Information & Management 49 (2012) 36–46

Contents lists available at SciVerse ScienceDirect

Information & Management
jou...
W.-H. Tsai et al. / Information & Management 49 (2012) 36–46

2. Literature review
2.1. Pre-implementation consideration f...
38

W.-H. Tsai et al. / Information & Management 49 (2012) 36–46

have real system deployment experience. However, consult...
W.-H. Tsai et al. / Information & Management 49 (2012) 36–46

39

SERVQUAL &
IS Success Model

Beforehand
Consideration Fa...
40

W.-H. Tsai et al. / Information & Management 49 (2012) 36–46

The pilot test survey was directed to the 5000 largest
c...
W.-H. Tsai et al. / Information & Management 49 (2012) 36–46
Table 5
Exploratory factor analysis of Pilot test data.

41

...
W.-H. Tsai et al. / Information & Management 49 (2012) 36–46

42

Table 7
Goodness of fit statistics for the overall struct...
W.-H. Tsai et al. / Information & Management 49 (2012) 36–46

43

Table 9
Testing results concerning ERP supplier selectio...
44

W.-H. Tsai et al. / Information & Management 49 (2012) 36–46

Fig. 4. ERP supplier selection criteria.

Fig. 5. ERP co...
W.-H. Tsai et al. / Information & Management 49 (2012) 36–46

5. Conclusions and limitations
ERP systems are, in principle...
W.-H. Tsai et al. / Information & Management 49 (2012) 36–46

46
Appendix B (Continued )
Measurement categories

Internal ...
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G1. a comprehensive-study-of-the-relationship-between-erp-selection-criteria-and-erp-success

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G1. a comprehensive-study-of-the-relationship-between-erp-selection-criteria-and-erp-success

  1. 1. Information & Management 49 (2012) 36–46 Contents lists available at SciVerse ScienceDirect Information & Management journal homepage: www.elsevier.com/locate/im A comprehensive study of the relationship between enterprise resource planning selection criteria and enterprise resource planning system success Wen-Hsien Tsai *, Pei-Ling Lee, Yu-Shan Shen, Hsiu-Ling Lin Department of Business Administration, National Central University, Taoyuan 32001, Jhongli, Taiwan A R T I C L E I N F O A B S T R A C T Article history: Received 23 February 2010 Received in revised form 3 June 2011 Accepted 30 September 2011 Available online 21 October 2011 We developed a conceptual framework for investigating how ERP selection criteria are linked to system quality and the service provided by suppliers and consultants, and thus how these influenced ERP implementation success. Through a cross-sectional survey of the top 5000 largest companies in Taiwan, using a balanced scorecard concept and path analysis, we showed that four system selection criteria (consultant’s suggestion, a certified high-stability system, compatibility between the system and the business process, and the provision of best practices) were positively related to system quality. Three supplier selection criteria (international market position, training support by the supplier and supplier technical support and experience) had a significant influence on supplier service quality, and two consultant selection criteria (consultant’s ERP implementation experience in a similar industry and consultant’s support after going live) were related to consultant service quality. However, we found that most organizations did not consider all these criteria when implementing ERP systems. Our study also suggested that enhanced system quality and service quality could increase user perspective and ERP success. ß 2011 Published by Elsevier B.V. Keywords: ERP systems ERP suppliers ERP consultants System quality SERVQUAL Balanced scorecard Path analysis Selection criteria 1. Introduction An ERP system is an integrated information technology (IT) that uses common databases and consistent cross-functional information flow to allow organizations to integrate information from different departments and locations. Their availability has prompted many small and medium sized organizations to shift their IT strategies from in-house development to purchasing application software [10]. Clearly, ERP systems can enhance business operations. However, there are several difficulties that must be overcome for the successful implementation of an ERP system [5]. The company must be aware of the factors that influence the success of its implementation to prevent failures. The perceived characteristics of the product play a major role in the final decision of companies considering buying an ERP system [1]. In choosing a specific system, organizations use a variety of criteria to determine the most suitable one for them. Several previous papers on ERP system selection have discussed the major criteria for the evaluation of the best implementation processes [3,22]. These have shown the differences in the characteristics of the available ERP systems and suppliers but the work has mainly been confined to the ERP selection criteria. Few studies have * Corresponding author. Tel.: +886 3 4267247; fax: +886 3 4222891. E-mail address: whtsai@mgt.ncu.edu.tw (W.-H. Tsai). 0378-7206/$ – see front matter ß 2011 Published by Elsevier B.V. doi:10.1016/j.im.2011.09.007 examined the relationship between ERP selection criteria and ERP system success among Taiwanese companies. The selection of an ERP system involves more than interviewing a few suppliers. After deciding that an ERP is right for the company, choosing the most suitable supplier becomes important. The correct supplier will become a long-term partner. Furthermore, ERP suppliers must incorporate the latest IT trends in their systems to remain competitive [20]. During the ERP implementation process, most organizations collaborate with their ERP suppliers and/or consultants. These suppliers and consultants are external facilitators that affect ERP success [25]. ERP suppliers and consultants help not only in improving the quality of the ERP products, but also in ensuring user knowledge and involvement. Most research to date has considered only ERP suppliers, ERP systems, or ERP consultants, or two of these; seldom have all three been considered simultaneously in their effect on the success of ERP implementation. Furthermore, the research has mainly discussed the pre-implementation stage; few researchers have done further investigation on subsequent effects, such as the influence that the selection criteria have on the success of the organization. We therefore decided to develop a conceptual framework for investigating how ERP selection criteria are linked to system quality and service quality and thus influence ERP system implementation success. Our study adopted a cross-sectional survey of the top 5000 largest companies in Taiwan to examine the influence of various ERP selection criteria.
  2. 2. W.-H. Tsai et al. / Information & Management 49 (2012) 36–46 2. Literature review 2.1. Pre-implementation consideration factors The factors that underlie ERP system success are not the same as those that determine implementation success. For the former, selecting the right solution is critical, whereas the latter depends on software and hardware characteristics. Organizations usually treat system suppliers and implementation consultants as major external support. It is therefore important that consultants and suppliers understand the business and translate the ERP requirements to the organizational and process levels [17]. In this stage the determination of pre-implementation factors, such as ERP system selection criteria, ERP supplier selection criteria, and ERP consultant selection criteria, are critical. 2.1.1. ERP system selection criteria The characteristics of the ERP system should match the criteria used by the company to select an IS. Youakim and Jean [26] pointed out that risk management is an important part of every successful business model when dealing with socio-economic changes. Also, security concerns play a major role in minimizing risk by protecting a business’ intangible resources and knowledge. Therefore, risk management and security control might be considered major factors that should be used to evaluate system quality. Functionality, system reliability, and fit with the systems of parent and/or other allied organizations were found to be the three most important criteria by Kumar et al. [14]. In an overlapping study, Birdogan and Kemal used 17 selection criteria as the determinants of ERP package selection: fit with parent organization systems, cross-module integration, compatibility with other systems, references of the vendor, vision, functionally, system reliability, consultancy, technical aspects, implementation time, methodology of the software, market position of the vendor, ease of customization, best fit with organizational structure, service and support, and cost and domain knowledge of the vendor. Based on a review of the criteria used in prior studies, we chose eleven ERP system selection criteria as the most important for our study: a) consultant’s suggestions, b) flexibility in adjusting demands according to business requirements, c) a complete mechanism for risk management and security control, d) the ability to integrate different platforms and data, e) ERP systems that are used by customers and suppliers, f) ease of integration with other systems (e.g., CRM and SCM), g) a certified high-stability system, h) ease of use and maintenance, i) compatibility between the system and the business process, j) the provision of best practices, and k) implementation time. 37 Table 1 Results of the literature review of ERP system selection criteria. Selection criteria References a. Consultant’s suggestions b. Flexibility in adjusting demands according to business requirements c. A complete mechanism for risk management and security control d. The ability to integrate different platforms and data e. ERP systems that are used by customers and suppliers f. Ease of integration with other systems (e.g., CRM and SCM) g. A certified high-stability system h. Ease of use and maintenance i. Compatibility between the system and the business process j. The provision of best practices k. Implementation time [3] [3,14] [26] [3,14] [3] [3,14,21] [3,14] [3] [3,5] [14] [3] assistance, emergency maintenance, updates, service responsiveness, solutions provision, design, customization support, and user training [27]. Some companies also place emphasis on learning from past experience and service infrastructure when selecting their ERP system. Thus, among the supplier evaluation process criteria, consideration should be given to supplier reputation, financial stability, and supplier vision. Moreover, sales references and the internationality of the supplier may be important in the selection process. As Somers and Nelson [19] mention, many projects have failed due to a lack of proper training support during the ERP implementation process. Finally, to cope with technical or other problems during installation, implementation, or go-live periods, companies need support from suppliers in terms of IT expertise and domain knowledge. Verville and Halingten proposed three supplier evaluation criteria: size, financial stability, and reputation. From these suggestions, we selected six criteria as for our study: a) b) c) d) e) f) international market position, a good reputation in the field, training support by the supplier, financial position, technical support and experience, and support for maintaining and updating the system after going live. Table 2 summarizes the results of the literature review on ERP supplier selection criteria. H3a–H3f. ERP supplier selection criteria (a–f) is positively correlated with ERP supplier service quality. H4a–H4f. ERP supplier selection criteria (a–f) is positively correlated with net benefits. Table 1 summarizes the literature on the ERP system selection criteria. 2.1.3. ERP implementation consultant selection criteria Many organizations use external consulting organizations to help them in the ERP selection and implementation process and in the technical evaluation of the chosen solution. Consultants are not only well trained in ERP implementation methodologies but also H1a–H1k. ERP system selection criteria (a–k) is positively correlated with ERP system quality Table 2 Results of the literature review of ERP supplier selection criteria. H2a–H2k. ERP system selection criteria (a–k) is positively correlated with net benefits. 2.1.2. ERP supplier selection criteria In addition to the ERP system selection criteria, the selection of the system supplier also affects ERP system success. Here, primary considerations generally includes the need for extended technical Selection criteria References a. International market position b. A good reputation in the field c. Training support by the supplier d. Financial position e. Technical support and experience f. Support for maintaining and updating the system after going live [3,14] [3,14,22] [14,19,27] [22] [3,14,21,23,27] [3,14,27]
  3. 3. 38 W.-H. Tsai et al. / Information & Management 49 (2012) 36–46 have real system deployment experience. However, consultant selection demands a complex decision making process. Although consultants transfer the knowledge and business practices to the client, they must also have as system knowledge and industryspecific expertise and this complicates their function and role [23]. The consultant’s domain knowledge and fee also affect the implementation [15,16]. Cheung et al. [6] structured their consultant selection criteria: on past performance, capacity to accomplish the work, project approach, and fees. Furthermore, the transfer of consultants’ implementation knowledge was deemed critical to meeting the perceived needs of the client, responding to changing business processes, conducting ongoing administration and maintenance, minimizing training, gaining new in-house capabilities, and deploying a high-quality system [13]. After a literature review and discussions with experts, we selected seven consultant selection criteria as those to use in our study: a) b) c) d) e) consulting fee, consultant’s project management abilities, consultant’s domain knowledge, consultant’s ERP implementation experience, consultant’s ERP implementation experience in a similar industry, f) consultant’s ERP implementation approaches and tools, and g) consultant’s support after going live. Table 3 summarizes the results of the literature review on the ERP consultant selection criteria. H5a–H5g. ERP consultant selection criteria (a–g) is positively correlated to ERP consultant service quality. H6a–H6g. ERP consultant selection criteria (a–g) is positively correlated to net benefits. 2.2. IS success model IS success is a difficult notion to define. Many prior studies have attempted to explain and justify their assessment of IS success [9]. The original and updated DeLone and McLean IS success model provided perhaps the most comprehensive system for success measurement to date, and it has been widely used by others. It has been validated and applied to measure the success of several ecommerce ventures [2]. The purpose of the original model was to synthesize work involving individual assessments into a single coherent model. The authors posited that system quality and information quality were the two main determinants of IS use and user satisfaction, and they directly influenced individual impact, and that this, in turn, had a positive effect on net benefits. The updated model speculated that information quality, system quality, and service quality affected subsequent use and user satisfaction, and that net benefits occurred as a result of this use and resulting user satisfaction. Subsequently, Ifinedo [11] Table 3 Results of the literature review of ERP consultant selection criteria. Selection criteria References a. Consulting fee b. Consultant’s project management abilities c. Consultant’s domain knowledge d. Consultant’s ERP implementation experience e. Consultant’s ERP implementation experience in a similar industry f. Consultant’s ERP implementation approaches and tools g. Consultant’s support after going live [6,15] [6,23] [15,23] [6,13,16] [6,23] [16] [13] extended the model, incorporating two new factors: the vendor/ consultant quality (VQ) and workgroup impact (WI). He identified the relevance measures relating to the cooperative role and system suppliers as success measures, grouping them together and incorporating them into the original model. A series of concrete measurement constructs for the updated success model was applied to evaluate performance during implementation. The model included six IS success factors: (1) system quality ! the performance characteristics of the ERP systems, including reliability, flexibility, ease of use, and response time [18], (2) information quality ! such as credibility, timeliness, usefulness, understandability, and relevance of output, (3) service quality ! the overall support delivered by the service supplier: users are now customers and poor user support can translate into lost customers and lost sales, (4) the use of an ERP system (the frequency with which an IS is used, including its use in decision making, charges for ERP system use, and connection time), (5) user satisfaction ! successful interaction between the IS and its users, covering the entire customer experience cycle from information retrieval through purchase, payment, receipt, etc., and (6) net benefits ! capturing the balance of positive and negative impacts of the system on the organization. In addition to the updated IS success model of DeLone and MacLean, we utilized the balanced scorecard (BSC) technique as a financial measure to evaluate the ERP performance of the net benefits dimension. The four constructs of the BSC are; financial, customer, internal business process, and learning and growth. We arrived at two hypotheses: H7. ERP system quality is positively related to the user’s perspective of an ERP system. H8. The user’s perspective of an ERP system is positively related to net benefits. 2.3. Measurement of service quality – SERVQUAL Service quality was explored via an extensive series of focus group interviews. These centered on the idea that it involved customer evaluations made on the basis of their expectations of service and their perception of the way the service had been performed. SERVQUAL, an instrument developed in the marketing arena, has been used as a measure of IS service quality. The majority of such research has attempted to use SERVQUAL to measure service quality [21]. The SERVQUAL instrument has been a major technique used to evaluate the degree of satisfaction with a service, as indicated by consumer perceptions of quality. In our study, we examined the applicability of SERVQUAL in the ERP environment. The SERVQUAL instrument uses the dimensions of tangibles, reliability, responsiveness, assurance, and empathy to measure service quality. The instrument items include: (1) Tangibles: Suppliers and/or Consultants provide up-to-date hardware and software. (2) Reliability: Suppliers and/or Consultants are reliable. (3) Responsiveness: Suppliers and/or Consultants provide prompt service to users. (4) Assurance: Suppliers and/or Consultants have the knowledge to do their job well. (5) Empathy: Suppliers and/or Consultants have users’ best interests at heart.
  4. 4. W.-H. Tsai et al. / Information & Management 49 (2012) 36–46 39 SERVQUAL & IS Success Model Beforehand Consideration Factor H2a+~H2k+ System Selections H1a+~H1k+ Net Benefits System Quality H7+ Service Quality Supplier Selections Consultant Selections H3a+~H3f+ H5a+~H5g+ Suppliers Quality Direct Impact H4a+~H4f+ H9+ Consultants Quality Financial Perspectives User Perspective User Satisfaction Individual Impact H8+ Customer Perspectives Indirect Impact Internal Business Perspectives Innovation & Learning H6a+~H6g+ Fig. 1. Research model and the hypotheses. Here, we hypothesized a positive relationship between the degree of satisfaction with the service quality of an ERP supplier and/or consultant and user satisfaction with an ERP system. H9. The degree of satisfaction with the service quality of an ERP supplier is positively related to user perspective of an ERP system. 2.4. Balanced scorecard Kaplan and Norton developed the balanced scorecard concept, which includes financial and non-financial measures for the estimation of the state of an organization. It incorporates four measurement dimensions: financial, customer, internal business, and innovation and learning. Its measure of success is the financial perspective. Financial measures calculate the fiscal influences of activities, in this case specifying whether the implementation of an ERP system contributes to the company’s financial position. Customer perspective is used to determine the degree of customer satisfaction in areas such as on-time delivery ratio, on-time invoice ratio, and response time to customer problems/complaints. The internal business perspective involves the efficiency of the production processes. Thus a company should recognize activities and major processes involved in achieving the aims with respect to the financial and customer perspectives. The innovation and learning perspectives include the aims and measures determining the progress of the organization and employee growth. Our research model is presented in Fig. 1. 3. Methodology The survey examined the ERP implementation experiences of the top 5000 largest corporations in Taiwan to explore the status of their ERP implementation. We used questionnaires that focused on five areas: the characteristics of ERP implementation, the ERP implementation status, the motivation and evaluation of the preimplementation process, the implementation experience and ERP system configuration, and the benefits of the ERP system and future directions. In our study, we used some of our survey data to examine whether there were any significant effects on the net benefits stemming from the choice of selection criteria. 3.1. Survey procedures and sample characteristics Our research instrument was developed by adapting existing scales to enhance validity; the items were also slightly altered to fit our research context. Based on an extensive literature review of ERP systems, eleven, six, and seven selection criteria were identified for the ERP system, supplier, and consultant, respectively. To ensure the face validity and clarify the wording of the questionnaire, we conducted an iterative process of personal interviews with eight knowledgeable people (two IS faculty, two ERP suppliers, two ERP consultants, and two manager-users) and the questionnaire was suitably modified before sending it out. Empirical data for testing the hypothesized relationships were obtained using a mail survey of Taiwanese manufacturing firms that had implemented ERP systems. Data collected from a pilot test were used for instrument validation and refinement and factorial validity, whereas data from the major survey were used for confirmatory analysis of the measurement properties and hypothesis testing of the model, as shown in Fig. 1. Usually, multiple surveys should not be aggregated unless the population surveyed, questionnaire, and sampling methodology are identical [12]. In our study, therefore, the two samples could not be combined because they were not homogeneous: the pilot test involved the top 5000 largest corporations in Taiwan, whereas the second sample included companies of different sizes (via stratified sampling) but only those in the manufacturing sector. In both cases, the survey questionnaires were mailed to the ERP project managers and senior project team members of the selected companies along with a letter outlining the purpose of the research and soliciting participation in the survey; a postage-paid return envelope was included for the completed response forms. No incentive was provided to participants for completing the survey other than the promise of a copy of the aggregated results.
  5. 5. 40 W.-H. Tsai et al. / Information & Management 49 (2012) 36–46 The pilot test survey was directed to the 5000 largest corporations in Taiwan in 2008; a total of 620 responses were obtained after three rounds of follow-up requests to nonrespondents. After deleting missing responses and unusable ones, this resulted in a data response rate of 14.4%. The second survey was directed at a stratified sample of Taiwan manufacturing firms. We decided to focus our analysis on manufacturing firms for two reasons: first, ERP implementation seemed to be particularly widespread among these firms, and second, we wanted to decrease potential confounding effects due to industry variation. To alleviate concerns about the sample distribution, we stratified the total employment numbers of approximately 4300 firms in the top 5000 largest corporations in 2009 into four categories based on their employee counts: 1000 and over, 500–999, 100–499, and less than 99. The survey was mailed to a random sample of firms in each of the four strata. In our study, only organizations with prior experience in implementing ERP systems were used in our samples. A total of 603 firms were contacted; after three followup mailings, a total of 278 usable responses were obtained, for a respectable response rate of 46%. A time-trend extrapolation test was used to examine non-response bias. We assumed that the responses of the non-respondents were more similar to late than early respondents. Taking the first 25% as early and the last 25% as non-respondents, a multivariate analysis of variance of all 37 variances indicated no significant difference (Wilk’s Lambda = 0.972; p = 0.264). This finding is consistent with the absence of non-response bias. In the questionnaires, YES/NO selections were used to determine if the selection criteria had been taken into account by the firms when selecting their ERP packages, ERP suppliers, and implementation consultants. In the second stage, the respondents were asked to evaluate the ERP performance improvement level and its importance using 7-point Likert-type scale ranging from 1 (substantial deterioration) to 7 (substantial improvement) and from 1 (extremely unimportant) to 7 (extremely important), respectively. The importance measures were based on SERVQUAL and focused on the respondents’ degree of satisfaction with system quality, suppliers quality, and consultants quality, and their evaluation of the individual impact and the net benefits of the system. The data on importance levels were applied to determine the relative weights of the measures. Although the original SERVQUAL scale is gap-based, performance-only service quality measures have been demonstrated to be superior to gap-based measures [4]; therefore, we adopted a performance-only approach. Table 4 shows the descriptive statistics and the response rate by firm strata. 3.2. Data operation Although the DeLone and McLean IS success model depicts a bidirectional effect between system use and user satisfaction, we considered, only user satisfaction because the sample data included only those organizations that had implemented an ERP software package. Since all organizations had used ERP systems, determinants of system use were dropped from the model. To explore important factors influencing ERP performance improvement and develop appropriate performance measures, we combined the IS success measurement categories in the DeLone and McLean original and updated IS success models. The scheme is presented in Fig. 1. In order to evaluate the importance level of multiple measures and their degree of improvement in a reliable way, we used weighted averages of the measures: a two-stage approach in the design of the performance evaluation questionnaires was adopted. Stage 1: To find the relative weight of the kth measure of the jth dimension relative to the measures within the jth dimension, we Table 4 Characteristics of the samples (N = 278). Firms in database (frequency) Survey returned (%) Employment numbers <100 100–499 500–999 1000 and over 65 139 32 42 23 50 12 15 Company age <10 years 10–<20 years 20–<30 years !30 years 37 72 61 108 13 26 22 39 Annual revenue (NT$ billion) <$20 7 $10–<$50 47 $50–<$100 61 $100–<$250 62 $250–<$500 43 !$500 58 3 17 22 22 15 21 Capital amount (NT$ billion) 17 <$8 $8–<$20 45 $20–<$50 79 $50–<$100 50 $100–<$250 39 $250–<$500 19 !$500 29 6 16 28 18 14 7 11 calculated the importance level and the degree of performance improvement: ¯ W jk ¼ PN W i jk ; N i¼1 i ¼ 1 to N (1) In this, Wijk is the importance level score (1–7) of the kth measure of the jth dimension as perceived by the ith respondent’s ¯ company. W jk is the average importance level score of the kth measure of the jth dimension as perceived by N respondents. Stage 2: Since average importance score rankings were acquired at Stage 1, the level of performance improvement of the jth dimension for the ith respondent’s company can be calculated as: 0 1 lj X P i jk à W jk ¯ @ A; Pi j ¼ Pl j ¯ k¼1 k¼1 W jk i¼1 to N and j¼1À3 (2) Pj ¯ ¯ Here, ðW jk = lk¼1 W jk Þ, is the relative weight of the kth measure of the jth dimension relative to the measures within the jth dimension. Meanwhile, Pijk is the performance improvement level score of the kth measure of the jth dimension for the ith respondent’s company, and lj is the number of chosen measures of the jth dimension and P i j is the performance improvement level score of the jth dimension for the ith respondent’s company. Note j that l is the number of chosen measures for the jth dimension. 4. Data analysis and findings 4.1. Data analysis and model testing Initially, we tested for common method bias using a post hoc procedure. Under principal components factor analysis, evidence for common method bias exists when a single factor emerges from the analysis, or one general factor accounts for the majority of the covariance in the interdependent and dependent variables. Because the analysis of our pilot data (the first survey), and data from the second survey resulted in multiple factors, we concluded that the data do not indicate substantial common method bias.
  6. 6. W.-H. Tsai et al. / Information & Management 49 (2012) 36–46 Table 5 Exploratory factor analysis of Pilot test data. 41 Table 6 Factor loadings, reliability and validity. Items 1 2 3 4 5 SEVQUAL1-CON3 SEVQUAL2-CON2 SEVQUAL3-CON4 SEVQUAL4-SUP3 SEVQUAL5-CON5 SEVQUAL6-SUP4 SEVQUAL7-SUP2 SEVQUAL8-SUP5 SEVQUAL9-SUP1 SEVQUAL10-CON1 0.853 0.819 0.809 0.807 0.801 0.796 0.779 0.754 0.691 0.678 0.144 0.195 0.014 0.204 À0.009 0.073 0.247 0.072 0.129 0.095 0.158 0.051 0.286 À0.007 0.218 0.130 À0.043 0.113 À0.102 0.035 À0.004 0.014 À0.012 0.055 À0.002 0.143 0.132 0.143 0.093 À0.041 0.083 0.119 0.175 0.041 0.135 0.123 0.063 À0.022 0.095 0.048 UserPec1-USER4 UserPec2-USER3 UserPec3-USER2 UserPec4-USER5 UserPec5-USER1 UserPec6-IND3 UserPec7-IND2 UserPec8-IND4 UserPec9-IND5 UserPec10-IND1 0.197 0.166 0.230 0.200 0.218 0.127 0.170 0.151 0.141 0.241 0.823 0.817 0.808 0.792 0.749 0.628 0.579 0.558 0.546 0.531 0.234 0.259 0.200 0.213 0.193 0.500 0.490 0.515 0.504 0.524 0.218 0.191 0.283 0.225 0.303 0.129 0.207 0.102 0.167 0.206 0.216 0.056 0.149 0.201 0.268 0.237 0.259 0.307 0.288 0.261 NetBenefitII1-IBP2 NetBenefitII2-IBP 3 NetBenefitII3-IBP1 NetBenefitII4-IL1 NetBenefitII5-IL3 NetBenefitII6-IL2 0.118 0.091 0.144 0.146 0.006 0.094 0.207 0.383 0.204 0.363 0.243 0.503 0.790 0.669 0.666 0.596 0.591 0.543 0.179 0.250 0.230 0.333 0.414 0.399 0.245 0.257 0.352 0.137 À0.129 0.122 NetBenefitDI1-FP1 NetBenefitDI2-FP 3 NetBenefitDI3-FP 2 NetBenefitDI4-CP1 NetBenefitDI5-CP2 NetBenefitDI6-CP3 System quality2 – SQ2 System quality4 – SQ4 System quality3 – SQ3 System quality1 – SQ1 System quality5 – SQ5 0.076 0.055 0.092 0.090 0.053 0.075 0.187 0.180 0.155 0.143 0.189 0.245 0.267 0.229 0.285 0.265 0.151 0.115 0.401 0.205 0.393 0.334 0.076 0.184 0.250 0.396 0.486 0.507 0.292 0.146 0.401 0.033 0.305 0.778 0.775 0.757 0.705 0.676 0.592 0.187 0.320 0.155 0.347 0.267 0.352 0.267 0.285 0.110 0.023 0.248 0.779 0.713 0.697 0.674 0.571 Inventory Notes. Principal axis factoring: Promax with Kaiser normalization. Data from the first survey were subjected to exploratory factor analysis (EFA) to gain insights as to the multidimensionality of the items (see Table 5). The results of this data analysis revealed five factors with an eigenvalue greater than one and no single factor was consistent with the absence of a significant variance common to the measures. Data from the second survey (manufacturing companies) were used for a confirmatory factor analysis (CFA) on the measurement scales and to test the model hypotheses, because the construct validity was preliminarily verified with an EFA from the first survey. A CFA using a complete standardized solution in LISREL 8.52 showed that all 37 items loaded on their corresponding factors, which provided strong empirical evidence of construct independence and the validity of the construct. Moreover, most reliability measures were well above the recommended level of 0.7, indicating adequate internal consistency. SPSS 17 was used for the analysis of construct item reliability. As indicated by Cronbach’s a coefficients, all constructs exhibited high composite reliability. Table 6 shows that most of the measures had significant loadings that were much higher than the suggested threshold. The two exceptions were the first two items in the system supplier and consultant scales of SERVQUAL, where loadings were slightly below 0.7. Content validity defines how representative and comprehensive the items were in testing the hypotheses. Our definitions of systems quality, service quality, user perspective, and net benefits were based on our literature review. For unidimensionality testing, a measurement model was constructed using the five latent factors. Each item was restricted to load on Service quality SERVQUAL of system suppliers Tangibles-SUP1 Reliability-SUP2 Responsiveness-SUP3 Assurance-SUP4 Empathy-SUP5 SERVQUAL of consultants Tangibles-CON1 Reliability-CON2 Responsiveness-CON3 Assurance-CON4 Empathy-CON5 System quality Reliability-SQ1 Flexibility-SQ2 Ease of use-SQ3 Accuracy-SQ4 Response time-SQ5 User perspective User satisfaction Information satisfaction-USER1 Software satisfaction-USER2 System Interface satisfaction-USER3 System satisfaction-USER4 ERP Project satisfaction-USER5 Individual impact Improved job performance-IND1 Improved productivity-IND2 Improved decision making ability-IND3 Improved identification problem ability-IND3 Improved trouble-shooting ability-IND4 Net benefits – direct impact Financial perspective Inventory levels-FP1 Purchasing costs-FP2 Inventory turnover-FP3 Customer perspective On time delivery ratio-CP1 Response time customer complaint-CP2 On time invoice ratio-CP3 Net benefits – indirect impact Internal business perspective Internal data transmission time-IBP1 Internal interaction frequency-IBP2 Response time to environmental volatility-IBP3 Innovation and learning Understanding of business process-IL1 Job achievement of employees-IL2 Product development to market-IL3 Standard loading CR AVE Cronbach’s 0.95 0.64 0.917 0.94 0.75 0.919 0.97 0.76 0.948 0.95 0.75 0.928 0.93 0.68 0.914 a 0.65 0.78 0.80 0.81 0.73 0.67 0.87 0.92 0.87 0.85 0.87 0.88 0.80 0.97 0.83 0.89 0.89 0.86 0.92 0.91 0.86 0.87 0.87 0.83 0.83 0.86 0.90 0.90 0.86 0.81 0.84 0.85 0.90 0.90 0.80 0.83 0.62 All ratio of improvement were measured by using seven-point Likert-type scales ranging from 1 (substantial deteriorate) to 7 (substantial improvement). only one specific latent variable, and one latent variable per indicator was allowed. Convergent validity is demonstrated when items load highly (loading >0.7) on their associated factors and constructs have an average variance extracted (AVE) of at least 0.5 and composite reliability (CR) of at least 0.7. After the assessment of reliability and validity, the overall fit and the explanatory power of the research model were examined together with the relative strengths of the individual causal path. Seven common modelfitting measures were used to assess the model’s overall goodnessof-fit: a normalized x2 test (x2 divided by degrees of freedom; x2/ df), a goodness-of-fit index (GFI), an adjusted goodness-of-fit index
  7. 7. W.-H. Tsai et al. / Information & Management 49 (2012) 36–46 42 Table 7 Goodness of fit statistics for the overall structure model. Fit Indices Threshold x /DF GFI AGFI NFI NNFI CFI RMSEA RMR Output <2 >0.80 >0.80 >0.80 >0.80 >0.8 <0.05 <0.1 2 1.26 0.96 0.88 0.95 0.86 0.95 0.00 0.05 (AGFI), a normalized fit index (NFI), a non-normalized fit index (NNFI), a comparative fit index (CFI), a root mean square error of approximation (RMSEA), and a root mean square residual (RMR). All reflective indicators were standardized to be consistent with recommendations for dealing with interactions [7]. Table 6 summarizes the overall fit statistics of the measurement model. All model indices fit well with the recommendations suggested by earlier studies. Therefore, the research model provided a good fit to the data (Table 7). We further examined the significance of individual paths, and the results are shown summarized in Fig. 2. All paths displayed a significant effect, with p-values less than 0.05. As hypothesized, system quality and service quality had positive effects on user perspectives, with path coefficients of 0.61 and 0.17, respectively. Thus, Hypotheses 7 and 9 were supported. Consistent with Hypothesis 8, user perspective had a positive effect on net benefits, with a path coefficient of 0.74. Table 8 presents the four system selection criteria, consultant’s suggestion, a certified highstability system, compatibility between the system and the business process, and the provision of best practices, had significant influences on system quality. Thus, Hypotheses 1a, 1g, 1i, and 1j were supported. Table 9 shows that three selection criteria, international market position, suppliers’ technical support and experience, and support for maintaining and updating the system after going live, had significant influences on supplier service quality. Therefore, Hypotheses 2a, 2e, and 2f were supported. In Table 10, the Beforehand Consideration Factor System Selections Supplier Selections H1a+~H1k+ SERVQUAL & IS Success Model System Quality 0.61*** R2 =0.68 User Perspective H2a+~H2f+ R2 =0.65 0.74*** Net Benefits 0.17*** Service Quality Consultant Selections H3a+~H3g+ Note: *p-value<0.05, **p-value<0.01, ***p-value<0.001 Fig. 2. Result of the path analysis. Table 8 Testing results concerning ERP system selection criteria. ERP system selection criteria Consider or not Freq. p-Value ERP system selection criteria and system quality a. Consultant’s suggestions b. Flexibility in adjusting demands according to business requirements c. A complete mechanism for risk management and security control d. The ability to integrate different platforms and data e. ERP systems that are used by customers and suppliers f. Ease of integration with other systems (CRM, SCM) g. A certified high-stability system h. Ease of use and maintain i. Compatibility between the system and the business process j. The provision of best practices k. Implementation time * p < 10%; p < 5%; p < 1%; number of respondents: 278. ** *** No Yes No Yes No Yes No Yes No Yes No Yes No Yes No Yes No Yes No Yes No Yes 245 33 151 127 183 95 226 52 259 19 235 43 188 90 158 120 130 148 229 49 200 78 ERP system selection criteria and net benefits 0.042** 0.463 0.329 0.403 0.720 0.179 0.998 0.134 0.954 0.267 0.121 0.011 0.008 *** 0119 0.173 0.700 0.042** 0.279 ** 0.033 0.356 0.597 0.416
  8. 8. W.-H. Tsai et al. / Information & Management 49 (2012) 36–46 43 Table 9 Testing results concerning ERP supplier selection criteria. ERP supplier selection criteria Consider or not Freq. p-Value ERP supplier selection criteria and ERP supplier service quality a. International market position b. A good reputation in the field c. Training support by the supplier d. Financial position e. Technical support and experience f. Support for maintaining and updating after going live No Yes No Yes No Yes No Yes No Yes No Yes 0.003*** 0.137 0.261 0.364 0.553 0.623 0.904 0.476 0.015** 0.338 0.061* 215 63 163 115 182 96 249 29 132 146 138 140 ERP supplier selection criteria and net benefits 0.664 * p < 10%; p < 5%; *** p < 1%; number of respondents: 278. ** Table 10 Testing results concerning ERP consultant selection criteria. ERP consultant selection criteria Consider or not Freq. p-Value ERP consultant selection criteria and ERP consultant service quality a. Consulting fee b. Consultant’s project management abilities c. Consultant’s domain knowledge d. Consultant’s ERP implementation experience e. Consultant’s ERP implementation experience in a similar industry f. Consultant’s ERP implementation approaches and tools g. Consultant’s support after going live ** No Yes No Yes No Yes No Yes No Yes No Yes No Yes 180 98 143 135 113 165 76 202 124 154 210 68 198 80 p < 5%, ***p < 1%; number of respondents: 278. * p < 10% Fig. 3. ERP system selection criteria. ERP consultant selection criteria and net benefits 0.278 0.609 0.262 0.378 0.211 0.209 0.207 0.261 * 0.062 0.770 0.256 0.601 0.088* 0.595
  9. 9. 44 W.-H. Tsai et al. / Information & Management 49 (2012) 36–46 Fig. 4. ERP supplier selection criteria. Fig. 5. ERP consultant selection criteria. selection criteria, consultant’s ERP implementation experience in a similar industry, and consultant’s support after going live, exhibited strong correlations with consultant service quality. Hypotheses 3e and 3g were thus supported. Fig. 2 also shows the explanatory power of the research model: the R2 values show that system quality and service quality accounted for 68% of the variance in user perspective, which accounted for 65% of the variance in net benefits. Given the high explanatory power of the resulting model, it is likely that the model can predict the results of a quality process from a user perspective and enhance ERP systems understanding. The results also implied that the selection criteria had positive influences on net benefits through service quality process and user perspective. 4.2. Results and management implications The survey evidence for the percentages and the ranking of selection criteria are listed in Figs. 3–5. Our first findings were that: the results were consistent with previous IS success model research [8] and a balanced scorecard was able to translate ERP performance more effectively across the entire enterprise and manage it effectively throughout its execution. Second, we found that system quality and service quality in terms of pre-implementation consideration factors may have major and moderating effects on user perspectives. These results may imply that the consistency of perceived system quality and service quality between ERP systems, ERP system suppliers, and implementation consultants is not only important in increasing the degree of user satisfaction but also critical in accommodating firms’ operations needs and determining the value of an ERP [24]. Consequently, we emphasize the importance of a good fit between these two roles (i.e., one with the smallest gap) when selecting an ERP package, and hiring an implementation consultant with the highest degree of fit competence. Last, but not least, we found that among the system selection criteria, consultant’s suggestion presented a significant influence on the service quality provided by the implementation consultants, and, among the consultant selection criteria, consultant’s ERP implementation experience in a similar industry and consultant’s support after going live presented significant influences. However, most organizations only considered compatibility between the system and the business process among the selection criterion; the three remaining significant software selection criteria were not the main considerations when organizations selected ERP software.
  10. 10. W.-H. Tsai et al. / Information & Management 49 (2012) 36–46 5. Conclusions and limitations ERP systems are, in principle, applicable to all industries and although it is costly, in general, it may bring major benefits. In our study, we evaluated the various selection criteria that may directly influence the service quality of an ERP and its further success. By confirming our success model, we found that enhanced system quality and service quality can increase user satisfaction. Although system quality and service quality were as important in ERP systems as in the traditional IS environment, a balanced scorecard concept played an important role in the evaluation of ERP performance. Moreover, our findings revealed that the selection criteria which most users considered to be important for ERP implementation actually had no obvious influence on the success of an ERP system. This finding has not been reported to date! We conclude that for an ERP implementation process to be reliable, corporations should place more emphasis on new selection criteria. Specifically, they should focus on the criteria shown to facilitate successful ERP implementation and system success. Some limitations of our study need mentioning. First, our results reflected only Taiwanese perspectives; different industries, cultural contexts and environmental settings may generate different results. Second, there is an obvious survey bias in the sample, because those who felt dissatisfied with the ERP systems were inclined to refuse to participate in the survey. Third, although there are numerous factors affecting the success of ERP systems, we only focused on the IS success constructs in our research model. Finally, the performance evaluation could be a limitation. In a questionnaire investigation, it is difficult to estimate the various variables by using the practical data obtained from real financial statements, management reports, and so on. f) support for maintaining and updating the system after going live. 3. Selection criteria for ERP consultant a. consulting fee, b. consultant’s project management abilities, c. consultant’s domain knowledge, d. consultant’s ERP implementation experience, e. consultant’s ERP implementation experience in a similar industry, f. consultant’s ERP implementation approaches and tools, and g. consultant’s support after going live. Appendix B. Benefits of an ERP system Please mark the appropriate number to indicate the extent to which the important factors influencing the ERP performance improvement. Using 7-point Likert-type scales ranging from 1 (substantial deterioration) to 7 (substantial improvement) and from 1 (extremely unimportant) to 7 (extremely important) to evaluate the ERP performance improvement levels and its importance for their degree, respectively. Measurement categories Please mark the selection criteria which you considered before you implemented an ERP system (multiple selection). Service quality SERVQUAL of system suppliers Tangibles Reliability Responsiveness Assurance Empathy SERVQUAL of consultants Tangibles Reliability Responsiveness Assurance Empathy System quality Reliability Flexibility Ease of use Accuracy Response Time 1. Selection criteria for an ERP system a) consultant’s suggestions, b) flexibility in adjusting demands according to business requirements, c) a complete mechanism for risk management and security control, d) the ability to integrate different platforms and data, e) ERP systems that are used by customers and suppliers, f) ease of integration with other systems (e.g., CRM and SCM), g) a certified high-stability system, h) ease of use and maintenance, i) compatibility between the system and the business process, j) the provision of best practices, and k) implementation time. 2. Selection criteria for ERP supplier a) international market position, b) a good reputation in the field, c) training support by the supplier, d) financial position, e) technical support and experience, and User perspective User satisfaction Information satisfaction Software satisfaction System interface satisfaction System satisfaction ERP project satisfaction Individual impact Improved job performance Improved productivity Improved decision-making ability Improved identification problem ability Improved trouble-shooting ability Net benefits-direct impact Financial perspective Inventory levels Purchasing costs Inventory turnover Customer perspective On time delivery ratio Customer complaint response time On time invoice ratio Net benefits-indirect impact Internal business perspective Acknowledgment The authors would like to thank the National Science Council of Taiwan for financially supporting this research under Grant NSC96-2416-H-008-015. Appendix A. Motivation and evaluation of pre-implementation process 45 The performance improvement levels after ERP implementation Important factors of the categories 1234567 1234567 1234567 1234567 1234567 1234567 1234567 1234567 1234567 1234567 1234567 1234567 1234567 1234567 1234567 1234567 1234567 1234567 1234567 1234567 1234567 1234567 1234567 1234567 1234567 1234567 1234567 1234567 1234567 1234567 1234567 1234567 1234567 1234567 1234567 1234567 1234567 1234567 1234567 1234567 1234567 1234567 1234567 1234567 1234567 1234567 1234567 1234567 1234567 1234567 1234567 1234567 1234567 1234567 1234567 1234567 1234567 1234567 1234567 1234567 1234567 1234567
  11. 11. W.-H. Tsai et al. / Information & Management 49 (2012) 36–46 46 Appendix B (Continued ) Measurement categories Internal data transmission time Internal interaction frequency Response time to environmental volatility Innovation and learning Understanding of business process Job achievement of employees Product development to market The performance improvement levels after ERP implementation Important factors of the categories 1234567 1234567 1234567 1234567 1234567 1234567 1234567 1234567 1234567 1234567 1234567 1234567 References [1] M. Al-Mashari, A. Al-Mudimigh, M. Zairi, Enterprise resource planning: a taxonomy of critical factors, European Journal of Operational Research 146 (2), 2003, pp. 352–364. [2] E.W.N. Bernroider, IT governance for enterprise resource planning supported by the DeLone-McLean model of information systems success, Information & Management 45 (5), 2008, pp. 257–269. [3] B. Birdogan, C. 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McLean, Measuring e-commerce success: applying the DeLone and McLean information systems success model, International Journal of Electronic Commerce 9 (1), 2004, pp. p.31–p.47. [9] W.H. DeLone, E.R. McLean, The DeLone and McLean model of information systems success: a ten-year update, Journal of Management Information System 19 (4), 2003, pp. 9–30. [10] K.K. Hong, Y.G. Kim, The critical success factors for ERP implication: an organizational fit perspective, Information & Management 40 (1), 2002, pp. 25–40. [11] P. Ifinedo, Extending the Gable et al. enterprise systems success measurement model: a preliminary study, Journal of Information Technology Management 17 (1), 2006, pp. 14–33. [12] J. Karimi, T. Somers, A. Bhattacherjee, The impact of ERP implementation on business process outcomes: a factor-based study, Journal of Management Information Systems 24 (1), 2007, pp. 101–134. [13] D.G. Ko, Consultant competence trust doesn’t pay off, but benevolent trust does! 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Nelson, A taxonomy of players and activities across the ERP project life cycle, Information & Management 41 (3), 2004, pp. 257–278. [20] F.K. Stage, H.C. Carter, A. Nora, Path analysis: an introduction and analysis of a decade of research, Journal of Educational Research 98 (1), 2004, pp. 5–12. [21] W.H. Tsai, M.J. Shaw, Y.W. Fan, J.Y. Liu, K.C. Lee, H.C. Cheng, An empirical investigation of the impacts of internal/external facilitators on the project success of ERP: A structural equation model, Decision Support Systems 50 (2), 2011, pp. 480–490. [22] J. Verville, A. Halingten, An investigation of the decision process for selecting ERP software: the case of ESC, Management Decision 40 (3), 2002, pp. 206–216. [23] E.T.G. Wang, K. Gary, J.J. James, ERP Misfit: country of origin and organizational factors, Journal of Management Information Systems 23 (1), 2006, pp. 263–292. [24] E.T.G. Wang, H.F. Chen Jessica, Effects of internal support and consultant quality on the consulting process and ERP system quality, Decision Support Systems 42, 2006, pp. 1029–1041. [25] J. Wu, Y. Wang, Measuring ERP success: the key-users’ viewpoint of the ERP to produce a viable IS in the organization, Computer in Human Behavior 23 (3), 2007, pp. 1582–1596. [26] B. Youakim, S. Jean, Security and risk management in supply chins, Journal of Information Assurance and Security (2), 2007, pp. 288–296. [27] Z. Zhang, M.K.O. Lee, P. Huang, L. Zhang, X. Huang, A framework of ERP systems implementation success in China: an empirical study, International Journal of Production Economics 98 (1), 2005, pp. 56–80. Wen-Hsien Tsai is a professor of accounting and information systems in the Department of Business Administration, National Central University, Taiwan. He is also a certified consultant of SAP financial module. He received his PhD degree in industrial management from the National Taiwan Science and Technology University. He received his MBA degree and his MS degree in Industrial Engineering from the National Taiwan University and National Tsing-Hwa University, respectively. His research interests include ERP implementation and auditing, activity-based costing (ABC), green production and optimization decision, and International Financial Reporting Standards (IFRS). He has published several papers in Decision Support Systems, Omega – The International Journal of Management Science, Transportation Science, Industrial Marketing Management, Journal of the Operational Research Society, Tourism Management, Family Business Review, Enterprise Information Systems, Int. J. Production Economics, Computers and Operations Research, Computers and Industrial Engineering, Int. J. Production Research, etc. Pei-Ling Lee is a PhD candidate in financial management at the Department of Business Administration, National Central University, Taiwan. She has done her MSc in finance and investment management at the University of Aberdeen in Scotland, UK and her BA in industrial engineering at Chun-Yuan Christian University in Taiwan. Her current research interests are focus on ERP implementation and management, ERP performance measurement, multi-criteria decision making, entrepreneurial policy in SME, management accounting and financial management. Yu-Shan Shen is a PhD student in financial management at the Department of Business Administration, National Central University, Taiwan. She has completed her MSc in international business administration at Chinese Culture University in Taiwan. Her BA in the department of International Trade at Chinese Culture University in Taiwan. Her current research interests include ERP implementation and management, ERP performance measurement, activity-based costing and entrepreneurial policy in SME. Hsiu-Ling Lin is a PhD student in financial management in the Department of Business Administration at National Central University, Taiwan. She is also secretary general, Taiwan Corporate Governance Association. Her research interests include ERP auditing, corporate governance, ERP performance measurement and activity-based costing.

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