The role of readiness for change in ERP implementation: Theoretical basesand 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 Koreab Korea University Business School, Anam-dong 5 ga, Seongbuk-gu, Seoul 136-701, Republic of Korea

Information & Management 45 (2008) 474–481

A R T I C L E I N F O

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

A B S T R A C T

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.

� 2008 Elsevier B.V. All rights reserved.

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1. Introduction

Organizations are continually faced with the need to changetheir structures, objectives, processes, and technologies. Thus, theymust be able to make changes to sustain their competitiveadvantage. Many have adopted ERP systems to help do this. Studieshave reported that ERP adoption is about 80% in Fortune 500companies [23].

However, despite its popularity, ERP implementations havebeen plagued with high failure rates and inability to realizepromised benefits. The failure rate has been estimated as 60–90%.Some prior studies indicated that a major reason for failure was theresistance of the user to change [21]. ERP systems are oftenassociated with fundamental change to organizational processesthat involve different stakeholders [24]. Therefore, though ERPsystems could be implemented successfully from a technicalperspective, success may depend on employees being willing touse the delivered system.

Creating readiness for change has been proposed as a majorprescription 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.

0378-7206/$ – see front matter � 2008 Elsevier B.V. All rights reserved.

doi:10.1016/j.im.2008.07.002

how readiness for change could affect the perceived value of thesystem and thus increase the intention to use ERP.

We explored the role of readiness for change in ERP implementa-tion and its impact on usage intention. To do so, we defined a modelof readiness by incorporating TAM and TPB. We included twoantecedents 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 toERP usage intention. The model was then tested using a sample of283 responses from 72 Korean organizations that had alreadyimplemented enterprise-wide ERP systems.

2. Theoretical background

2.1. Underlying theories

The IS literature has become a stage for social psychology-basedand attitude-based models predicting usage and acceptance. Butalthough both PU and PEU are important predictors of use, they donot explain individual attitude and behavior. Prior research hasindicated the need for a better understanding of key determinantsand suggested that TAM should be integrated into a broader modelwith variables related to human and organizational dimensions.

On the other hand, it has been argued that TPB is difficult toapply across diverse user contexts [22]. TPB accounts forconditions where individuals do not have complete control over

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 asubjective norm as a determinant of IS usage is unclear; someresearch has not found a significant relationship between it andusage intention. In contrast, perceived behavioral control appar-ently does play a critical role in understanding people’s PEU inperforming a behavior of interest. Therefore, the stronger theindividual feels about his or her ability to execute the behavior, themore he or she will utilize available resources and opportunities toexecute the behavior. Subsequently, individuals will thus gainconfidence from perceived higher behavioral control [6].

To overcome the problems and enhance the understandability ofIS usage and IS acceptance behaviors, we proposed a model thatwould be relevant to enterprise-wide initiatives, by identifying notonly 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 attitudestoward behavior – readiness for change – the extent to whichorganizational members hold positive views about the need fororganizational change, as well as their belief that changes are likelyto have positive implications for them and the organization. Thisattitude can determine whether an individual supports or resists achange. Of course, a change may give satisfaction to some and notto others.

Organizational commitment (the relative strength of an indivi-dual’s identification with, and involvement in, a particularorganization) and perceived personal competence (the degree ofthe individual’s feelings of competence in the work role) play keyroles in employees’ acceptance of change.

3. Research model and hypotheses

To explore how readiness for change affected an individual’sreaction to implementation of an ERP system, we developed amodel that considered its psychological consequences andantecedents. This is shown in Fig. 1.

3.1. The importance of readiness for change

Readiness for change plays a crucial role in mitigatingresistance to change and thus in reducing the failure rate [14].Effective ERP system implementation requires enterprise-wide

Fig. 1. Researc

initiatives, bringing large-scale change generally requiring largeinvestment of resources; a failure results in significant loss.

Organizational change should be a continuous process [9].Change initiatives can be characterized as push systems wheresenior managers and experts cause change. However, a pull systemmay be needed for a successful effort; in this, transitioning to newtechnologies is forced by the people who will manage them. Thepull system can be achieved by focusing on user readiness forchange and identifying the circumstance under which users arereceptive to it.

3.2. The effects of readiness for change

Creating the belief that organizational change is neededrequires agreement that there is a gap between the current anddesired end states. In general, an ERP system is introduced into acompany to improve its organizational effectiveness and fill anyperformance gap. Organizational members who have favorableperceptions of organizational transformation and are ready for itwill be more likely to participate positively in the change andexpect enhanced performance after its implementation. A priorstudy of ERP implementation [3] suggested that a push for changefrom top management was likely to produce positive perception.When employees are positive about and ready for organizationalchange, they appear to be more willing to try out a system. Theythink that they might miss benefits if they do not try out thesystem [30]. Also, when informed about the ERP system and itsimpact they have less uncertainty about the technical changes[12]. Thus, when employees are ready for change, they will find thesystems more useful. Therefore, we proposed the hypothesis:

H1. Readiness for change has a positive effect on the perceivedusefulness of an ERP system.

Previous studies have paid attention to individual traits, such asinnovativeness or technology readiness, to describe the indivi-dual’s attitude toward change [8]. Parasuraman [25] has definedtechnology readiness as a state of mind that affected ‘‘people’spropensity to embrace and use new technologies for accomplish-ing goals. . .’’ He argued that this related to the degree of readinessthat the individual felt in using a technology. The technologicallyready individual was more likely to see it as easy to use. Similarly,Walczuch et al. showed that more innovative individuals wereperceived to have a smoother transition into a new technologywithout much cognitive effort. Therefore, we expected thatindividuals ready for change believed they could easily learnhow to use the system with little effort. This is particularly true for

h model.

Table 1Respondent 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) 474–481476

ERP systems, because users must overcome knowledge barriersand rid themselves of what was the previous operation [27].Therefore we hypothesized:

H2. Readiness for change has a positive effect on the perceivedease 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. Ahigh level of perceived competence resulting from prior workingexperiences results in self-confidence and employees tend tobelieve that they can execute their job well when performingslightly different tasks. Thus, we posited:

H3. Perceived personal competence has a positive effect on readi-ness for change.

Individuals with strong organizational commitment should bemore willing to accept organizational change if it does not alterbasic values and goals and is seen as beneficial; they are also thenwilling to expend more effort on behalf of the organization. Thissuggests that individuals’ commitment to the organization hasvarying effects on their readiness for change. Thus we made thefollowing hypothesis:

H4. Organizational commitment has a positive effect on readinessfor change.

3.4. Perceived technological attributes and usage intention

A system must be useful and easy to learn. Consequently, weadded the following two hypotheses for completeness:

H5. Perceived usefulness has a positive effect on the usage inten-tion of an ERP system.

H6. Perceived ease of use has a positive effect on the usageintention of an ERP system.

3.5. Control variable

Among the determinants of both PU and PEU, computer self-

efficacy (the person’s belief that he or she can perform a job and isconfident of this) has been proposed as an important antecedent[29]. Many, for example [18,19] found experimental evidencesupporting this relationship.

4. Research methodology

A field study using a convenience sample was employed to testthe model. The unit of analysis was the individual who worked foran organization that had already implemented an enterprise-wideERP system.

4.1. Instrument development

The items used to measure the constructs in our study wereadopted and modified, as needed, from previous studies. Eachsurvey item was first discussed with and scrutinized by two ISresearchers to check its face validity. All research variables weremeasured using multi-item scales, as shown in Appendix A.

Measures of readiness for change were based on an instrumentdeveloped by Dunham et al. [13], which originally consisted of 18

items. From these, we selected seven that revealed highexplanatory power, were not reverse coded because of theirpotential negative effect on unidimensionality, and representedappropriate readiness for change of individuals in terms of content[17]. Organizational commitment was measured with six itemsselected from the instrument developed by Allen and Meyer [2].This instrument originally had 24 items that represented threesubgroups (affective, continuance, and normative commitment).As the multidimensionality of these three was not a concern in ourresearch and because reverse coded items were excluded, wedecided to reduce the items to six by selecting two from eachsubgroup while maintaining their original meaning. Perceivedpersonal competence was measured using five items from Allenand Meyer’s measurement. PU and PEU were each measured by sixitems, which were adopted from the previously validatedmeasurement inventory and then modified to suit the context ofthe present research. Two items to measure usage intention werebased on Davis: items for measuring PU, PEU, and usage intentionwere modified by changing the target IS into ‘‘the ERP system’’ toreflect our research context [11]. Finally, to measure thepsychometric properties of computer self-efficacy as a controlvariable, we adopted 10 items from an instrument developed byCompeau and Higgins [10]. All question items were measuredusing 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 ourstudy. We asked the vendor to select its client companies that hadrecently finished ERP implementation and had implemented atleast more than two ERP modules. We distributed a total of 350questionnaires to 72 organizations in Korea through the vendor.The data were collected from employees who worked with ERPsystems to perform their tasks. Of the 350 questionnairesdistributed, 312 were returned. After being screened for usability

Table 2Analysis of non-response biases

Measures Early

respondents

(n = 40)

Late

respondents

(n = 40)

Significance

(P)

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

K.-Y. Kwahk, J.-N. Lee / Information & Management 45 (2008) 474–481 477

and reliability, 283 responses were found to be complete andusable, representing a response rate of about 81%. Table 1 presentsthe respondent’s demographics. On average, the respondents were29.2 years old. The respondents had about 4.7 years of workexperience, and most had worked for less than 7 years.

Because this study used a convenience sample, it was notpossible to test non-response bias by comparing the respondentsand non-respondents. Instead, non-response bias was assessed bycomparing the responses of early and late respondents, defined asthe first and last 40 questionnaires received [20]. The average agesfor the early and late respondents were 28.9 and 28.6, respectively,and these were not significant. No significant differences in tenure

Table 3Convergent validity test

Constructs Items Factor loadi

Readiness for change (RFC) RFC2 0.76

RFC3 0.79

RFC4 0.88

RFC5 0.88

RFC6 0.86

RFC7 0.85

Perceived ease of use (PEU) PEU1 0.84

PEU2 0.84

PEU3 0.85

PEU4 0.86

PEU5 0.90

PEU6 0.87

Perceived usefulness (PUS) PUS1 0.85

PUS2 0.91

PUS3 0.86

PUS4 0.87

PUS5 0.88

PUS6 0.86

Usage intention (UIT) UIT1 0.90

UIT2 0.86

Organizational commitment (OCM) OCM1 0.82

OCM2 0.76

OCM3 0.82

OCM4 0.88

OCM6 0.67

Perceived personal competence (PPC) PPC1 0.78

PPC2 0.76

PPC3 0.81

PPC4 0.70

PPC5 0.78

Computer self-efficacy (CSE) CSE1 0.75

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

were observed between the two groups (first group = 5.3; lastgroup = 5.7 years). There were no significant differences in themajor variables, as can be seen in Table 2, suggesting that non-response bias was low.

5. Data analysis and results

LISREL was used for data analysis. Our objective was to test theproposed factors that lead to usage intention in a holisticframework. Data analysis was carried out in accordance with atwo-step methodology [5] to avoid the possible interactionbetween measurement and structural equation models. Thestructural model describes the relationships among the theoreticalconstructs, while the measurement model consists of the relation-ships between the observed variables (items) and the latentconstructs they measure. According to this procedure, after themodel 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 usingLISREL 8.7. The overall effectiveness of the measurement modelwas examined using seven common model fit measures: normed

ng Composite reliability Average variance extracted

0.93 0.70

0.94 0.74

0.95 0.76

0.88 0.78

0.89 0.63

0.88 0.59

0.98 0.70

K.-Y. Kwahk, J.-N. Lee / Information & Management 45 (2008) 474–481478

x2 (x2 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), androot mean square error of approximation (RMSEA). Themeasurement model in the CFA was revised by removing itemsthat had large standardized residuals with other items, one at atime. After dropping two items (RFC1 and OCM5), the measure-ment model exhibited overall good fit. The normed x2 was 1.82,which was satisfactory, being below the maximum desired cut-off of 3.0 [15]. RMSEA was 0.05, indicating a good fit, below themaximum desired cut-off of 0.06. Also the RMR was 0.04, lowerthan the desired maximum cut-off of 0.05. GFI was 0.81, whichwas above the recommended threshold of 0.8. The other fitindices were all satisfactory: CFI = 0.99, NFI = 0.97 andNNFI = 0.99, suggesting that the measurement model fit thedata adequately [16].

Further analysis was conducted to assess the psychometricproperties of the scales. The construct validity of the researchinstrument determines the extent to which the operationalizationof a construct actually measures what it is designed to measure.Convergent validity was assessed using three measures, as shownin Table 3: factor loading, composite construct reliability, andaverage variance extracted.

First, in determining the appropriate minimum factor loadingsrequired for the inclusion of an item within a construct, factorloadings greater than 0.70 were considered highly significant. All ofthe factor loadings of the items in the measurement model weregreater than 0.70, except for OCM6. Each item loaded significantly

Table 4Discriminant validity test

Constructs Mean (S.D.) RFC PEU

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

UIT 5.03 (1.06) 0.68 0.70

OCM 4.67 (1.04) 0.50 0.49

PPC 4.66 (0.89) 0.54 0.48

CSE 4.81 (0.98) 0.44 0.45

Note: Leading diagonals represent the square root of the average variance extracted betw

among constructs.

Fig. 2. Model tes

(p < 0.01 in all cases) on its underlying construct. Second, thecomposite construct reliabilities were within the commonlyaccepted range greater than 0.70. Finally, the average variancesextracted were all above the recommended level of 0.50. Therefore,all constructs had adequate convergent validity.

To confirm discriminant validity, the average variance sharedbetween the construct and its indicators should be larger than thevariance shared between the construct and other constructs. Asshown in Table 4, all constructs share more variances with theirindicators than with other constructs. The discriminant validity ofthe constructs was further validated by fixing the correlationbetween various constructs at 1.0, and then re-estimating themodified model [28]. Significant differences in the x2 statistic ofthe constrained and unconstrained models imply high discrimi-nant validity. From the constrained testing, the x2 statistic of theunconstrained model was significantly better than any possibleconstrained models, thereby providing positive support for thediscriminant validity.

5.2. Structural model

The structural model was examined using the cleansedmeasurement model. The overall fit with the data was evaluatedby the same set of fit indices used in the measurement model. Thenormed x2 was 1.84, which is within the recommended level of 3.0,while the structural model exhibited a fit value satisfying thecommonly recommended threshold for the respective indices, thusproviding evidence of a good model: GFI = 0.81, NFI = 0.97,

PUS UIT OCM PPC CSE

0.87

0.81 0.88

0.49 0.50 0.79

0.50 0.51 0.55 0.77

0.41 0.52 0.47 0.69 0.84

een the constructs and their measures, while off diagonal entries are the correlations

ting 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. Theseresults suggested that the structural model fit the data adequately.

The standardized LISREL path coefficients and overall fit indicesare shown in Fig. 2. Two variables (readiness for change andcomputer self-efficacy) were significantly related to PU andexplained 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 PEUand 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 sig-nificantly related to readiness for change and explained 37% of itsvariance: organizational commitment (b = 0.30, p < 0.01) andperceived personal competence (b = 0.39, p < 0.01). Finally, twovariables (PU and PEU) were significantly related to usageintention 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 inturn influenced the PU and the PEU of the system. It was alsoobserved that readiness for change played an important role inexplaining two attributes by identifying the increased variances:readiness for change and computer self-efficacy accounted for 57% ofthe variance in PU. The addition of readiness for change contributedto an increase in the explained variance of 38% over and above thevariance explained by computer self-efficacy. Readiness for change

and computer self-efficacy together explained 43% of the variance inthe PEU, while computer self-efficacy alone explained 23% of thevariance in the PEU. The addition of readiness for change increasedthe explained variance by 21%. We also examined how readinessfor change could be formed. One was through organizationalcommitment; another was through perceived competence. More-over, PU and PEU had a significant positive effect on the usageintention of ERP systems.

This study has limitations that circumscribe the interpretationof its findings. First, measures of all constructs were gathered at thesame time and through the same instrument. Consequently,common method variance exists. Due to the cross-sectional andretrospective nature of this study, causality could only be inferredvia theory: a longitudinal approach needs to be considered. Second,although we attempted to incorporate computer self-efficacy intothe model, other factors may affect the technological attributes ofthe system. Third, although our study was conducted in the contextof ERP systems, their introduction is not representative of all kindsof IT-driven change. Therefore, caution must be exercised ingeneralizing our findings.

6.2. Implications

From a theoretical perspective, our study developed anintegrated framework that provides a rich understanding of ISimplementation. It also provided evidence for the value of usingsocio-technical systems (STS) theory in the context of new ISintroduction [7].

From the practical perspective, our findings shed light onwhy and when managers should pay attention to the role ofreadiness for change in ERP system implementation. Despite thepromised benefits, ERP system implementation is inherentlyrisky because it requires enterprise-wide initiatives, andorganizations often adjust slowly to complex enterprise systempackages [4]. Therefore, our findings emphasized the impor-tance of managing employees’ attitudes toward change. For thesuccessful adoption of an ERP system, the management andproject team should pay attention to promoting readiness forchange in their users.

7. Conclusion

Our study examined the role of readiness for change in thecontext of ERP systems implementation. The empirical findingsshowed how readiness for change indirectly influenced thebehavioral intention to use ERP systems through PU and PEU,and was directly affected by organizational commitment andperceived personal competence.

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

K.-Y. Kwahk, J.-N. Lee / Information & Management 45 (2008) 474–481480

References

[1] I. Ajzen, The theory of planned behavior, Organizational Behavior and HumanDecision Processes 50, 1991, pp. 179–211.

[2] N.J. Allen, J.P. Meyer, The measurement and antecedents of affective, continuanceand normative commitment to the organization, Journal of Occupational Psy-chology 63, 1990, pp. 1–18.

[3] K. Amoako-Gyampha, Perceived usefulness, user involvement and behaviouralintention: an empirical study of ERP implementation, Computers in HumanBehavior 23, 2007, pp. 1232–1248.

[4] K. Amoako-Gyampha, A.F. Salam, An extension of the technology acceptancemodel in an ERP implementation environment, Information and Management 41,2004, pp. 731–745.

[5] J.C. Anderson, D.W. Gerbing, Structural equation modeling in practice: a reviewand recommended two-step approach, Psychological Bulletin 103 (3), 1988, pp.411–423.

[6] A. Bandura, Self-Efficacy: The Exercise of Control, W.H. Freeman, New York, 1997.[7] R.P. Bostrom, J.S. Heinen, MIS problems and failures: a socio-technical perspec-

tive. Part I. The causes, MIS Quarterly 1 (3), 1977, pp. 17–32.[8] M. Chandrashekaran, R.K. Sinha, Isolating the determinants of innovativeness: a

split-population tobit (SPOT) duration model of timing and volume of first andrepeat purchase, Journal of Marketing Research 32, 1995, pp. 444–456.

[9] C. Clegg, S. Walsh, Change management: time for a change, European Journal ofWork and Organizational Psychology 13 (2), 2004, pp. 217–239.

[10] D.R. Compeau, C.A. Higgins, Computer self-efficacy: development of a measureand initial test, MIS Quarterly 19 (2), 1995, pp. 189–211.

K.-Y. Kwahk, J.-N. Lee / Information & Management 45 (2008) 474–481 481

[11] F.D. Davis, Perceived usefulness, perceived ease of use, and user acceptance ofinformation technology, MIS Quarterly 13 (3), 1989, pp. 319–339.

[12] L. Dong, Modeling top management influences on ES implementation, BusinessProcess Management Journal 7 (3), 2001, pp. 243–250.

[13] R.B. Dunham, J.A. Grube, D.G. Gardner, L.L. Cummings, J.L. Pierce, The develop-ment of an attitude toward change instrument, in: Proceedings of Academy ofManagement Annual Meeting, Washington, DC, 1989.

[14] L.T. Eby, D.M. Adams, J.E.A. Russell, S.H. Gaby, Perceptions of organizationalreadiness for change: factors related to employees’ reactions to theimplementation of team-based selling, Human Relations 53 (3), 2000, pp.419–442.

[15] D. Gefen, D.W. Straub, M.-C. Boudreau, Structural equation modeling and regres-sion: guidelines for research practice, Communications of the Association forInformation Systems 4 (7), 2000, pp. 1–70.

[16] J.T. Hair, R.E. Anderson, R.L. Tatham, W.C. Black, Multivariate Data Analysis, Fifthed., Prentice Hall, Upper Saddle River, NJ, 1998.

[17] J. Herche, B. Engelland, Reverse-polarity items and scales unidimensionality,Journal of the Academy of Marketing Science 24 (4), 1996, pp. 366–374.

[18] W. Hong, J.Y.L. Thong, W.M. Wong, K.Y. Tam, Determinants of user acceptance ofdigital libraries: an empirical examination of individual differences and systemcharacteristics, Journal of Management Information Systems 18(3), Winter 2001–2002, pp. 97–124.

[19] P.J.H. Hu, T.H.K. Clark, W.W. Ma, Examining technology acceptance by schoolteachers: a longitudinal study, Information and Management 41, 2003, pp. 227–241.

[20] E. Karahanna, D.W. Straub, N.L. Chervany, Information technology adoptionacross time: a cross-sectional comparison of pre-adoption and post-adoptionbeliefs, MIS Quarterly 23 (2), 1999, pp. 183–213.

[21] L. Lapointe, S. Rivard, A multilevel model of resistance to information technologyimplementation, MIS Quarterly 29 (3), 2005, pp. 461–491.

[22] P. Legris, J. Ingham, P. Collerette, Why do people use information technology? Acritical review of the technology acceptance model Information and Management40, 2003, pp. 191–204.

[23] META Group, Market Research: The State of ERP Services (Executive Summary),META Group Inc., Stamford, CT, 2004.

[24] F.F.-H. Nah, X. Tan, S.H. Teh, An empirical investigation on end-users’ acceptanceof enterprise systems, Information Resources Management Journal 17 (3), 2004,pp. 32–53.

[25] A. Parasuraman, Technology readiness index (TRI): a multi-item scale to measurereadiness to embrace new technologies, Journal of Service Research 2 (4), 2000,pp. 307–320.

[26] S.K. Piderit, Rethinking resistance and recognizing ambivalence: a multidimen-sional view of attitudes toward an organizational change, Academy of Manage-ment Review 25 (4), 2000, pp. 783–794.

[27] D. Robey, J. Ross, M.C. Boudreau, Learning to implement enterprise systems: anexploratory study of the dialectics of change, Journal of Management InformationSystems 19 (1), 2002, pp. 17–46.

[28] A.H. Segars, Assessing the unidimensionality of measurement: a paradigm andillustration 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’ technologyreadiness 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 & Manage-

ment, International Journal of Information Manage-

ment, 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 Informa-

tion 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, Commu-

nications 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.