The Relationship Between Implementation Variables and ...

Proceedings of the First Workshop on Knowledge Economy and Electronic Commerce

The Relationship Between Implementation Variables and Performance Improvement of ERP Systems Wen-Hsien Tsai*, Julian Ming-Sung Cheng*, Jun-Der Leu* Yi-Wen Fan**, Ping-Yu Hsu*, Li-Wen Chou*, Ching-Chien Yang* *Dept. of Business Administration, National Central University Jung-Li, Taiwan Email: [email protected]

**Dept. of Information Management, National Central University Jung-Li, Taiwan E-mail: [email protected]

Abstract The purpose of this paper is to explore the relationship between some implementation variables and performance improvement of ERP systems. DeLone and McLean (1992) surveyed 180 articles attempting to measure information systems (IS) success and proposed an analysis framework, composed of six dimensions, for assessing the ERP performance in the post-implementation stage. In this paper, DeLone-&McLean’s framework will be used to develop the ERP performance measures fit for ERP adopters of Taiwan. The implementation variables explored in this paper are ERP implementation statuses (all the planned modules having been implemented or not), ERP system sources (packaged ERP systems or nonpackaged ERP systems), and ERP implementation strategies (integral or step-by-step planning; Big Bang or phased approach). Structured questionnaire were sent to those companies listed in the TOP500 The Largest Corporations in Taiwan 2001. The research findings indicate that the companies using nonpackaged ERP systems, integral planning and having implemented all the planned ERP modules will have the better performance improvement. Keywords: ERP System Sources, ERP Implementation Strategies, Performance Improvement 1. Introduction In recent years, companies throughout the world gradually adopt Enterprise Resource Planning (ERP) systems to enhance competitiveness, to enhance the ability of responding more quickly to change, to enable easier access to information and faster retrieval of information or reports, to improve information for strategic planning and operational control, and to achieve other benefits (Mirani and Lederer, 1998). The main purpose of ERP projects is the automation and integration of many basic processes in order to integrate information across the enterprise and eliminating complex, expensive interfaces between computer systems (Teltumbde, 2000). Since all business functions are involved in ERP systems, they will be highly complex information systems. And, it is expensive and time consuming to implement an ERP system (Sarkis and Gunasekaran, 2003). Due to the constraints of budget and time, some companies may employ a phased implementation approach, that is, modules are implemented one at a time or in a group of modules, often a single location at a time. Phased implementations require substantial attention and maintenance given to legacy systems in order to facilitate integration with the new ERP system. Moreover, there may not be enough modules implemented to achieve functionality. However, there also are some advantages and disadvantages for a Big Bang implementation approach, where an entire suite of ERP modules is implemented at all locations at the same time (Thanasankit, 2001; Mabert et al., 2003). Generally, so-called ERP systems are the packaged ERP software purchased from vendors. Nevertheless, in our knowledge, some companies in Taiwan employed the non-packaged ERP systems that came from evolution of legacy systems, self-redevelopment, or outsourcing. ERP vendors designed their packaged ERP systems to be the universal package software for various industries and organizations. Also, Packaged ERP systems often offer numerous options representing best practices (Teltumbde, 2000). Even so, it is impossible for any organization to install a packaged ERP system without any tailoring or add-on. Thus, it is not advantageous to adopt an ERP system if it requires considerable modifications. In view of the issues mentioned above, the purpose of this research is to explore the relationship between some implementation variables and performance improvement of ERP systems. The implementation variables explored in this paper are ERP implementation statuses (all the planned modules

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having been implemented or not), ERP system sources (packaged ERP systems or non-packaged ERP systems), and ERP implementation strategies (integral or step-by-step planning; Big Bang or phased approach). Besides, the Information System (IS) success model of DeLone and McLean (1992) is used to develop the ERP performance measures for measuring the performance improvement levels. 2. ERP Performance Measures In this paper, we utilize DeLone and McLean (1992) IS success model to develop ERP performance measures. DeLone and McLean (1992) divided IS success measure into six dimensions or categories as follows: (1). System Quality: measures of the information processing system itself. (2). Information Quality: measures of the information system output. (3). System Use: measures of recipient use of information system. (4). User Satisfaction: measures of recipient response to the use of information system. (5). Individual Impact: measures of the effect of information on the behavior of the recipient. (6). Organizational Impact: measures of the effect of information on organizational performance. This study selected ERP performance measures from the related literature (DeLone and McLean, 1992; DeLone and McLean, 2003; Saarinen, 1996; Skot et. al., 2001; Mirani and Lederer, 1998; Lee et al., 2002; Liberatore and Miller, 1998; Mabert et al., 2000). As for Organizational Impact, the Balanced Scorecard (BSC) approach is used to divide ERP performance measures of Organization Impact dimension into four categories (Kaplan and Norton, 1992; Roseman and Wiese, 1999; Lipe and Salterio, 2000). Among those authors researching on the DeLone and McLean’s IS success model, Li (1997) and Skok et al., (2001) explored the importance of IS success measures using 7-point and 9-point Likert-type scales of survey questionnaires respectively. In this research, we will use 7-point Likert-type scales. 3. Methodology 3.1 Research Hypotheses As mentioned in Section 1, the purpose of this research is to exploring the relationship between some implementation variables and performance improvement of ERP systems. The dependent variables are the performance improvement levels of System Quality, Information Quality, System Use, User Satisfaction, Individual Impact, Organizational Impact, and Composite Performance after having implemented ERP systems. The implementation variables (dependent variables) explored in this paper are: (1) ERP implementation statuses (all the planned modules having been implemented or not), (2) ERP system sources (packaged ERP systems or non-packaged ERP systems), and (3) ERP implementation strategies (integral or step-by-step planning; Big Bang or phased approach). The research hypotheses of this research are as follows: H1: There is no difference in performance improvement levels between the companies that have implemented all the planned modules and that have implemented the partial modules. H2: There is no difference in performance improvement levels between the companies that have implemented ERP systems with different ERP system sources. H3: There is no difference in performance improvement levels between the companies that have implemented ERP systems with different ERP implementation strategies. 3.2 Data Collection There are two stages in this research described as follows: Stage1: Listing ERP Performance Measures and Evaluating their Importance by a Small Sample Survey The list of ERP performance measures is obtained after a literature review. These ERP performance measures are categorized according to the six dimensions of DeLone and McLean’s model. Then the importance of these ERP performance measures (83 measures in total) are evaluated by companies that have implemented ERP systems by using 7-point Likert-type scales. In this stage, 260 questionnaires were sent to the companies that had implemented ERP systems. The number of usable responses is 45, and the

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usable response rate is 17.31%. Stage 2: Redesigning the Survey Questionnaire Concerning ERP Performance Measures and Implementation Variables. Collecting the Data by a Large Sample and Analyzing the Data Collected According to the average importance score rankings obtained from Stage 1, top five important performance measures were chosen for each success dimension of DeLone and McLean’s model except the Organizational Impact dimension where 12 measures were chosen. The 37 chosen measures are as shown in Table 3. In this stage, 3597 questionnaires were sent to the companies of manufacturing and services industries, listed in the TOP 5000 The Largest Corporations in Taiwan on 2001. Of the 3597 questionnaires mailed, 657 (18.27% of 3597) were usable responses. Among 657 usable responses, 93 (14.16% of 657) were under implementation and there were no module going-live, 137 (20.85% of 657) had implemented partial modules, and 146 (22.22% of 657) had implemented all the planned modules. In this paper, 283 companies, that had implemented all the planned modules or the partial planned modules, will be analyzed. The characteristics of the respondents are shown in Table 1. 3.3 Measurement of Performance Improvement Levels In the questionnaire of Stage 2, we asked respondents to evaluate the performance improvement level and importance level for each of the 37 chosen ERP performance measures by using 7-point Likert-type scales ranging from 1 (Substantial Deterioration) to 7 (Substantial Improvement) and from 1 (Extremely unimportant) to 7 (Extremely important), respectively. The data of importance levels are used to calculate the relative weights of measures. We used these data and the following equations to determine the performance improvement levels of System Quality, Information Quality, System Use, User Satisfaction, Individual Impact, Organizational Impact, and Composite Performance after implementing ERP systems: th

th

1.The performance improvement level of the j dimension for the i respondent’s company: lj

Pij = ∑ Pijk ∗ k =1

W

i = 1 , 2 , 3 ,..., N ,

,

jk

j = 1, 2 , 3 ,..., 6

lj

∑W

jk

k =1

where

W jk = the average importance level score of the k th measure of the j th dimension as perceived by N respondents N

=

( ∑ W ijk ) i =1

,

N

Wijk = the importance level score (1 to 7) of the k th measure of the j th dimension as perceived by the

i th respondent,

Pijk = the performance improvement level score (1 to 7) of the k th measure of the j th dimension th

for the i respondent’s company,

l j = the number of chosen measures for the j th dimension. th

2.The composite performance improvement level for the i respondent’s company: lj

6

Pi = ∑ ( Pij ∗ j =1

∑W

jk

),

k =1 6 lj

∑∑ W

i =1, 2, 3, …, N

jk

j =1 k =1

where

Pij , W jk , and l j are defined as above.

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Table 1. Characteristics of the Respondents Enterprise Employment Fewer than 100 employees 10 to 300 More than 300

Freq. 42 127 114

Percentage 14.84% 44.88% 40.28%

Annual Revenue (NT$ bil.) $0.2 or