The Impacts of User Readiness on Perceived Value

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1 The Impacts of User Readiness on Perceived Value Chorng-Shyong Ong Department of Information Management, National Taiwan University No. 1, Sec. 4, Roosevelt Road, Taipei, Taiwan, R.O.C. Ching-Tsung Lin Department of Information Management, National Taiwan University No. 1, Sec. 4, Roosevelt Road, Taipei, Taiwan, R.O.C. ABSTRACT Implementing of cloud computing continues to lead to changes in organizations, as it is connected with underlying organizational developments reducing across functional and organizational limitations. However, the endeavor is frequently regarded as a failure, partially because potential users resist this technology and changes driven by its introduction. This study highlights technology readiness (TR) and readiness for change (RC) as a way to ease an ITdriven organizational change, incorporating cloud computing implementation. User readiness plays a significant role in easing resistance to such endeavors. Consequently, this study has tested the impacts of TR and RC on the perceived value of cloud computing resulting in its satisfaction. This study has developed a research model integrating TR and RC. This model has been then empirically verified employing data collected from users of high-tech organizations in Taiwan. Structural equation analysis adopting LISREL has given significant evidences for all proposed hypotheses. Keywords Technology readiness, readiness for change, cloud computing. INTRODUCTION The expectation-confirmation model (ECM) proposed by Bhattacherjee (2001) has been already widely received much attention and been applied to various contexts of technology implementation by academia and practitioners in recent decades. ECM model also has been considerably stable for assessing post-acceptance and predicting user behavior effectively. Numerous organizations, moreover, refer to this model in order to increase success rate as ASIST 2013, November 1-6, 2013, Montreal, Quebec, Canada. implementing information technology (IT). Furthermore, satisfaction in information system (IS) success model developed by DeLone and McLean (1992) is a significant construct affecting IS use and user satisfaction has the strongest influence on IS success (Igbaria & Tan, 1997). Success might rely on users being willing to use and being satisfied with IT more than other technical factors. An implementation of IT would not result in the strategic benefits to the implementing organization if users are not satisfied with it. In ECM model, perceived usefulness (PU) and confirmation as perceived value are viewed as two primary determinants for satisfaction and there are rich empirical results to support this argument (Roca, Chiu, & Martinez, 2006). PU is the only concept consistently affecting user intention in both adoption and post-adoption stages (Limayem & Cheung, 2008). Besides, ECM related satisfaction and perceived usefulness to the level with which user s expectations about the use of an IS are confirmed. Expectation is the baseline level against which users determine their satisfaction. User satisfaction is depended on both perceived usefulness and confirmation of expectation. Moreover, confirmation has an influence on perceived usefulness (Lin, Wu, Hsu, & Chou, 2012). More and more studies focused on ECM research have extended their scope of antecedents of ECM. Lin and Wang (2012) explore the antecedents of ECM from system perspectives in the context of e-learning system and Roca et al. (2006) examine ECM from subjective norm views in a similar background. Those works indeed enrich ECMrelated researches and the factors, thus, impacting ECM have become a significant research issue. More specifically, there are diverse antecedents arising from the interactions among user, task, environment, and technology (Kwahk & Kim, 2008). On the other hand, organizations are also continually faced with the demand to change structures, goals, processes, and technologies existed in order to fit changes driven by IT implementation to exert their strategic benefits (Kwahk & Lee, 2008). Such organizational changes have been often seen in modern organizations. Thus, IT implementation needs changes not only in technologies but also in processes and other facets. However, IT implementation 1

2 has been worried about high failure rates and difficulties in achieving their strategic benefits (Gattiker & Goodhue, 2005; Kim & Pan, 2006) although introduction of IT is up to 80% in organizations (Kwahk & Lee, 2008). An implementation of IT failure would cause a great loss to the implementing organization since it needs enterprise-wide initiatives, brings enterprise-wide changes in the whole organizations, and needs huge resource investment. The failure rate in realizing the objectives of an implementation of IT is estimated to be between 60 and 90% (Kwahk & Kim, 2008). It is widely noted that the origins of IT implementation failures are not restricted to technical issues. In view of this, why would implementing organizations face with high failure rates? This might be a reason that many researches in the past have placed much emphasis on system perspective or other technical factor but usersrelated studies are partly ignored. IT-driven organizational change would have a great impact and influence on users. Some studies have reported that one of the most major reasons for failure is resistance of users to technology or change (Jiang, Muhanna, & Klein, 2000; Parasuraman, 2000). IT implementation is usually connected with underlying change to organizational processes that cover varied users (Nah, Tan, & Teh, 2004). As indicated above, IT implementation success might rely on users being willing to use and being satisfied with it more than other technical factors. An implementation of IT would not result in the strategic benefits to the implementing organization if users are not satisfied with it. Furthermore, an implementation of IT is not only IT itself but also brings changes. Hence, users have to face with the IT and changes simultaneously as implementing IT. Developing technology readiness (TR) and readiness for change (RC) has been recommended as a critical instruction for easing resistance (Piderit, 2000). TR and RC might act to ease the possibility of resistance to technology and change in nature, increasing the potential for change endeavors to be more effective (Armenakis, Harris, & Mossholder, 1993). It has been proposed that TR and RC foster the effectiveness with which such organizational changes are implemented while various factors impact successful organizational changes, (Armenakis et al., 1993; Parasuraman, 2000). This study, therefore, would like to analyze how TR and RC could impact the perceived value of IT and thus increase user satisfaction to implement IT. This paper attempts to study the impacts of TR and RC on perceived value in the context of cloud computing implementation. To do so, this study extends ECM by integrating user readiness as antecedents of ECM. This paper incorporates two kinds of user readiness (TR and RC) as antecedents for two perceived value variables (perceived usefulness and confirmation) resulting in cloud computing satisfaction. The model is then verified employing a sample of 212 responses from 5 Taiwanese high-tech organizations that had already implemented cloud computing. This study has discovered the roles of TR and RC in cloud computing implementation and their impacts on perceived value. This paper gives several contributions to theory and practice. From the theoretical view, this study has confirmed two antecedents of perceived value and reflected on what roles they play by clarifying their impacts on the implementation of cloud computing by users. From the practical view, this study has provided managerial insights for cloud computing practitioners on the development and impacts of TR and RC in cloud computing implementation. CONCEPTUAL BACKGROUND AND MODEL HYPOTHESES Cloud Computing and Organizations Cloud computing is an emerging technology which promised to provide chances for delivering various computer applications (Truong, 2010) and attracts a great deal of attention recently. With cloud computing, organizations and users could have access to applications all over the world through any web browser. A cloud computing system could be imagined as a virtualized computer system that covers all software and applications required for organizations. Cloud computing gives organizations a fundamentally different model of operation in which the service providers are responsible for tough works in using software such as installation, upgrade, maintenance, backups, and security (Truong, 2010). Thus, the users of cloud computing services would perceive the improved reliability and cost decline in virtue of economies of scale. Organizations could save a large investment in their own servers or employ staff to take care of them. These advantages are significant for organizations, particularly for those who have limited resources. Using cloud computing services would save them a great deal of time and money and make their operations more successful. A definition for cloud computing as follows which is defined by Truong (2010): Cloud computing is a virtualized, self-maintained and managed platform that gives organizations various scalable resources on demand. Figure 1. Research framework for impacts of user readiness on perceived value. 2

3 How to leverage cloud computing in creating and supporting competitive advantage for organizations is particularly significant such as cost and time saving,reliability of the services, responsive delivery (Hayes, 2008). It does not stand alone but needs to act in conjunction with other resources to offer strategic benefits although cloud computing provides a variety of benefits to organizations. Research has indicated that the organizational performance depends on how technology is integrated with human resource (Powell & Dent-Micallef, 1997). Therefore, organizations would also have to face some potential challenges if they use cloud computing. Cloud computing introduces significant concerns about this emerging technology itself and changes driven by its implementation for users. There might be the primary reason for resistance of cloud computing implementation among users. Cloud computing of successful implementation might rely on users being willing to use and being satisfied with it more than other technical aspects. An implementation of cloud computing would not result in strategic benefits to the implementing organization if users are not satisfied with it. Thus, this paper attempts to study the impacts of users TR and RC on perceived value in the context of cloud computing implementation to bridge the gap in the literature of IS which has focused mainly on technical issues. Conceptual Background Expectation confirmation theory (ECT) is derived from marketing and argues that consumers intention to repurchase a product or service is significantly affected by their prior experience with that product or service (Oliver, 1980). Satisfactory experience is viewed as a core enabler for developing and retaining a long-term consumer relationship. ECT has been widely applied to clarify consumers satisfaction and repurchase decisions in postpurchase conditions (Lin et al., 2012). In general, it has been applied to clarify the pre-behavior (expectation) and post-behavior (perceived performance) variables rather than only pre-behavior variables (Lin, Wu, & Tsai, 2005). Consumers first shape expectations of a product or service before the purchasing decision has been made according to ECT. Consumers form their perceptions about the performance based on consumption experiences after consuming that product or service. Subsequently, an evaluation is made to compare perceived performance with the initial expectation. The perceived performance might either confirm or contradict the pre-purchase expectations. Eventually, there is a positive relationship between expectation and satisfaction. Simultaneously, the confirmation of expectation results in consumers satisfaction with the product or service. Higher satisfaction could be perceived as expectation is either high or has been confirmed. As a result, satisfied consumers have higher repurchase intention compared with those who are dissatisfied (Lin et al., 2012). Drawing on ECT which is initially evolved in the area of consumer behavior (Oliver, 1980), Bhattacherjee (2001) proposed the expectation-confirmation model (ECM) to clarify user post-adoption of information technologies. He considers that the IS users continuance decision is similar to consumers repurchase decision because both decisions follow an initial (acceptance or purchase) decision, are impacted by the initial use (of IS or product) experience, and could potentially result in an ex post facto reversal of the initial decision (Hayashi, Chen, Ryan, & Wu, 2004). However, he argues that the original ECT neglects potential changes in consumers expectations following their consumption experience, which influences later cognitive processes. Pre-acceptance expectation is typically based on the opinions of others, while post-acceptance expectation is tempered by the consumers firsthand experience and is, hence, more rational (Bhattacherjee, 2001). As ECM focuses entirely on post-acceptance variables, the impacts of any pre-acceptance variables are already captured within the confirmation and satisfaction constructs (Hayashi et al., 2004). In ECM attention is also drawn to the substantive dissimilarities between acceptance and continuance behaviors. Empirical results confirm the above arguments and suggest that continuance intention is a function of satisfaction and PU of continued IS use. User satisfaction is depended on both PU and confirmation of expectation. Besides, confirmation has an influence on PU (Lin et al., 2012). As indicated above, IS users continuance intention is mainly depended on satisfaction and PU. The model mirrors current thinking that PU is the only concept consistently affecting user intention in both adoption and post-adoption stages (Limayem & Cheung, 2008). Besides, the model related satisfaction and PU to the level with which user s expectations about the use of an IS are confirmed. Expectation is the baseline level against which users determine their satisfaction. It is believed that satisfaction with the use of an IS would strengthen users intention to continue using it. The confirmation of users evaluations of their use of an IS would also influence their perception about its usefulness, as well as their satisfaction with it and this, in turn, would affect their satisfaction and intention to continue using the IS. Model Hypotheses The Relationships of Perceived Usefulness, Confirmation, and Satisfaction Satisfaction is defined as users feelings about prior cloud computing use. PU is defined as users perception of the expected benefits of cloud computing use. Confirmation is defined as users perception of the coincidence between expectation of cloud computing and its actual performance (Bhattacherjee, 2001). The level of confirmed initial expectations is supposed to play a critical role in the establishment of beliefs about usefulness (Bhattacherjee, 2001). A user with high initial 3

4 usefulness perceptions would tend to use a new IT. Users might experience cognitive inconsistency or psychological tension if their usefulness perceptions are disconfirmed during actual usage. A rational user might attempt to remedy this perceived inconsistency by distorting or modifying his/her initial perceptions if those initial expectations are disconfirmed through actual use, that is, to be more consistent with reality. As a result, confirmation would tend to encourage user s PU and disconfirmation would lower such perceptions. H1. Users perceived confirmation positively affects PU of cloud computing Users PU of technology has a positive impact on their satisfaction with technology by working as a criterion for reference against confirmation judgements (Thong, Hong, & Tam, 2006). PU is expected to be the most salient expectation which affects user satisfaction (Bhattacherjee, 2001), that is, PU is a significant determinant of user satisfaction. Hence: H2. User PU positively affects their satisfaction of cloud computing Confirmation is positively related to satisfaction with technology use because it implies realization of the expected benefits through users usage experiences with technology, while disconfirmation indicates failure to accomplish expectation (Bhattacherjee, 2001). That is, users confirmation level and expectations positively affect their satisfaction with a technology. Users tend to be more satisfied when their expectations are confirmed; conversely, they tend to be dissatisfied when the perceived performance could not meet prior expectations. The confirmationsatisfaction relationship is supported through a number of IS studies and a great deal of industry reports provide strong support for this relationship (Bhattacherjee, 2001; Sorebo & Eikebrokk, 2008). Thus, the following hypothesis is proposed: H3. Users perceived confirmation positively affects their satisfaction of cloud computing Impacts of Technology Readiness on Perceived Usefulness and Confirmation Enterprises implementing cloud computing require to comprehend their users readiness to use cloud computing because user personality traits tend to affect an individual s adoption of technology (Lin & Chang, 2011). Also, previous study has shown the demand for a better understanding of critical determinants and suggested that acceptance model should be integrated into a broader model with variables related to user dimensions (Kwahk & Lee, 2008). Earlier studies on personality traits toward technology use have discovered factors such as selfefficacy, computer anxiety, and personal innovativeness. Those factors do not adequately mirror user personality traits of technology adoption since users could simultaneously hold favourable and unfavourable 4 perspectives of adoption (Parasuraman, 2000). Within ECM studies, a need occurs to integrate user traits that mirror users readiness to adopt technologies, incorporating the involution of opposite positive and negative feelings about such technology acceptance. TR (Parasuraman, 2000) adequately mirrors this involution of user traits, and, for extension of the ECM, forms a more complete construct than existing personality traits. Verhoef et al. (2009) also implies technology adoption study should incorporate TR to examine how it might affect technology use. TR refers to user s propensity to embrace and adopt new technologies for achieving objectives in home life and at work (Parasuraman, 2000). The TR construct could be regarded as an overall state of mind originating from a gestalt of mental enablers and inhibitors that jointly influence a user s predisposition toward technologies (Parasuraman, 2000). TR mirrors a set of beliefs about technology but is not an indicator of a user s ability in using it (Walczuch, Lemmink, & Streukens, 2007). The construct could be classified into four dimensions: optimism, innovativeness, discomfort, and insecurity. Optimism and innovativeness are positive enablers of TR, fostering users to use technology and to hold a positive attitude toward it. By contrast, discomfort and insecurity are negative inhibitors, making users resistant to use technology. Including TR into ECM is viewed as a user trait. Davis (1989) proposed that perceptions completely mediate the impacts of external variables such as individual differences on technology use. Following the foundation of this research stream, this study argues that TR has a direct influence on perceived value (PU and confirmation). Prior studies have revealed a positive relationship between TR and PU (Lin, Shih, & Sher, 2007; Walczuch et al., 2007). Of the specific TR dimensions, optimism, a positive enabler of TR, relates to a positive perspective of technology and a belief that technology provides user increased control, flexibility, and efficiency. Hence, optimists perceive a given technology as being more useful since they concern less about possible negative results (Scheier & Carver, 1992; Walczuch et al., 2007). Scholars have also discovered that early adopters, who are more innovative individuals, have less complicated belief sets about technology (Karahanna, Straub, & Chervany, 1999). On the other hand, Chen, Gillenson, and Sherell (2002) have confirmed that some critical barriers of technology adoption are as a result of security and privacy concerns. High personal insecurity and discomfort with technology result in lower PU of a specific technology. Because TR originates from the interplay of positive enablers and negative inhibitors, this study argues that users with higher TR propensities would be more likely to perceive the usefulness of cloud computing, leading to the following hypothesis: H4. TR is positively related to PU of cloud computing For a user to use a technology, his/her personality trait contains the basic concept and attempt to the use of a technology. When his/her TR gets higher, representing

5 he/she has a stronger interest for a technology and then his/her use intention would be greater (Lin et al., 2007). A user s concept for a technology would be better if his/her personality trait gets more positive. Previous research has shown that personality trait influenced confirmation significantly (Hsu, Yen, Chiu, & Chang, 2006). Therefore, this study argues that the higher TR of a user, his /her recognition of perceived confirmation would be greater. H5. TR is positively related to perceived confirmation of cloud computing Impacts of Readiness for Change on Perceived Usefulness and Confirmation Organizations are continually implementing cloud computing to maintain their competitive advantage although cloud computing is complex. In the meanwhile, organizations are also continually faced with the demand to change structures, goals, processes, and technologies existed in order to fit changes for cloud computing implementation to exert their strategic benefits (Kwahk & Lee, 2008). RC is widely acknowledged as a critical factor in successful organizational change and plays a crucial role in relieving resistance to change and thus in lowering the failure rate (Eby, Adams, Russell, & Gaby, 2000). Self and Schraeder (2009) propose that the first step in the process of implementing a change initiative is creating RC. The prior empirical results support the perspective that there is a positive correlation between the level of RC and the successful implementation of change (Todnem, 2007). Effective cloud computing implementation needs enterprise-wide initiatives, bringing large-scale change usually requiring huge investment of resource. RC is mirrored in the attitude toward organizational change of organizational users. This attitude could determine whether a user supports or resists a change. Users are not likely to resist change if they hold a positive attitude toward it and are ready for it. RC refers to the extent to which organizational users hold positive perspectives about the demand for organizational change, as well as the extent to which they believe that such changes are likely to have positive meaning for themselves and the organization (Armenakis et al., 1993). Therefore, an organizational user s attitude toward change could play a critical role in determining whether the individual selects to support or resist a change (Kirton & Mulligan, 1973). Developing the belief that organizational change is needed requires understanding that there is a gap between the current and desired goal states. Generally speaking, cloud computing are introduced into an enterprise to promote its organizational effectiveness and fulfil any performance gap. Organizational users who have favourable perceptions of organizational transition and are ready for it would be more likely to join positively in the change and look for improved performance after its implementation. Users seem to be more willing to experience a technology when they are positive about and ready for organizational change. They believe that they might miss benefits if they do not 5 experience the technology (Walczuch et al., 2007). Besides, they have less uncertainty about the technical changes when told about the cloud computing and their impact (Dong, 2001). Hence, users would find the cloud computing more useful when they are ready for change. Thus, this study proposes the hypothesis: H6. RC is positively related to PU of cloud computing Earlier studies have paid attention to individual traits, such as innovativeness or TR, to report the individual s attitude toward change (Chandrashekaran & Sinha, 1995). RC is an attitude toward change (Bouckenooghe, Devos & Van Den Broeck, 2009). Walczuch et al. (2007) have exhibited that individuals ready for change are perceived to have a more successful transition into a technology without much cognitive endeavor. Therefore, this study argues that the higher RC of a user, his /her recognition of perceived confirmation would be greater. H7. RC is positively related to perceived confirmation of cloud computing Finally, all research hypotheses are exhibited in Figure 1. METHODOLOGY Measurement Development The items utilized to assess the constructs in this survey are adopted and revised, as needed, from prior researches. Each survey item is continually discussed with and reviewed by two IS scholars to examine its face validity. All research variables are assessed adopting multi-item scales, as exhibited in Table 1. Measures of TR are derived from a measurement evolved by Parasuraman (2000), which included of 36items at first. From these, this survey chooses 16 that showed high explanatory power, are not reverse coded on account of their potential negative influence on unidimensionality, and reveals adequate TR of users in terms of content (Herche & Engelland, 1996). RC is assessed with 13 items picked from the measurement evolved by Bouckenooghe et al. (2009), and reverse coded items are excluded. Thus, multidimensionality of those two constructs is not a concern in this study. PU is assessed utilizing 6 items from Davis s (1989) instrument. Confirmation and satisfaction are each assessed by 3 items, which are derived from the verified instrument inventory formerly and then revised to fit the context of the present study. Items for assessing all constructs are revised by changing the target information systems into cloud computing to suit this study conditions (Davis, 1989). All question items are assessed utilizing a five-point Likert-type scale with anchors ranging from strongly disagree to strongly agree.

6 Table 2. Respondent characteristics (N=212). Table 1. The structure of survey instrument. Data Collection and Sample Characteristics A field survey utilizing a convenience sample is used to verify this research model. The unit of analysis is the user who worked for an enterprise that had already implemented cloud computing. Five of the directors of Taiwanese hightech companies agree to support this survey, we ask the directors to choose the divisions that had recently completed cloud computing implementation. This study distributes a total of 500 questionnaires to five enterprises in Taiwan through the directors. The data are gathered from users who worked with cloud computing to conduct their tasks. Of the 500 questionnaires distributed, 334 are returned. After being screened for usability and reliability, 212 responses are discovered to be complete and usable, exhibiting a response rate of about 42%. Table 2 provides the respondent s demographics. On average, the respondents are 28.7 years old. The respondents have about 3.2 years of work experience, and most have worked for less than 5 years. Because this survey adopts a convenience sample, it is not likely to examine non-response bias by comparing the respondents and non-respondents. Thus, non-response bias is measured by comparing the responses of early and late responses, defined as the first and last 40 questionnaires received (Karahanna et al., 1999). The average ages for the early and late responses are 28.6 and 28.1, respectively, and these are not significant. No significant differences in tenure are noted between the two groups (early responses=2.7; late responses=2.9 years). There are no significant differences in the critical variables, implying that non-response bias is insignificant. DATA ANALYSIS AND RESULTS Scale Validation This study is performed data analysis according to a twostage methodology (Anderson & Gerbing, 1988) utilizing LISREL to avoid the possible interaction between measurement and structural equation models. The first step in the data analysis is to examine the convergent and discriminant validity of the constructs. This study verifies the structural model in the second step. This study first requires to examine the unidimensionality of each construct to examine convergent validity. The measurement model contains the relationships between the observed variables (items) and the latent constructs they assess. According to the suggested methodological procedures (Anderson & Gerbing, 1988; Gefen, Straub, & Boudreau, 2000), this analysis starts to modify the measurement model by dropping items, one at a time, items which shared a high extent of residual variance with other items. After removing one question (TR9), the measurement model has overall good fit and then the structural equation model could be analyzed. Further analysis is carried out to measure the psychometric properties of the scale. The construct validity of the survey instrument confirms the degree to which the operationalization of a construct really measures what it is designed to measure. Convergent validity is then examined with three measures. First, standardized path loadings, which are indicators of the extent of connection between the underlying latent factor and each item, should exceed 0.7 and statistically significant (Gefen et al., 2000). Second, the composite reliability (CR) and the Cronbach s α for each construct must be greater than 0.7 (Nunnally & Bernstein, 1994). Third, the average variance extracted (AVE) for each factor should be larger than 0.5 (Fornell & Larcker, 1981). As represented in Table 3, the standardized path loadings are all significant (t-value>1.96) and larger than 0.7. The composite reliability and Cronbach s α for all constructs are greater than 0.7. The average variance extracted for each factor is larger than 0.5. Thus, all constructs in this study have adequate convergent validity (Anderson & Gerbing, 1988; Kumar, Scheer, & Steenkamp, 1998). 6

7 Table 3. Results of convergent validity testing. Next, this study examines discriminant validity by comparing the square root of AVE for each construct with the correlations between that construct and other constructs (Fornell & Larcker, 1981). As exhibited in Table 4, the square root of AVE for each construct is greater than the correlations between that construct and other constructs (Bagozzi & Warshaw, 1990). Thus, discriminant validity is verified. Hypotheses Tests This study examines the hypotheses utilizing the structural model of LISREL. The overall effectiveness of the structural model is assessed utilizing six common model fit measures: normedχ² (χ² to degree of freedom), goodnessof-fit index (GFI), normalized fit index (NFI), nonnormalized fit index (NNFI), comparative fit index (CFI), and root mean square error of approximation (RMSEA). The normed χ² is 2.691, which is satisfactory, being less than the desired maximum cut-off of 5.0 (Bhattacherjee, 2001). RMSEA is 0.067, revealing a good fit, being less than the desired maximum cut-off of 0.08 (Browne & Cudeck, 1993). GFI is and adjusted goodness-of-fit index (AGFI) was 0.873, both of which are larger than the minimum threshold of 0.8 (Hair, Anderson, Tatham, & Black, 1998). The other fit indices are all satisfactory: CFI=0.961, NFI=0.920, and NNFI=0.942 are all greater than 0.9 (Bhattacherjee, 2001). These results reveal that the structural model fitted the data appropriately (Hair et al., 1998). Table 4. Results of discriminant validity testing. Figure 2. Model testing results. The structural model represents the relationships among the theoretical constructs. Figure 2 exhibits the standardized LISREL path coefficients. Both TR and RC are significant related to PU and confirmation, and explained 25.4% of variance in confirmation: TR (path coefficient=0.494, ρ<0.05), RC (path coefficient=0.527, ρ<0.05) and 31.8% of variance in PU: TR (path coefficient=0.413, ρ<0.05), RC (path coefficient=0.441, ρ<0.01), and confirmation (path coefficient=0.365, ρ<0.01). Next, two variables (PU, and confirmation) are significantly related to satisfaction and explained 38.7% of variance in satisfaction: PU (path coefficient=0.358, ρ<0.05), confirmation (path coefficient=0.282, ρ<0.05). Hence, all hypotheses are supported. CONCLUSIONS AND DISCUSSION It has been reported that more than two thirds of IT implementation leads to failure (Kwahk & Kim, 2008). One of the significant failure reasons is resistance to technology and change from employees or potential users. This study has tested the impacts of TR and RC on IT implementation by emphasizing that user readiness is a way for easing resistance to technology and change in IT-driven organizational change such as cloud computing implementation. This study is based on ECM developed by Bhattacherjee (2001) as research framework and extends TR and RC as two major antecedents. The establishment of this research framework is to explore the impacts of personality trait and attitude on perceived value while users face cloud computing. This framework analyzes those constructs such as TR, RC, PU, confirmation, and satisfaction to discover the influences of TR and RC on PU and confirmation as implementing cloud computing. In the present study, TR and RC extended as antecedents are verified significant positively related with PU and confirmation and TR and RC have an indirect impact on satisfaction. That is, satisfaction of a user adopting cloud computing would also be improved once personality trait and attitude of user for technology and change are encouraged. Research Contributions Throughout all the empirical results completed by this study, there are several research contributions as follows: 7

8 The practitioners in the past investigated the factors of ECM usually neglect the antecedents of TR and RC which are much more significant constructs. A successful IT implementation might rely on users being willing to use and being satisfied with it more than other technical factors, however, the previous studies often ignore these two concepts or only focus one of two constructs. Armenakis et al. (1993) and Parasuraman (2000) propose that user readiness plays a critical role in IT implementations. Thus, this study simultaneously integrates TR and RC into the ECM to explore their impacts. As the above mentioned, an implementation of IT would not result in the expected strategic benefits to the implementing organization successfully if users are not satisfied with it. Nevertheless, the prior studies often explore IT implementations from technical factors rather than user perspectives. This study completely examines IT implementations from user views. Derived from the empirical results of this study, the findings exhibit how both TR and RC indirectly affect satisfaction to use IT through PU and confirmation. It offers managers a way for easing resistance to technology and change in IT-driven organizational change such as cloud computing implementation. This study has once again verified the assumptions in ECM developed by Bhattacherjee (2001) except continuance intention. Both PU and confirmation are significantly related to satisfaction, and confirmation is positively related to PU as well. Theoretical and Practical Implications Throughout all the empirical results completed by this study, there are several research implications as follows: This paper gives several implications to theory and practice. From a theoretical view, this study develops an integrated framework that offers a rich understanding of cloud computing implementation. It also offers evidence for the value of using IS theory in the context of cloud computing introduction (Bostrom & Heinen, 1977). From the practical view, the empirical results explain why and when managers should pay more attention to the role of TR and RC in cloud computing implementation. Cloud computing implementation is risky in essence because it needs enterprise-wide initiatives, and organizations continually adapt to complex IT (Amoako-Gyampah & Salam, 2004) despite the strategic benefits. Hence, the empirical results highlight the significance of encouraging users traits toward a technology and managing their attitudes toward change. For the successful implementation of IT, the management should pay more much attention to fostering TR and RC in their users. In short, this paper contributes toward theoretical advancements on the issue of user readiness for ECM and the empirical results provide organizations practical insights for managing user readiness for technology and change in IT implementations. Limitations and Future Research This paper has limitations that restrain the explanation of the empirical results. First, assessments of all constructs were collected at the same point in time and through the same scale. Therefore, the potential of common method variance for some of the result occurs. Besides, causality could only be concluded through theory, and so a longitudinal approach requires to be taken into account in future research as a result of the cross-sectional and retrospective essence of this research. Second, other factors might influence the perceived value or satisfaction of cloud computing although this study only focused on integrating TR and RC into research model. 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