Supply Chain Innovation and Business Success: The Mediating Roles of Logistics Performance and Service Quality in the Bangkok Frozen Seafood Industry
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This study examines how supply chain innovation relates to the business success of frozen seafood firms in Bangkok and whether this relationship operates through operational capabilities. Grounded in the Resource-Based View and the Dynamic Capabilities View, integrated into a Resource–Capability–Performance framework, the study positions logistics performance and service quality as parallel mediators. A quantitative, cross-sectional survey of 400 senior executives from frozen seafood enterprises was analyzed using partial least squares structural equation modeling (PLS-SEM). All six hypotheses were supported (p < .001). Supply chain innovation was strongly and positively associated with logistics performance (β = 0.507) and service quality (β = 0.529), and both capabilities were positively associated with business success (β = 0.358 and 0.310). The two specific indirect effects were significant (β = 0.181 and 0.164; total indirect effect = 0.345), indicating that supply chain innovation is associated with business success through significant indirect relationships transmitted by logistics performance and service quality, rather than directly. The model showed moderate in-sample explanatory power (R² = 0.257–0.289) with supported out-of-sample predictive relevance. Importance–performance map analysis identified supply chain innovation as the highest-priority leverage point—high in importance yet lowest in current performance. Theoretically, the study integrates the “what” of the Resource-Based View with the “how” of the Dynamic Capabilities View to explain a mediating pathway rarely tested in a developing-economy cold-chain context. In practice, it advises firms—especially resource-constrained SMEs—to invest not only in innovation but also in the logistics and service systems that translate innovation into operational excellence, ultimately supporting sustainable growth and export competitiveness in a strategically vital sector.
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Agi, M. A. N., & Jha, A. K. (2022). Blockchain technology in the supply chain: An integrated theoretical perspective of organizational adoption. International Journal of Production Economics, 247, 108458. https://doi.org/10.1016/j.ijpe.2022.108458
Alfian, G., Syafrudin, M., Farooq, U., Ma’arif, M. R., Syaekhoni, M. A., Fitriyani, N. L., Lee, J., & Rhee, J. (2020). Improving efficiency of RFID-based traceability system for perishable food by utilizing IoT sensors and machine learning model. Food Control, 110, 107016. https://doi.org/10.1016/j.foodcont.2019.107016
Arora, M. (2022). Analysis and management of issues in cold chain of frozen food products [Doctoral dissertation, University of Petroleum and Energy Studies]. UPES Repository. http://hdl.handle.net/123456789/4064
Barney, J. (1991). Firm resources and sustained competitive advantage. Journal of Management, 17(1), 99–120. https://doi.org/10.1177/014920639101700108
Bhatti, S. H., Wan Hussain, W. M. H., Khan, J., Sultan, S., & Ferraris, A. (2024). Exploring data-driven innovation: What’s missing in the relationship between big data analytics capabilities and supply chain innovation? Annals of Operations Research. https://doi.org/10.1007/s10479-022-04772-7
Boonchunone, S., Nami, M., Krommuang, A., Karnpakdee, C., & Suwunnamek, O. (2024). The impact of logistics service quality through the perceived organizational image on performance: Cold chain logistics provider in Thailand. Acta Logistica, 11(4), 651–663. https://doi.org/10.22306/al.v11i4.564
Cannas, V. G., Ciano, M. P., Saltalamacchia, M., & Secchi, R. (2024). Artificial intelligence in supply chain and operations management: A multiple case study research. International Journal of Production Research, 62(9), 3333–3360. https://doi.org/10.1080/00207543.2023.2232050
Creswell, J. W., & Creswell, J. D. (2023). Research design: Qualitative, quantitative, and mixed methods approaches (6th ed.). SAGE.
Department of Business Development. (2024). DBD DataWarehouse: Business registration statistics [Data set]. Ministry of Commerce, Thailand. https://datawarehouse.dbd.go.th/
Dubey, R., Gunasekaran, A., Childe, S. J., Bryde, D. J., Giannakis, M., Foropon, C., Roubaud, D., & Hazen, B. T. (2020). Big data analytics and artificial intelligence pathway to operational performance under the effects of entrepreneurial orientation and environmental dynamism. International Journal of Production Economics, 226, 107599. https://doi.org/10.1016/j.ijpe.2019.107599
Eslami, M. H., Jafari, H., Achtenhagen, L., Carlbäck, J., & Wong, A. (2024). Financial performance and supply chain dynamic capabilities: The moderating role of Industry 4.0 technologies. International Journal of Production Research, 62(22), 8092–8109. https://doi.org/10.1080/00207543.2021.1966850
Fernandes, A. C., Sampaio, P., Sameiro, M., & Truong, H. Q. (2017). Supply chain management and quality management integration: A conceptual model proposal. International Journal of Quality & Reliability Management, 34(1), 53–67. https://doi.org/10.1108/IJQRM-03-2015-0041
Fugate, B. S., Mentzer, J. T., & Stank, T. P. (2010). Logistics performance: Efficiency, effectiveness, and differentiation. Journal of Business Logistics, 31(1), 43–62. https://doi.org/10.1002/j.2158-1592.2010.tb00127.x
Garg, A., & Vemaraju, S. (2025). Green logistics management effect on sustainable logistics performance. Journal of Environmental & Earth Sciences, 7(2), 175–186. https://doi.org/10.30564/jees.v7i2.7685
Hair, J. F., Risher, J. J., Sarstedt, M., & Ringle, C. M. (2019). When to use and how to report the results of PLS-SEM. European Business Review, 31(1), 2–24. https://doi.org/10.1108/EBR-11-2018-0203
Henseler, J., Ringle, C. M., & Sarstedt, M. (2015). A new criterion for assessing discriminant validity in variance-based structural equation modeling. Journal of the Academy of Marketing Science, 43(1), 115–135. https://doi.org/10.1007/s11747-014-0403-8
Hitt, M. A., Xu, K., & Carnes, C. M. (2016). Resource based theory in operations management research. Journal of Operations Management, 41, 77–94. https://doi.org/10.1016/j.jom.2015.11.002
Kalaiarasan, R., Olhager, J., Agrawal, T. K., & Wiktorsson, M. (2022). The ABCDE of supply chain visibility: A systematic literature review and framework. International Journal of Production Economics, 248, 108464. https://doi.org/10.1016/j.ijpe.2022.108464
Kaplan, R. S., & Norton, D. P. (1992). The balanced scorecard—Measures that drive performance. Harvard Business Review, 70(1), 71–79.
Khan, T., & Emon, M. M. H. (2025). The role of digital supply chain practices in enhancing firm performance: Insights from the manufacturing sector of Bangladesh. Brazilian Journal of Operations & Production Management, 22(2), e20252493. https://doi.org/10.14488/BJOPM.2493.2025
Kock, N. (2015). Common method bias in PLS-SEM: A full collinearity assessment approach. International Journal of e-Collaboration, 11(4), 1–10. https://doi.org/10.4018/ijec.2015100101
Kock, N., & Hadaya, P. (2018). Minimum sample size estimation in PLS-SEM: The inverse square root and gamma-exponential methods. Information Systems Journal, 28(1), 227–261. https://doi.org/10.1111/isj.12131
Krungsri Research. (2023). Industry outlook 2023–2025: Processed seafood industry. Bank of Ayudhya. https://www.krungsri.com/en/research/industry/industry-outlook/food-beverage/processed-seafood/io/processed-seafood-2023-2025
Kshetri, N. (2021). Blockchain and sustainable supply chain management in developing countries. International Journal of Information Management, 60, 102376. https://doi.org/10.1016/j.ijinfomgt.2021.102376
Kumar, A., Singh, R. K., & Modgil, S. (2020). Exploring the relationship between ICT, supply chain management practices and organizational performance in agri-food supply chain. Benchmarking: An International Journal, 27(3), 1003–1041. https://doi.org/10.1108/BIJ-11-2019-0500
Liengaard, B. D., Sharma, P. N., Hult, G. T. M., Jensen, M. B., Sarstedt, M., Hair, J. F., & Ringle, C. M. (2021). Prediction: Coveted, yet forsaken? Introducing a cross-validated predictive ability test in PLS-SEM. Decision Sciences, 52(2), 362–392. https://doi.org/10.1111/deci.12445
Mordor Intelligence. (2025). Frozen seafood market size & share analysis – growth trends & forecasts (2025–2030). https://www.mordorintelligence.com/industry-reports/frozen-seafood-market
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.
Piprani, A. Z., Jaafar, N. I., & Ali, S. M. (2022). Multi-dimensional supply chain flexibility and supply chain resilience: The role of supply chain risks exposure. Operations Management Research, 15, 307–325. https://doi.org/10.1007/s12063-021-00232-w
Ringle, C. M., & Sarstedt, M. (2016). Gain more insight from your PLS-SEM results: The importance-performance map analysis. Industrial Management & Data Systems, 116(9), 1865–1886. https://doi.org/10.1108/IMDS-10-2015-0449
Sarstedt, M., Hair, J. F., Cheah, J.-H., Becker, J.-M., & Ringle, C. M. (2019). How to specify, estimate, and validate higher-order constructs in PLS-SEM. Australasian Marketing Journal, 27(3), 197–211. https://doi.org/10.1016/j.ausmj.2019.05.003
Shmueli, G., Sarstedt, M., Hair, J. F., Cheah, J.-H., Ting, H., Vaithilingam, S., & Ringle, C. M. (2019). Predictive model assessment in PLS-SEM: Guidelines for using PLSpredict. European Journal of Marketing, 53(11), 2322–2347. https://doi.org/10.1108/EJM-02-2019-0189
Teece, D. J. (2007). Explicating dynamic capabilities: The nature and microfoundations of (sustainable) enterprise performance. Strategic Management Journal, 28(13), 1319–1350. https://doi.org/10.1002/smj.640
Thai Frozen Foods Association. (2024). Report of fisheries products export and import statistics: January–December 2023. https://www.thai-frozen.or.th/
Toorajipour, R., Sohrabpour, V., Nazarpour, A., Oghazi, P., & Fischl, M. (2021). Artificial intelligence in supply chain management: A systematic literature review. Journal of Business Research, 122, 502–517. https://doi.org/10.1016/j.jbusres.2020.09.009
Tortorella, G. L., Fogliatto, F. S., Saurin, T. A., Tonetto, L. M., & McFarlane, D. (2023). The impact of Industry 4.0 on supply chain capability and supply chain resilience: A dynamic resource-based view. International Journal of Production Economics, 259, 108821. https://doi.org/10.1016/j.ijpe.2023.108821
Zhao, G., Liu, S., Lopez, C., Lu, H., Elgueta, S., Chen, H., & Boshkoska, B. M. (2019). Blockchain technology in agri-food value chain management: A synthesis of applications, challenges and future research directions. Computers in Industry, 109, 83–99. https://doi.org/10.1016/j.compind.2019.04.002
Zhao, X., Lynch, J. G., & Chen, Q. (2010). Reconsidering Baron and Kenny: Myths and truths about mediation analysis. Journal of Consumer Research, 37(2), 197–206. https://doi.org/10.1086/651257