Main Article Content
Abstract
The Indonesian logistics industry is undergoing rapid digital transformation through the adoption of artificial intelligence (AI), Logistics Information System (LIS) integration, and Warehouse Management Systems (WMS). However, empirical studies examining the simultaneous effects of these technologies on operational efficiency and customer satisfaction remain limited. This study investigates the influence of AI implementation, LIS integration, and WMS adoption on operational efficiency and customer satisfaction in Indonesian logistics companies. A quantitative explanatory research design was applied using purposive sampling. Data were collected from 350 respondents, including logistics managers, warehouse supervisors, IT managers, and supply chain coordinators across Indonesia. The data were analyzed using Partial Least Squares Structural Equation Modeling (PLS-SEM) with SmartPLS 4. The results show that AI implementation, LIS integration, and WMS adoption have positive and significant effects on operational efficiency. Operational efficiency also significantly improves customer satisfaction. Among the three digital technologies, AI implementation has the strongest standardized effect, followed by LIS integration and WMS adoption. The findings confirm that operational efficiency plays a crucial role in linking digital technology adoption with customer-related outcomes. This study contributes to TAM, RBV, TOE, and Digital Transformation Theory and provides practical insights for improving logistics performance.
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References
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- Podsakoff, P. M., MacKenzie, S. B., Lee, J. Y., & Podsakoff, N. P. (2003). Common method biases in behavioral research: A critical review of the literature and recommended remedies. Journal of Applied Psychology, 88(5), 879–903. https://doi.org/10.1037/0021-9010.88.5.879
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- Teece, D. J. (2018). Business models and dynamic capabilities. Long Range Planning, 51(1), 40–49. https://doi.org/10.1016/j.lrp.2017.06.007
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- Vial, G. (2019). Understanding digital transformation: A review and a research agenda. The Journal of Strategic Information Systems, 28(2), 118–144. https://doi.org/10.1016/j.jsis.2019.01.003
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References
Bag, S., Dhamija, P., Gupta, S., & Sivarajah, U. (2021). Examining the role of artificial intelligence in supply chain resilience and performance. International Journal of Production Research, 59(16), 1–18. https://doi.org/10.1080/00207543.2021.1884313
Bag, S., Dhamija, P., Gupta, S., & Sivarajah, U. (2024). Artificial intelligence and digital transformation in supply chains: A systematic review and research agenda. International Journal of Production Research, 62(5), 1520–1543. https://doi.org/10.1080/00207543.2023.2172834
Büyüközkan, G., & Göçer, F. (2018). Digital supply chain: Literature review and a proposed framework for future research. Computers in Industry, 97, 157–177. https://doi.org/10.1016/j.compind.2018.02.010
Christopher, M. (2016). Logistics & supply chain management (5th ed.). Pearson.
Creswell, J. W., & Creswell, J. D. (2018). Research design: Qualitative, quantitative, and mixed methods approaches (5th ed.). Sage Publications.
Dubey, R., Gunasekaran, A., Childe, S. J., Bryde, D. J., Giannakis, M., & Foropon, C. (2020). Big data analytics and artificial intelligence pathway to operational performance under the effects of entrepreneurial orientation and environmental dynamism. Production Planning & Control, 31(7), 1–14. https://doi.org/10.1080/09537287.2019.1631458
Faber, N., de Koster, R. B. M., & van de Velde, S. L. (2018). Linking warehouse complexity to warehouse planning and control structure. International Journal of Physical Distribution & Logistics Management, 48(5), 1–20. https://doi.org/10.1108/IJPDLM-10-2016-0305
Fornell, C., & Larcker, D. F. (1981). Evaluating structural equation models with unobservable variables and measurement error. Journal of Marketing Research, 18(1), 39–50. https://doi.org/10.1177/002224378101800104
Golini, R., Kalchschmidt, M., & Landoni, P. (2023). The role of warehouse management systems in enhancing logistics performance. International Journal of Logistics Management, 34(2), 325–345. https://doi.org/10.1108/IJLM-08-2021-0397
Gu, J., Goetschalckx, M., & McGinnis, L. F. (2019). Research on warehouse operation: A comprehensive review. European Journal of Operational Research, 177(1), 1–21. https://doi.org/10.1016/j.ejor.2006.02.025
Gunasekaran, A., Subramanian, N., & Rahman, S. (2017). Supply chain resilience: Role of complexities and strategies. International Journal of Production Research, 55(22), 1–14. https://doi.org/10.1080/00207543.2016.1195248
Hair, J. F., Hult, G. T. M., Ringle, C. M., & Sarstedt, M. (2022). A primer on partial least squares structural equation modeling (PLS-SEM) (3rd ed.). Sage Publications.
Hair, J. F., Ringle, C. M., & Sarstedt, M. (2019). Partial least squares structural equation modeling. In Handbook of market research (pp. 1–40). Springer. https://doi.org/10.1007/978-3-319-05542-8_15-1
Huo, B., Ye, Y., Zhao, X., & Shou, Y. (2014). The impact of human capital on supply chain integration and competitive performance. International Journal of Production Economics, 178, 132–143. https://doi.org/10.1016/j.ijpe.2016.05.009
Ivanov, D., & Dolgui, A. (2024). A digital supply chain twin for managing the disruption risks and resilience in logistics networks. Transportation Research Part E: Logistics and Transportation Review, 183, Article 103346. https://doi.org/10.1016/j.tre.2023.103346
Juniansyah, D., Hasan, S., & Putra, A. H. P. K. (2023). The role of consumer emotional value, and service innovation on consumer purchase decisions: TAM theory approach. Golden Ratio of Marketing and Applied Psychology of Business, 3(1), 52–65. https://doi.org/10.52970/grmapb.v3i1.170
Kamble, S. S., Gunasekaran, A., & Sharma, R. (2020). Analysis of the driving and dependence power of barriers to adopt Industry 4.0 in Indian manufacturing industry. Computers in Industry, 101, 107–119. https://doi.org/10.1016/j.compind.2018.06.004
Klumpp, M., Hintze, M., & Immonen, M. (2023). Smart logistics and artificial intelligence: Opportunities and performance implications. Computers & Industrial Engineering, 179, Article 109211. https://doi.org/10.1016/j.cie.2023.109211
Kotler, P., & Keller, K. L. (2016). Marketing management (15th ed.). Pearson.
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
Parasuraman, A., Zeithaml, V. A., & Malhotra, A. (2005). E-SERVQUAL: A multiple-item scale for assessing electronic service quality. Journal of Service Research, 7(3), 213–233. https://doi.org/10.1177/1094670504271156
Podsakoff, P. M., MacKenzie, S. B., Lee, J. Y., & Podsakoff, N. P. (2003). Common method biases in behavioral research: A critical review of the literature and recommended remedies. Journal of Applied Psychology, 88(5), 879–903. https://doi.org/10.1037/0021-9010.88.5.879
Queiroz, M. M., Telles, R., & Bonilla, S. H. (2021). Blockchain and supply chain management integration: A systematic review of the literature. Supply Chain Management: An International Journal, 26(2), 241–254. https://doi.org/10.1108/SCM-03-2018-0143
Richards, G. (2018). Warehouse management: A complete guide to improving efficiency and minimizing costs in the modern warehouse (3rd ed.). Kogan Page.
Rosenfeld, Y., & Trautmann, S. (2024). Digital integration and organizational readiness in logistics transformation. Journal of Business Logistics, 45(1), 89–108. https://doi.org/10.1111/jbl.12345
Sarjono, H., Mahira, T., & Soeratin, B. S. (2025). E-supply chain management and customer satisfaction in Indonesian e-commerce. Golden Ratio of Mapping Idea and Literature Format, 6(1), 173–188. https://doi.org/10.52970/grmilf.v6i1.1381
Saunders, M., Lewis, P., & Thornhill, A. (2019). Research methods for business students (8th ed.). Pearson.
Shao, F., & Zheng, L. (2025). Artificial intelligence adoption in logistics operations: Evidence from supply chain analytics. Computers & Industrial Engineering, 181, Article 109401. https://doi.org/10.1016/j.cie.2024.109401
Teece, D. J. (2018). Business models and dynamic capabilities. Long Range Planning, 51(1), 40–49. https://doi.org/10.1016/j.lrp.2017.06.007
Venkatesh, V., & Davis, F. D. (2000). A theoretical extension of the technology acceptance model: Four longitudinal field studies. Management Science, 46(2), 186–204. https://doi.org/10.1287/mnsc.46.2.186.11926
Vial, G. (2019). Understanding digital transformation: A review and a research agenda. The Journal of Strategic Information Systems, 28(2), 118–144. https://doi.org/10.1016/j.jsis.2019.01.003
Wamba-Taguimdje, S. L., Fosso Wamba, S., Kala Kamdjoug, J. R., & Tchatchouang Wanko, C. E. (2020). Influence of artificial intelligence (AI) on firm performance: The business value of AI-based transformation projects. Business Process Management Journal, 26(7), 1893–1924. https://doi.org/10.1108/BPMJ-10-2019-0411
World Bank. (2023). Connecting to compete 2023: Trade logistics in an uncertain global economy—The logistics performance index and its indicators. World Bank Publications. https://doi.org/10.1596/978-1-4648-1908-5
Zeithaml, V. A., Bitner, M. J., & Gremler, D. D. (2018). Services marketing: Integrating customer focus across the firm (7th ed.). McGraw-Hill.