exploring managerial factors affecting erp implementation ... · exploring managerial factors...

Exploring managerial factors affecting ERPimplementation: an investigation of theKlein-Sorra model using regression splinesKweku-Muata Osei-Bryson,* Linying Dong,† Ojelanki Ngwenyama‡

*Department of Information Systems and the Information Systems Research Institute,Virginia Commonwealth University, Richmond, VA 23284, USA, email:[email protected], †Ted Rogers School of Information Technology Management,Ryerson University, Toronto ON M5B 2K3, Canada, email: [email protected], and‡Institute for Innovation and Technology Management, Ted Rogers School ofManagement, Ryerson University, Toronto ON M5B 2K3, Canada, email:[email protected]

Abstract. Predicting successful implementation of enterprise resource planning(ERP) systems is still an elusive problem. The cost of ERP implementation failuresis exceedingly high in terms of quantifiable financial resources and organizationaldisruption. The lack of good explanatory and predictive models makes it difficult formanagers to develop and plan ERP implementation projects with any assuranceof success. In this paper we investigate the Klein & Sorra theoretical model ofimplementation effectiveness. To test this model we develop and validate a datacollection instrument to capture the appropriate data, and then use multivariateadaptive regression splines to examine the assertions of the model and suggestadditional significant relationships among the factors of their model. Our researchoffers new dimensions for studying managerial interventions in IT implementationand insights into factors that can be managed to improve the effectiveness of ERPimplementation projects.

Keywords: ERP implementation, IT implementation management, informationsystems success, information systems management, multivariate adaptiveregression splines

1. INTRODUCTION

Enterprise resource planning (ERP) systems have the potential for operational, managerialand strategic benefits to the organization when successfully implemented (Murphy & Simon,2002; Shang & Seddon, 2002). However, recent studies (e.g. Robbins-Gioia, 2002) have foundthat more than 70% of ERP implementations have not met the expectations of managers.Various prescriptions have been proposed for improving ERP implementation success, includ-

doi:10.1111/j.1365-2575.2008.00309.x

Info Systems J (2008) 1

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ing: (a) standardize business practices to fit with the ERP software (Kremers & Dissel, 2000;Markus et al., 2000a; Sumner, 2000); (b) avoid customizing the software (Mabert et al., 2001;Murray & Coffin, 2001; Parr & Shanks, 2000); (c) provide appropriate training in the use of thesystem (Bingi et al., 1999; Sumner, 2000; Al-Mudimigh et al., 2001); and (d) provide projectteam education and proper project management (Bingi et al., 1999; Holland & Light, 1999;Kumar & Crook, 1999; Willcocks & Sykes, 2000). Some researchers have also attempted todevelop frameworks of essential characteristics of good ERP implementation management,generally referred to as critical success factors (CSFs; Bingi et al., 1999; Holland & Light, 1999;Nah et al., 2001), and other studies have adopted or extended various predictive models oftechnology acceptance to the problem of ERP systems (Kositanurit et al., 2006). However,studies examining managerial factors that influence ERP implementation success arescarce.

According to Markus et al. (2000a) ERP systems are ‘commercial software packages thatenable the integration of transactions-oriented data and business processes throughout anorganization’. They are a class of information systems (IS) designed to integrate the enter-prise’s core processes, information processing and information management (Holland &Light, 1999). Some common examples of these are SAP, BAAN and PeopleSoft. While ERPsystems have been around for more than two decades, research on their implementationstarted in the 1990s. Many of these ERP implementation studies have built upon the priorresearch on implementing IS in organizations (Markus et al., 2000a; Kositanurit et al., 2006).However, much of the IS implementation research has focused on the individual user’sperspective on the adoption and use (Venkatesh et al., 2003). A significant limitation of thisIS implementation research is the lack of attention to the managerial dimension; this is alsomanifest in the ERP implementation research which builds upon it (Parr & Shanks, 2000;Hong & Kim, 2002; Kumar, Maheshwari & Kumar et al., 2003). Recently, some researchershave pointed to the need for research on IS implementation that moves beyond the existingIS implementation models (Chin & Marcolin, 2001b). Others have pointed to the lack oftheories or models that investigate managerial perspective factors that affect successful ISimplementation (Holahan et al., 2004). The theoretical model of innovation implementationproposed by Klein & Sorra (1996) focuses on managerial factors affecting successful imple-mentation of new technologies. This model (see Section 3) is used as a basis for ourexploration of managerial factors that lead to successful ERP implementation. The primaryresearch question is: What are the managerial factors that affect successful ERP implemen-tation? Our exploration included collecting survey data from ERP environments, factor analy-sis using partial least squares (PLS), and hypothesis testing using a relatively new statisticalanalysis technique, multivariate adaptive regression splines (MARS). We developed a ques-tionnaire, conducted a survey in six companies and analyzed the data collected from 239respondents. The findings of our study suggest new and fertile areas for researching themanagerial perspective of IS implementation. The rest of the paper is organized as follows:in Section 2 we discuss prior research and still open questions about IS Implementation. InSection 3 we outline the key details of the Klein and Sorra model. In Section 4 we describethe research methodology and the steps we followed in conducting the study. In Section 5 we

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present the MARS analysis and findings. Finally, in Section 6 we conclude with a discussionon the contributions of this research.

2. PRIOR RESEARCH AND OPEN QUESTIONS

It has long been accepted that users and managers are two key players in IS implementation(Kwon & Zmud, 1987; Leonard-Barton & Deschamps, 1988). The role of managers is topromote system innovations and influence users to adopt them; individual users, on the otherhand, evaluate the system and social factors before making adoption decisions (Shang &Seddon, 2002; Chang, 2006). While research into IS implementation reflects these twoperspectives (individual user and managerial), more attention has been given to the individualuser perspective. Research on the individual perspective has a relatively long history and alarge and growing body of work, while the managerial perspective has been overlooked andonly few studies have focused on it. Due to space limitations we provide a brief overview ofthese two perspectives below, for more detailed discussions the reader should refer to thespecific references.

2.1 The individual user perspective

The major contributions to IS implementation research from the individual user perspective fallinto categories (a) user satisfaction and performance; and (b) task-technology fit (see Table 1).The user satisfaction and performance studies, focus on identifying the conditions under whichusers are satisfied with the IS they use. Doll & Torkzadeh (1988) define user satisfaction as theuser’s opinion of a IS application that she or he uses. The fundamental argument of thisapproach is that high levels of user satisfaction lead to high levels of user performance. Muchof the early work focused on developing instruments for measuring user satisfaction. Forexample, Bailey & Pearson (1983) conducted a literature review to identify influencing factorsand developed a questionnaire for investigating user satisfaction. Ives et al. (1983) replicatedand extended Bailey & Pearson’s (1983) study to provide evidence of the validity of theinstrument. Doll & Torkzadeh (1988) also developed an instrument that would measureend-user computing satisfaction. These studies paved the way for later research that linked theconstructs of user satisfaction, system characteristics and user performance. Some of thisresearch has also focused specifically on clarifying and confirming the relationship betweenuser satisfaction and end-user performance (DeLone & McLean, 1992). This argument wasalso the central point of the nomological net model of Igbaria & Tan (1997). In anotherimportant study, DeLone & McLean (1992) validate the constructs, system quality, informationquality, use, user satisfaction, and individual and organizational performance. Later, Etezadi-Amoli & Farhoomand (1996) developed a questionnaire instrument and tested the relationshipbetween end-user satisfaction and user performance.

The task-technology-fit (TTF) approach postulates that when the user’s task and the tech-nology are congruent, user performance will be high (Goodhue & Thompson, 1995; Goodhue,

Managerial factors affecting ERP implementation 3


Table 1. IS implementation research from the individual perspective

Focus of the study Method of data collection Method of data analysis Citations

TTF: the effect of task

demands and graphical

format on information

processing strategies

Experiment ANOVA Jarvenpaa (1989)

TTF: the fit between job and

PC capabilities

Surveys (questionnaires) Partial least square

analysis

Thompson et al. (1991)

TTF: computer graphs and

fit with question types and

question complexity levels

Laboratory Experiment Two-way analysis of

variance and Wilcoxon

matched pairs

signed-ranks test

Wilson and Addo (1994)

TTF: Surveys (questionnaires) Regression analysis Goodhue (1995)

TTF: model validation Surveys (questionnaires) Regression analysis Goodhue & Thompson

(1995)

TTF Surveys (questionnaires) Establishing instrument

validity

Goodhue (1998)

TTF Laboratory experiment Regression analysis Mathieson & Keil (1998)

TTF and fitness-for-use

(FFU)

Surveys (questionnaires) Regression analysis and

path analysis

Dishaw & Strong (1998)

TTF and TAM Surveys (questionnaires) Path analytic technique Dishaw & Strong (1999)

TTF Experiment and

questionnaire

Regression analysis and

logistic regression

Goodhue et al. (2000)

TTF: CASE-task fit and

software developer’s

performance

Surveys (questionnaires) Hierarchical regression

analysis

Lai (1999)

User satisfaction Surveys (questionnaires) Measurement development Bailey & Pearson (1983)

User satisfaction Surveys (questionnaires) Measurement development Ives et al. (1983)

User satisfaction Surveys (questionnaires) Measurement development Doll & Torkzadeh (1988)

User satisfaction Surveys (questionnaires) Establishing instrument

validity

Torkzadeh & Doll (1991)


validity

Doll et al. (1994)


validity

Hendrickson et al. (1994)


validity, structural

equation model,

regression analysis

Etezadi-Amoli &

Farhoomand (1996)

User satisfaction Surveys (questionnaires) Partial least square testing Igbaria & Tan (1997)

User satisfaction Surveys (questionnaires) Structural equation based

on partial least square

Blili et al. (1998)

User satisfaction and TAM Surveys (questionnaires) Structural equation model

using LISREL

Al-Gahtani & King (1999)

User satisfaction Surveys (questionnaires)

and observation through

metamonitoring system

analysis that

automatically, tracked and

recorded users’ activities.

Z-tests Downing (1999)

FFU, fitness-for-use; TAM, Technology Acceptance Model; TTF, task-technology-fit.



1995; Dishaw & Strong, 1998; Mathieson & Keil, 1998). Consequently, studies falling underthis approach try to define task and technology characteristics and what is ‘goodness of fit’between specific technologies and end-user tasks (Dishaw & Strong, 1998; Mathieson & Keil,1998; Goodhue et al., 2000). Several researchers have used the TTF approach to explain theimpact of IS and task characteristics on individual performance (Goodhue & Thompson, 1995;Dishaw & Strong, 1998; Ferratt & Vlashos, 1998). This approach supports the argument thatwhen there is fit between user task characteristics and characteristics of the IS, utilization ofthe system will be high and user performance will also be high. Goodhue & Thompson (1995)found support for the relationships TTF and performance, and utilization and performance.

Recent research by Venkatesh et al. (2003) has attempted to integrate the technologyacceptance model and user acceptance into a unified model called, unified theory of accep-tance and use of technology. Further, Kositanurit et al. (2006) developed and tested anintegrated task-technology fit user performance model for the ERP environment. But neitherof these has addressed the managerial dimension of the ERP implementation problem.The managerial perspective remains under investigated, even though managers play a salientrole in diffusing an information technology into a user community (Kwon & Zmud, 1987,pp. 124–125).

2.2 The managerial perspective

IS implementation research that focused on managerial issues can be categorized into twogroups: (a) case studies that have identified critical success factors for IS implementation; and(b) survey studies focused on validating a narrow set of constructs. Management interventionshave been examined from different perspectives (see Table 2). For example, Thompson et al.(1991) developed the construct facilitating conditions that suggests factors to which managersmust attend in order to cultivate a supportive environment for individual technology adoption.Davis (1989) and Venkatesh et al. (2003) investigated how managers might affect useradoption behaviors by setting norms. Compeau & Higgins (1995a) and Igbaria et al. (1997)have investigated the importance of management support by providing resources and trainingfor users of new technologies. More recently however, managerial research on ERP imple-mentation has been dominated by case studies, which explore the complex and dramaticorganizational changes (Besson & Rowe, 2001; Lee & Myers, 2004) during and after imple-mentation of these systems (see Table 2). Some researchers have focused on explicatingvarious managerial problems from strategic alignment (Amrani et al., 2006; Clemons & Simon,2001; Grant, 2003; Soh & Sia, 2004) to change management (Besson & Rowe, 2001; Lee &Myers, 2004; Chae & Poole, 2005; Pozzebon & Pinsonneault, 2005) and knowledge transfer(Volkoff, 1999; Lee & Lee, 2000; Robey et al., 2002; Ko et al., 2005). Much of the managerialresearch on ERP implementation has been prescriptive or normative focusing on what wouldbe considered ‘best practice’ for successful implementation of ERP systems (Davenport, 1998;Besson & Rowe, 2001). Some researchers have attempted to develop frameworks to assistmanagers in defining and analysing critical success factors (Akkermans et al., 1999; Bingiet al., 1999; Holland & Light, 1999; Nah et al., 2001), risk factors (Sumner, 2000), to ensure



Table 2. IS implementation research focusing on the managerial perspective

Focus of the study

Method of data

collection Method of data analysis Citations

Success measures of ERP

implementations Issues

emerging in each phase of

ERP implementations

Case studies Qualitative data analysis Markus et al. (2000a)

Strategy and ERP

implementation

Cases NA Davenport (1998)

Critical factors of ERP

implementations

Literature review Qualitative data analysis Nah et al. (2001)

Risk factors in ERP

implementations

Case studies Qualitative data analysis Sumner (2000)

Critical success factors Case studies Qualitative analysis Holland & Light (1999)

Critical issues Prescriptive NA Bingi et al. (1999)

Interrelationships among

critical factors

Case studies Qualitative analysis Akkermans et al. (1999)

Management influence on

user interpretation

Case studies Qualitative analysis Chae & Poole (2005)

Power and politics on ERP

implementations

Single case study Qualitative analysis Lee & Myers (2004)

Strategic alignment Single case study Qualitative analysis Grant (2003)

Knowledge transfer Case study Qualitative analysis Volkoff et al. (2004)

Knowledge transfer Case study Qualitative analysis Lee & Lee (2000)

Knowledge transfer Case studies Qualitative analysis Robey et al. (2002)

Knowledge transfer Survey PLS Ko et al. (2005)

Power and politics Case study Metaphor analysis Pozzebon & Pinsonneault

(2005)

Organization alignment with

ERP

Case studies Qualitative analysis Soh and Sia (2004)

Management approaches Case studies Qualitative analysis Ward et al. (2005)

ERP implementation

strategies

Case study Qualitative analysis Light et al. (2001)

Issues emerging from ERP

implementation

Survey Descriptive analysis Themistocleous et al. (2001)

Organizational strategies and

ERP configuration

Conceptual NA Clemons & Simon (2001)

Dynamics of ERP

implementation and

task-related conflicts

Secondary data

and case studies

Qualitative analysis Besson & Rowe (2001)

Project management model of

ERP

Cases Comparative case analysis Parr & Shanks (2000)

Role of CIO Prescriptive NA Willcocks and Sykes (2000)

Emerging implementation

issues

Descriptive NA Huang & Palvia (2001)

Organizational learning Case studies Qualitative analysis Scott & Vessey (2000)

CIO, Chief Information Officer; ERP, enterprise resource planning.



effective planning and decision making about ERP implementation projects, and for buildingintra-organizational coalitions for success (Pozzebon & Pinsonneault, 2005; Ward et al.,2005). Nah et al. (2001) have suggested what is good management practice and Willcocks &Sykes (2000) offer advice on the roles of Chief Information Officer and IT functions during ERPimplementations.

While much of the managerial research has been oriented to developing concepts fromempirical field observations, some studies have attempted to use organizational influence theory(Avital & Vandenbosch, 2000; Chae & Poole, 2005) to understand organizational power shiftsand institutional theory (Markus et al., 2000b; Soh & Sia, 2004; Boersma & Kingma, 2005), tounderstand mutual adaptation between ERP technologies and the organization. There is also asmall body of work that investigates problems of knowledge transfer between various actors inthe ERP implementation project and their implications for success (Lee & Lee, 2000; Robeyet al., 2002; Volkoff et al., 2004). In summary, ERP studies on management issues, by applyingcase study method, offer an in-depth understanding of challenges faced by management in ERPimplementations. However, there is a lack of empirical studies focused on developing predictivemodels to assist managerial decision-making and intervention to improve ERP implementationoutcomes. Presently, few studies have focused on developing predictive models for ERPimplementation outcomes (Amoako-Gyampah & Salam, 2004; Kositanurit et al., 2006).Amoako-Gyampah & Salam (2004) extended the technology acceptance model for ERPimplementation environments and Kositanurit et al. (2006) proposed and tested a user perfor-mance and task technology model for the ERP environment. Both of these studies took theindividual user perspective but did not focus attention on the managerial perspective.

3. THE KLEIN AND SORRA MODEL

The Klein and Sorra model (see Figure 1), is one of few proposed predictive models thataddressed the managerial perspective of IS implementation. Klein and Sorra argue that keydeterminants of implementation effectiveness are: (a) climate for implementation, whichdescribes employees’ ‘shared perceptions of the events, practices, and behaviors that arerewarded, supported, and expected in a setting’ and (b) innovation-values fit, which depicts theextent to which the innovation is consistent with shared employee values. A strong organiza-tion implementation climate, is one that includes organizational influence and compliancemechanisms, establishes necessary user skills and provides appropriate incentives for use(and disincentives for non-use), and removes obstacles to use (Klein & Sorra, 1996). Thestronger the organizational climate for implementation is, the more likely it is that targetedusers will apply the system frequently, consistently and enthusiastically (more on this later).

Two studies have tested parts of the Klein & Sorra (1996) model. Klein et al. (2001)confirmed the relationship between implementation climate and implementation effectivenessacross 39 factory environments. And Holahan et al. (2004) confirmed the influence of imple-mentation climate on implementation effectiveness but failed to confirm innovation-values fit asa key determinant of implementation effectiveness. Neither of these tested the complete set of



factors that Klein & Sorra (1996) postulated as impacting implementation effectiveness. Con-sequentially, the Klein and Sorra model still remains a set of conjectures based on theoreticalspeculation about the phenomena. Our interest is in empirically examining their conjectures inorder to determine how useful this model is in explaining or accounting for successful ISimplementation. The basic objective of this investigation is on testing the conjectures of themodel, proposing alternative conjectures and finding the ones that can offer the potential for abetter explanation of the empirical evidence within the context of ERP systems. This approachto scientific investigation of models and conjectures with the intention of identifying the betterexplanatory model or theory is well discussed in philosophy of science (cf. Day & Kincaid,1994). The basic principle of this approach suggests that a model M1 is preferred when itsconjectures or hypotheses offer a better explanation of the evidence than the conjectures orhypotheses of another model M0 with regard to the same empirical evidence.

The Klein and Sorra model of implementation is based on Sussmann & Vecchio (1982), workon organizational influence, which in turn is built upon the social influence theory of Kelman

Implementation Climate (CLIM )

Innovation -values Fit (FIT )

Absence of Obstacles

Implementation Effectiveness

(ENDORS)

Incentives

Skills

Commitment

Figure 1. Model of Klein and Sorra (1996).



(1961). Social influence theory suggests three processes through which organizations influ-ence employees’ behavior: (a) compliance, in which an employee complies with an organiza-tional influence to gain specific rewards and to avoid punishments; (b) identification, in whichan employee accepts the organizational influence to engage in a satisfying role-relationshipwith another person or a group; and (c) internalization, in which an employee internalizes theorganizational influence because it is congruent with his value systems or because it isintrinsically rewarding. Klein and Sorra argue that internalization and compliance are twoimportant processes for effective implementation of new technologies. In their model, imple-mentation effectiveness is defined as ‘quality and consistency of use of an adopted innovation’(Klein & Sorra, 1996, p. 1056). Two determinants of implementation effectiveness are: (a)climate for implementation, which describes employees’ ‘shared perceptions of the events,practices, and behaviors that are rewarded, supported, and expected in a setting’ (Schneider,1990, p. 384) and (b) innovation-values fit, which depicts the extent to which the innovation isconsistent with the values of the organization members (Klein & Sorra, 1996, p. 1063).

4. METHODOLOGICAL DETAILS OF OUR INVESTIGATION

Our methodology for investigating the Klein-Sorra model can be described as a four-phasedprocess: (a) development and validation of an instrument for collecting data about theconstructs defined in the model; (b) survey data collection that includes 209 participants insix organizations which implemented enterprise systems from different venders (e.g. SAP,PeopleSoft, Oracle, Bann, JDE); (c) the use of PLS analysis to validate the relevant constructsdefined in Klein and Sorra’s model; and (d) the use of MARS analysis to evaluate and describecausal links existing between factors in Klein and Sorra’s model. We discuss this final phasein Section 4 of the paper.

4.1 Phase 1: scale development

Three of the seven constructs (i.e. innovation-values fit, implementation effectiveness andcommitment) in Klein and Sorra’s model have related measures developed by previous studies(for details see Appendix A). In particular, we adapted measures of TTF (i.e. quality describingthe extent to which an IS provides current, updated, and useful information and locatibilitydescribing how easily it is to find data in the system) by Goodhue (1995; 1998) and measuresof work processes evaluating degree of repetitiveness by Valle et al. (2000) to evaluate asystem’s fit with users’ task-related values. Implementation effectiveness was evaluated bythe measures developed by Klein et al. (2001). Other constructs including implementationclimate, skills, absence of obstacles, and commitment became the focal point of the scaledevelopment.

To develop measurement items for these constructs, we first created a pool of raw mea-surement items based on the review of prior studies. In particular, the measurements forimplementation Climate were created based on four core dimensions of implementation



Climate – task support (i.e. the extent to which employees perceive that they are beingsupplied with the material, equipment, services and resources necessary to perform their jobs),reward emphasis (i.e. the extent to which employees perceive that various organizationalrewards are to be allocated on the basis of their job performances), mean emphasis (i.e. theextent to which managers make known the methods and procedures that employees areexpected to use in performing their jobs) and goal emphasis (i.e. the extent to which managersmake known the types of outcomes and standards that employees are expected to accom-plish; Kopelman et al., 1990). Measures for users’ Skills were developed to evaluate whethera user has comprehended basic concepts, applications, and products of a system, how wellthe user inputs and interprets information in the system, and whether the user can do somesimple troubleshooting (Simon et al., 1996). For incentives and absence of obstacles, wefocused on their definitions and created measures to examine whether users are givenincentives to use an IS (incentives) and the extent to which individual use of an IS is deterredby the organization (absence of obstacles). Measures for commitment were developed basedon seven-item measures for affective commitment of Meyer & Allen (1991) to assess users’emotional attached to a newly.

The created measurement items were then reviewed by four IS researchers who wereknowledgeable about the IS innovation and implementation and three people who participatedin IS implementations, and then were entered into the card sorting after rewording unclearitems, deleting the items that did not make sense, and adding the items suggested by thereviewers. The remaining candidate items were further explored using the popular four-roundcard-sorting method of Moore & Benbasat (1996). In comparing the card-sorting results of thefirst round with those of the fourth round, we notice not only that the number of itemsdramatically reduced to 35 from 50, but also the Cohen’s Kappa index dramatically increased,suggesting that the coherence among other items under their assigned constructs wasenhanced (see Table 3). As a result, the card sorting improved the validity and reliability of theconstruct measurements. With the refined items, we went ahead with the full survey.

4.2 Phase 2: data collection

We randomly selected and contacted 800 midsized or large Canadian companies from twosources: ‘Canada Top 1000’ online database (from The Globe and Mail 2002), and SCOTT, a

Table 3. Results of the four rounds of card sorting

Results of card sorting Judges A–D First round Second round Third round Fourth round

Kappa AB 0.50 0.71 0.86 0.88

AC 0.60 0.77 0.85 0.86

AD 0.51 0.68 0.85 0.86

BC 0.56 0.75 0.81 0.89

BD 0.65 0.70 0.91 0.88

CD 0.65 0.74 0.87 0.85

Average 0.58 0.72 0.86 0.87

Number of items 50 44 40 35



comprehensive database of Canadian companies. Fifteen organizations qualified havingrecently implemented ERP systems, and consented to participate in our research. After a fewrounds of communications clarifying the responsibilities of the companies, only six of themwere committed to the research project. We asked the six companies to provide us with thename of ERP system recently implemented and the contact information of the targeted users.All together 422 users were identified and we sent out questionnaires to each of them. Wereceived 239 responses (a 56.6% response rate). Of the 239 responses only 209 were usable.We then assessed non-response bias by comparing the responses from the first batch(received within the first 2 weeks after the survey questionnaire was delivered) with those fromthe last batch (received 2 months after) (Armstrong & Overton, 1977), but did not findsignificant differences in age, work experience, or job tenure (Hotelling’s Trace = 1.034,p = 0.235), nor did we find any significant discrepancies in position (Chi-square = 1.29,p = 0.256), gender (Chi-square = 1.718, p = 0.190), or education (Chi-square = 2.992, p =0.224). The sample size for each organization varied from 30 to 42. In total, the averagerespondent age was 40 years, the average time spent in service of the organization wasapproximately eight and one half years, and the average time holding the current position wasa little more than 5 years. Of all the participants, 81 (38.8%) had a completed university orgraduate degree, and 54 (25.8%) had completed high school or some college or universitystudy. Approximately 56% of respondents were male.

4.3 Phase 3: validation of the model constructs

We applied PLS, a second-generation statistical method, to obtain factor scores for eachconstruct in the model. PLS was adopted in this paper due to its rigor, ease to use, and itsability to analyze both formative and reflective constructs (Chin, 1998a; 1998b; Gefen et al.,2000). The sample exceeded the ‘10 times’ PLS sample size heuristic described by Chin et al.(2003). In our analysis implementation climate is the construct with the greatest number offormative indicators, it contained four items. Implementation effectiveness is the construct withthe greatest number of structural paths (i.e. two) leading into it. The model required a minimumof 40 cases to be estimated. To obtain the factor scores for our analysis we used the PLS 3.0bootstrap method with 200 re-sampling. And two criteria were adopted to evaluate constructconvergent validity: (a) each measurement item of every construct should have loadings above0.70 (Chin, 1998b) and (b) average variance extracted score for each construct should beabove 0.50 (Fornell & Larcker, 1981). However, we relaxed the 0.70 threshold level to 0.60 dueto the exploratory nature of the study (Wixom & Watson, 2001). Internal consistency reliabilitywas evaluated by composite reliability (Werts et al., 1974), a better estimate than Cronbach’salpha for composite reliability uses actual item loadings to calculate the reliability (Chin &Gopal, 1995). The common cutoff point for composite reliability is 0.70.

The measurements for the five first-order constructs (skills, commitment) in the model weredirectly entered into PLS. Since PLS could not handle second-order constructs directly, weassessed the two second-order constructs (i.e. implementation climate, innovation-values fit)by deploying a two-step procedure proposed by Chin & Gopal (1995). PLS was first run



between first-order factors and the adjacent construct(s) to obtain factor scores for thesefirst-order factors. The generated factor scores are deemed to ‘more accurately [reflect] theunderlying constructs than any of the individual items by accounting for the unique factors anderror measurements that may also affect each item’ (Chin & Gopal, 1995, p. 50). These factorscores are then treated as indicators of second-order constructs and entered into PLS.

4.3.1 Results from construct validation

The first run of factor analysis indicated that a couple of items had weak loadings (e.g. one ofmeasurement items of the construct incentive ‘I am given incentives to use the system’ had afactor loading of 0.2646), and thus these items were discarded. For the second round of PLSanalysis all constructs were shown to possess satisfactory internal consistency reliability andconvergent validity. The lowest composite reliability was 0.78 and the lowest factor loading was0.65, which surpasses the threshold level 0.60 for exploratory studies (Wixom & Watson,2001). Further, the discriminant validity of the constructs was validated since factor loading foreach measurement item is higher than the item’s correlations with other constructs. With thevalid and reliable measurements, we used PLS to generate factor scores, which were thenentered into MARS for additional analysis.

5. MARS ANALYSIS OF THE DATA

5.1 Overview on MARS

MARS is a technique for discovering, evaluating and describing the causal links betweenfactors in any theoretical model. Ordinary regression equations model the relationship betweenoutcome and predictor variables using a single function (e.g. linear and log linear) of thepredictor variables, describing the contribution of each predictor (independent) variable with asingle coefficient. As Hastie & Tibshirani (1990) point out non-linearity in ordinary regressionis captured in higher order terms (x2, x3, etc.) but the coefficients of these terms are estimatedusing the data globally and thus, local features of the true function might not be captured.However, the regression splines (RS) approach models the relationship between outcome andpredictor variables as a piecewise polynomial function f(x) which can be obtained by dividingthe range of each predictor variable into one or more intervals and representing f by a separatepolynomial in each interval (Hastie & Tibshirani, 1990). A regression spline function can beexpressed as a linear combination of piecewise polynomial basis functions (BF) that are joinedtogether smoothly at the knots, where a knot specifies the end of one region of data and thebeginning of another (Steinberg & Martin, 1999). The coefficient of each BF is estimated byminimizing the sum of square errors, which is similar to the estimation process of linearregression, but involving local data for the given region.

Although regression splines have only been used recently in IT studies, RS based analysishas been successfully applied in various fields including software engineering (Briand et al.,



2000a; 2000b), electrochemistry (e.g., Carey & Yee, 1992), geography (Abraham & Steinberg,2001), communication (Ekman & Kubin, 1999), chemical studies (De Veaux et al., 1993;Nguyen-Cong & Rode, 1996), cancer research (Mallick et al., 1997), genetics (York & Eaves,2001), engineering (Jin et al., 2000), geochemistry (Griffin et al., 1997), epidemiology (Kuhnertet al., 2000), finance (Abraham, 2002) and biological sciences (Prasad & Iverson, 2000). Itshould be noted that both regression and regression splines could identify the order ofimportance of the independent variables in a predictive model, and estimate the value of thecoefficient for each independent variable. However, if the impact of an independent variable onthe dependent variable is conditional, then regression splines can identify such conditionswhile regression cannot. Thus, some questions cannot be answered using regression since itdoes not provide means for exploring those questions. On the other hand, regression splinescan provide the means for exploring our research questions in greater depth than would havebeen possible using regression.

5.2 MARS analysis of the Klein-Sorra model

In the following subsections we discuss the MARS analysis. In Subsection 5.2.1 we discuss thecausal links between the independent factors (climate and fit) and the mediators (skill, incen-tives, absence of obstacles and user commitment) of the Klein-Sorra model. In Subsection5.2.2 we discuss the causal links between the mediator factors (skill, incentives, absence ofobstacles and user commitment) and implementation effectiveness. For these analyses weuse the Salford System’s MARS software. For the forward phase modeling we set theparameters as follows:

• Maximum number of BFs: 180

• Minimum number of observations between knots: 1

• Method for determining degrees-of-freedom charge per knot: 10-fold cross validation

A high value for the maximum number of BFs parameter setting reduces the chance thatrelevant BFs might not be identified. While our desire was to generate as many relevant BFsin the forward phase as was permitted by computer memory constraints, we did not want tohave too much redundancy; consequently we set the minimum number of observationsbetween knots to one. If we had more observations in the dataset we might have increased thevalue of this parameter.

5.2.1 Relationships between independent factors and mediators

The Klein and Sorra model postulates that each mediator factor (skill, incentives, absence ofobstacles and user commitment) has a single independent factor (climate or fit) as its predictor.However, since Klein and Sorra did not subject their model to empirical validation, theseassertions about the causal links are nothing more than conjectures. Consequently, wecommence our analysis by examining possible causal links for each of the mediator factorsdescribed in the model. We explore the possible causal links from two perspectives: (a) we



investigate the relationships as they are described in the Klein and Sorra model; and (b) weinvestigate the possibility that either or both of the independent factors could be potentialpredictors of any of the mediator factors. Our analysis for the second case is well supported byMARS as it allows us to retrieve both a model with two predictors of SKILL, namely CLIM andFIT (cf. Table 4.1a), and a model with a single predictor of SKILL, namely FIT (cf. Table 4.1a).We then compare these models from the first perspective (i.e. only one independent factor isa predictor) to the models of the second perspective (i.e. both independent factors arepredictors) based on their R2 values. However, in order to allow for a fair comparison, we selectonly models that have the same number of BF. Our interest here is identifying the better model.The criteria for identifying the better model is as follows: If the adjusted R2 of the two-predictormodel is greater than the adjusted R2 of the single predictor model suggested by Klein andSorra, and if this difference is greater than 0.05 then we take the position that data suggeststhat the given two predictor model provides a better description of the causal relationship thanthe given single predictor model suggested by Klein and Sorra.

Given our interest in not overburdening the reader with all the details of our analysis, we willlimit ourselves to providing a full discussion of our analysis and results for one of theindependent factors (i.e. skill), and have a reduced, but nevertheless substantive discussionfor the other variables. Table 4.1a displays models for predicting the factor Skill. The readermay observe that the R2 (0.48) of the two predictor model (Input: CLIM, FIT; Output: CLIM, FIT)is greater than the R2 (0.25) of the single predictor model suggested by Klein and Sorra (Input:CLIM; Output: CLIM), and this difference is greater than 0.05 (i.e. 0.23) thus we conclude thatthe evidence suggests that the given two-predictor model provides a better description of thecausal relationship than the single predictor model suggested by Klein and Sorra. Interestinglythe R2 (0.42) of the other single predictor model (Input: CLIM, FIT; Output: FIT) is also greater

Table 4.1a. MARS models for predicting SKILL

Target Input Output R2 MARS model: BFs and equation

Skill (SKILL) CLIM (CLIM) 0.25 BF3 = max(0, CLIM + 2.136);

BF5 = max(0, CLIM + 2.420);

BF33 = max(0, CLIM - 0.139);

SKILL = -1.865 - 5.136 * BF3 + 5.238 *

BF5 + 0.622 * BF33

CLIM, FIT (FIT) 0.42 BF93 = max(0, FIT + 1.220);

SKILL = -0.970 + 0.753 * BF93

(FIT, CLIM) 0.48 BF3 = max(0, CLIM + 2.176);

BF17 = max(0, CLIM + 2.420);

BF93 = max(0, FIT + 1.220);

SKILL = -2.457 - 5.533 *

BF3 + 5.648 * BF17 + 0.669 * BF93

D = R2 (CLIM, FIT) - R2 (CLIM) 0.23

BF, basis functions; CLIM, implementation climate, FIT, Innovation-values fit; MARS, multivariate adaptive regression splines.



than the R2 (0.25) of the single predictor model suggested by Klein and Sorra (Input: CLIM;Output: CLIM) even though the former has fewer BF. Together these alternate models stronglysuggest that FIT should also be considered to be a predictor of SKILL. We therefore select thetwo-predictor model (Input: CLIM, FIT; Output: CLIM, FIT) as the ‘best’ of these models.

The MARS results displayed in Table 4.1b suggests that the rate of impact of climate on skillis not uniform, but rather depends on the value of the climate factor. When the climate CLIM)is below -2.420, climate has no impact on skill. Beyond this point, there is positive impact ofclimate on skill but the rate of impact decreases at the point where the factor score of the CLIMis above -2.176. Similarly the results for the impact of the fit on skill suggest that its rate ofimpact is not uniform, and could be positive or zero. The positive impact of fit on skill occurswhen the factor score of fit is above -1.220.

Table 4.2, illustrates our results for the impacts on the target variables incentives, absenceof obstacles and user commitment respectively. In the left column of the table the targetvariable is given followed by the predictor variable, then the region of the regression splinefollowed by the b coefficient and the direction of the impact. The last column of the tableprovides the data on the model fit. The reader will notice that the relationship betweenINCENTIVE and CLIM is complex; in two regions of the regression spline it is positive and theother it is negative. We will discuss this more later.

5.2.2 Relationships between mediators and implementation effectiveness

The Klein and Sorra model postulates that each mediator factor (skill, incentives, absence ofobstacles and user commitment) is a predictor of implementation effectiveness. But as statedearlier none of these conjectures were subjected to empirical validation, consequently, we willnow examine the possible causal links between each of the mediator variables and implemen-tation effectiveness as they are described in the model. We commence our exploration byexamining the impact of each mediator on implementation effectiveness then examine theircollective impact of implementation effectiveness. We then compare the models from the firstcase (i.e. a single mediator as a predictor of implementation effectiveness) to the model of the

Table 4.1b. The best MARS model predicting skill

Target variable

(Dependent

variable)

Predictor

variables

(Factors)

Regression

spline

region

Direct impactModel fit

adjusted R2Beta coefficient Direction

SKILL CLIM (-•, -2.420) 0.000 CNone 0.48

(-2.420, -2.176) 5.648 Positive

(-2.176, +•) 0.115 (= 5.648 - 5.533) Positive

FIT (-•, -1.220) 0.000 None

(-1.220, +•) 0.669 Positive

CLIM, implementation climate, FIT, Innovation-values fit; MARS, multivariate adaptive regression splines.



Tab

le4.

2.D

irect

impa

cts

onea

chpr

edic

tor

varia

ble

inth

eth

ree

best

mod

els

Var

iabl

es

Reg

ress

ion

splin

ere

gion

Dire

ctim

pact

Mod

elfit

adju

sted

R2

Tar

get

(dep

ende

ntva

riabl

e)P

redi

ctor

(fac

tors

)B

eta

coef

ficie

ntD

irect

ion

Ince

ntiv

eC

CLI

M(-

�,

-1.2

80)

0.00

0N

one

0.45

(-1.

280,

-1.0

33)

6.39

3P

ositi

ve

(-1.

033,

-0.8

63)

-7.0

62(=

6.39

3-

13.4

55)

Neg

ativ

e

(-0.

863,

+�)

0.40

4

(=6.

393

-13

.455

+7.

466)

Pos

itive

FIT

(-�

,-1

.356

)0.

000

Non

e

(-1.

356,

-1.0

75]

3.17

3P

ositi

ve

(-1.

075,

+�)

0.29

2(=

3.17

3-

2.88

1)P

ositi

ve

Abs

ence

ofob

stac

les

CLI

M(-

�,

-0.4

87]

0.00

0N

one

0.41

(-0.

487,

+�)

-0.5

48N

egat

ive

FIT

(-�

,-0

.234

]0.

000

Non

e

(-0.

234,

-0.0

08]

-2.9

24N

egat

ive

(-0.

008,

+�)

-0.1

17(=

-2.9

24+

2.80

7)N

egat

ive

Use

rco

mm

itmen

tF

IT(-

�,

-1.8

60]

0.00

Non

e0.

42

(-1.

860,

-0.9

21]

1.33

4P

ositi

ve

(-0.

921,

+�)

0.58

4(=

1.33

4-

0.75

0)P

ositi

ve

CLI

M,

impl

emen

tatio

ncl

imat

e,F

IT,

Inno

vatio

n-va

lues

fit.



second case (i.e. all mediators are predictors) based on their R2 values. The results of ourMARS analysis are four models for predicting implementation effectiveness (cf. Appendix B).An examination of these models points to the four–predictor model postulated by Klein andSorra as the most appropriate with an R2 of 0.46. The basic characteristics of this model aredisplayed in Table 4.3 below. The model illustrates that for each mediator, the correspondingrate of impact is complex and non-uniform, and depends on the value of the mediator.

For example, on different regions of the regression spline defining the relationship betweenskill and implementation effectiveness, the impact of skill on implementation effectiveness ispositive or negative, depending on the value of this factor. The results also suggest absenceof obstacles has no impact on implementation effectiveness when the value of this factor isbelow -1.898 but has a negative impact above this value. The results also show that usercommitment has no impact on implementation effectiveness when the value of this factor is inthe range (-�, -0.738), a positive impact when it is in the range (-0.738, -0.568), a negativeimpact when in the range (-0.568, 0.146), and a positive impact when it is greater than 0.146.

6. CONCLUSION

In this research we set out to conduct a complete and systematic empirical evaluation of theinnovation implementation model postulated by Klein & Sorra (1996) which until now was onlypartially tested. Our reason for selecting this model is that it offered a conceptualization of themanagerial dimension of implementation that is missing from existing models. The results fromour research suggest that the causal relationships postulated by model all exist, but in somecases they are more complex (e.g. non-linear) than suggested by Klein & Sorra (1996).Further, our MARS analysis offers evidence for the existence of additional causal relationshipsnot postulated in the original model. For example, in the original model, implementation climateis believed to be a sole predictor of skills, incentives, and absence of obstacles; but ouranalysis indicates that innovation-values fit also affects these variables as well (see Figure 2).In particular, our findings indicate that both implementation climate and innovation-values fithave strong influence on implementation effectiveness. implementation climate affects imple-mentation effectiveness through enhancing user skills, reducing obstacles, and increasingincentives. Further, innovation-values fit also impacts on implementation effectiveness byincreasing the users’ commitment to the new ERP system. We have also found that innovation-values fit is the best predictor of implementation effectiveness and it also has direct impact onincentives, absence of obstacles and user commitment. Our findings offer three main contri-butions to the IS literature: (a) to ERP implementation theory; (b) to IS implementation; and (c)to ERP implement management practice; which we discuss below.

6.1 Contributions to ERP implementation research

Recent research on predictive models of ERP implementation build on research findings fromvoluntary system usage context, and do not consider the managerial dimension (Kositanurit



Tab

le4.

3.T

hebe

stM

AR

Sm

odel

pred

ictin

gim

plem

enta

tion

effe

ctiv

enes

s

Var

iabl

es

Reg

ress

ion

splin

ere

gion

Dire

ctim

pact

Mod

elfit

adju

sted

R2

Tar

get

(Dep

ende

ntva

riabl

e)

Pre

dict

or

(Ind

epen

dent

)

Bet

a

coef

ficie

ntD

irect

ion

Impl

emen

tatio

n

effe

ctiv

enes

s

Ski

ll(-

2.61

8,-2

.059

)3.

919

Pos

itive

0.46

(-2.

059,

-0.4

85)

-0.5

11(=

3.91

9-

4.43

0)N

egat

ive

(-0.

485,

-0.4

10)

15.2

46

(=3.

919

-4.

430

+15

.757

)

Pos

itive

(-0.

410,

-0.3

58)

-40.

711

(=3.

919

-4.

430

+15

.757

-55

.957

)

Neg

ativ

e

(-0.

358,

-0.2

97)

24.4

69(=

3.91

9-

4.43

0+

15.7

57-

55.9

57+

65.1

80)

Pos

itive

(-0.

297,

+�)

0.01

6

(=3.

919

-4.

430

+15

.757

-55

.957

+65

.180

-24

.453

)

Pos

itive

Ince

ntiv

e(-

�,

-2.1

91)

0.00

0N

one

(-2.

191,

+�)

0.24

4P

ositi

ve

Abs

ence

ofob

stac

les

(-�

,-1

.898

)0.

000

Non

e

(-1.

898,

+�)

-0.2

33N

egat

ive

Use

rco

mm

itmen

t(-

�,

-0.7

38)

0.00

0N

one

(-0.

738,

-0.5

68)

5.42

1P

ositi

ve

(-0.

568,

0.14

6)-1

.057

(=5.

421

-6.

478)

Neg

ativ

e

(0.1

46,

+�)

0.35

6

(=5.

421

-6.

478

+1.

413)

Pos

itive



et al., 2006; Amoako-Gyampah & Salam, 2004) that is relevant to the context of mandatorysystem usage. However, given the mission-critical role of ERP systems in organizations,managers need to pay careful attention to cultivating and managing factors that could have apositive impact on the implementation. Our study if the Klein and Sorra model suggests two keyfactors, implementation climate and innovation-values fit, that managers can manipulate toachieve effective ERP implementation. An unexpected finding of this study is the influence ofinnovation-values fit on implementation effectiveness. Our MARS analysis suggest that highinnovation-values fit influences users to obtain better skills, perceive less obstacles, and feelmore motivated in using the ERP system. A possible explanation is that if users perceive that theERP will help them solve their work related problems; they internalize the benefits of the ERP.Consequently, they are more open to learning and mastering the system, thus becomingintrinsically motivated. Some studies have found that when system usage is not intrinsicallydriven, system suffers underutilization or users’ intentional sabotage (Markus & Keil, 1994;

Figure 2. Model suggested by empirical exploration.



Brown et al., 2002). As ‘higher level of intrinsic motivation typically leads to willingness to spendmore time on the task’ (Venkatesh, 2000, pp. 348–349), it is understandable that users whoperceive high innovation-values fit will be more skillful and highly motivated in using the ERP.This finding is particularly useful for the implementation where system usage is mandated byorganizations. Therefore, in mandatory system usage contexts, it is particularly important formanagers to help individual users understand how the ERP system fits with the organization’splans for innovating the business, for the expected benefits from the system to be realized.

6.2 Contributions to IS implementation research

Our empirical evaluation of the Klein and Sorra model in an ERP environment is a contributionto the general literature on IS implementation research for the following reasons: First, a keycontribution of the Klein and Sorra model is the formalization of the idea that it is committedadoption that enables organizations to obtain expected benefits when implementing newinformation technologies (Shrednick et al., 1992), and it is crucial to understand the antecedentsof committed adoptions. The Klein and Sorra model recognizes that individual users makecompromises, which could undermine their system usage, and consequently affect organiza-tional performance negatively. By highlighting key determinants of users’ commitment andquality system usage the model offers insights into effective management interventions in ISimplementations. Second, the model introduces unexplored constructs such as implementationclimate and innovation-values fit, which can be applied to various implementation contexts andhelp enrich our understanding of IS adoption and implementation (Agarwal, 2000; Chin &Marcolin, 2001a). Other researchers have also suggested that attitudinal elements (e.g.,enthusiasm) and quality elements (e.g., skillful and consistent use) are also implicated in ISimplementation outcomes (Bailey & Pearson, 1983; Gelderman, 1998; Kositanurit et al., 2006).Third the model offers new possibilities for extending research into the managerial factors thataffect ERP (and more generally IS) implementation success. As stated earlier much of the ISimplementation research has focused on the individual user perspective or the task technology-fit perspective (see Table 1), and there has been an attempt to unify these perspectives (e.g.Venkatesh et al., 2003), little attention has focused on expending beyond these two perspec-tives. As others have pointed out (Parr & Shanks, 2000; Hong & Kim, 2002; Kumar, Maheshwari& Kumar et al., 2003), this is a significant limitation of this IS implementation research. Ourresearch offers the potential of adding a managerial perspective to the existing implementationmodels. Thus making them more complete and better predictors of IS implementation success.Our complete evaluation of the Klein and Sorra model is a step in the development of a moregeneral model of IS implementation that would include managerial factors.

In addition to a better understanding of two key determinants for implementation effective-ness under the mandatory IS usage context, our research contributes to the existing literatureby offering a set of validated constructs for future studies of IT implementation climate. Agarwal(2000) specifically mentioned the lack of studies on managerial interventions such as climate.The constructs developed here can be deployed in other studies on the effect of managementinterventions in various IS implementation contexts. Furthermore, we demonstrate MARS as



an effective tool to not only testing existing causal relationships conjectured in Klein and Sorramodel, but also exploring the existence of additional causal relationships. For any suchrelationship that is suggested by our statistical analysis, we also offered theoretical justificationfor its existence. The result is an extended model that offers a richer description and expla-nation of the impacts of managerial factors on implementation effectiveness that is supportedby both theory and data analysis. It appears to us that even beyond this research project, suchan approach to the validation and development of theory seems to be potentially valuable toresearchers given the fact that it is burdensome and almost impossible for the researcher tospecify and evaluate all relevant hypotheses. Thus, the researcher cannot discover additionalimportant relationships that may exist in the data if they were not explicitly included in the setof hypotheses.

6.3 Contributions to ERP implementation practice

As the first study exploring the two key determinants for implementation effectiveness, ourresearch offers several important implications to practitioners. First, our findings shed light oneffective strategies for management intervention during an ERP implementation. In particular,we indicate that managers need to focus on cultivating a strong climate for ERP implementa-tion and helping users identify the innovation-values fit. The four dimensions of implementationclimate (i.e. goal emphasis, mean emphasis, task support, and reward emphasis) highlightwhat managers can do to foster a strong climate. For example, managers need to emphasizevisions behind the system implementation, explain to users changes to their daily tasks, andoffer rewards and necessary assistance. The three dimensions of innovation-values fit (i.e.,quality, business processes and locatibility) suggest that managers need to stress the benefitsof a new ERP in these areas to help users recognize the value of the new system andinternalize its usage. An ERP implementation without a strong climate fails to equip users withnecessary skills and motivation, and an ERP implementation that is not perceived (innovation-values fit) by users as solving the organization’s problems negatively influences users resultingin users becoming disinterested in the system.

Second, our research findings inform managers and researchers that mandatory systemusage does not necessarily equate to users’ commitment and quality system usage. In fact, wehave found that users realizing the fit like to pay extra effort to master the system, and as aresult, tend to be more skilful, enthusiastic, and committed to the system than do users who failto see the fit. It is reasonable to argue that implementations with committed and skillful usershave a higher possibility to realize the benefits of the ERP than do implementations withdisinterested and unskillful users. Accordingly, in promoting a new ERP system, managersshould not neglect users’ internalization of an enterprise system just because its usage ismandatory. Rather, considering the importance of fit in implementation effectiveness, manag-ers need to direct their efforts at helping users internalize the benefits of a new system. Finally,it is well known that organizations have been experiencing high failure rates in implementingERP systems. Despite the wealth of findings gained through the existing IS implementationliterature, there has been lacking a strong theory conceptualizing about the managerial



dimension in studies of ERP (or IS) implementation effectiveness. Most of the studies of ERP(or IS) implementation have taken the individual user focus. Our research has contributed tothe advancement of understanding how management interventions can positively impact ERPimplementations. Our research offers insights immediately useful for managers and futureresearch on IT implementation, a primary topic in the field of IS.

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Biographies

Kweku-Muata Osei-Bryson is Professor of Information

Systems at Virginia Commonwealth University. He holds a

PhD in Applied Mathematics (Management Science and

Information Systems) from the University of Maryland at

College Park. He currently does research in various areas

including: data mining, decision support systems, knowl-

edge management, IT and productivity, IS outsourcing,

e-commerce, IS security, multicriteria decision analysis.

Currently he serves as an Associate Editor of the

INFORMS Journal on Computing, and is a Member of the

Editorial Board of the Computers & Operations Research

Journal and the International Advisory Board of the Journal

of the Operational Research Society.

Linying Dong is an Assistant Professor in the Ted

Rogers School of Information Technology Management,

Ryerson University. She earned her PhD from the Richard

Ivey School of Business, the University of Western

Ontario. Her research focuses on management support,

leadership, privacy, and IS implementations and adoption.

Her work has appeared in the Journal of Information

Technology, European Journal of Information Systems,

Communications of the AIS, and Business Process Man-

agement Journal.

Ojelanki Ngwenyama is Professor and Director of the

Institute for Innovation and Technology Management at

the Ted Rogers School of Management, Ryerson Univer-

sity, Toronto, Canada and Research Professor at Aarhus

School of Business, University of Aarhus, Denmark. His

research focuses on a range of topics including the critical

social theory approach to information systems research

and decision models for IS management. His papers have

appeared in a range of international scholarly journals

including MISQ, European Journal of Operational Re-

search, European Journal of Information Systems, IEEE

Transactions on Engineering Management and Informa-

tion Systems Journal. He is a Member of IFIP WG 8.2 and

is presently on the Editorial Advisory Board of the Scandi-

navian Journal of Information System.



APPENDIX A: CONSTRUCT MEASURES AND SOURCES

CONSTRUCT ITEM

ImplementationClimate(CLIMATE) (new)

Mean Emphasis:� Employees were told about the new work procedures for using the system

� Employees were told about the changes in the work procedures due to the

implementation of the system

� Employees were told about the methods for using the system

Goal Emphasis:� Employees were told that what they needed to accomplish in using the system

� Employees were told the standards they had to meet in using the system

� Employees were told the types of outcomes that they needed to accomplish in using the

system

Task Support:� Employees were provided all computer technology (e.g., hardware and software)

necessary to perform their tasks with the system

� Helpful books, manuals, and online documents were available when employees had

problems with the new system

� Employees were given sufficient time to learn the new system before they had to use it

� A ‘Help Desk’ was available whenever people needed help with the system

� Additional training for the new system was available on request

� Money was readily available to support activities related to the implementation of the

system

Reward Emphasis:� Employees were told the potential risk if they did not use the new system

� Employees perceived that the more they knew about the new system, the better their

chances were of getting a job promotion

� Employees perceived that the better they were at using the new system, the more likely

they were to get a bonus or raise

� Employees knew how their individual performances in using the new system was

evaluated

� Employees perceived that they were going to be recognized for time and effort they

spent in learning the system

Innovation-Values Fit(FIT) (Valle et al.,2000; Goodhue,

1995)

Fit with business process (FITBP):� The system supports the repetitive and predictable work processes

� The system supports cooperation between departments

� The system assists me in developing diverse abilities and capabilities that are required

to complete my job

Fit re: Quality (FITQL):� The system keeps data up-to-date for my task

� The system is missing critical data that would be very useful to my task

� The system helps me to get data that is current enough to meet my business needs

� The system maintains data I need to carry out my task

� Sufficiently detailed data is maintained by the system

� The system keeps data at an appropriate level of details so that I can complete my

tasks

Fit re: Locatibility (FITLO):� The definition of data fields relating to my task is easy to find out in the system

� The system helps me locate corporate or department data very easily



APPENDIX A: cont.

CONSTRUCT ITEM

� It is easy to find out what data the system maintains on a given subject

� The system helps me understand the meaning of data very easily

Absence of Obstacles(OBSTACLES)

(new)

� Due to the lack of organizational resources (e.g., time, training), I have faced a lot of

difficulties in learning to use the system

� Due to the lack of technical support, I have found the system difficult to use

� There are a lot of organizational barriers that prevent me from using the system

effectively

Incentives (INCENT)

(new)

� I am motivated to use the system

� I am discouraged from using the system

Skills (SKILLS) (new) � I am very knowledgeable about how the system works

� I understand all of the special features of the system

� I can enter data into the system whenever I need to

� I know how data in my functional department links to data in other departments

� I know which departments receive the information I input into the system

� I can interpret the data shown in the system without problems

Commitment (Meyer

and Allen, 1991)

� Using the system is personally meaningful to me

� I enjoy discussing my experiences in using the system with my colleagues

� I really feel as if the system is my system

ImplementationEffectiveness (IE)

(Klein et al., 2001)

� I think the system is a waste of time and money for our organization (reverse)

� If I had my way, this plant would go back to the old way and forget the system (reverse)

� I am happy to do my part to make the system effective at this organization

� I think the system is an improvement over the system(s) that we used to use



APPENDIX B: THE SET OF MARS MODELS PREDICTING IMPLEMENTATION

EFFECTIVENESS

Target Input Output

Adjusted

R2 Model

Implementation

Effectiveness

SKILL,

INCEN,

ABSOB

1 0.25 BF88 = max(0, INCEN + 2.191);

ENDOR = -1.124 + 0.511 * BF88;

2 0.33 BF29 = max(0, SKILL + 2.618); BF30 = max(0,

SKILL + 2.059); BF88 = max(0, INCEN + 2.191);

ENDOR = -2.888 + 2.974 * BF29 - 2.780 *

BF30 + 0.385 * BF88;

3 0.40 BF29 = max(0, SKILL + 2.618); BF30 = max(0,

SKILL + 2.059); BF48 = max(0, SKILL + 0.358);

BF58 = max(0, SKILL + 0.410); BF60 = max(0,


BF80 = max(0, USRCO + 0.738); BF88 = max(0,

INCEN + 2.191); ENDOR = -2.937 + 4.030 *

BF29 - 4.426 * BF30 + 59.666 * BF48 - 51.432 *

BF58 - 22.169 * BF60 + 14.429 * BF62 + 0.188 *

BF80 + 0.329 * BF88;

4 0.46 BF3 = max(0, ABSOB + 1.898); BF29 = max(0,





USRCO + 0.738); BF84 = max(0, USRCO - 0.146);

BF86 = max(0, USRCO + 0.568); BF88 = max(0,

INCEN + 2.191); ENDOR = 2.252 - 0.233 *

BF3 + 3.919 * BF29 4.430 * BF30 + 65.180 *

BF48 - 55.957 * BF58 - 24.453 * BF60 + 15.757 *

BF62 + 5.421 * BF80 + 1.413 * BF84 - 6.478 *

BF86 + 0.244 * BF88;

D = R2 (4) - R2 (3) 0.06



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