EXAMINING SERVICE QUALITY GAPS AND OPERATIONS–SUPPLY CHAIN PERFORMANCE IN THE INDONESIAN AUTOMOTIVE PARTS INDUSTRY
Published:
2025-11-30Downloads
Abstract
Automotive-parts customers experience supply-chain performance through delivery reliability, timely information, and effective problem resolution. This article develops a diagnostic framework that connects adapted SERVQUAL gaps with operational indicators guided by the APICS/ASCM Supply Chain Operations Reference (SCOR) model. A dataset contains 96 records across 24 Indonesian firms. Five service dimensions are assessed using perception minus expectation scores. Paired comparisons and correlations demonstrate the proposed analysis. Reliability has the largest illustrated gap (−0.96), followed by responsiveness (−0.77). The overall gap correlates positively with the operational composite (r = .47), on-time-in-full delivery (r = .40), and forecast accuracy (r = .39), and negatively with order-cycle time (r = −.51) and defects (r = −.34). These patterns show how managers could connect customer concerns to process improvement. The contribution is a transparent measurement results that distinguish diagnostic priorities from causal explanations.
Keywords:
supply chain performance SERVQUAL SCOR APICS ASCM IndonesiaReferences
Bartos, K. E., Schwarzkopf, J., Mueller, M., & Hofmann-Stoelting, C. (2022). Explanatory factors for variation in supplier sustainability performance in the automotive sector: A quantitative analysis. Cleaner Logistics and Supply Chain, 5, Article 100068. https://doi.org/10.1016/j.clscn.2022.100068
Cronin, J. J., Jr., & Taylor, S. A. (1992). Measuring service quality: A reexamination and extension. Journal of Marketing, 56(3), 55–68. https://doi.org/10.1177/002224299205600304
Dubey, R., Bryde, D. J., Blome, C., Dwivedi, Y. K., Childe, S. J., & Foropon, C. (2024). Alliances and digital transformation are crucial for benefiting from dynamic supply chain capabilities during times of crisis: A multi-method study. International Journal of Production Economics, 269, Article 109166. https://doi.org/10.1016/j.ijpe.2024.109166
Franco, C. W., Benitez, G. B., de Sousa, P. R., Kliemann Neto, F. J., & Frank, A. G. (2024). Managing resources for digital transformation in supply chain integration: The role of hybrid governance structures. International Journal of Production Economics, 278, Article 109428. https://doi.org/10.1016/j.ijpe.2024.109428
Gu, M., Zhang, Y., Li, D., & Huo, B. (2023). The effect of high-involvement human resource management practices on supply chain resilience and operational performance. Journal of Management Science and Engineering, 8(2), 176–190. https://doi.org/10.1016/j.jmse.2022.12.001
Hsieh, C.-H., & Zhang, M. (2022). Critical factors affecting performance of logistics operation planning considering interdependency: A case study in automotive aftermarket. Asian Transport Studies, 8, Article 100055. https://doi.org/10.1016/j.eastsj.2022.100055
Manalu, I. K., Firmansyah, C., Syam, M. F., & Syamil, A. (2024). Comparison of inventory management methods: Reliability centered spares (RCS), ABC analysis and fixed timed period. Jurnal Mantik, 8(1), 122–131.
Mentzer, J. T., Flint, D. J., & Hult, G. T. M. (2001). Logistics service quality as a segment-customized process. Journal of Marketing, 65(4), 82–104. https://doi.org/10.1509/jmkg.65.4.82.18390
Nguyen, T. T. H. (2024). Measuring supply chain performance using the SCOR model. Operations Research Forum, 5(2), Article 37. https://doi.org/10.1007/s43069-024-00314-y
Ning, Y., Li, L., Xu, S. X., & Yang, S. (2023). How do digital technologies improve supply chain resilience in the COVID-19 pandemic? Evidence from Chinese manufacturing firms. Frontiers of Engineering Management, 10(1), 39–50. https://doi.org/10.1007/s42524-022-0230-4
Parasuraman, A., Zeithaml, V. A., & Berry, L. L. (1988). SERVQUAL: A multiple-item scale for measuring consumer perceptions of service quality. Journal of Retailing, 64(1), 12–40.
Pranugroho, B., Miftachudin, Bakti, A. K., & Syamil, A. (2023). Organizational support and work engagement factor for the implementation of MH02 technology with the Delone McLean model. The Spirit of Society Journal, 7(1), 47–56.
Qian, C., Dion, P. A., Wagner, R., & Seuring, S. (2023). Efficacy of supply chain relationships: Differences in performance appraisals between buyers and suppliers. Operations Management Research, 16(3), 1302–1320. https://doi.org/10.1007/s12063-023-00354-3
Singh, S. P., Adhikari, A., Majumdar, A., & Bisi, A. (2022). Does service quality influence operational and financial performance of third party logistics service providers? A mixed multi criteria decision making-text mining-based investigation. Transportation Research Part E: Logistics and Transportation Review, 157, Article 102558. https://doi.org/10.1016/j.tre.2021.102558
Syamil, A., Doll, W. J., & Hong, P. (1996). Organizational learning practices in new product development. In Proceedings of the Annual Meeting of the Decision Sciences Institute (p. 1293). Decision Sciences Institute.
Syamil, A., Doll, W. J., & Quesada, G. (2003). A measurement model for supplier performance in new product development. In Proceedings of the Annual Meeting of the Decision Sciences Institute. Decision Sciences Institute.
Trivedi, S. D., Nigam, A., & Pareek, A. (2023). Service quality dimensions in the manufacturer–distributor dyadic relationship: Manufacturer-to-distributor context. International Journal of Retail & Distribution Management, 51(3), 407–423. https://doi.org/10.1108/IJRDM-08-2022-0283
Urbaniak, M., Rogala, P., & Kafel, P. (2023). Expectations of manufacturing companies regarding future priorities of improvement actions taken by their suppliers. Operations Management Research, 16(1), 296–310. https://doi.org/10.1007/s12063-022-00307-2
Valdivia, C. A. S., Mamédio, D. F., Loures, E. de F. R., & Tortato, U. (2024). Dimensions of digital transformation for digital supply chains: Evidence from an automotive OEM group. Research-Technology Management, 67(2), 57–68. https://doi.org/10.1080/08956308.2023.2298655
Wu, L., Huang, J., Wang, M., & Kumar, A. (2024). Unleashing supply chain agility: Leveraging data network effects for digital transformation. International Journal of Production Economics, 277, Article 109402. https://doi.org/10.1016/j.ijpe.2024.109402
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