Abstract
In today’s competitive business environment, service providers have a strong objective to satisfy the customers with low cost to ensure a patronage/loyalty. Performance measurement defines the information or feedback on actions to meeting strategic objectives and client satisfaction. Generally, performance evaluation of the service provider is a time consuming complicated process, depends customer satisfaction. Over the past two decades several researchers have proposed methods to measure service and quality performance in order to improve the performance efficiency of the organization, since there is a considerable room exists. Hence, in this paper, we analyse efficient and inefficient levels of service performance using data envelopment analysis (DEA) and balance scorecard (BSC) techniques, to bridge the exist gap. The DEA approach has been used to measure the performance of automobile dealers from different areas to know their service levels and also treats the quality of service by making use of different cross-efficiency data envelopment analysis models to discriminate the units. Then, a BSC approach analyzes which aspects of decision making units are inefficient, grounded on four perspectives like as; customers, financial, internal business process and learning and growth, based on the study carried out on ten automobile dealers from various areas. The results identify that dealers are inefficient in learning about customer’s growth, which help the dealers to transform from inefficient into efficient. In addition, this study also focused on various insights related to performance evaluation and provide some useful recommendations which can be practiced in future.
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This research was supported in part by the National Natural Science Foundation of China (Grant Nos. 71302005, 71402084), the major Program of the National Social Science Fund of China (Grant No. 13&ZD147).
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Tan, Y., Zhang, Y. & Khodaverdi, R. Service performance evaluation using data envelopment analysis and balance scorecard approach: an application to automotive industry. Ann Oper Res 248, 449–470 (2017). https://doi.org/10.1007/s10479-016-2196-2
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DOI: https://doi.org/10.1007/s10479-016-2196-2