The initiation and adoption of client–server technology in organizations

Information & Management 35 (1999) 77±88

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 distributed 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.1 In a client±server environment, clients initiate service requests and servers respond to those requests via the network. There are different types of client±server systems, depending on the extent to which a server distributes *Corresponding author. Tel.: +1-518-442-4028; fax: +1-518442-2568; e-mail: [email protected] 1 Distributed processing became popular during the late 1970s and early 1980s, while client±server processing emerged during the late 1980s and early 1990s.

computing tasks to clients. The Gartner Group delineates ®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 controls both application and data management processing.  Remote presentation ± clients provide only presentation services, while the server provides both, application and data management processing.  Distributed function ± clients provide presentation services and perform part of application processing, while the server provides the remaining application processing and data management processing.  Remote data management ± clients provide both presentation services and application processing,

0378-7206/99/$ ± see front matter # 1999 Elsevier Science B.V. All rights reserved PII: S-0378-7206(98)00077-9

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while the server provides just data management processing.  Distributed database ± clients provide presentation services, application processing, and data management 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 environmental characteristics on the initiation and adoption of client±server systems to gain insight into the effective deployment of this technology.3 We 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, routinization, and infusion. These need not occur sequentially because companies can conduct two or more in parallel. 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 preimplementation process of identifying and responding to a company's problems and/or opportunities, searching for appropriate IT solutions, and assessing the 2

We use the original, 1991 classification; a new classification is now in circulation. 3 For a detailed account of this survey see Ref. [2].

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 farreaching 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 external and internal forces (e.g. awareness of competitors' actions, existing technology base, and market conditions). Five contextual factors that may affect initiation and adoption of IT are: environmental (e.g. general level of competition); organizational (e.g. degree of centralization); technological (e.g. compatibility with existing systems); user (e.g. level of education); and task (e.g. amount of autonomy permitted). We consider only environmental, organizational, and technological factors here.5 3.1. Environmental factors Environmental forces, such as competition, technology, and government regulation, precipitate initiation and adoption 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 systems. 3.2. Organizational factors Organizational characteristics affect the initiation and adoption process as well. Moch and Morse 4 Thus, our definition stems from Refs. [3, 8, 13]. Moreover, it also precludes implementation and post-implementation stages. 5 Lack of data on users and tasks precluded any analysis on other contextual factors.

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Fig. 1. Initiation and adoption frameworks.

[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 consensus 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 decentralized. 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 companies, 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.

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Finally, a company's internal approach for migrating to other computing 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 applications. 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 strategy 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¯uences 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 application's technical complexity (i.e. hardware, software, and communications architecture) may hinder implementation success [16]. Generally, client±server applications 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, companies are discovering that the implementation of client± server systems involves hidden costs, including custom 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¯uences the initiation and adoption process.

H6: The scope of the client±server application in¯uences the initiation and adoption process. H7: The cost of the client±server application in¯uences 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 extensively to ensure that it was suitable for the undertaking. For this study, we relied primarily on items appearing in the background section of the questionnaire, including market position, size, and organizational 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 components), middle (installation of network with some clients and servers), and late (completed recently). We split market position into three classes: (a) dominant market leader, (b) major competitor, (c) and minor competitor. Organizational structure and culture had three variables: (a) centralized vs. decentralized, (b) pioneering vs. traditional, and (c) top-down vs. bottom-up management style. Recognizing that companies 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 centralized'. We used the same procedure for the other two variables. To measure a company's size, we used the number of employees and total sales (or assets for banks). We created two groups for each of these variables, based on their medians. We used two items to capture migration strategy. The ®rst asked if the application was mission-critical, and the second asked if the application was implemented across all business functions at the same time. We measured the responses on

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a 5-point Likert scale with `1ˆStrongly Disagree' and `5ˆStrongly 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 organizational 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 `1ˆNot at All a Motivation' and `5ˆ 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 signi®cant (Bartlett's ˆ1295.44, p<0.00), suggesting Table 1 Average ratings for initiation and adoption incentives a Items

Means

Increase productivity Manage and control information better Improve customer services Enhance organizational flexibility Empower users Reduce cycle time Decrease costs Increase profit/revenue Re-engineer business processes Exploit leading-edge technologies Respond to competitive pressures Maximize return on investments in desktop technologies Escape from old proprietary platforms Create new products/services Create new sales opportunities Avoid mainframe facilities Create external linkages with supplier/customers Downsize organization Reduce buyers'/suppliers' power Other (please specify)

4.12 3.96 3.92 3.48 3.45 3.28 3.22 3.10 3.03 2.91 2.90 2.89

a

2.71 2.62 2.59 2.36 2.24 1.88 1.32 1.05

350 respondents rated each of the above items on a scale of 1±5 with 1ˆ`Not at all a motivation' and 5ˆ`motivated to a great extent'.

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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 sampling adequacy was 0.76, well within the acceptable range [4]. We used the maximum likelihood extraction method with an oblimin rotation (ˆ0) 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 mainframe facility's costs and escaping old proprietary platforms. The last factor, operational motives, represents a company's wish to achieve a more ¯exible organization; it includes better management and control of information, and enhanced organizational ¯exibility. 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 productivity' and `enhance organizational ¯exibility', portray the assessment of a technology's bene®ts. The technical motives, including `avoid mainframe facilities' and `escape proprietary platforms', describe a search for appropriate information technology solutions. Collectively, the four motive factors represent the afore-

6 We dropped those variables that had low communalities (<0.50) and low loadings (<0.30), and re-ran the factor analysis. The ratio of sample size to number of items was approximately 23 : 1, exceeding the suggested minimum ratio of 5 : 1.

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Table 2 Summary of factor analysis a Factors

Items

Eigenvalues

Percentage of explained variation

n

Reliabilities

Competitive motives Efficiency motives Technical motives Operational motives

6 3 2 4

3.86 2.07 1.41 1.26

24.1 12.9 8.8 7.9

348 350 348 349

0.75 0.64 0.72 0.61

a

Overall measure of sampling adequacyˆ0.76.

Table 3 Initiation and adoption factors and item loadings a Factors

Items

Loadings

Competitive motives

create new sales opportunities create new products/services increase profit/revenue respond to competitive pressures Create external linkages with supplier/customers improve customer services

1.00 0.56 0.55 0.52 0.39

Efficiency motives

decrease costs increase productivity reduce cycle time

0.60 0.52 0.39

Technical motives

avoid mainframe facilities escape from old proprietary platforms

0.75 0.73

Operational motives

enhance organizational flexibility manage and control information better empower users re-engineer business processes

0.58 0.50

0.33

0.46 0.33

a

We excluded items that exhibited low communalities and low loadings from the factor analysis.

mentioned pre-implementation facets of the initiation and adoption process considered here.7 5. Sample and sample profile From a commercially available, nation-wide database, we selected a random sample of 5000 execu7

Client±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.

tives.8 We 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.9 We made telephone follow-up interviews, with repeated callbacks on a random sample of 160 non-respondents. 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 (2ˆ14.78, pˆ0.01) 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 managers 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 dis8 We 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 marketing assistance. 9 We mailed fewer than 5000 questionnaires because 407 (5000± 4593) entries had incomplete data (e.g. missing name). 10 Due to the small sample size, we followed Cochran's rule, collapsing just the categories that had frequencies below five.

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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. Seventyseven percent of the respondents classi®ed their companies 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,11 and 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 functions 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 multivariate analysis of variance (MANOVA) (see Table 4).12 The dependent variables were the competitive, ef®ciency, technical, and operational factors. The independent variables were the aforementioned contextual factors. The MANOVA allowed us to analyze simultaneously the effect of each of the contextual factors on the four initiation and adoption factors. 11 There were only 21 banks in our sample; their median assets were $4 billion. 12 The assumption of homogeneity of variances holds in all cases.

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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 applications 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 performed a MANOVA with early, middle, and late implementation stages as independent variables. The results showed no difference (Fˆ1.05, pˆ0.40) between the four motive factors through the three stages of implementation, indicating that the reasons why our companies began client±server implementations 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 applications 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).

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Table 4 Multivariate ANOVA statistics for the motive factors and the contextual factors Contextual factors Market position

2 b

p

a

Effects d

Fc

p

2.83

0.00

30.35

0.06

market position of the firm

6.72

0.75

centralized organization top-down management style pioneering organization interaction (centraltop-down) interaction (top-downpioneer) interaction (centralpioneer) interaction (centraltop-downpioneer)

1.82 0.83 1.51 1.16 1.40 0.42 0.41

0.13 0.51 0.20 0.33 0.23 0.79 0.80

18.72

0.54

number of employees sales of the firm interaction (employeessales)

1.36 0.22 1.03

0.25 0.93 0.39

9.96

0.44

mission-critical application implementation across all functions at a time interaction (mission-criticalall functions)

3.05 1.56 0.65

0.02 0.18 0.66

Scale of application

13.40

0.86

number of clients number of servers interaction (clientsservers)

2.98 0.08 0.59

0.02 0.99 0.67

Scope of application

29.82

0.07

number of functional areas spanned number of organizational levels served interaction (areaslevels)

3.32 3.28 1.40

0.01 0.01 0.23

Cost of application

14.29

0.82

duration of project (in months) budget (in millions) interaction (monthsbudget)

0.67 2.85 0.60

0.62 0.02 0.66

Structure and culture

Firm size

Migration strategy

a

The dependent variables are the average ratings assigned to the four motive factors. Tests the assumption of homogeneity of variances. c Multivariate F, derived from Pillai's trace. d The three market positions are dominant market leader, major competitor, and minor competitor. b

Table 5 Univariate ANOVA statistics for the significant contextual factors

a

Variables

Competitive

Efficiency

Technical

Operational

Market position of the firm Mission-critical application b Number of clients c Number of functional areas spanned c Number of levels served c Budget (millions) c

0.34 (pˆ0.71) 5.87 (pˆ0.02) 3.86 (pˆ0.05) 12.65 (pˆ0.00) 1.52 (pˆ0.22) 1.99 (pˆ0.16)

0.37 8.83 0.08 2.63 1.70 1.27

1.13 1.92 4.15 0.18 9.18 6.90

9.01 4.40 5.94 0.85 6.79 6.47

a

(pˆ0.69) (pˆ0.00) (pˆ0.78) (pˆ0.11) (pˆ0.19) (pˆ0.26)

(pˆ0.32) (pˆ0.17) (pˆ0.04) (pˆ0.67) (pˆ0.00) (pˆ0.01)

(pˆ0.00) (pˆ0.04) (pˆ0.02) (pˆ0.36) (pˆ0.01) (pˆ0.01)

The dependent variable is the average rating assigned to a motive factor. We combined responses of `strongly agree' and `agree' to form one group and the remaining responses formed the second group. c We divided the data into two groups based on the median. b

I. Chengalur-Smith, P. Duchessi / Information & Management 35 (1999) 77±88 Table 6 Average motive factor ratings across groups of contextual factors Motive factors

Market position dominant

major competitor

minor competitor

Competitive Efficiency Technical Operational

2.94 3.66 2.59 3.47

2.99 3.61 2.61 3.65

3.06 3.48 2.32 3.11

Motive factors

Mission-critical application no

yes

Competitive Efficiency Technical Operational

2.58 3.22 2.20 3.28

3.02 3.65 2.65 3.54

Motive factors

Number of clients 100

>100

Competitive Efficiency Technical Operational

2.78 3.49 2.36 3.34

3.07 3.60 2.76 3.65

Motive factors

Number of areas 3

>3

Competitive Efficiency Technical Operational

2.71 3.43 2.49 3.39

3.13 3.66 2.59 3.57

Motive factors

Number of levels 4

>4

Competitive Efficiency Technical Operational

2.79 3.45 2.35 3.34

3.06 3.65 2.78 3.65

Motive factors Competitive Efficiency Technical Operational

Budget $1 million

> $1 million

2.83 3.48 2.36 3.38

3.05 3.65 2.87 3.67

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 consider operational and ef®ciency factors to be of equal

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importance in their initiation and adoption process (see Table 7). They considered these factors to be more important than competitive and technical considerations, suggesting that the initiation and adoption of client±server systems in these companies are primarily 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 competitive advantages against dominant market leaders and other major competitors. Minor competitors considered competitive, operational and ef®ciency factors to be equally important. All three types of companies downplayed the importance of technical considerations. 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 differing 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 scalability 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 process: companies rate competitive, ef®ciency, and operational factors as more important in missioncritical applications than in non-mission-critical applications. In companies with mission-critical applications, 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 expectations of signi®cant operational and ef®ciency gains.

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Table 7 Average motive factor ratings within groups of contextual factors a Market position Dominant leader Major competitor Minor competitor

(technologicalˆ2.59, competitiveˆ2.94)<(operationalˆ3.47, efficiencyˆ3.66) technologicalˆ2.61
Mission-critical application No Yes

(technologicalˆ2.20, competitiveˆ2.58)<(efficiencyˆ3.22, operationalˆ3.28) technologicalˆ2.65< competitiveˆ3.02<(operationalˆ3.54, efficiencyˆ3.65)

Number of clients 100 >100

technologicalˆ2.36< competitiveˆ2.78<(operational ˆ3.34, efficiencyˆ3.49) (technologicalˆ2.76, competitiveˆ3.06)<(efficiencyˆ3.60, operationalˆ3.65)

Number of areas 3 >3

(technologicalˆ2.49, competitiveˆ2.71)<(operationalˆ3.39, efficiencyˆ3.43) technologicalˆ2.59< competitiveˆ3.13<(operationalˆ3.57, efficiencyˆ3.66)

Number of levels 4 >4

technologicalˆ2.35< competitiveˆ2.79<(operationalˆ3.34, efficiencyˆ3.45) (technologicalˆ2.77, competitiveˆ3.06)<(efficiencyˆ3.65, operationalˆ3.65)

Budget $1 million >$1 million

technologicalˆ2.36< competitiveˆ2.83<(operationalˆ3.38, efficiencyˆ3.48) (technologicalˆ2.87, competitiveˆ3.05)<(efficiencyˆ3.65, operationalˆ3.67)

a

Average ratings for factors contained within parentheses are not signi®cantly different from each other.

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 competitive, technical, and operational factors than companies with 100 or less clients. Concerning companies 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, reducing 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 applications 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 processes 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 environmental characteristics signi®cantly affect them. We did not, however, consider certain groups of factors,

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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 potential 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 control information better'. Thus, the motives for employing 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 competitiveness. 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 characteristics of the system. Interestingly, neither organizational 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, competitive, operational and technical gains. Thus, if the system is built to service major segments of the organization (both horizontal and vertical), management 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-

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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 information research: a technological diffusion approach, Management Science 36(2), 1990, pp. 123±139. [4] J.F. Hair, R.E. Anderson, R.L. Tatham, W.C. Black, Multivariate 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 Management 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 environment on the management of information systems, Information 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 organization adoption of innovations, 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 mainframe and client-server environments, Information and Management 32(3), 1997, pp. 137±146.

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[15] R.A. Schultheis, D.B. Bock, Bene®ts and barriers to client server computing, Journal of Systems Management, February 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 University of New York at Albany. She received her doctorate from Virginia Polytechnic Institute and State University, Blacksburg. Her research interests include information quality, decision making and technology implementation. Her publications have appeared in various journals including the Communications of the ACM, Transportation Research and International Journal of Production Research. She has worked on industry sponsored projects in the areas of quality control, transportation cost models and technology implementation.

Peter Duchessi is an Associate Professor of Management Science and Information Systems at the School of Business, University at Albany. His areas of expertise include general business analyses; development of advanced computer-based systems, such as DSS, ES, and on-line analytic processing (OLAP) systems; operations management; and service management. He has provided consulting and management education to a number of companies, including Jet Aviation Business Jets AG; GE Corporate Research and Development; and GE Fanuc ± NA. He has published articles in numerous journals, including Communications of the ACM, Interfaces, and the California Management Review.