Research on the influence of after-sales service quality factors on customer satisfaction |
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| Affiliation: | 1. Department of Industrial and Systems Engineering, Seoul National University of Science and Technology, 172 Gongreung 2-dong, Nowon-gu, Seoul 139-746, Republic of Korea;2. Department of Technology and Systems Management, Induk University, Choansan-ro 14, Nowon-gu, Seoul 139-050, Republic of Korea;1. Dr. B. R. Ambedkar Institute of Management & Technology, Baghlingampally, Hyderabad-500044, India;2. Graduate School of Business, (Formerly, faculty member, University of Washington), PLAZA Universitaria, 55thth floor, University of Puerto Rico, San Juan, PR, P.O.Box 23332, 00931, USA;1. The School of Management, Xi’an Jiaotong University, The State Key Lab for Manufacturing Systems Engineering, The Key Lab of the Ministry of Education for Process Control & Efficiency Engineering, Xi’an 710049, China;2. Department of Logistics and Maritime Studies, The Hong Kong Polytechnic University, Hung Hom, Kowloon, Hong Kong Special Administrative Region;3. Department of Industrial and Systems Engineering, The Hong Kong Polytechnic University, Hung Hom, Kowloon, Hong Kong Special Administrative Region |
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| Abstract: | Determining customer satisfaction elements in retailing after-sales services have been well explored; however, the increasing competition in this area demands the investigation of actual instrumentality of these elements on satisfaction of customers. In the present research, we have proposed a framework for assessing the instrumentality of after-sales services on customer satisfaction. Kano model and SERVQUAL framework were used to categorize customer satisfaction elements. In addition, in order to address behavioral dissimilarities among customers, RFM clustering technique was used for analysing 243,180 customers of automobile after-sales services. Accordingly, dissatisfaction decrement index and satisfaction increment index were measured for every cluster separately. We identified a group of 21 quality elements and demonstrated the instrumentality and quality of these quality elements on customer satisfaction. RFM clustering technique is applied to address customer dissimilarities and we demonstrated the preferences and desires of customers in each cluster. While some papers have already identified the influential factors of after-sales services on customer satisfaction, this is for the first time that the instrumentality of after-sales services is being identified. Accordingly, this study demonstrates how different after-sales services quality elements affect customer satisfaction. Therefore, the results of this study can help companies to allocate their resources more efficiently. |
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| Keywords: | After-sales services Quality element Fuzzy kano model RFM model SERVQUAL |
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