Research
The initiation and adoption of client±server
technology in organizations
InduShobha Chengalur-Smith
*, Peter Duchessi
Management Science and Information Systems, School of Business, University at Albany, SUNY, Albany, NY 12222, USA
Received 15 October 1997; accepted 23 August 1998
Abstract
A large number of companies are adopting client±server systems. We investigated the relationship between several contextual factors and the initiation and adoption process of this important technology. The contextual factors included: (a) environmental, such as market position; (b) internal factors, namely organizational structure/culture, size, and migration strategy; and (c) technological, such as scope, scale, and cost of a system. An analysis of data from 350 companies revealed that a company's market position, its migration strategy, and the system's scope and scale have a signi®cant effect on the initiation and adoption process.#1999 Elsevier Science B.V. All rights reserved
Keywords: Client-server systems; Initiation and adoption of technology; Contextual factors
1. Introduction
Client-server systems represent a form of distrib-uted processing. The systems distribute information and computing tasks among computers that are linked by a network. They are based on the relatively new notion that speci®c servers (e.g. a database server) handle some computing tasks best.1In a client±server environment, clients initiate service requests and ser-vers respond to those requests via the network.
There are different types of client±server systems, depending on the extent to which a server distributes
computing tasks to clients. The Gartner Group deline-ates ®ve different types of client±server applications; they appear below in order of increasing complexity:
Distributed presentation ± clients and the server
share presentation services, while the server con-trols both application and data management proces-sing.
Remote presentation ± clients provide only
presen-tation services, while the server provides both, application and data management processing.
Distributed function ± clients provide
presenta-tion services and perform part of applicapresenta-tion pro-cessing, while the server provides the remaining application processing and data management pro-cessing.
Remote data management ± clients provide both
presentation services and application processing,
*Corresponding author. Tel.: 442-4028; fax: +1-518-442-2568; e-mail: ic307@cnsibm.albany.edu
1Distributed processing became popular during the late 1970s
and early 1980s, while client±server processing emerged during the late 1980s and early 1990s.
while the server provides just data management processing.
Distributed database ± clients provide presentation services, application processing, and data manage-ment processing.2
The proliferation of client±server systems has been so rapid that several practitioners have hailed this information technology (IT) as the next signi®cant computing paradigm.
Using data from a nation-wide survey, we examined the impact of technological, organizational, and envir-onmental characteristics on the initiation and adoption of client±server systems to gain insight into the effec-tive deployment of this technology.3We also address several important questions, including the following: Are the initiation and adoption processes used by larger companies different from those of smaller ones? Does the technical complexity of a client±server system in¯uence the initiation and adoption process?
2. Initiation and adoption of client±server technology
The implementation of client±server systems may progress through several stages (see Fig. 1). Cooper and Zmud [3] and Kwon and Zmud [8] propose one framework that contains six implementation phases: initiation, adoption, adaptation, acceptance, routiniza-tion, and infusion. These need not occur sequentially because companies can conduct two or more in par-allel. Preece [13] discusses another framework that also consists of seven stages: initiation, progression, investment decision, planning and systems design, installation, operationalization, and evaluation of the new technology. The ®rst two stages, initiation and progression, ®t with the initiation phase; whereas the third stage, namely investment decision, equates to adoption. We resolve semantic differences by using the term `initiation and adoption' to represent the pre-implementation process of identifying and responding to a company's problems and/or opportunities, search-ing for appropriate IT solutions, and assesssearch-ing the
technology's bene®ts for management's approval.4
The consequences of using IT include several practical bene®ts, including increased pro®t, increased market share, and higher quality services. These may have far-reaching effects (e.g. they may initiate consideration of future IT and strategic initiatives). Thus, the bene®ts can entail technical, operational, and competitive advantages.
3. Factors affecting the initiation and adoption of client±server systems
In planning for IT change, managers consider exter-nal and interexter-nal forces (e.g. awareness of competitors' actions, existing technology base, and market condi-tions). Five contextual factors that may affect initia-tion and adopinitia-tion of IT are: environmental (e.g. general level of competition); organizational (e.g. degree of centralization); technological (e.g. compat-ibility with existing systems); user (e.g. level of education); and task (e.g. amount of autonomy per-mitted). We consider only environmental, organiza-tional, and technological factors here.5
3.1. Environmental factors
Environmental forces, such as competition, tech-nology, and government regulation, precipitate initia-tion and adopinitia-tion of client±server systems. A company's desire to be ahead of the competition is a major factor in adopting IT [7]. Companies that are dominant in a particular market tend to be leaders; either they are responsible for IT innovations or are very quick to adopt them as they are introduced by competitors [10]. Thus, we state the ®rst hypothesis:
H1: The market position of a company in¯uences the initiation and adoption process of client±server sys-tems.
3.2. Organizational factors
Organizational characteristics affect the initiation and adoption process as well. Moch and Morse
2We use the original, 1991 classification; a new classification is
now in circulation.
3For a detailed account of this survey see Ref. [2].
4Thus, our definition stems from Refs. [3, 8, 13]. Moreover, it
also precludes implementation and post-implementation stages.
5Lack of data on users and tasks precluded any analysis on other
[12] found that decentralized organizations adopt innovations faster than more centralized ones. Companies that have a middle-out management style, where decisions are made through the con-sensus of independent business managers, often seek technology solutions that are tailored to
departmental needs, not necessarily overall
organizational needs [11]. Client±server systems enable decentralization and, as a consequence, are compatible with companies that operate in this
way. Alternatively, centralized companies may
consider the technology as a means to become decen-tralized.
The returns for investing in client±server systems are hard to predict for ®rst-time users because of their lack of experience in using them. Pioneering compa-nies, which initiate and adopt innovations early [5], may be more active in applying client±server systems for their business problems, though the returns are not immediately obvious. Large companies can more easily absorb the risks and costs of implementing client±server systems.
Finally, a company's internal approach for migrat-ing to other computmigrat-ing platforms can affect initiation and adoption. Some authors (e.g. Schultheis and Bock [15]) advocate an iterative strategy: start with pilot projects and gradually grow into more critical appli-cations. Others (e.g. Atre [1]) promote a bolder approach: go with a mission-critical application at the outset. Thus, the type of in-house migration strat-egy determines the nature of the client±server solution considered by a company.
Hence these hypotheses follow:
H2: The structure and culture of a company in¯uences the initiation and adoption process for client±server systems.
H3: The size of a company in¯uences the initiation and adoption process for client±server systems.
H4: A company's in-house migration strategy in¯u-ences the initiation and adoption process for client± server systems.
3.3. Technological factors
Technological issues are important during initiation and adoption as companies evaluate the ®t between the new technology and their existing systems. An appli-cation's technical complexity (i.e. hardware, software, and communications architecture) may hinder imple-mentation success [16]. Generally, client±server appli-cations require considerable investment in new operating systems, communication protocols, and hardware components, especially when companies move from mainframe-based legacy applications to the client±server architecture. Furthermore, compa-nies are discovering that the implementation of client± server systems involves hidden costs, including cus-tom coding and developer training [6].
As the expense and degree of complexity go up, companies need to make a strong case to justify the effort, possibly requiring a complete explanation of problems resolved and/or opportunities realized. The next hypotheses, therefore, are:
H5: The scale of the client±server application in¯u-ences the initiation and adoption process.
H6: The scope of the client±server application in¯u-ences the initiation and adoption process.
H7: The cost of the client±server application in¯u-ences the initiation and adoption process.
4. Measurement issues
Our data originated from a questionnaire that was developed for a nation-wide study of client±server implementation. We tested the questionnaire exten-sively to ensure that it was suitable for the under-taking. For this study, we relied primarily on items appearing in the background section of the question-naire, including market position, size, and organiza-tional culture, as well as items about the application, including number of servers, number of clients, and project budget.
4.1. Contextual factors
To determine the implementation stage, we used:
early (identi®ed application but no installed
compo-nents), middle (installation of network with some
clients and servers), and late (completed recently). We split market position into three classes: (a) domi-nant market leader, (b) major competitor, (c) and
minor competitor.Organizational structure and culture had three variables: (a) centralizedvs.decentralized, (b) pioneering vs. traditional, and (c) top-downvs.
bottom-up management style. Recognizing that com-panies and management styles could fall anywhere between these extremes, we used a sliding scale with the extremes as anchors. We classi®ed ®rms that checked either of the two scale positions closest to centralized as `centralized' and those that checked any of the remaining three scale positions as `not centra-lized'. We used the same procedure for the other two variables.
a 5-point Likert scale with `1Strongly Disagree' and
`5Strongly Agree'. For each variable, we split
respondents into two groups as follows: we assigned respondents who checked `Strongly Agree' or `Agree' to one group and the remaining respondents to the second group. We measured project scope and scale using the number of servers, clients, and organiza-tional levels covered. We captured project cost with project duration and budget. Again, we used the medians of these variables to create two groups.
4.2. Initiation and adoption factors
To capture initiation and adoption considerations, we used a number of motives, including `respond to competitive pressures', `avoid mainframe facilities', and `increase revenues', measured on a 5-point rating
scale with `1Not at All a Motivation' and `5
Motivated to a Great Extent' (see Table 1).
To reduce the number of motives, we ran a factor analysis. Bartlett's test for sphericity was highly sig-ni®cant (Bartlett's 1295.44, p<0.00), suggesting
signi®cant correlations among at least some of the motives. The measure of sampling adequacy for each item was acceptable and the overall measure of sam-pling adequacy was 0.76, well within the acceptable range [4]. We used the maximum likelihood extraction
method with an oblimin rotation (0) because the
underlying dimensions were generally correlated with each other (i.e. we did not force orthogonality). We determined the number of factors on the basis of the minimum eigenvalue (greater than one) and the scree plot.6 We assessed the reliabilities of our measures with Cronbach alphas for each of the categories and found that the reliabilities were consistently above 0.50 (see Table 2). The factor analysis revealed four underlying dimensions, which collectively explain 54% of the total variance in initiation and adoption (see Table 3).
The ®rst factor, competitive motives, represents a company's desire to gain or maintain a competitive advantage; while the second factor, ef®ciency motives, depicts a company's desire to attain internal ef®ciencies by reducing costs, increasing productivity, and reducing cycle time. The third factor, technical motives, portrays a company's desire to develop a modern approach to computing by avoiding main-frame facility's costs and escaping old proprietary platforms. The last factor, operational motives, repre-sents a company's wish to achieve a more ¯exible organization; it includes better management and con-trol of information, and enhanced organizational ¯ex-ibility.
Competitive motives, such as `create new sales opportunities' and `respond to competitive pressures', describe the process of identifying and responding to a company's problems and/or opportunities. Ef®ciency and operational motives, including `increase produc-tivity' and `enhance organizational ¯exibility', portray the assessment of a technology's bene®ts. The tech-nical motives, including `avoid mainframe facilities' and `escape proprietary platforms', describe a search for appropriate information technology solutions. Col-lectively, the four motive factors represent the
afore-Table 1
Average ratings for initiation and adoption incentivesa
Items Means
Increase productivity 4.12
Manage and control information better 3.96
Improve customer services 3.92
Enhance organizational flexibility 3.48
Empower users 3.45
Reduce cycle time 3.28
Decrease costs 3.22
Increase profit/revenue 3.10
Re-engineer business processes 3.03
Exploit leading-edge technologies 2.91
Respond to competitive pressures 2.90
Maximize return on investments in desktop technologies
2.89
Escape from old proprietary platforms 2.71
Create new products/services 2.62
Create new sales opportunities 2.59
Avoid mainframe facilities 2.36
Create external linkages with supplier/customers 2.24
Downsize organization 1.88
Reduce buyers'/suppliers' power 1.32
Other (please specify) 1.05
a350 respondents rated each of the above items on a scale of 1±5
with 1`Not at all a motivation' and 5`motivated to a great extent'.
6We dropped those variables that had low communalities (<0.50)
mentioned pre-implementation facets of the initiation and adoption process considered here.7
5. Sample and sample profile
From a commercially available, nation-wide
data-base, we selected a random sample of5000
execu-tives.8We deliberately used a large sample, because the database contained organizations without client± server systems. We mailed the questionnaires to 4593
executives, and received 350 usable responses.9We
made telephone follow-up interviews, with repeated callbacks on a random sample of 160 non-respon-dents. Of those having a client±server implementation, we found no signi®cant differences (from our mail sample results) for market position, business sector, and client±server application classi®cation. We found a signi®cant difference (214.78,p0.01) for title of respondent, with 39% of the callback contacts being senior IT managers and IT staff (vs. 58% of our mail sample) and 30% being other managers (vs. 8% of our mail sample). While there are differences between the titles of the respondents and callback contacts, the absence of other signi®cant differences suggests that the degree of non-response bias is slight.10
In our sample, executive managers, such as CEOs, presidents, and chairpersons, initiated over half (53%) of the client±server projects. IS managers, including CIOs, Chief Technology Of®cers, and Vice Presidents of IT, began 15% of client±server projects, with the remaining 32% of the applications started by man-agers from other functional areas. Sixty percent of the applications spanned three or more functional areas with the top three areas being customer service, operations, and accounting/®nance.
Using the Gartner Group's classi®cation, we found that almost half (48%) of the respondents classi®ed their application as distributed function; 19% as
dis-Table 2
Summary of factor analysisa
Factors Items Eigenvalues Percentage of explained variation n Reliabilities
Competitive motives 6 3.86 24.1 348 0.75
Efficiency motives 3 2.07 12.9 350 0.64
Technical motives 2 1.41 8.8 348 0.72
Operational motives 4 1.26 7.9 349 0.61
aOverall measure of sampling adequacy0.76.
Table 3
Initiation and adoption factors and item loadingsa
Factors Items Loadings
Competitive create new sales opportunities 1.00 motives create new products/services 0.56 increase profit/revenue 0.55 respond to competitive pressures 0.52 Create external linkages with
supplier/customers
0.39
improve customer services 0.33
Efficiency decrease costs 0.60
motives increase productivity 0.52
reduce cycle time 0.39
Technical avoid mainframe facilities 0.75 motives escape from old proprietary
platforms
0.73
Operational enhance organizational flexibility 0.58 motives manage and control information
better
0.50
empower users 0.46
re-engineer business processes 0.33
aWe excluded items that exhibited low communalities and low
loadings from the factor analysis.
7Client±server applications can present risks and challenges; for
example, vulnerability to security breaches [14]. Problems can reduce benefits, but there was no way for us to determine the extent to which potential problems influence the initiation and adoption process. As a caveat, respondents sometimes overstate favorable outcomes and understate less favorable ones, imparting some degree of bias to any study such as this one.
8We purchased the list from CMP Direct Marketing Services, a
company that provides marketing lists, maintains databases of subscribers to high-tech publications, and provides direct market-ing assistance.
9We mailed fewer than 5000 questionnaires because 407 (5000±
4593) entries had incomplete data (e.g. missing name).
10Due to the small sample size, we followed Cochran's rule,
tributed database; 14% as remote data management; 11% as remote presentation; and 7% as distributed presentation. Thus, a majority of the systems fell into the more complex half of the spectrum.
Concerning implementation stage, we found that 17% of our respondents were in early implementation stage; 65% fell in the middle implementation stage; and 18% had completed implementation. Seventy-seven percent of the respondents classi®ed their com-panies as either the dominant market leader or a major competitor, while the rest classi®ed themselves as minor competitors. Slightly over half the respondents (52%) claimed that their organizational culture was centralized to some degree, and less than half (46%) considered themselves to be pioneering in nature. Exactly 50% reported that their management style is top-down, rather than bottom-up.
The median sales of the organizations were $63
million,11and the median number of employees was
450. Though three-quarters of our respondents classi-®ed their application as mission-critical, only 20% had implemented the application across all business func-tions at the same time. For our sample, the typical implementation of 100 clients and three servers lasted about 16 months and had a budget of about $1 million. The scope of the applications was large: 62% of the applications provided service to all levels of the company from top management to line supervisors. The top ®ve areas served were: customer service, operations, accounting/®nance, sales, and marketing.
6. Research methodology
To examine how the initiation and adoption of client±server systems is affected by our contextual variables (e.g. market position), we performed a
multi-variate analysis of variance (MANOVA) (see
Table 4).12The dependent variables were the compe-titive, ef®ciency, technical, and operational factors. The independent variables were the aforementioned contextual factors. The MANOVA allowed us to ana-lyze simultaneously the effect of each of the contex-tual factors on the four initiation and adoption factors.
Stage of implementation could be a confounding factor in this analysis. For example, companies that are leaders in implementing client±server systems have initially little information about the potential pitfalls and probable bene®ts, and this could affect initiation and adoption of client±server technology, possibly limiting the scale and scope of the applica-tions considered. [9] reports that some IT managers fear premature use of new technology and prefer to adopt a new technology only after it is proven.
In order to investigate the effect of implementation stage on the initiation and adoption process, we per-formed a MANOVA with early, middle, and late implementation stages as independent variables. The results showed no difference (F1.05, p0.40) between the four motive factors through the three stages of implementation, indicating that the reasons why our companies began client±server implementa-tions are independent of implementation stage.
We employed a factorial design to examine the impact of each measure separately and the interaction effects among measures of the same variable. The factorial design allows us to answer questions of the type: ``Is the initiation and adoption process for appli-cations that have a large number of clients different from applications with a small number of clients?'' ``Is this difference the same when there are a large number of servers or a small number of servers?'' Similarly, we used a factorial design to account for possible interactions among the three organizational structure and culture variables. We found none of the interaction effects to be signi®cant for any of the variables considered.
7. Effects of contextual factors on initiation and adoption
7.1. Environmental factors
We found that environmental forces, as measured by market position, affect the initiation and adoption process for client±server systems, with operational motives as the differentiating factor (see Tables 4 and 5). It appeared that minor competitors rate operational motives signi®cantly lower than do major competitors and dominant market leaders (see Table 6).
11There were only 21 banks in our sample; their median assets
were $4 billion.
Table 4
Multivariate ANOVA statistics for the motive factors and the contextual factors a
Contextual factors 2 b p Effects Fc p
Market position 30.35 0.06 market position of the firmd 2.83 0.00
Structure and culture 6.72 0.75 centralized organization 1.82 0.13
top-down management style 0.83 0.51
pioneering organization 1.51 0.20
interaction (centraltop-down) 1.16 0.33
interaction (top-downpioneer) 1.40 0.23
interaction (centralpioneer) 0.42 0.79
interaction (centraltop-downpioneer) 0.41 0.80
Firm size 18.72 0.54 number of employees 1.36 0.25
sales of the firm 0.22 0.93
interaction (employeessales) 1.03 0.39
Migration strategy 9.96 0.44 mission-critical application 3.05 0.02
implementation across all functions at a time 1.56 0.18 interaction (mission-criticalall functions) 0.65 0.66
Scale of application 13.40 0.86 number of clients 2.98 0.02
number of servers 0.08 0.99
interaction (clientsservers) 0.59 0.67
Scope of application 29.82 0.07 number of functional areas spanned 3.32 0.01
number of organizational levels served 3.28 0.01
interaction (areaslevels) 1.40 0.23
Cost of application 14.29 0.82 duration of project (in months) 0.67 0.62
budget (in millions) 2.85 0.02
interaction (monthsbudget) 0.60 0.66
aThe dependent variables are the average ratings assigned to the four motive factors. bTests the assumption of homogeneity of variances.
cMultivariateF, derived from Pillai's trace.
dThe three market positions are dominant market leader, major competitor, and minor competitor.
Table 5
Univariate ANOVA statistics for the significant contextual factors a
Variables Competitive Efficiency Technical Operational
Market position of the firm 0.34 (p0.71) 0.37 (p0.69) 1.13 (p0.32) 9.01 (p0.00) Mission-critical applicationb 5.87 (p0.02) 8.83 (p0.00) 1.92 (p0.17) 4.40 (p0.04) Number of clientsc 3.86 (p0.05) 0.08 (p0.78) 4.15 (p0.04) 5.94 (p0.02) Number of functional areas spannedc 12.65 (p0.00) 2.63 (p0.11) 0.18 (p0.67) 0.85 (p0.36) Number of levels servedc 1.52 (p0.22) 1.70 (p0.19) 9.18 (p0.00) 6.79 (p0.01) Budget (millions)c 1.99 (p0.16) 1.27 (p0.26) 6.90 (p0.01) 6.47 (p0.01) aThe dependent variable is the average rating assigned to a motive factor.
Looking at the data from another angle, we checked for signi®cant differences among the four motive factors for a given market position. We found that dominant market leaders and major competitors con-sider operational and ef®ciency factors to be of equal
importance in their initiation and adoption process (see Table 7). They considered these factors to be more important than competitive and technical con-siderations, suggesting that the initiation and adoption of client±server systems in these companies are pri-marily in¯uenced by the technology's ability to increase productivity, reduce costs, and reduce cycle time through operational improvements. Additionally, we found that major competitors rate competitive motives higher than technical motives, indicating that they feel client±server applications may yield compe-titive advantages against dominant market leaders and other major competitors. Minor competitors consid-ered competitive, operational and ef®ciency factors to be equally important. All three types of companies downplayed the importance of technical considera-tions. Apparently, it is not the technical bene®ts (e.g. avoid mainframe facilities) that make the difference, but the technology's business bene®ts that affects the initiation and adoption of client±server systems.
7.2. Organizational factors
We found no signi®cant differences in the initiation and adoption process between companies with differ-ing structures, cultures, sizes, and management styles. This is understandable because, during the late 1980s and early 1990s, many companies recognized the need to use IT to respond to threats and opportunities. Moreover, large and small companies are able to act in a similar way because the ¯exibility and scal-ability of client±server systems makes them affordable in a variety of large and small architectures. Thus, internal factors, such as culture, size, etc., relevant in the 1970s and early 1980s, are less apt to affect the initiation and adoption of client±server technology.
With regard to migration strategy, the nature of the application affected the initiation and adoption pro-cess: companies rate competitive, ef®ciency, and operational factors as more important in mission-critical applications than in non-mission-mission-critical appli-cations. In companies with mission-critical applica-tions, technical bene®ts were the least important factors, while operational and ef®ciency gains received the most attention during the initiation and adoption process. It appears that companies pursuing mission-critical applications may have high expecta-tions of signi®cant operational and ef®ciency gains.
Table 6
Average motive factor ratings across groups of contextual factors
Motive factors Market position
dominant major competitor
minor competitor
Competitive 2.94 2.99 3.06
Efficiency 3.66 3.61 3.48
Technical 2.59 2.61 2.32
Operational 3.47 3.65 3.11
Motive factors Mission-critical application
no yes
Competitive 2.58 3.02
Efficiency 3.22 3.65
Technical 2.20 2.65
Operational 3.28 3.54
Motive factors Number of clients
100 >100
Competitive 2.78 3.07
Efficiency 3.49 3.60
Technical 2.36 2.76
Operational 3.34 3.65
Motive factors Number of areas
3 >3
Competitive 2.71 3.13
Efficiency 3.43 3.66
Technical 2.49 2.59
Operational 3.39 3.57
Motive factors Number of levels
4 >4
$1 million > $1 million
Competitive 2.83 3.05
Efficiency 3.48 3.65
Technical 2.36 2.87
7.3. Technological factors
We found that the number of clients affects the initiation and adoption process: companies with more than 100 clients give greater consideration to compe-titive, technical, and operational factors than compa-nies with 100 or less clients. Concerning compacompa-nies with 100 or more clients, technical and competitive factors received the least amount of attention, while operational and ef®ciency gains were most important. Clearly, companies that have many users are looking for competitive, technical, and operational gains. The scalability of the architecture makes the addition of more servers to the system transparent to users, redu-cing the effect of this factor on the initiation and adoption process.
We found signi®cant differences in the initiation and adoption process for number of functional areas spanned and number of organizational levels served. Competitive issues were most important in applica-tions that span three or more functional areas, while technical and operational issues predominated in applications serving more than four organizational levels. Additionally, we found that operational and ef®ciency bene®ts received the greatest consideration,
especially for applications spanning more than three functional areas or four functional levels. Generally, systems that link the activities of multiple functional areas and organizational levels improve business pro-cesses and are more dif®cult to implement because they often require business process redesign. Yet, this is highly correlated with bene®ts achieved through the implementation of client±server technology.
With regard to system cost, we found that the budget is an important consideration: companies with applications that exceed $1 million rate technical and operational factors as more important than companies with less expensive applications. Regardless of budget size, ef®ciency and operational gains are sought after with equal intensity.
8. Summary and conclusions
Based on a sample of 350 managers, we examined differences in the initiation and adoption process, as measured by our four motive factors, to understand which technological, organizational, and environmen-tal characteristics signi®cantly affect them. We did not, however, consider certain groups of factors,
Table 7
Average motive factor ratings within groups of contextual factorsa
Market position
Dominant leader (technological2.59, competitive2.94)<(operational3.47, efficiency3.66) Major competitor technological2.61<competitive2.99<(efficiency3.61, operational3.65) Minor competitor technological2.32<(competitive3.06, operational3.11, efficiency3.48)
Mission-critical application
No (technological2.20, competitive2.58)<(efficiency3.22, operational3.28) Yes technological2.65< competitive3.02<(operational3.54, efficiency3.65)
Number of clients
100 technological2.36< competitive2.78<(operational3.34, efficiency3.49) >100 (technological2.76, competitive3.06)<(efficiency3.60, operational3.65)
Number of areas
3 (technological2.49, competitive2.71)<(operational3.39, efficiency3.43) >3 technological2.59< competitive3.13<(operational3.57, efficiency3.66)
Number of levels
4 technological2.35< competitive2.79<(operational3.34, efficiency3.45) >4 (technological2.77, competitive3.06)<(efficiency3.65, operational3.65)
Budget
$1 million technological2.36< competitive2.83<(operational3.38, efficiency3.48) >$1 million (technological2.87, competitive3.05)<(efficiency3.65, operational3.67)
including user and task characteristics. Moreover, the issue of causality here remained untouched. For instance, we found a signi®cant relationship between our initiation and adoption factors and the project budget, but do projects with considerable operational and competitive impact result in larger budgets? Or, does the size of the budget frame the initiation and adoption process?
The typical initiation and adoption process does not delve into technical details, focusing instead on poten-tial bene®ts and responses to external opportunities and/or internal problems. Overall, the areas that get the most attention are operational and ef®ciency gains, such as `increase productivity' and `manage and con-trol information better'. Thus, the motives for employ-ing this technology are more business-driven than technical in nature.
We ®nd that the initiation and adoption process for client±servers systems has four distinct dimensions of motivation. The primary factors appear to be potential gains in technology, operations, ef®ciency, and com-petitiveness. However, the relative importance of each of these driving forces is affected by the market position of the ®rm, whether or not the application is mission-critical, and certain technological charac-teristics of the system. Interestingly, neither organiza-tional size, structure, culture, nor management style has an impact on the initiation and adoption process. Concerning market position, during the initiation and adoption process, dominant market leaders and major competitors focus more on operational gains relative to minor competitors. We ®nd that companies with mission-critical applications in mind give more attention to competitive, operational, and ef®ciency gains during the initiation and adoption process. As the scope and scale (measured by the number of clients, number of functional areas spanned, and number of organizational levels served) of the system increases, so do expectations of ef®ciency, competi-tive, operational and technical gains. Thus, if the system is built to service major segments of the organization (both horizontal and vertical), manage-ment is counting on a correspondingly large positive impact on the business.
In summary, in today's business world, the initiation and adoption process for client±server systems involves the consideration of competitive, ef®ciency, technical, and operational factors. Adding to the
com-plexity, the issues become more or less important depending on the external environment in which the company operates as well as its internal characteristics.
Acknowledgements
This study has been partially funded by American Management Systems, Andersen Consulting, and Ernst and Young. We thank the anonymous reviewers and Editor for their constructive comments, which greatly improved the quality of this paper.
References
[1] S. Atre, Be assertive, get noticed, Information Week 18, 1996, pp. 104.
[2] I.N. Chengalur-Smith, P. Duchessi, Surviving client±server: some management pointers, Working paper, School of Business, University at Albany, 1997.
[3] R. Cooper, R.W. Zmud, Implementation technology informa-tion research: a technological diffusion approach, Manage-ment Science 36(2), 1990, pp. 123±139.
[4] J.F. Hair, R.E. Anderson, R.L. Tatham, W.C. Black, Multi-variate Data Analysis with Readings, 4th edn., Prentice-Hall, NJ, 1995.
[5] P.A. Herbig, A cusp catastrophe model of the adoption of an industrial innovation, Journal of Product Innovation Manage-ment 8(2), 1991, pp. 127±137.
[6] Rising from the Ashes, Information Week, May 27, 1996, pp. 44±50.
[7] A.S. Kunnathur, M.V. Ahmed, R.J.S. Charles, Expert systems adoption: an analytical study of managerial issues and concerns, Information and Management 30(1), 1996, pp. 15±25. [8] T. Kwon, R.W. Zmud, Unifying the fragmented models of
information systems implementation, in: R. Boland, R. Hirscheim (Eds.), Critical Issues in Information Systems Research, John Wiley & Sons, New York, 1987, pp. 227-251. [9] A.L. Lederer, A.L. Mendelow, The impact of the environ-ment on the manageenviron-ment of information systems, Informa-tion Systems Research 1(2), 1990, pp. 205±222.
[10] D. Leonard-Barton, The role of process innovation and adaptation in attaining strategic technological capability, IJTM Special Issue on Manufacturing Strategy, 1991, pp. 303±320. [11] D.N. Meyer, D.P. Gardner, Political Planning for Innovation,
Information Strategy: The Executive's Journal, Fall 1992, pp. 5±10.
[12] M.K. Moch, E.V. Morse, Size, centralization and organiza-tion adoporganiza-tion of innovaorganiza-tions, American Sociological Review 42, 1977, pp. 716±725.
[13] D.A. Preece, The whys and wherefores of new technology adoption, Management Decision 29(1), 1991, pp. 53±58. [14] S.D. Ryan, B. Bordloloi, Evaluating security threats in
[15] R.A. Schultheis, D.B. Bock, Bene®ts and barriers to client server computing, Journal of Systems Management, Febru-ary 1994, pp. 12±41.
[16] L.G. Tornatzsky, K. Klein, Innovation characteristics and innovation implementation: a meta-analysis of ®ndings, IEEE Transactions Engineering Management 29(1), 1982, pp. 28±45.
InduShobha Chengalur-Smith is an Associate Professor of Management Science and Information Systems in the School of Business at the State Uni-versity of New York at Albany. She received her doctorate from Virginia Polytechnic Institute and State Univer-sity, Blacksburg. Her research interests include information quality, decision making and technology implementation. Her publications have appeared in var-ious journals including the Communications of the ACM,
Transportation ResearchandInternational Journal of Production Research. She has worked on industry sponsored projects in the areas of quality control, transportation cost models and technology implementation.
