Keywords: ERP, Pre-implementation decisions, Misfit, Implementation risk. 1. Introduction

Transcription

1 The Relationship between Pre-Implementation Decisions and Implementation Risks in ERP Projects A Survey on Taiwan s Manufacturing Firms C.-Y. Ho and Y.-M. Lin Department of Information Management National Central University Jung-li City, Taoyuan, Taiwan chuckho@mgt.ncu.edu.tw Abstract Prior to the ERP implementation, crucial managerial decisions concerning what to justify in adopting a ready-made ERP software package, what criteria should be used to select ERP software and external consultants, and what specifications needed for the ERP project have to be made. There are quite a few studies on the critical success factors in ERP implementation, but pre-implementation factors are often unspecifically treated and their importance underestimated. Risks associated with this enterprise-wide ERP software are inevitable and sometimes necessary to realize the promised organizational performance. Not to minimize the risk but to recognize its existence and formation so that we can take necessary precautions throughout the implementation process and make duly pre-implementation decisions in the first place. This study is to investigate the perceived implementation risks that can be attributed to the misfits found in the pre-implementation decisions, including the motivational misfits and the misfits in the selection criteria for ERP software and implementation consultants. We also examined the moderating effects of various managerial actions and project specifications on the base relation between the pre-implementation misfits and the implementation risks. A mail survey is conducted over the CommonWealth Top 1000 Manufacturers in Taiwan. Out of 143 respondents, 98 firms have implemented ERP systems. The findings show that the ERP implementation risks can be classified into five categories; they are ERP systems design, user involvement and training, IT planning and integration, skill mix, and management structure and strategy. Among the various pre-implementation decisions, we found that if the selected ERP is less capable of satisfying our strategic, business, or technical requirements, the resulting implementation risks perceived to be very high. Secondly, the factors that would affect the base relation between the motivational misfit and the perceived implementation risks include the planning activities such as budget control and project specifications such as if the vendor of the selected ERP is a local company and the scope of ERP implementation includes primary activity. The other factors studied, though not affecting the base relation, are potential additional predictors and need further study to establish their contribution to implementation risks. Keywords: ERP, Pre-implementation decisions, Misfit, Implementation risk 1. Introduction In MIS implementation, many key decisions are made in pre-implementation stage. These pre-implementation decisions will have the greatest effect on an assessment of the project s probability of success or failure should be possible at that time (Ginzberg, 1981). However, pre-implementation factors are often unspecifically treated and their importance underestimated. Current ERP research concerning the pre-implementation only covers the survey of motivation and selection criteria for adopting ERP software (Mabert, 2000; Everdingen et al., 2000) and the way to depict and select appropriate ERP software (Sistach et al.,

2 1999; Franch and Pastor, 2000; Brown et al., 2000; Oliver and Romm, 2000; Stefanou, 2000). Wheather the chosen ERP software, vendor, and consultants can meet the company s motivation and selection criteria or not has not been discussed. Whether these misfits were related to any implementation problems also has not been addresses. Problems, or risks, in ERP implementation process are mostly reported by some case studies. (Markus and Tanis, 1999; Sumner, 2000; Markus et al., 2000; Scott and Vessey, 2002) Risks associated with this enterprise-wide ERP software are inevitable and sometimes necessary to realize the promised organizational performance. Not to minimize the risk but to recognize its existence and formation so that we can take necessary precautions throughout the implementation process and make duly pre-implementation decisions in the first place. This study is to investigate the perceived implementation risks that can be attributed to the misfits found in the pre-implementation decisions, including the motivational misfits and the misfits in the selection criteria for ERP software and implementation consultants. We also examined the moderating effects of various managerial actions and project specifications on the base relation between the pre-implementation misfits and the implementation risks. Research questions include the followings: (1) How the misfits in pre-implementation decisions made by Taiwan s manufacturing firms can contribute to ERP implementation risks? (2) What planning activities and project specifications will moderate the relationship between the misfits and the implementation risks? Using Koh et al. s (2000) four-phase process model of ERP implementation as a reference model, our study focused on the decisions made in the chartering phase and their effects on the subsequent project phase. We argue that inappropriate decisions (misfits) made in the planning stage will affect the severity of problems encountered during the implementation process. To address the research questions using a quantitative approach will enable us to evaluate the potential risks when making the pre-implementation decisions and therefore take necessary precautions throughout the implementation process. We will provide a literature review relevant to our research questions in section 2. In section 3, a research model is proposed to investigate the relationship between the misfits in pre-implementation decisions and the perceived implementation risks. Numerical analysis and conclusions will be given at the end of this paper. 2. Literature Review In this section, we will review the past research of implementation process and define the pre-implementation decisions and their effects on latter stages. Then we will review some studies on the factors of motivations and selection criteria, but very few of them concerning how these motivations and selection criteria can be satisfied. Lastly, we will review the implementation problems or risks. Markus and Tanis (1999) developed a four-phase model of ERP implementation, which includes chartering, project, shakedown, and onwards and upwards. The key activities in chartering phase are the development of the business case for the ERP package selection, identification of the project manager, and budget and schedule approval. Key players in this phase include vendors, consultants, company executives, and IT specialists. The key activities of the project phase are software configuration, system integration, testing, data conversion,

3 training and roll-out. After the review of the ERP implementation models and comparison with MIS implementation model, we define the pre-implementation decisions as the ERP package selection (Markus and Tanis, 1999; Parr and Shanks, 2000), the selection of vendors and consultants (Markus and Tanis, 1999), the determination of the steering committee and the project team and project manager (Bancroft et al., 1998; Markus and Tanis, 1999; Parr and Shanks, 2000), the project scope and size (Davis, 1974; Parr and Shanks, 2000), and budget and schedule approval (Markus and Tanis, 1999). Parr and Shanks (2000) classified ERP motivations as technical, operational, and strategic motivations. Summarized from a few studies, we can establish a list of the reasons of ERP adoption, arranged according to Parr and Shanks classification. Technical motivations include common platform and obsolescence of legacy systems; operational motivations include process improvement, data visibility, and operation cost reductions; the strategic motivations include Y2K compliance, multi-site standardization, customer responsiveness, decision-making improvement, need for efficiencies and integration, and business restructuring. Only in Reinhard and Bergamaschi s (2001) study, they listed 11 items of motivations and tested whether the motivations for initiating the project were achieved or not in project managers and users opinion. There are two ways to measure the risk of software projects. One is to ask experienced project managers to identify, rank and rate the risks they perceived. Boehm(1991) listed top 10 primary source of risk on software projects, based on a survey of several experienced project managers. Keil et al. (1998) also asked experienced project managers to identify 11 most severe risks. The other is to review the literature on ERP problems and focus on categorizing them. Discussing the risks of companies who are planning to implement ERP through asking former implemented companies project managers whether they face that problem or not. Sumner (2000) identified six categories of the risk factors in ERP projects, and they are organizational fit, skill mix, management structure and strategy, software systems design, user involvement and training, and technology integration. Hottenstein and Dean (1992) defined project risk as the likelihood that a project will fail to achieve its objectives. Risk is the degree of uncertainty concerning loss, and the higher complexity will result in higher uncertainty. Risk management strategies are not necessarily oriented toward the reduction of risk. Rather they are oriented toward giving the manager tools for successfully managing a project given its risk profile. If a risk profile is not acceptable, ways might be found to reduce risk at its sources, (Hottenstein and Dean, 1992). In our study, we examine the misfit of the selected ERP with the motivations and the selection criteria as the sources of risk and propose a research model in the following section. 3. Research Model and Hypotheses In this section we will propose the research model and the research hypotheses to be tested to address the research questions of how these inappropriately made pre-implementation decisions contribute to the perceived ERP implementation risks. 3.1 Research model The base relation in our research model is the causal relationship between the misfit in the pre-implementation decisions and ERP implementation risk. The effects of seven project specification factors on

4 the base relations are explored as moderating variables. The research model is illustrated in Fig. 1. Motivation misfit (Parr and Shanks, 2000) Selection criteria misfit -ERP system -ERP vendor (Everdigen et al., 2000) -Consultant (Basil et al. 1997) H1 H2 ERP project specification -source (H3) -scope (H4) -schedule (H5) -size (H6) -budget control (H7) -support (H8) -structure (H9) (Karakanian, 1999) ERP Implementation Risk (Sumner, 2000) Fig. 1 Research model 3.2 Research hypotheses The first category of the inappropriately-made pre-implementation decisions, motivational misfit, means the original functions of the firm s chosen ERP software cannot meet the firm s motivations of ERP adoption (Markus and Tanis, 2000; Koh et al., 2000). In the former discussion of firms motivations to adopt an ERP, we use Parr and Shanks (2000) classification scheme, including technical, operational, and strategic motivations. A firm may adopt an ERP for more than one of these reasons. However, because of the limitation of the functions of an ERP, and the limited resources of the firm, it is nearly impossible that the chosen ERP can meet all the firm s motivations. Thus, the ERP of the firm s choice may fit one of the motivations perfectly and leave others less satisfied or unsatisfied. We argue that these misfits are the unresolved problems of the pre-implementation, and might cause some problems in the latter implementation. Hence, for a firm who planned to implement ERP, the higher motivation misfit should lead to the level of perceived implementation risks higher. This leads to our first hypothesis. H1: There is a positive relationship between the degree of motivational misfit and the level of implementation risks. Selection criteria are used to guide the selection of software, hardware, vendor, and consultant. Selection criteria misfit means the firm s chosen ERP software, ERP vendor, and consultants cannot adhere to the firm s planned selection criteria (Markus and Tanis, 2000; Koh et al., 2000). However, because of the limited resources of the firm, the limited choices of ERP vendor, software, and consultants that a firm can choose, it is nearly impossible that all the selection criteria can be met simultaneously. Moreover, there exist some conflicting criteria. For example, there is a trade-off between the total cost and the function of the software. We argue that these misfits are the unresolved problems of the pre-implementation, and might cause some problems in the latter implementation. This leads to our second hypothesis. H2: There is a positive relationship between the degree of selection criteria misfit and the level of implementation risks. Since the interactions with other variances will change how the motivational and selection criteria misfits

5 affect the ERP implementation, the variances are included in our research model as moderating factors. The variances of pre-implementation decisions that have not yet been discussed include ERP source, scope, schedule, size, budget control, top management support, and the structure of selection team. The corresponding hypotheses are given as the following. H3-9a: There is a moderating effect of the variable X on the relationship between the motivational misfit and the level of perceived implementation risks. H3-9b: There is a moderating effect of the variable X on the relationship between the selection criteria misfit and the level of perceived implementation risks. From hypotheses 3 to 9, the variable X are the source of ERP, the scope of ERP implementation, the contracted ERP project schedule, the contracted amount of ERP project, the level of project budget control, the level of top management support, and the project structure, respectively. 4. Research Method and Results A mail survey is conducted over the CommonWealth Top 1000 Manufacturers in Taiwan. Out of 143 respondents, 98 firms have implemented ERP systems and accounted for an effective response rate of 9.8%. We were using SPSS for Windows 10.0 to perform all the quantitative analysis in this study. 4.1 Descriptive statistics This study used descriptive statistics to understand the characteristics of the responding firms. The characteristics include total asset values, number of employees, number of IT department employees, among other characteristics. Table 1 Descriptive statistics of responding organizational profiles Company Category No. of Percentage characteristics respondents Less than NT$ 80 million 0 0% NT$ 80 million ~ NT$ 0.5 billion % NT$ 0.5 billion ~ NT$ 1 billion % Total asset NT$ 1 billion ~ NT$ 2 billion % NT$ 2 billion ~ NT$ 5 billion % NT$ 5 billion ~ NT$ 10 billion 9 9.1% Over NT$ 10 billion % Less than % 200 ~ % Number of employees 501 ~ 1, % 1,001 ~ 1, % Over 1, % Less than % 4 ~ % 7 ~ % Number of IT 11 ~ % employees 21 ~ % Over % Missing value 2 2% Primary type of ODM (original design manufacturer) %

6 operation Respondent s position OEM (original equipment manufacturer) % OBM (original brand manufacturer) 48 49% Missing value 1 1.0% Manager in other department as PM/PL 8 8.2% IS manager % Lower than the level of IS manager in IS % Missing value 2 2.0% Both selection and implementation phase % Period of Only the implementation phase % participation After implemented % Missing value 1 1.0% Table 2 presents detailed information for packaged ERP implementation. According to our respondents, the most widespread ERP system used by Taiwan s manufacturing companies is Data systems (24.5%), the second is Oracle (17.4%), and the third is SAP (12.3%). From the view of source, half of them are supplied by local vendors (50%) and half are world-wide (48%). As to the external consulting sources, the majority of the responding firms (70.4%) rely on the ERP vendor for implementation support. Table 2 Descriptive statistics of responding ERP project specifications ERP project specifications ERP Vender Consulting firm Estimated implementation duration Estimated ERP system cost Number of modules implemented Category No. of Percentage respondents Data systems % Oracle % SAP % IE ERP 6 6.1% SSA GT 5 5.1% Proyoung Business Information System 4 4.1% Baan 4 4.1% FAST Tech 2 2.0% J. D. Edwards 1 1.0% Others % Provided by ERP vender % Touche 5 5.1% HP 4 4.1% Andersen TN Soong & Co % Advanced Tek.com, Inc % KPMG 2 2.0% Others % Less than 6 month % 6 month ~ 1 year % 1 year ~ 1.5 year % 1.5 year ~ 2 year % Over 2 year 4 4.3% Missing value 5 5.1% Less than NT$ 6 million % NT$ 6 million ~ NT$ 15 million % Over NT$ 15 million % Missing value % 1 ~ 2 modules 8 6% 3 ~ 4 modules % 5 ~ 6 modules % 7 ~ 8 modules % Over 9 modules 3 2.2%

7 Module implemented Missing value 1 0.8% Purchasing % Material management % Financial accounting % Production planning % Sales % Asset management % Human resource management % Distribution/ logistics % Quality management % Financial control % R and D management % Financing management 9 9.2% Investment management 4 4.1% Other 7 7.1% Whether the data collected were representative of the population or not was tested in two ways. First, the non-response bias test was conducted. A random sample of 50 non-respondents was compared with the 50 respondents randomly selected from the 98 effective respondents in total assets and number of employees. A t-test showed that no significant differences in these two variables between these two samples. Second, early and late respondents were compared upon two descriptive variables, total assets and employee numbers. To conduct the test, the sample was split into two subgroups according to whether it mailed back in one week or not. The numbers of the two groups are 59 and 39. The results of t-tests depicted in Table 5.5 indicated that no significant differences in these two variables between early and late respondents. Thus, there is homogeneity of variance in the sample. Three major forms of validity are content validity, criterion-related validity, and construct validity. The content validity is measured by judgmental. Firstly, it was established through a careful assessment of the literature review. Secondly, two executives from manufacturing industry and one from consulting firm provided useful feedback for purification of scale items. Thirdly, the pre-test is done by 30 respondents from manufacturing industry excluding the formal sample, and some refinement is done according to the statistics results and suggestions. The construct validity is measured by convergent-discriminant techniques (Cooper and Schindler, 2001). To establish the unidimensionality of each construct, we eliminate an item if its item-total correlation was below 0.4 (Kerlinger, 1986) and its factor loading was below 0.5 (Hair et al., 1992). Cronbach s α is a widely used measure of scale reliability. Typically, a scale is reliable if its α is 0.70 or higher (Nunnally, 1978; Werts, Linn, and Joreskog, 1974). Reliability tests were performed through the calculation of Cronbach s α for each construct. All α results ranged from to , except that of organizational fit dimension. Thus the items of this dimension were excluded to preserve the internal consistency of the scales as in Table 3. As a summary, three items were eliminated from the Motivational misfits (), one item is eliminated from the Software (SCSF) in the Selection criteria misfit (), and three items were eliminated from the Vendor (SCVE) in the Selection criteria misfit (). The rest items are used for further analysis. Table 3 Reliability analysis

8 Construct Dimension of each dimension of the construct Motivational misfit () Selection criteria misfit () Software (SCSF) Vendor (SCVE) Consultant (SCCO) ERP project specification Budget control (PBUD) Support (PSUP) Structure (PSTR) Organizational fit (RIMO) Skill mix (RIMS) Implementation risks (RIM) Management structure and strategy (RIMM) Software system design (RIMD) User involvement and training (RIMU) Technology planning/ integration (RIMT) Hypotheses testing Table 4 Correlation between independent and dependent variables Var RIMD RIMU RIMT RIMS RIMM RIM ** RIMD.258* RIMU ** RIMT *.460**.357** RIMS.296** **.585**.417** RIMM.357**.303**.657**.563**.495**.624** RIM.286**.255*.838**.833**.631**.792**.850** Base relation Multiple regression analysis was used to test the base relation (H1 and H2) among the independent variables, motivational misfits and selection criteria misfits, and the dependent variable, the level of perceived implementation risks as shown in table 5. The results indicate that the relationship between independent variables (motivational misfit and selection criteria misfit) and the level of implementation risks was significant (F(2,95)=5.666; p<0.01). The regression coefficient for motivational misfit was positive and significant (t=0.2098, p<0.05). Among the independent variables, motivational misfit was positively related to ERP implementation risk with significance level of 0.05, however, selection criteria misfit was not significant (t=1.628; p>0.05). Thus H1 is supported and H2 is not supported by our empirical data.

9 Table 5 Multiple regression analysis on H1 and H2 Dependent variable: RIM Independent Variable B Std. err. t-value p-value * R 2 =.107; Adjusted R 2 =.088; F(2,95)=5.666; p-value=0.005** Moderator effects The preferable way to measure the dichotomous moderator effect is to measure the effect of the independent variable on the dependent variable by unstandardized (not beta) regression coefficients for each group (Baron and Kenny, 1986). The source of ERP is a dichotomy variable. Thus we divided the whole sample into two subgroups, one for international vendor and the other for local vendor, and repeated the regression analyses for testing H1 and H2. We find that when the ERP is from international vendors, both the regression coefficients were not significant (p>0.05), while when the ERP is from local vendors, the regression coefficient of the motivational misfit was significant (t=2.142; p<0.05). Given the source of ERP is not correlated to the level of ERP implementation risks, we can establish that the source of ERP is a pure moderator to the base relation between the motivational misfit and the perceived implementation risks, that is, H3a is supported, but not H3b. Table 6 Moderating effects of the source of ERP Source B p-value R 2 / Adjusted R 2 P-value All (98) * / ** International (49) / Local (49) * / * Table 7 Moderating effects of the scope of ERP implementation Scope B p-value R 2 / Adjusted R 2 p Primary (89) * / ** Support (89) / * As for the scope of ERP implementation, we test whether the base relation will be influenced by the kind of module, i.e., to support primary activities or support activities. We choose the firms that adopt the module of material management to represent the primary activity module as sample 1. The sample 2 includes the firms that adopt the module of financial accounting, which represents the support activity module. We found that when the module is support activity one, the regression coefficients were not significant (p>0.05), while when the module is primary activity one, the regression coefficient of motivational misfit was significant (t=2.280; p<0.05), but the regression coefficient of selection criteria misfit was not. Given the scope of ERP is not correlated to the level of implementation risks, we can also establish that the scope of ERP implementation is a pure moderator to the relationship between motivational misfit and implementation risk, while it plays no moderating effect on the

10 relationship between selection criteria misfits and the level of implementation risks. We examine the continuous moderator according to Sharma et al. (1981). From the correlation with the predictor or criterion and the interaction with the predictor in the regression, the moderator can be classified into one of the three categories, they are pure moderator, quasi moderator, and homologizer. The results are summarized in table 8. Table 8 Moderating effects of the continuous variables Moderating variables (interaction with) p-value (interaction with) R 2 p R R ** PLEN * PSIZ ** PBUD * ** PSUP * PSTR ** Only budget control is a significant quasi moderator in the base relation between the motivational misfit and the level of perceived implementation risks. However, the schedule, the size, top management support, and the structure have no significant interaction. The schedule is a homoligizer. In such a situation, the error term is posited to be a function of the moderator variable. That means there might be factors hidden in the error term. The size, top management support, and the structure, which means the user involvement in the selection phase, might be an intervening, exogenous, antecedent, suppressor, or additional predictor (independent) variable depending rather than a moderator. That is, those of the ERP project specifications might have greater effect than its interaction with the independent variable does. 5. Conclusion For the first time we have some quantitative empirical evidence to enhance our understanding of the ERP implementation risks. Among the various pre-implementation decisions, we found that if the selected ERP is less capable of satisfying our strategic, business, or technical requirements (i.e., the degree of motivational misfit is high), the resulting implementation risks perceived to be very high. This confirms that to succeed in implementing complex enterprise systems such as ERP, the company has to establish its requirements first and strive to appropriate a solution that fits its requirements as much as possible. Secondly, the factors that would affect the base relation between the motivational misfit and the perceived implementation risks include the planning activities such as budget control and project specifications such as if the vendor of the selected ERP is a local company and the scope of ERP implementation includes primary activity. The other factors studied, though not affecting the base relation, are potential additional predictors and need further study to establish their contribution to implementation risks. 6. References (Omitted)