Research on E-Commerce Supply Chain Alliance Performance Evaluation Based on Importance-Performance Analysis

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1 Research on E-Commerce Supply Chain Alliance Performance Evaluation Based on Importance-Performance Analysis LUO Hanyang 1, JI Wenli 2 1. Shenzhen Graduate School, Harbin Institute of Technology, P.R.China, College of Management, Shenzhen University, P.R.China, [email protected] Abstract: In the E-commerce environment, the evaluation of the supply chain alliance performance (SCAP) has its characteristics and faces new challenges. As a valid and powerful technique, Importance-Performance Analysis (IPA) has become a popular managerial tool that has been broadly used to identify the strengths and weakness in various industries recently. It is easy to be used and offers considerable value for the evaluation of SCAP. This paper mainly explores how to use IPA to evaluate five parts of SCAP in E-commerce environment, namely customer satisfaction, enterprise satisfaction, business process, economic efficiency and innovation and development ability of supply chain alliance. The interpretation of the IPA is graphically presented on a grid divided into four quadrants: Keep Up the Good Work, Concentrate Here, Low Priority and Possible Overkill. IPA technique helps the supply chain alliance locate bottlenecks and areas of inefficiency and provides a useful tool for supply chain alliance to understand and improve the SCAP. Keywords: E-commerce, Importance-performance analysis, Evaluation, Supply chain alliance performance 1 Introduction With the development of the network technology, the supply chain management tightly combines with E-commerce, which makes sharing the information of supply and demand easier, the commercial connection closer and the all-directional alliance s cooperation more feasible among the enterprises in the supply chain. E-commerce environment provides the supply chain management with the network tool which dynamically combines all the trading partners of the supply chain alliance together. The electronic managerial tools bring the network benefit of management for the enterprises, but at the same time, they put forward new challenges for the evaluation of the supply chain alliance performance and the validity of the management [1]. The next section discuses the indexes for the performance evaluation of the supply chain alliance. The third section explains the use of Importance-Performance Analysis (IPA) for assessing the indexes importance to the supply chain alliance performance. The fourth section presents the research methods, analyzes and interprets the study results using IPA. And at last, the fifth section is the conclusion. 2 The Construction of E-commerce Supply Chain Alliance Performance Evaluation Indexes Edward, Ron Basu [2] pointed out that different strategic supply chain should have different key performance indexes. Beamon [3], Lambert and Terrance, Gilmour [4] emphasized the total supply chain performance, they pointed out that it should evaluate the performance from all parts of the supply chain. Stefan, Bechtel and Jayaram [5] thought that the evaluation must pay attention to the influence among trading partners across the supply chain. Shao Xiaofeng, Ji Jianhua and Huang Peiqing made a systematic analysis on the evaluation of the supply chain system s competitiveness [6]. Zeng Xiangyun [7] proposed the performance evaluation indexes of the supply chain enterprises from the supply chain theory. Xu Xianhao, Ma Shihua and Chen Rongqiu [8] explored the performance evaluation of the supply chain system in terms of the production operation. Liu Xiaoping and Li Hongfu [9] put forward a model of evaluating supply chain efficiency with respect to strategy, tactics and operation. Xu Zhongyan and 771

2 Sun Rui [10] discussed the requirement of the supply chain organization s performance evaluation in the e-commerce environment, and provided an evaluation indexes system about the supply chain alliance performance according to its characteristic. This paper, based on the evaluation indexes system in previous research [10], mainly explores how to introduce IPA to evaluate the performance of the supply chain alliance. From Tab.1 we can know that the evaluation indexes system includes five aspects of the supply chain alliance performance (SCAP), as shown in the following:customer satisfaction (CS),supply chain alliance s enterprise satisfaction (SCAES),supply chain alliance s business process (SCABP),supply chain alliance s economic efficiency (SCAEE),supply chain alliance s innovation and development ability (SCAIDA). 3 Importance-Performance Analysis IPA is a useful technique for evaluating the above five aspects of the SCAP. IPA conceptually underlay the multi-attribute models in the late 1970s. Martilla and James [11] applied the IPA technique to analyze the performance of the automobile industry. Originating from the marketing discipline, the technique is used to evaluate the importance and performance of various attributes. It s a valid and powerful technique, and has become a popular managerial tool that has been broadly used to identify the strengths and weakness in various industries in recent years. Skok et al. used IPA as a tool for diagnosis of information systems success in the heath club industry. Kee.Hung Lai used IPA to assess the relative importance and performance of various supply chain performance factors from the view of service providers in the transport logistics industry. Hemmasi, Strong and Taylor measured the service quality of hospital services using IPA as an alternative to the traditional SERVQUAL instrument devised by Parasuraman, Zeithaml and Berry. IPA is easy to be used and offers considerable value for the evaluation of the performance of the supply chain alliance in E-commerce environment. In this study, IPA technique is used to help the supply chain alliance locate bottlenecks and areas of inefficiency and identify the strengths and weakness in different aspect of SCAP. This means a lot for improving the efficiency of the supply chain activities. IPA begins with identifying the critical factors to be evaluated. Next, a survey instrument is developed to collect data about the importance and performance of each factor from the samples, often using numerical scales. Performance and importance are calculated for each factor and plotted, typically with performance along the X-axis and importance along the Y-axis. The point corresponding to each factor determines its placement on the grid. According to Martilla and James [18], positioning the vertical and horizontal axes is a matter of judgment by the researcher, based on relative rather than absolute levels of importance and performance. The interpretation of the IPA is graphically presented on a grid divided into four quadrants. Fig.1 illustrates the IPA grid. The Y-axis denotes perceived importance of selected factors, and the X-axis shows the product's (or service's) performance in relation to these factors. The four identifiable quadrants are: Concentrate Here, Keep Up the Good Work, Low Priority and Possible Overkill. In the Concentrate Here quadrant, factors are perceived to be very important to respondents, but their performance levels are seen as fairly low. This sends a direct message that improvement efforts should concentrate here. In the Keep Up the Good Work quadrant, factors are perceived to be very important to respondents, and at the same time, the organization seems to have high levels of performance in relation to these factors. In the Low Priority quadrant, factors have low importance and low performance. Although performance levels may be low in this quadrant, managers should not be overly concerned since the factors in this quadrant are not perceived to be very important. Limited resources should be expended on this Low Priority quadrant. Lastly, the Possible Overkill quadrant contains factors of low importance, but of relatively high performance. Respondents are satisfied with the performance of the organizations, but managers should consider present efforts on the factors of this quadrant as being over-utilized. 4 Using IPA to evaluate SCAP and the analysis of the results In this study, the four quadrants of the Fig.1 are interpreted in the following way. In QuadrantⅠ, 772

3 Ⅱ indexes are considered to be very important to the supply chain alliance s enterprises, while they also Ⅲ Ⅰ achieve high levels of performance on these indexes, suggesting that Ⅳ they should Keep Up the Good Work. In QuadrantⅡ, indexes are perceived to be very important, but performance levels are relatively low. This suggests that they should pay more attention to these indexes Concentrate Here. In Quadrant Ⅲ, both the importance and performance levels of the indexes are perceived to be relatively low, suggesting that they should put a Low Priority for improvement on these indexes. In Quadrant Ⅳ, the indexes are perceived to be of relatively low importance but the performances of the enterprises on these indexes are relatively high. This suggests that these Possible Over killed performance areas have consumed excessive resources and the enterprises located within this quadrant should consider the reallocation of resources to other areas in need of strengthening. QUADRANT QUADRANT Concentrate Here Keep Up the Good Work High Importance High Importance Low Performance High Performance QUADRANT QUADRANT Low Priority Possible Overkill Low Importance Low Importance Low Performance High Performance Importance Fig.1 Importance-Performance Matrix To evaluate the five aspects of SCAP, the 24-item indexes system in Tab.1 is adopted. The survey questionnaire is designed to facilitate the use of IPA in data analysis. The respondents are requested to respond to each of the 24 indexes twice by determining their perceptions of each factor; one is about the level of importance of a certain index and the other is related to the level of performance of the supply chain alliance with respect to the given index. All the indexes in the survey questionnaire were assessed on a five-point scale, with an anchor from 5(very important) to 1(very unimportant) for the level of importance part, and from 5(superior to competitor) to 1(much worse than competitor) for the level of performance part. In the questionnaire, the managers or directors of the E-commerce supply chain alliance s companies are selected as the study targets in this study because they have the knowledge and experience about the industry under investigation. But under the domestic environment, such questionnaire often involves some business secret of the companies, so it s very difficult to collect real and valid data we need in the study. So the following analysis is on the basis that we have got the data. The emphasis of this study is the application of the IPA technique, so lacking of the real data will not have much influence on the study. Tab.1 shows the respective mean and standard deviation of both the importance and performance rating for each of the 24 indexes of the SCAP. In Tab.1 we also apply a series of T-tests to find that whether any significant differences exist between the importance and performance of the SCAP indexes. From Tab.1 we can see there are significant differences, i.e. t>2, between the mean of importance and performance for all the SACP indexes. Using IPA technique, we can get five Importance-Performance Matrixes (as shown in Fig.2-Fig.6) for the five aspects of the SCAP, namely CS, SCAES, SCABP, SCAEE and SCAIDA. From Tab.1 we can know the mean score for the Index 1(Product quality) is very high (Importance mean=4.86 and Performance mean=4.27), so Index 1 falls into the quadrant Ⅰ(Keep Up the Good Work). As is shown in Fig.2 Importance-Performance Matrixes for CS, three indexes (1, 2, 4) fall into Quadrant Ⅰ. This indicates that such indexes are considered to be very important to the evaluation of CS, while they also achieve high levels of performance, suggesting that they should Keep Up the Good Work. But Index 3 (Delivery time) and Index 5 (Efficiency) score relative low, either the Importance or the Performance. Such indexes fall into Quadrant Ⅲ(Low Priority). It suggests that the respondents under the investigation view these indexes as the least important ones in CS of SCAP. So limited 773

4 resources should be expended on this Low Priority quadrant. Tab.1 Means and Standard Deviations (SD) in Importance and Performance Ratings on SCAP Importance Performance Performance No. Indexes Minus Importance Mean SD Mean SD Mean t-value 1 Product quality Service level CS 3 Delivery time Information communication Efficiency Rate of delivery on time SCAES 7 Rate of cost-profit Product qualification rate Information communication level Output flexibility Delivery flexibility SCABP 12 Sales-output rate Demands-output rate Data sharing rate Profit growth rate Market share SCAEE 17 Rate of product cost reduction Rate of return on supply chain capital Capital turnover Intelligence-capital rate Average training time SCAID 22 Average training cost A 23 New products development cycle New products (Service) sales rate Fig.2 Importance-Performance Matrix for CS Fig.3 Importance-Performance Matrix for SCAES From Fig.3 Importance-Performance Matrix for SCAES, we can see that Index 8 (Product qualification rate) scores relative well (Importance mean=4.5 and Performance mean=4.12) in evaluating SCAES. There are three indexes (8, 6, 9) falling into Quadrant Ⅰ(Keep Up the Good Work). It shows that these indexes are very important to the evaluation of SCAES, and they do perform well. Their advantage should be kept because they are the strengths of the organization. Index 7 (Rate of cost-profit) falls into the quadrant Ⅲ(Low Priority). Its low importance and low performance makes it a 774

5 candidate for discontinuation of resources or effort. Ⅲ Fig.4 Importance-Performance Matrix for SCABP Fig.5 Importance-Performance Matrix for SCAEE From Fig.4 Importance-Performance Matrix for SCABP, it is clear that Index 10 (Output flexibility) and 11(Delivery flexibility) fall into Quadrant Ⅱ(Concentrate Here). Such indexes are perceived to be very important to the evaluation of SCABP, but their performance levels are relatively low. It suggests that they should be paid more attention. This also sends a direct message that improvement efforts should concentrate here and these indexes should be given top priority. Index 12 (Sales-output rate) and 13 (Demands-output rate) fall into Quadrant Ⅳ(Possible Overkill). These indexes have low importance but relatively high performance on SCABP. Respondents are satisfied with the performance, but managers should consider present efforts on the indexes of this quadrant as being over-utilized. Index (Data sharing rate) falls into Quadrant (Low Priority), which should be given low priority. Tab.2 Distribution of SCAP Indexes in the Importance-Performance Matrixes Index Number QuadrantⅠ: 1,2,4,6,8,9, Keep Up the Good Work 16,17,22,24 QuadrantⅡ: 10,11,15,21 Concentrate Here QuadrantⅢ: 3,5,7,14,18, Low Priority 19,20,23 Fig.6 Importance-Performance Matrixes for SCAIDA Ⅳ: Quadrant Possible Overkill 12,13 From Fig.5 Importance-Performance Matrix for SCAEE, we can know that Index 16 (Market share) and 17(Rate of product cost reduction) fall into Quadrant Ⅰ(Keep Up the Good Work). These indexes are very important to the evaluation of SCAEE, and they perform well, so their advantage should be kept, too. Index 15 (Profit growth rate) falls into Quadrant Ⅱ(Concentrate Here). Managers should put more efforts on this index. There are two indexes (18, 19) still falling into Quadrant Ⅲ(Low Priority). From Fig.6 Importance-Performance Matrix for SCAIDA, it shows that Index 22 (Average training 775

6 cost) and 24(New products (Service) Sales rate) fall into Quadrant Ⅰ(Keep Up the Good Work),while Index 21 (Average training time) falls into QuadrantⅡ (Concentrate Here) and the two indexes (20, 23) fall into Quadrant Ⅲ(Low Priority) during the evaluation of SCAIDA. Tab.2 summarizes the above analyses about five aspects of the evaluation of SCAP using IPA. 5 Conclusions The purpose of this paper is to explore how to use IPA to evaluate the five parts of supply chain alliance performance in E-commerce environment. This study has provided a self-evaluation of SCAP in the supply chain alliance companies. To improve SCAP in E-commerce, companies must communicate their improvement priorities with their supply chain partners. Using IPA technique can help the supply chain alliance determine the bottlenecks and areas of inefficiency and identify the strengths and weakness in different aspect of SCAP. The results of this study provide a useful tool for supply chain alliance s organizations to understand and improve the SCAP. However, this study has its limitations. First, the sample of the questionnaire in this study is just on the assumption that we have got the data. If we are to collect data again, the study results could be dramatically different. Second, we cannot ensure respondents seriousness about the questionnaires. References [1] Stanley Baiman, Paul E.Fischer and Madhav V.Rajan. Performance Measurement and Design in Supply Chain. Management Science, 2001, 47(1): [2] Basu R. New Criteria of Performance Management: A transition from Enterprise to Collaborative Supply Chain. Measuring Business Excellence, 2001, 5(4): 7-13 [3] Beamon B M. Measuring Supply Chain Performance. International Journal of Operations and Production Management, 1989, 19(3): [4] Gilmour P. A Strategic Audit Framework to Improve Supply Chain Performance. Journal of Business and Industrial Marketing, 1999, 14(5): [5] Bechtel C, Jayaram J. Supply Chain Management: A Strategic Perspective. The International Journal of Logistics Management, 1997, 8 (1): [6] SHAO Xiao-feng, JI Jian-hua, HUANG Pei-qing. Performance Measurement System for Analyzing Supply Chain Competitiveness. Forcasting, 2000,(6): (in Chinese) [7] ZENG Xiang-yun. The Evaluation of Enterprise Performance Based on the Theory of Supply Chain Management. Economic Management, 2001,(22): 23-27(in Chinese) [8] XU Xian-hao, MA Shi-hua, CHEN Rong-qiu. A Study on the Performance Evaluating Index in Supply Chain. Journal of HUST,2000,(2):70-72(in Chinese) [9] LIU Xiao-ping, LI Hong-fu. Tactics and Index System for Supply Chain Efficiency Evaluation. Industrial Engineering and Management, 2004, (6):15-20 (in Chinese) [10] XU Zhong-yan, SUN Rui. The Performance Evaluation in E-commerce Supply Chain Organization. Commercial Economy Studies, 2004,(21): (in Chinese) [11] Martilla.J.A and James.J.C. Importance-Performance Analysis. Journal of Marketing,1977, 41(1):77-79 Acknowledgement This research is supported by Shenzhen University Scientific Research Foundation(No ). 776