Information & Management

Information & Management 45 (2008) 474 481 Contents lists available at ScienceDirect Information & Management journal homepage: www.elsevier.com/locate/im The role of readiness for change in ERP implementation: Theoretical bases and empirical validation Kee-Young Kwahk a,1, Jae-Nam Lee b, * a School of Business IT, Kookmin University, 861-1 Jeongneung-dong, Seongbuk-gu, Seoul 136-702, Republic of Korea b Korea University Business School, Anam-dong 5 ga, Seongbuk-gu, Seoul 136-701, Republic of Korea ARTICLE INFO ABSTRACT Article history: Received 19 April 2006 Received in revised form 12 June 2008 Accepted 5 July 2008 Available online 9 September 2008 Keywords: Organizational change Readiness for change ERP systems User behavior toward IT Technology acceptance model Theory of planned behavior Implementation of ERP systems continues to drive change in organizations. However, the effort is often considered a failure, partially because potential users resist the change. Readiness plays an active role in reducing resistance to such efforts. Therefore, we examined the formation of readiness for change and its effect on the perceived technological value of an ERP system leading to its use. We developed a model of readiness for change incorporating TAM and TPB. The model was then empirically tested using data collected from users of ERP systems in Korea. Structural equation analysis using LISREL provided significant support for all proposed relationships. Specifically, we found that readiness for change had an indirect effect on behavioral intention to use an ERP system. At the same time, readiness for change was found to be enhanced by two factors: organizational commitment and perceived personal competence. ß 2008 Elsevier B.V. All rights reserved. 1. Introduction Organizations are continually faced with the need to change their structures, objectives, processes, and technologies. Thus, they must be able to make changes to sustain their competitive advantage. Many have adopted ERP systems to help do this. Studies have reported that ERP adoption is about 80% in Fortune 500 companies [23]. However, despite its popularity, ERP implementations have been plagued with high failure rates and inability to realize promised benefits. The failure rate has been estimated as 60 90%. Some prior studies indicated that a major reason for failure was the resistance of the user to change [21]. ERP systems are often associated with fundamental change to organizational processes that involve different stakeholders [24]. Therefore, though ERP systems could be implemented successfully from a technical perspective, success may depend on employees being willing to use the delivered system. Creating readiness for change has been proposed as a major prescription for reducing resistance [26]. We therefore examined * Corresponding author. Tel.: +82 2 3290 2812; fax: +82 2 922 7220. E-mail addresses: kykwahk@kookmin.ac.kr (K.-Y. Kwahk), isjnlee@korea.ac.kr (J.-N. Lee). 1 Tel.: +82 2 910 4738; fax: +82 2 910 4519. how readiness for change could affect the perceived value of the system and thus increase the intention to use ERP. We explored the role of readiness for change in ERP implementation and its impact on usage intention. To do so, we defined a model of readiness by incorporating TAM and TPB. We included two antecedents of readiness for change (perceived personal competence and organizational commitment) and two process outcome variables (perceived usefulness (PU) and perceived ease of use (PEU)) leading to ERP usage intention. The model was then tested using a sample of 283 responses from 72 Korean organizations that had already implemented enterprise-wide ERP systems. 2. Theoretical background 2.1. Underlying theories The IS literature has become a stage for social psychology-based and attitude-based models predicting usage and acceptance. But although both PU and PEU are important predictors of use, they do not explain individual attitude and behavior. Prior research has indicated the need for a better understanding of key determinants and suggested that TAM should be integrated into a broader model with variables related to human and organizational dimensions. On the other hand, it has been argued that TPB is difficult to apply across diverse user contexts [22]. TPB accounts for conditions where individuals do not have complete control over 0378-7206/$ see front matter ß 2008 Elsevier B.V. All rights reserved. doi:10.1016/j.im.2008.07.002

K.-Y. Kwahk, J.-N. Lee / Information & Management 45 (2008) 474 481 475 their behavior. Thus, behavioral intention depends on attitude, subjective norm, and perceived behavioral control [1]. The role of a subjective norm as a determinant of IS usage is unclear; some research has not found a significant relationship between it and usage intention. In contrast, perceived behavioral control apparently does play a critical role in understanding people s PEU in performing a behavior of interest. Therefore, the stronger the individual feels about his or her ability to execute the behavior, the more he or she will utilize available resources and opportunities to execute the behavior. Subsequently, individuals will thus gain confidence from perceived higher behavioral control [6]. To overcome the problems and enhance the understandability of IS usage and IS acceptance behaviors, we proposed a model that would be relevant to enterprise-wide initiatives, by identifying not only the PU and the PEU but also the perceived behavioral control (i.e., perceived personal competence) and attitude toward behavior (i.e., readiness for change) as major factors of a successful ERP project. 2.2. Organizational factors for successful ERP implementation In our study, we decide to focus mainly on positive attitudes toward behavior readiness for change the extent to which organizational members hold positive views about the need for organizational change, as well as their belief that changes are likely to have positive implications for them and the organization. This attitude can determine whether an individual supports or resists a change. Of course, a change may give satisfaction to some and not to others. Organizational commitment (the relative strength of an individual s identification with, and involvement in, a particular organization) and perceived personal competence (the degree of the individual s feelings of competence in the work role) play key roles in employees acceptance of change. 3. Research model and hypotheses To explore how readiness for change affected an individual s reaction to implementation of an ERP system, we developed a model that considered its psychological consequences and antecedents. This is shown in Fig. 1. 3.1. The importance of readiness for change Readiness for change plays a crucial role in mitigating resistance to change and thus in reducing the failure rate [14]. Effective ERP system implementation requires enterprise-wide initiatives, bringing large-scale change generally requiring large investment of resources; a failure results in significant loss. Organizational change should be a continuous process [9]. Change initiatives can be characterized as push systems where senior managers and experts cause change. However, a pull system may be needed for a successful effort; in this, transitioning to new technologies is forced by the people who will manage them. The pull system can be achieved by focusing on user readiness for change and identifying the circumstance under which users are receptive to it. 3.2. The effects of readiness for change Creating the belief that organizational change is needed requires agreement that there is a gap between the current and desired end states. In general, an ERP system is introduced into a company to improve its organizational effectiveness and fill any performance gap. Organizational members who have favorable perceptions of organizational transformation and are ready for it will be more likely to participate positively in the change and expect enhanced performance after its implementation. A prior study of ERP implementation [3] suggested that a push for change from top management was likely to produce positive perception. When employees are positive about and ready for organizational change, they appear to be more willing to try out a system. They think that they might miss benefits if they do not try out the system [30]. Also, when informed about the ERP system and its impact they have less uncertainty about the technical changes [12]. Thus, when employees are ready for change, they will find the systems more useful. Therefore, we proposed the hypothesis: H 1. Readiness for change has a positive effect on the perceived usefulness of an ERP system. Previous studies have paid attention to individual traits, such as innovativeness or technology readiness, to describe the individual s attitude toward change [8]. Parasuraman [25] has defined technology readiness as a state of mind that affected people s propensity to embrace and use new technologies for accomplishing goals... He argued that this related to the degree of readiness that the individual felt in using a technology. The technologically ready individual was more likely to see it as easy to use. Similarly, Walczuch et al. showed that more innovative individuals were perceived to have a smoother transition into a new technology without much cognitive effort. Therefore, we expected that individuals ready for change believed they could easily learn how to use the system with little effort. This is particularly true for Fig. 1. Research model.

476 K.-Y. Kwahk, J.-N. Lee / Information & Management 45 (2008) 474 481 ERP systems, because users must overcome knowledge barriers and rid themselves of what was the previous operation [27]. Therefore we hypothesized: H 2. Readiness for change has a positive effect on the perceived ease of use of an ERP system. 3.3. Creating readiness for change We considered two major antecedents of readiness for change: perceived personal competence and organizational commitment. A high level of perceived competence resulting from prior working experiences results in self-confidence and employees tend to believe that they can execute their job well when performing slightly different tasks. Thus, we posited: H 3. Perceived personal competence has a positive effect on readiness for change. Individuals with strong organizational commitment should be more willing to accept organizational change if it does not alter basic values and goals and is seen as beneficial; they are also then willing to expend more effort on behalf of the organization. This suggests that individuals commitment to the organization has varying effects on their readiness for change. Thus we made the following hypothesis: H 4. Organizational commitment has a positive effect on readiness for change. 3.4. Perceived technological attributes and usage intention A system must be useful and easy to learn. Consequently, we added the following two hypotheses for completeness: H 5. Perceived usefulness has a positive effect on the usage intention of an ERP system. H 6. Perceived ease of use has a positive effect on the usage intention of an ERP system. 3.5. Control variable Among the determinants of both PU and PEU, computer selfefficacy (the person s belief that he or she can perform a job and is confident of this) has been proposed as an important antecedent [29]. Many, for example [18,19] found experimental evidence supporting this relationship. 4. Research methodology A field study using a convenience sample was employed to test the model. The unit of analysis was the individual who worked for an organization that had already implemented an enterprise-wide ERP system. 4.1. Instrument development The items used to measure the constructs in our study were adopted and modified, as needed, from previous studies. Each survey item was first discussed with and scrutinized by two IS researchers to check its face validity. All research variables were measured using multi-item scales, as shown in Appendix A. Measures of readiness for change were based on an instrument developed by Dunham et al. [13], which originally consisted of 18 items. From these, we selected seven that revealed high explanatory power, were not reverse coded because of their potential negative effect on unidimensionality, and represented appropriate readiness for change of individuals in terms of content [17]. Organizational commitment was measured with six items selected from the instrument developed by Allen and Meyer [2]. This instrument originally had 24 items that represented three subgroups (affective, continuance, and normative commitment). As the multidimensionality of these three was not a concern in our research and because reverse coded items were excluded, we decided to reduce the items to six by selecting two from each subgroup while maintaining their original meaning. Perceived personal competence was measured using five items from Allen and Meyer s measurement. PU and PEU were each measured by six items, which were adopted from the previously validated measurement inventory and then modified to suit the context of the present research. Two items to measure usage intention were based on Davis: items for measuring PU, PEU, and usage intention were modified by changing the target IS into the ERP system to reflect our research context [11]. Finally, to measure the psychometric properties of computer self-efficacy as a control variable, we adopted 10 items from an instrument developed by Compeau and Higgins [10]. All question items were measured using a seven-point Likert-type scale with anchors ranging from strongly disagree to strongly agree. 4.2. Data collection and sample characteristics One of the directors of an ERP vendor agreed to sponsor our study. We asked the vendor to select its client companies that had recently finished ERP implementation and had implemented at least more than two ERP modules. We distributed a total of 350 questionnaires to 72 organizations in Korea through the vendor. The data were collected from employees who worked with ERP systems to perform their tasks. Of the 350 questionnaires distributed, 312 were returned. After being screened for usability Table 1 Respondent characteristics Respondent profiles Frequency Percent (%) Cumulative (%) Gender Male 144 52.7 52.7 Female 129 47.3 100.0 Age 29 126 62.1 62.1 30 39 65 32.0 94.1 40 12 5.9 100.0 Educational level High school 36 13.7 13.7 University 222 84.8 98.5 Postgraduate 4 1.5 100.0 Tenure 3 109 41.6 41.6 4 6 82 31.3 72.9 7 10 52 19.8 92.7 11 19 7.3 100.0 Role Clerical 157 60.2 60.2 Supervisory 73 28.0 88.2 Middle management 31 11.8 100.0 Industry type Manufacturing 44 61.1 61.1 Service 16 22.2 83.3 Information and communication 7 9.7 93.0 Food and beverage 5 7.0 100.0

K.-Y. Kwahk, J.-N. Lee / Information & Management 45 (2008) 474 481 477 Table 2 Analysis of non-response biases Measures Early respondents (n = 40) Late respondents (n = 40) Tenure 5.27 5.73 0.68 Age 28.94 28.67 0.84 Readiness for change (RFC) 5.26 4.88 0.12 Perceived usefulness (PUS) 5.25 4.95 0.19 Perceived ease of use (PEU) 4.72 4.43 0.23 Usage intention (UIT) 5.23 5.15 0.76 Significance (P) and reliability, 283 responses were found to be complete and usable, representing a response rate of about 81%. Table 1 presents the respondent s demographics. On average, the respondents were 29.2 years old. The respondents had about 4.7 years of work experience, and most had worked for less than 7 years. Because this study used a convenience sample, it was not possible to test non-response bias by comparing the respondents and non-respondents. Instead, non-response bias was assessed by comparing the responses of early and late respondents, defined as the first and last 40 questionnaires received [20]. The average ages for the early and late respondents were 28.9 and 28.6, respectively, and these were not significant. No significant differences in tenure were observed between the two groups (first group = 5.3; last group = 5.7 years). There were no significant differences in the major variables, as can be seen in Table 2, suggesting that nonresponse bias was low. 5. Data analysis and results LISREL was used for data analysis. Our objective was to test the proposed factors that lead to usage intention in a holistic framework. Data analysis was carried out in accordance with a two-step methodology [5] to avoid the possible interaction between measurement and structural equation models. The structural model describes the relationships among the theoretical constructs, while the measurement model consists of the relationships between the observed variables (items) and the latent constructs they measure. According to this procedure, after the model has been modified to create the best measurement model, the structural equation model can be analyzed. 5.1. Measurement model Confirmatory factor analysis (CFA) was conducted using LISREL 8.7. The overall effectiveness of the measurement model was examined using seven common model fit measures: normed Table 3 Convergent validity test Constructs Items Factor loading Composite reliability Average variance extracted Readiness for change (RFC) RFC2 0.76 0.93 0.70 RFC3 0.79 RFC4 0.88 RFC5 0.88 RFC6 0.86 RFC7 0.85 Perceived ease of use (PEU) PEU1 0.84 0.94 0.74 PEU2 0.84 PEU3 0.85 PEU4 0.86 PEU5 0.90 PEU6 0.87 Perceived usefulness (PUS) PUS1 0.85 0.95 0.76 PUS2 0.91 PUS3 0.86 PUS4 0.87 PUS5 0.88 PUS6 0.86 Usage intention (UIT) UIT1 0.90 0.88 0.78 UIT2 0.86 Organizational commitment (OCM) OCM1 0.82 0.89 0.63 OCM2 0.76 OCM3 0.82 OCM4 0.88 OCM6 0.67 Perceived personal competence (PPC) PPC1 0.78 0.88 0.59 PPC2 0.76 PPC3 0.81 PPC4 0.70 PPC5 0.78 Computer self-efficacy (CSE) CSE1 0.75 0.98 0.70 CSE2 0.74 CSE3 0.80 CSE4 0.83 CSE5 0.87 CSE6 0.87 CSE7 0.86 CSE8 0.89 CSE9 0.87 CSE10 0.86

478 K.-Y. Kwahk, J.-N. Lee / Information & Management 45 (2008) 474 481 x 2 (x 2 to degree of freedom), goodness-of-fit index (GFI), normalized fit index (NFI), non-normalized fit index (NNFI), comparative fit index (CFI), root mean square residual (RMR), and root mean square error of approximation (RMSEA). The measurement model in the CFA was revised by removing items that had large standardized residuals with other items, one at a time. After dropping two items (RFC1 and OCM5), the measurement model exhibited overall good fit. The normed x 2 was 1.82, which was satisfactory, being below the maximum desired cutoff of 3.0 [15]. RMSEA was 0.05, indicating a good fit, below the maximum desired cut-off of 0.06. Also the RMR was 0.04, lower than the desired maximum cut-off of 0.05. GFI was 0.81, which was above the recommended threshold of 0.8. The other fit indices were all satisfactory: CFI = 0.99, NFI = 0.97 and NNFI = 0.99, suggesting that the measurement model fit the data adequately [16]. Further analysis was conducted to assess the psychometric properties of the scales. The construct validity of the research instrument determines the extent to which the operationalization of a construct actually measures what it is designed to measure. Convergent validity was assessed using three measures, as shown in Table 3: factor loading, composite construct reliability, and average variance extracted. First, in determining the appropriate minimum factor loadings required for the inclusion of an item within a construct, factor loadings greater than 0.70 were considered highly significant. All of the factor loadings of the items in the measurement model were greater than 0.70, except for OCM6. Each item loaded significantly (p < 0.01 in all cases) on its underlying construct. Second, the composite construct reliabilities were within the commonly accepted range greater than 0.70. Finally, the average variances extracted were all above the recommended level of 0.50. Therefore, all constructs had adequate convergent validity. To confirm discriminant validity, the average variance shared between the construct and its indicators should be larger than the variance shared between the construct and other constructs. As shown in Table 4, all constructs share more variances with their indicators than with other constructs. The discriminant validity of the constructs was further validated by fixing the correlation between various constructs at 1.0, and then re-estimating the modified model [28]. Significant differences in the x 2 statistic of the constrained and unconstrained models imply high discriminant validity. From the constrained testing, the x 2 statistic of the unconstrained model was significantly better than any possible constrained models, thereby providing positive support for the discriminant validity. 5.2. Structural model The structural model was examined using the cleansed measurement model. The overall fit with the data was evaluated by the same set of fit indices used in the measurement model. The normed x 2 was 1.84, which is within the recommended level of 3.0, while the structural model exhibited a fit value satisfying the commonly recommended threshold for the respective indices, thus providing evidence of a good model: GFI = 0.81, NFI = 0.97, Table 4 Discriminant validity test Constructs Mean (S.D.) RFC PEU PUS UIT OCM PPC CSE RFC 4.93 (1.07) 0.84 PEU 4.53 (1.08) 0.61 0.86 PUS 5.00 (1.01) 0.74 0.60 0.87 UIT 5.03 (1.06) 0.68 0.70 0.81 0.88 OCM 4.67 (1.04) 0.50 0.49 0.49 0.50 0.79 PPC 4.66 (0.89) 0.54 0.48 0.50 0.51 0.55 0.77 CSE 4.81 (0.98) 0.44 0.45 0.41 0.52 0.47 0.69 0.84 Note: Leading diagonals represent the square root of the average variance extracted between the constructs and their measures, while off diagonal entries are the correlations among constructs. Fig. 2. Model testing results.

K.-Y. Kwahk, J.-N. Lee / Information & Management 45 (2008) 474 481 479 NNFI = 0.98, CFI = 0.99, RMR = 0.05, and RMSEA = 0.05. These results suggested that the structural model fit the data adequately. The standardized LISREL path coefficients and overall fit indices are shown in Fig. 2. Two variables (readiness for change and computer self-efficacy) were significantly related to PU and explained 57% of the variance in PU: readiness for change (b = 0.70, p < 0.01) and computer self-efficacy (b = 0.12, p < 0.01). The same two variables were also significantly related to the PEU and explained 43% of the variance in PEU: readiness for change (b = 0.53, p < 0.01) and computer self-efficacy (b = 0.23, p < 0.01). Two variables (organizational commitment and PEU) were significantly related to readiness for change and explained 37% of its variance: organizational commitment (b = 0.30, p < 0.01) and perceived personal competence (b = 0.39, p < 0.01). Finally, two variables (PU and PEU) were significantly related to usage intention and explained 73% of the variance in usage intention: PU (b = 0.63, p < 0.01) and perceived ease of use (b = 0.35, p < 0.01). Thus, all hypotheses were supported. 6. Discussion and implications 6.1. Findings and limitations In our analysis, we found that behavioral intention to use an ERP system was affected indirectly by readiness for change, which in turn influenced the PU and the PEU of the system. It was also observed that readiness for change played an important role in explaining two attributes by identifying the increased variances: readiness for change and computer self-efficacy accounted for 57% of the variance in PU. The addition of readiness for change contributed to an increase in the explained variance of 38% over and above the variance explained by computer self-efficacy. Readiness for change and computer self-efficacy together explained 43% of the variance in the PEU, while computer self-efficacy alone explained 23% of the variance in the PEU. The addition of readiness for change increased the explained variance by 21%. We also examined how readiness for change could be formed. One was through organizational commitment; another was through perceived competence. Moreover, PU and PEU had a significant positive effect on the usage intention of ERP systems. This study has limitations that circumscribe the interpretation of its findings. First, measures of all constructs were gathered at the same time and through the same instrument. Consequently, common method variance exists. Due to the cross-sectional and retrospective nature of this study, causality could only be inferred via theory: a longitudinal approach needs to be considered. Second, although we attempted to incorporate computer self-efficacy into the model, other factors may affect the technological attributes of the system. Third, although our study was conducted in the context of ERP systems, their introduction is not representative of all kinds of IT-driven change. Therefore, caution must be exercised in generalizing our findings. 6.2. Implications From a theoretical perspective, our study developed an integrated framework that provides a rich understanding of IS implementation. It also provided evidence for the value of using socio-technical systems (STS) theory in the context of new IS introduction [7]. From the practical perspective, our findings shed light on why and when managers should pay attention to the role of readiness for change in ERP system implementation. Despite the promised benefits, ERP system implementation is inherently risky because it requires enterprise-wide initiatives, and organizations often adjust slowly to complex enterprise system packages [4]. Therefore, our findings emphasized the importance of managing employees attitudes toward change. For the successful adoption of an ERP system, the management and project team should pay attention to promoting readiness for change in their users. 7. Conclusion Our study examined the role of readiness for change in the context of ERP systems implementation. The empirical findings showed how readiness for change indirectly influenced the behavioral intention to use ERP systems through PU and PEU, and was directly affected by organizational commitment and perceived personal competence.

480 K.-Y. Kwahk, J.-N. Lee / Information & Management 45 (2008) 474 481 Appendix A. The structure of the survey instrument Constructs Items Question items Readiness for change (RFC) RFC1 I look forward to changes at work RFC2 I find most change to be pleasing RFC3 Other people think that I support change RFC4 I am inclined to try new ideas RFC5 I usually support new ideas RFC6 I often suggest new approaches to things RFC7 I intend to do whatever is possible to support change Perceived ease of use (PEU) PEU1 Learning to operate the ERP system is easy PEU2 It is easy to remember how to use the ERP system PEU3 I find it easy to get the ERP system to do what I want it to do PEU4 My interaction with the ERP system is clear and understandable PEU5 It is easy to become skillful at using the ERP system PEU6 I find the ERP system easy to use Perceived usefulness (PUS) PUS1 Using the ERP system enables me to have more accurate information PUS2 Using the ERP system enhances my effectiveness in performing my task PUS3 Using the ERP system is useful for performing my task PUS4 Using the ERP system increases my productivity in performing my task PUS5 Using the ERP system enables me to access more relevant information PUS6 Using the ERP system enables me to acquire high-quality information Usage intention (UIT) UIT1 I intend to use the ERP system for performing my job as often as needed UIT2 To the extent possible, I would frequently use the ERP system in my job Organizational commitment (OCM) OCM1 I would be very happy to spend the rest of my career with this organization OCM2 I enjoy discussing my organization with people outside it OCM3 I really feel as if this organization s problems are my own OCM4 This organization has a great deal of personal meaning for me OCM5 It would be very hard for me to leave my organization right now, even if I wanted to OCM6 Too much in my life would be disrupted if I decided I wanted to leave my organization now Perceived personal competence (PPC) PPC1 In general, the work I am given to do at my organization is challenging and exciting PPC 2 The requirements of my job are demanding PPC 3 In this organization, I am encouraged to feel that the work I do makes important contributions to the larger aims of the organization PPC 4 I am usually given feedback concerning my performance on the job PPC 5 In my organization, I am allowed to participate in decisions regarding my workload and performance standards Computer self-efficacy (CSE) CSE1 I could complete a job using the information system if there is no one around to tell me what to do CSE2 I could complete a job using the information system if I have never used an information system like it before CSE3 I could complete a job using the information system if I have only the system manuals for reference CSE4 I could complete a job using the information system if I have seen someone else using it before trying it myself CSE5 I could complete a job using the information system if I could call someone for help if I got stuck CSE6 I could complete a job using the information system if someone else helps me get started CSE7 I could complete a job using the information system if I have a lot of time to complete the job for which the information system was provided CSE8 I could complete a job using the information system if I have just the built-in help facility for assistance CSE9 I could complete a job using the information system if someone shows me how to do it first CSE10 I could complete a job using the information system if I have used similar information systems like this one before in doing the job References [1] I. 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Ross, M.C. Boudreau, Learning to implement enterprise systems: an exploratory study of the dialectics of change, Journal of Management Information Systems 19 (1), 2002, pp. 17 46. [28] A.H. Segars, Assessing the unidimensionality of measurement: a paradigm and illustration within the context of information systems research, Omega 25 (1), 1997, pp. 107 121. [29] V. Venkatesh, F.D. Davis, A model of the antecedents of perceived ease of use: development and test, Decision Sciences 27 (3), 1996, pp. 451 481. [30] R. Walczuch, J. Lemmink, S. Streukens, The effect of service employees technology readiness on technology acceptance, Information and Management 44, 2007, pp. 206 215. Kee-Young Kwahk is an Associate Professor of management information systems at the School of Business IT of Kookmin University in Seoul, Korea. He received his B.A. in Business Administration from Seoul National University, his M.S. and Ph.D. in MIS from the Graduate School of Management of the Korea Advanced Institute of Science and Technology (KAIST) in Seoul. His research interests include strategic agility based on IT, IT assimilation, IT-enabled organizational change, knowledge management, and electronic commerce. His research papers appear in Behavior & Information Technology, Communications of the AIS, Decision Support Systems, Information & Management, International Journal of Information Management, Journal of Database Management, and others. He has presented several papers at AMCIS, DSI International Meeting, and HICSS. Jae-Nam Lee is an Associate Professor in the Business School of Korea University in Seoul, Korea. He was formerly on the faculty of the Department of Information Systems at the City University of Hong Kong. He holds M.S. and Ph.D. degrees in MIS from the Graduate School of Management of the Korea Advanced Institute of Science and Technology (KAIST) in Seoul. His research interests are IT outsourcing, knowledge management, e-commerce, and IT deployment and impacts on organizational performance. His published research articles appear in MIS Quarterly, Information Systems Research, Journal of MIS, Journal of the AIS, Communications of the AIS, IEEE Transactions on Engineering Management, European Journal of Information Systems, Communications of the ACM, Information & Management, and others. He has presented several papers at the ICIS, HICSS, ECIS, DSI and IRMA Conferences, and serves on the editorial boards of MIS Quarterly, Information Systems Research, and Journal of the AIS.