Technological innovations: a framework for
communicating diffusion effects
Franklin J. Carter Jr.
a,1, Thani Jambulingam
a, Vipul K. Gupta
a,*, Nancy Melone
b 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 *Corresponding author. Tel.:1-610-660-1622;
fax:1-610-660-1229.
E-mail addresses: fcarter@sju.edu (F.J. Carter Jr.),
tjambuli@sju.edu (T. Jambulingam), gupta@sju.edu (V.K. Gupta). 1Tel.:1-610-660-1463.
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. Diffudiffu-sion 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 engiengi-neering 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.
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 `intermediate-level' advocacy on the adop-tion of innovaadop-tions. An excepadop-tion 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 `inter-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 innovaadop-tions [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 `out 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].
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.
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 perper-sonnel, (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.
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 producorganiza-tion 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 `event-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.
2Event-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.
3.5. 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 D0)
Adopt (mean for D0)
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
aLog-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
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 innovareduc-tions 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,H1cis 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 forH1d.
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 andH1b 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 implementaorganiza-tion 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 H2a
andH2b.
Table 3
Binary probit analysis implementationa
Parameter Non-adopt (mean for D0)
Adopt (mean for D1)
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
aLog-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
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 (p0:001). 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 adopadop-tion process, communicaadop-tion 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 `intermediate-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 personperson-nel. 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.
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 adopinnova-tion 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 theInformation and Management: An International Journal of Information Technologyand the Journal of Healthcare Manage-ment Science.
Vipul K. Guptais 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 asOmega,Interfaces, andInternational 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.
