Research
Organizational adoption of open systems:
a `technology-push, need-pull' perspective
P.Y.K. Chau
a,*, K.Y. Tam
baSchool of Business, The University of Hong Kong, Pokfulam, Hong Kong, PR China bDepartment of Information and Systems Management, School of Business and Management, Hong Kong University of Science and Technology, Clear Water Bay, Kowloon, Hong Kong, PR China
Received 23 December 1998; accepted 12 September 1999
Abstract
The growing popularity of open systems in organizational computing has made it important to understand the key determinants of open-systems adoption. Existing innovation diffusion theories, however, have been criticized for their inability to provide an adequate explanation for diffusion of complex organizational technology. This study used the `technology-push' (TP) and `need-pull' (NP) concepts, borrowed from the engineering/R&D management literature to examine the key factors in the adoption decision. Based on this theory, a research model was developed and tested by collecting data from senior IT executives in 89 organizations. The results generally offered support for the model and for the usefulness of applying the TP-NP theory to explain the adoption decision. Organization size had the largest impact on the decision. Migration costs was the next greatest in¯uence. We also found that the organization would be less likely to adopt the new technology, unless the existing systems appeared to be unsatisfactory.#2000 Elsevier Science B.V. All rights reserved.
Keywords:Technology adoption; Open systems; Technology-push; Need-pull
1. Introduction
Rapid advances in information technology (IT) and telecommunications systems have created a dilemma for organizations. On the one hand, they provide enormous opportunities for skillful managers to reshape internal operations and their relationships with their suppliers, customers, and even rivals. On the other hand, the short life cycle of computer
hard-ware platforms and systems softhard-ware has made it increasingly dif®cult for MIS directors and corporate IT-systems designers to keep abreast of the latest developments. Open systems are advocated as a solu-tion to this dilemma, because they allow those same people to rely on a stable suite of interfaces, services, and protocols that function on even the latest plat-forms. This, in turn, permits application developers to ensure that their applications continue to be compa-tible despite changes in the supporting hardware and basic systems software. The essence of an open-sys-tems strategy is that the adopter bene®ts from a much simpler method of integrating all the IS by making technology interoperate more easily and enabling *Corresponding author. Tel.:852-2859-1025;
fax:852-2858-5614.
E-mail address: [email protected] (P.Y.K. Chau)
information to be more portable. In simple terms, open systems promote vendor independence and applica-tions transparency[2].
The decision to adopt open systems has signi®cant rami®cations on the IT infrastructure and its align-ments with the organizational structure. However, there is little work published on factors that affect the adoption of open systems in an organization [5].
Studies on the adoption of IT innovations have been well documented. Many (see, e.g. [23]) have based their research models on Rogers' [40] diffusion of innovations (DOI) theory. Example works include Hoffer and Alexander [21], Moore and Benbasat [29] and Ramamurthy and Premkumar [38]. In DOI, the theory posits that diffusion depends on ®ve general attributes: relative advantage, compatibility, complexity, observability, and trialability. Tornatzky and Klein [44] conducted a meta-analysis of ®ndings from studies on innovation characteristics and innova-tion adopinnova-tion and concluded that compatibility, com-plexity, and relative advantage are consistently important during adoption decisions. Nevertheless, researchers on complex IS have criticized the `de®-ciencies' of the DOI theory. For example, Brancheau and Wetherbe [3] noted that it was clear that DOI theory did not provide a complete explanation for technology diffusion. In a review of IT innovation studies, Fichman [13] argued that classical diffusion variables by themselves are unlikely to be strong predictors of complex IT adoption and diffusion, suggesting that additional factors should be added. In studies of adoption, Prescott and Conger [37] concluded that ``DOI factors are not as appropriate for inter-organizational information technologies as they are for the others,. . . traditional DOI ®ndings
must be modi®ed. . .''
Zmud [48] suggested using the `technology-push' (TP) and `need-pull' (NP) concepts borrowed from the engineering/R&D management literature to explain behavior in adoption of new technology. In his study, he developed a model of process innovation to explain practices in the adoption of software using responses in a questionnaire from 47 software development managers. Though the investigation failed to validate the concepts, the author concluded that ``the general support observed for the overall research model should encourage future research. . .''
This study follows Zmud's suggestion by develop-ing an adoption model for open systems. The objective is twofold:
1. to examine a set of factors that facilitate or inhibit the adoption of open systems; and
2. to provide an empirical test of the validity of the concepts applied to technology adoption of open systems.
2. Background
2.1. The technology-push and need-pull (TP-NP) concepts
The concepts of technology-push and need-pull were introduced by Schon [42] as the underlying motivations and driving forces behind the innovation of a new technology [6]. Two schools of thought, namely the TP and the NP, propose and support two different arguments. The TP school suggests that innovation is driven by science, and thus drives tech-nology and application: scienti®c discovery triggers the sequence of events which end in diffusion or application of the discovery [30]. The TP force stems from recognition of a new technological means for enhancing performance. Porter and Millar [35] argued that, with appropriate structure and strategy, adoption of new technology could create substantial and sus-tainable competitive advantages.
From the classical economics' point of view, tech-nology is basically a means of changing the factors of production. J.A. Schumpeter asserted that the pace and direction of innovation would be determined by advances in the underlying scienti®c base. His view was corroborated by Phillips [34], who argued that the user needs had a relatively minor role in determining the pace and direction of innovation.
Gauvin and Sinha [16] suggested two types of opportunities for adoption of new technology: from productivity gains achieved with a new technology, and from expansion of resulting demand or from replacement of the technological base.
that more than 70% of the innovations could be classi®ed as need-pull, and suggested that organiza-tions should pay more attention to needs for innova-tion than in maintaining technical competence. Langrish [23] examined the issue again and concluded that both, the TP and NP models existed, but that the NP model was generally more prevalent. Zmud also noted that ``need-pull innovations have been found to be characterized by higher probabilities for commercial success than have technology-push inno-vations.''
Some researchers proposed that a successful inno-vation would occur when a need and the means to resolve it simultaneously emerge [14]. Munro and Noori [30], in their study on commitment to new manufacturing technology, included both, the TP and the NP factors. Their ®ndings suggested that the integration of both generally contributed to more innovativeness. Thus, adoption of a new technology may be induced by
1. the recognition of a promising new technology, 2. a performance gap, or
3. the motivating forces of both.
2.2. Characteristics of open systems
An open systems environment is
A comprehensive and consistent set of interna-tional information technology standards and functional standards profiles that specify inter-faces, services and supporting formats to accomplish interoperability or portability of applications, data and people [32].
Each hardware vendor, applications developer and end-user participating in the development of an open system speci®cation has his or her interests, and reconciling various differences can be dif®cult. Thus, it is often necessary for some to lead the way and pioneer its adoption. Open systems can be viewed as an organizational innovation that requires both, tech-nical and administrative innovation [9]. The adoption of an architecture leads to a radical redesign of the IT infrastructure of the organization. Thus, it is a radical technical process innovation [10].
The changes in administrative procedures accom-panying the adoption of open systems make such adoption an administrative innovation [24]. Adoption
of open systems requires an organization to revise its procedures to deal with hardware/software procure-ments, resources allocation, staff training, and opera-tion and management. An organizaopera-tion must also possess three characteristics of an administrative inno-vation, as suggested by Loh and Venkatraman [25].
3. Research model and hypotheses
The research model consists of three sets of vari-ables: TP factors, NP factors, and two other variables. All these factors are assumed to in¯uence the adoption decision of open systems. The model is illustrated in Fig. 1.
3.1. Technology-push factors
The two TP-related factors are the bene®ts obtained from adopting the technology and the costs associated with its adoption. The gains should be greater than the costs. In the context of open systems, numerous bene®ts, mostly technical, have been mooted. They include:
providing a flexible environment unconstrained by proprietary systems;
offering more choices for hardware; promoting flexibility and integration; utilizing IT resources more effectively; and allowing transparent data access.
However, quantifying such benefits is generally diffi-cult. This leads to the following hypothesis:
H1. The extent of perception of benefits to be gained by adopting open systems will be positively related to the decision to adopt.
Higher cost for an innovation is negatively asso-ciated with its adoption [36]. In open systems, the cost of adoption may be associated with the technical or organizational uncertainties involved.
[7] proposed a concept of absorptive capacity, de®ned as an organization's ability to recognize the value of new information, assimilate it, and apply it to productive ends. They argued that it was the level of skills and knowledge gained over the course of the adopter's cumulative history of innovative activities and was a key determinant of an organiza-tion's capacity for innovation. Attewell [1] also emphasized the role of know-how in the adoption of innovation.
Organizational uncertainty may result from two sources: the dif®culty of estimating the administrative and operating costs of adoption and the infeasibility of replacing the current old technologies, in-house IT expertise and administrative processes. Open systems require discontinuous [12,45] and competence-destroyingchanges [46]. Adoption of such technology may cause the technologies, applications, expertise and administrative rules and regulations to become
obsolete. Iivari [22], in his study of adoption ofCASE
tools, noted that in addition to learning, adopting new complex technology might require unlearning of old practices. It would not be trivial if the underlying or supporting methodology was very different from the one currently being used [41].
The second hypothesis is, therefore:
H2. The extent of migration costs associated with adopting open systems will be negatively related to the decision for adoption.
3.2. Need-pull factors
There are two NP-related factors proposed in the research model: performance gap and market uncer-tainty.
computer systems, unacceptable price/performance ratio of the existing systems or inability to serve the organization's new needs. This argument leads to the following hypothesis:
H3. The level of satisfaction with the existing com-puting systems will be negatively related to the deci-sion for adoption.
In addition, the motivation to adopt new technology may be pressure from the external market (see, e.g. [39,43]). Mans®eld et al. [26] provided evidence that intense market competition appeared to stimulate the rapid diffusion of an innovation. Pfeffer and Leblebici [33] also argued that it was when the organization faced a complex and rapidly changing environment that IT was both, necessary and justi®ed. In a study of the adoption of telecommunications technologies in US organizations, Grover and Goslar [17] also found signi®cant relationships between environmental uncertainty and use of technology.
Market and environmental factors, such as the degree of competition, the stability of demand for products, and the degree of customer loyalty, cannot be controlled by the management of the organization, but can affect the way the business is conducted. From an IT viewpoint, as companies are facing an uncertain market environment, the competitive atmosphere demands more responsiveness and ¯exibility in IT support. This suggests the following hypothesis:
H4. The level of market uncertainty will be positively related to the decision for adoption.
3.3. Additional variables
Two additional variables are IT human-resource availability and formalization. Many researchers have suggested, and found, empirical support for the posi-tive association between human-resource availability and innovation behaviors [19,28]. The basic rationale is that large organizations have more resources so that the potential loss due to unsuccessful innovations can be tolerated more easily. Others studied a closely-related concept, organizational slack, and found a positive relationship between it and the adoption of IT [4]. Adoption of open systems requires a radical redesign of the IT infrastructure of an organization. This lead to the following hypothesis:
H5. IT human-resource availability will be positively related to the decision for adoption.
The degree of formalization of work procedures is also expected to in¯uence the adoption decision. Rogers de®ned formalization as the degree to which an organization emphasizes rules and procedures in the performance of its members and argued that such formalization may inhibit innovation. This sug-gests a negative relationship between the degree of formalization and the adoption decision. However, in studying the diffusion of laptop computers, Gatignon and Robertson [15] reported that organiza-tional standardization was a prerequisite for improv-ing productivity. Cooper and Zmud [8] also found that task±technology compatibility was a key factor associated with the adoption of a production and inventory control IS. Organizations which currently have a formal policy on systems±related matters are, therefore, believed to be better prepared for the adop-tion of open systems. This suggests the following hypothesis:
H6. The degree of formalization of systems develop-ment and managedevelop-ment will be positively related to the decision for adoption.
4. Methodology
4.1. Informants
A preliminary questionnaire was developed and pilot-tested with ®ve IS managers to assess logical inconsistencies, ease of understanding, sequence of questions, and task relevance. Instead of mailing out the questionnaires, face-to-face interviews were con-ducted to ensure that respondents clearly understood all the questions and terms used in the questionnaire. There were some modi®cations to the original tionnaire to clarify the meaning of particular ques-tions. None of the responses in the pilot test were used in the analysis reported in this study.
4.2. Construct operationalizations
To operationalize the constructs, direct use of ques-tionnaires employed in other studies of technology innovation adoption was believed to be inappropriate. Instead, items were adapted from either instruments used in other studies or popular IT periodicals and trade journals.
Bene®ts of adopting open systemswere measured by ®ve items adapted from various IT magazines for practitioners and pamphlets published by vendors of open-systems products. Respondents were asked to give their level of agreement or disagreement with the following ®ve potential bene®ts of going to an open system:
1. no longer constrained by proprietary systems; 2. more choice for hardware and software; 3. better utilization of IT resources; 4. promote flexibility and integration; and 5. allow transparent data access.
A seven-point Likert-type scale was employed. Migration costs associated with adopting open systemswas operationalized with three items. Respon-dents were asked to indicate the extent to which they agreed with statements relating to the migration costs of open systems:
1. high cost for migration;
2. existing IS personnel are only familiar with proprietary systems; and
3. infeasible to dispose of existing proprietary systems.
These items were based on IT adoption studies or were adapted from various open-systems surveys published in trade journals. A seven-point Likert-type scale was used.
Thesatisfaction level with existing computing sys-tems construct included two items:
1. Does your existing computing system serve the needs of the company? and
2. Are you satisfied with the price/performance of your system?
Respondents were asked to respond to these questions in a seven-point Likert-type scale with anchors from `to a great extent' to `only a little' and from `very satisfied' to `very dissatisfied', respectively.
Market uncertaintywas operationalized by asking respondents to describe:
1. the market for their company's products; 2. the competition for their company's products; 3. the demand of their major customers;
4. the degree of loyalty of their major customers; and 5. the frequency of price-cutting in their industry.
A seven-point Likert-type scale was used, with anchors (such as ranging from `extremely stable' to `extremely unstable') The five items were adapted from Robertson and Gatignon.
IT human-resource availability was measured by the number of IT personnel (excluding computer operators) in the organization. Bretschneider and Witt-mer [4] noted that personnel re¯ected resource com-mitments, more than hardware and software, which were generally one-time expenses. As suggested by Zmud, the measure was put in natural logarithm form. Degree of formalization was operationalized by counting the number of formal policies or standards (relating to tasks performed in systems development and management) being used in the organization, and then normalizing the result. Tasks included project control, feasibility study, budget estimation, schedule estimation, requirements analysis, systems design, program design, coding, testing, documentation, and conversion. This measure was similar to those of Moch and Morse [28] and Ettlie [11] in the innovation literature. The items were adapted from Zmud [47].
force/com-mittee may have been set up to investigate the feasi-bility of migration and/or some IT people in the organization may have already talked to certain ven-dors about open-systems products. While these activ-ities may be considered as tasks leading to the adoption decision, the adoption decision of open systems is not considered to be made until a formal migration plan for open systems has been developed. The plan must be already endorsed by top manage-ment together with a ®nancial budget and a migration schedule. An organization will not be treated as an open-systems adopter until it has developed the migra-tion plan such as operamigra-tionalizamigra-tion was used in pre-vious innovation studies (see, e.g. [1]).
4.3. Construct reliability and validity
Cronbach a was used to assess the reliability or internal consistency of the constructs. Thea values range from 0.63 to 0.73 (Table 1). `IT human-resource availability', `degree of formalization' and `open-sys-tems adoption decision' were single-item constructs and, thus, had no a value. The lower reliability for
`satisfaction level with existing systems' can be partly attributed to the small number of items in the factor as the calculation ofacan be affected by the length of the construct. Nunnally [31] suggested that reliability of at least 0.7 suf®ced for early stages of basic research. As most of the items of the constructs were adapted from either previous studies in related areas or popular IT periodicals and trade magazines, the content validity of the constructs is deemed acceptable.
In view of its data-driven nature, factor analysis was not used to identify constructs. Instead, this technique was used to examine the existence of the constructs and the groupings of the items. If all items in the independent variables are factor analyzed and loaded in accordance with the proposed ones, then construct validity is further supported. Therefore, principal components analysis with VARIMAX rotation and a
four-factor solution was performed. Table 2 shows the results of the factor analysis. Items of the four factors were loaded as theorized and the four factors altogether explained 56% of the total variance. There-fore, the construct validity was claimed.
4.4. Data analysis
Logistic regression analysis was performed to examine the signi®cance of the six proposed indepen-dent variables on the open-systems adoption decision. A multivariate statistical technique was chosen over a Table 1
Reliability of constructs
Construct Cronbacha
Benefits of adopting open systems 0.729 B1: no longer constrained by proprietary systems B2: more choice for hardware and software B3: better utilization of IT resources B4: promote flexibility and integration B5: allow transparent data access
Migration costs of open systems 0.713 U1: high cost for migration
U2: existing IS personnel only familiar with proprietary systems
U3: infeasible to dispose of existing proprietary systems
Satisfaction level with existing computing systems 0.629 S1: existing computing systems serve the
needs of the organization
S2: satisfied with the price/performance ratio of the existing system
Market uncertainty 0.701
M1: market for the company's major products M2: competition for the company's major products M3: demand of major customers
M4: degree of loyalty of major customers M5: frequency of price-cutting in the industry
Table 2
Results of factor analysis
Factor 1 Factor 2 Factor 3 Factor 4
B1 0.574 0.155 ÿ0.296 ÿ0.300
B2 0.615 0.067 ÿ0.181 ÿ0.047
B3 0.552 0.279 ÿ0.140 0.020
B4 0.683 0.145 ÿ0.238 ÿ0.246
B5 0.657 0.291 ÿ0.0442 ÿ0.246
U1 0.296 0.161 0.720 0.102
U2 0.201 0.148 0.802 ÿ0.066
U3 0.295 0.268 0.629 0.079
S1 0.289 0.067 ÿ0.129 0.707
S2 0.305 0.166 ÿ0.222 0.753
M1 ÿ0.193 0.736 ÿ0.225 0.187
M2 ÿ0.339 0.465 0.025 0.122
M3 ÿ0.269 0.723 0.089 0.074
M4 ÿ0.216 0.655 ÿ0.049 ÿ0.313
M5 ÿ0.367 0.451 ÿ0.203 ÿ0.206
Eigenvalue 2.710 2.279 1.925 1.498
multiple regression analysis, because the dependent variable in the model was a nominal variable. Using a nominal dependent variable in multiple regression analysis would violate the assumptions necessary for hypothesis testing. The signi®cance of the regres-sion coef®cients of the hypothesized independent variables was examined to determine support for the hypotheses. Wald statistic was used in the sig-ni®cance test as the coef®cients were all smaller than one [20]. Contribution of individual constructs to the model was measured by theR statistic.
5. Results
Table 3 shows the results of the logistic regression analysis. Both theÿ2 log likelihood statistic and the goodness-of-®t statistic indicated that the model was not signi®cantly different from a `perfect' model. This allowed us to proceed with the data analysis as planned.
The signi®cance of individual constructs was assessed by the Wald statistic and its corresponding p-value. The coef®cients of three constructs (migra-tion costs of open systems, satisfac(migra-tion level with existing computing systems and IT human-resource availability) were found to be signi®cantly different from zero whilst the coef®cients of the other three constructs (bene®ts of adopting open systems, market uncertainty and degree of formalization) were not. Also, based on the R statistic, IT human-resource availability had the only positive contribution to the model; both migration costs of open systems and satisfaction level with the existing computing systems had a relatively smaller, negative contribution to the model. Therefore, support was found for hypothesis 2, 3, and 5. Support was not found for the other three hypotheses.
6. Discussion
In this study, a research model using the TP-NP concepts as a basis was developed for examining the in¯uence of several factors on the decision of open-systems adoption. Speci®cally, six factors were pro-posed to be important and the results showed that three of them had signi®cant effects on the decision.
6.1. Impact of technology-push factors on the adoption decision
The research model proposed two TP factors: ben-e®ts of adopting open systems and uncertainty from adopting open systems. Organizations might be attracted or `pushed' to adopt open systems, because of perceived bene®ts of adopting that technology. Adopting open systems can provide an organization with many bene®ts. The study did not support these claims. Maybe many organizations have had bad experiences in adopting new IT, especially for orga-nizational innovation.
Uncertainty, and thus costs, might disincline an organization to adopt a new technology. This `nega-tive' TP factor was found to be signi®cant in the open-systems adoption decisions in this study. The higher the costs, the lower the chance of adopting open systems. The novelty of the open-systems technology may lead to uncertainty, and thus costs, as to the amount of technical know-how required and the cor-responding technological changes needed. Successful implementation of open systems requires competence in technologies, such as UNIX and TCP/IP, which are not yet dominant in corporate computing environ-ments. Expertise in these areas is scarce. The adoption decision also demands replacing current old
technol-Table 3
Results of the logistic regression analysisa
Factor Coefficient Wald statistic Significance Rstatistic
Benefits of adopting open systems 0.216 0.687 0.407 0.000
Migration costs of open systems ÿ0.376 3.971 0.046 ÿ0.126
Satisfaction level with existing computing systems ÿ0.509 4.628 0.032 ÿ0.146
Market uncertainty 0.051 0.049 0.826 0.000
IT human-resource availability 0.739 7.546 0.006 0.212
Degree of formalization 0.754 0.748 0.387 0.000
aÿ2 Log likelihood:
ogies, in-house IT expertise and administrative pro-cesses.
This suggests that in deciding whether or not to adopt open systems, organizations seem to pay more attention to the potential problems than to the potential bene®ts, that is most organizations are conservative.
6.2. Impact of need-pull factors on the adoption decision
In our research model, two NP factors were pre-dicted as having in¯uence on the adoption decision for open systems. Based on NP concepts, an organization would not consider adopting a new technology unless a need, such as a performance gap, was recognized. Therefore, in the context of adopting open systems, the satisfaction level with existing computing systems should be closely related to the need for improvement and, thus, the adoption decision. This assertion was supported in our study. Whenever the current systems satis®ed the needs of the organization, the propensity to change should be lower. The results also agreed with the ®ndings of other empirical studies.
In contrast to the ®nding of a signi®cant negative relationship between the satisfaction level with exist-ing computexist-ing systems and the adoption decision, the pressure coming from the external-market environ-ment was not found to be a signi®cant factor encoura-ging the organization to adopt open systems.
Thus, consistent with ®ndings concerning the impact of TP factors on the open-systems adoption decision, of the two NP factors examined, organiza-tions tended to emphasize the `internal' factor (satis-faction level with existing computing systems) rather than the in¯uence from the external-market environ-ment (market uncertainty).
6.3. Impact of IT human-resource availability and degree of formalization on the adoption decision
Consistent with prior technology-adoption studies, IT human-resource availability was found to be a signi®cant positive factor in the adoption decision for open systems. The ®nding supported the `Schump-eter hypothesis'. The results also indirectly supported the `information capacity hypothesis', postulating that organizations with greater capacities to obtain and evaluate information about a new technology should
adopt it sooner if the technology was evaluated as favorable to the organization.
As for the impact of degree of formalization of work procedures relating to systems development and man-agement on the adoption decision for open systems, this study did not ®nd any signi®cant relationship bet-ween the existence of formal policies on performing systems tasks and the decision to adopt open systems.
6.4. Overall validity of the research model
The overall validity of the research model devel-oped using the TP-NP concepts as a basis was gen-erally supported in the study. Although the study's hypotheses were not all supported, based on the statistics measuring the model ®t, the research model was statistically valid.
The results show that the NP factor had a more signi®cant in¯uence on the decision model than the TP factor in terms of partial contribution to the total variance of the model. However, it should be noted that the IT human-resource availability factor had a stronger in¯uence than either TP or NP factors in our study. This seems to indicate that when an organiza-tion has to decide whether or not to adopt open systems, availability of resources may be the most important consideration.
Of the three factors found to be signi®cant in affecting the adoption decision, the satisfaction level with existing systems factor is basically not in the Rogers' framework. The migration costs factor can be considered to `implicitly' include the complexity fac-tor in Rogers' framework. The migration costs facfac-tor in the present model deals with issues more than this complexity and includes (in)feasibility to dispose of existing proprietary systems. Lastly, IT human-re-source availability may be considered to be similar to organizational slack and/or size in Rogers' framework.
6.5. Limitations
interviewees. Second, open systems is an innovation quite different from other IT innovations. Although both reliability and validity were demonstrated, oper-ationalization of the factors in the research model had not been extensively used and tested in previous studies.
7. Conclusions
This study sought empirical support for a research model describing the key factors affecting the decision for open-systems adoption. The research model was developed using the TP-NP concepts as a basis plus two additional variables. The results generally sup-ported the model and the usefulness of applying the TP-NP concepts to explain the adoption decision. In making the adoption decision, organizations tended to worry more about the migration costs associated with the adoption, would be less likely to adopt unless the existing computing systems were unsatisfactory, and had a higher propensity to consider adopting new technology when they had more IT human resources. The organizational forces that underlie technology innovation and adoption are both, complex and varied. When the innovation context becomes more speci®c, using `universal' factors, such as perceived bene®ts of adoption may not be appropriate or suf®cient to explain the decision. This study adopted the TP-NP into the context of the open systems adoption decision and found it to be useful.
Of course, operationalization of key concepts in the TP-NP concepts is dif®cult. A very important issue after the adoption decision is the implementation of the technology. An adoption cannot be considered to be successful until the technology has been imple-mented as planned and has assisted the organization to achieve the results as expected.
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P.Y.K. Chauis Associate Professor of Information Systems at the University of Hong Kong. He received his Ph.D. in business administration from the Ri-chard Ivey School of Business (formerly Western Business School), The Univer-sity of Western Ontario, Canada. His research interests include decision-sup-port systems, information presentation and model visualization, and issues related to IS/IT adoption and implementation. He has papers published in major information systems journals including
Management Information Systems Quarterly,Journal of Manage-ment Information Systems, Decision Sciences, Decision Support Systems,Information and Management,Journal of Organizational Computing and Electronic Commerce, European Journal of Information Systems, andINFOR.
K.Y. Tam is currently Professor of Information Systems and Weilun Senior Fellow at the Hong Kong University of Science and Technology. His research interests include electronic commerce, adoption of information technology, and information technology applications. He has published extensively on these topics in major management science and infor-mation systems journals including Man-agement Science, MIS Quarterly, Information Systems Research,
