not consistent and there is still a great

deal to be learned about the effects of ìntermediate-

level' advocacy on the adoption process.

The results of this research also have implementa-

tions for the use of communication mechanisms to

support the adoption process for an innovations.

Extensive use of communication mechanisms was

found to affect timing more than probability of adop-

tion. Training provided by the organization to its staff,

a high resource, formal mechanism, generally had

positive impact on speed of adoption. This is true

whether the training is developed by in-house person-

nel or outside personnel. Also, the effect of commu-

nication mechanisms may vary based on the type of

innovation being considered. Extensive use of formal

communication mechanisms have a signi®cant, posi-

tive, impact on adoption. Communication mechan-

isms requiring a high level of organizational

commitment tend to have a signi®cant, positive asso-

ciation with adoption. However, this effect is most

evident when the mechanism is both high in resource

commitment and formalism.

We also found that mandates work. Bidding on

contracts which mandate the use of the innovation

is associated with both higher probability of


movement through stages and earlier adoption. Note

that these results do not imply anything about whether

the innovation is used properly by the organization,

once it is adopted. However, mandates do provide

strong incentives for organizations to rapidly adopt

and implement an innovation.

Finally, there is support for the assertion that diffu-

sion of innovations should be studied as a process

consisting of multiple stages and measures. Results

clearly show that the importance of adoption factors

varies by stage and by adoption measure considered.

Perceived advantage or disadvantages of the innova-

tion are especially important early in the adoption

process. Communication mechanisms have more

impact later in the process. The adoption history,

the smoothness and timing of the early stages, also

signi®cantly affects later stages. Overall, the results of

the research provide some general insights into the

adoption process of IT innovations.

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Thani Jambulingam is an Assistant

Professor in the Department of Pharma-

ceutical Marketing at Erivan K. Haub

School of Business, St. Joseph's Uni-

versity, Philadelphia, PA. He teaches

both Executive Pharmaceutical MBA

program and undergraduate program in

Pharmaceutical Marketing. He has

served as a consultant to several major

pharmaceutical and insurance compa-

nies. Thani did PhD work at the

University of Wisconsin, Madison. Thani's primary research areas

include role of technology in supply chain integration, alliances

and acquisitions, e-commerce and entrepreneurship in the health-

care industries. His work has been published in the Journal of

Research in Pharmaceutical Economics, Journal of Health Systems

Pharmacy, Journal of International Marketing, Journal of Business

Venturing, Journal of Social Administrative Pharmacy, Marketing

Educators Conference Proceedings, Society of Franchising Con-

ference Proceedings.

Franklin J. Carter Jr. (BS, 1978; MS,

1994; PhD, 1997 Ð Carnegie Mellon

University and MBA, 1982 Ð The

Wharton School at the University of

Pennsylvania) is an Assistant Professor

of Pharmaceutical Marketing at St.

Joseph's University. He received his

doctorate in marketing from Carnegie

Mellon University. In addition his work

experience include, general partner for

The Quaestus Group, regional sales

manager for Carnation Nutritional Products, group product

manager for IMS America, district sales manager for Princeton

Pharmaceuticals and manager of Product Planning and Research

and Product manager for Pfizer Pharmaceuticals. His primary areas

of research include business-to-business marketing, salesforce

management, and diffusion of innovation. His work will appear

in the Information and Management: An International Journal of

Information Technology and the Journal of Healthcare Manage-

ment Science.

Vipul K. Gupta is an Assistant Profes-

sor of Information Systems at the Erivan

K. Haub School of Business, St. Jo-

seph's University. He holds Bachelor's

degree from the Institute of Technology,

Varanasi, and Master's and PhD from

the University of Houston. Dr. Gupta's

areas of interest include strategic impact

of information technologies (IT), intel-

ligent decision support Systems, use of

Internet in supply-chain integration, and

customer relationship management. His previous work has

appeared in journals such as Omega, Interfaces, and International

Journal of Quality Science. He currently serves on the editorial

board of the Project Management Journal and is an active

consultant in financial services industry.

Dr. Nancy Paule Melone recently left

the security of her tenured faculty

position to pursue independently a

broader set of business and technology

interests, including the emerging areas

of electronic commerce and web-based

communications. Prior to her departure

from academia, she was on the faculties

of the University of Oregon and Carne-

gie Mellon University, where she spe-

cialized in information technology and

was integrally involved in the design of several interdisciplinary

programs, including CMU's M.S. in Software Engineering. Her

research has appeared in such journals as Management Science,

Journal of System Science, IHRIM Journal, Organization Science,

and Organizational Behavior and Human Decision Processes. She

currently serves on the editorial boards of IHRIM Journal and

Journal of Information Technology Cases and Applications. Dr.

Melone's corporate experience includes professional positions in

the computer and financial industries in strategic planning,

planning and research, and human computer interaction. She

earned her MBA and Ph.D. in information systems from the

University of Minnesota, where she held an IBM Fellowship and

was affiliated with the Center for Research in Human Learning.

She received her MLS and MAIR from the University of Iowa. For

fun, she competes in the performance and show rings with her

Bernese Mountain Dogs and teaches a course in electronic

commerce at Duquesne University.


Documents

Technological innovations: a framework for communicating diffusion effects