Bridging the Gap Between Managerial Perceptions and Resilience Performance in Supply Chains: A Mixed-Methods Analysis
Keywords:
Supply Chain Resilience, Managerial Perception, AI Dynamic Routing, Strategic Alignment, Operational Disruption, Decision Support Systems, Digital MaturityAbstract
Supply chain disruptions have become increasingly structural, driven by geopolitical tensions, pandemics, and climate-related events. This study investigates how managerial perceptions influence the selection and effectiveness of resilience strategies in the face of such disruptions. The objective is to assess whether perceptions align with actual performance outcomes of key resilience strategies. Using a mixed-methods approach, the study combines survey data from 214 supply chain managers with performance metrics from 180 companies. Five common strategies AI dynamic routing, digital integration, multi-sourcing, nearshoring, and inventory buffering were evaluated for cost, recovery speed, and resilience score. Quantitative analysis was supported by qualitative synthesis to understand behavioral influences on strategy adoption. The results show that AI-enabled strategies, particularly dynamic routing and digital integration, align strongly with managerial perceptions and deliver the highest resilience scores. Conversely, inventory buffering, while highly regarded by managers, underperforms in practice. This discrepancy suggests a cognitive bias influenced by traditional practices. Barriers such as risk aversion, overconfidence, and limited digital maturity hinder the adoption of effective strategies. The study identifies Protection Motivation Theory and Dynamic Capabilities Theory as key frameworks to understand how cognition shapes strategic choices. In conclusion, aligning managerial perceptions with data-driven insights is crucial for effective resilience strategy implementation. Investment in digital infrastructure, decision support systems, and a culture of adaptive learning are necessary to bridge perception-performance gaps. The findings contribute a behavioral-operational framework to guide resilience planning in modern supply chains.
References
Abaku, E. A., Edunjobi, T. E., & Odimarha, A. C. (2024). Theoretical Approaches to AI in Supply Chain Optimization: Pathways to Efficiency and Resilience. International Journal of Science and Technology Research Archive, 6(1), 92–107. https://doi.org/10.53771/ijstra.2024.6.1.0033
Adenuga, T., Ayanbode, N., Ayobami, T., & Okolo, F. C. (2024). Supporting AI in Logistics Optimization Through Data Integration, Real-Time Analytics, and Autonomous Systems. International Journal of Scientific Research in Science Engineering and Technology, 11(3), 511–546. https://doi.org/10.32628/ijsrset241487
Alnsour, Y., Johnson, M., Albizri, A., & Harfouche, A. (2023). Predicting Patient Length of Stay Using Artificial Intelligence to Assist Healthcare Professionals in Resource Planning and Scheduling Decisions. Journal of Global Information Management, 31(1), 1–14. https://doi.org/10.4018/jgim.323059
Arcidiacono, F., Ancarani, A., Mauro, C. D., & Schupp, F. (2022). The Role of Absorptive Capacity In the Adoption of Smart Manufacturing. International Journal of Operations & Production Management, 42(6), 773–796. https://doi.org/10.1108/ijopm-09-2021-0615
Arianpoor, A., & Zaidan, A. S. (2023). The Impact of Managerial Psychological Characteristics on the Supply Chain Management Efficiency. The TQM Journal, 37(1), 150–170. https://doi.org/10.1108/tqm-04-2023-0116
Atadoga, A., Osasona, F., Amoo, O. O., Farayola, O. A., Ayinla, B. S., & Abrahams, T. O. (2024). The Role of It in Enhancing Supply Chain Resilience: A Global Review. International Journal of Management & Entrepreneurship Research, 6(2), 336–351. https://doi.org/10.51594/ijmer.v6i2.774
Azar, G., Batsakis, G., Drogendijk, R., Pakseresht, A., & Geng, R. (2024). Institutional Distance and Location Choice: An Experimental Approach to the Perception Puzzle. International Marketing Review, 41(3/4), 717–744. https://doi.org/10.1108/imr-12-2022-0282
Baryannis, G., Dani, S., & Antoniou, G. (2019). Predicting Supply Chain Risks Using Machine Learning: The Trade-Off Between Performance and Interpretability. Future Generation Computer Systems, 101, 993–1004. https://doi.org/10.1016/j.future.2019.07.059
Bode, C., Macdonald, J. R., & Merath, M. (2021). Supply Disruptions and Protection Motivation: Why Some Managers Act Proactively (And Others Don’t. Journal of Business Logistics, 43(1), 92–115. https://doi.org/10.1111/jbl.12293
Craighead, C. W., Ketchen, D. J., & Darby, J. L. (2020). Pandemics and Supply Chain Management Research: Toward a Theoretical Toolbox*. Decision Sciences, 51(4), 838–866. https://doi.org/10.1111/deci.12468
Duoming, H., & Chin, T. A. (2022). A Systematic Review for Supply Chain Integration and Risks. International Journal of Academic Research in Business and Social Sciences, 12(1). https://doi.org/10.6007/ijarbss/v12-i1/11856
Esmaeilzadeh, P. (2020). Use of AI-based Tools for Healthcare Purposes: A Survey Study From Consumers’ Perspectives. BMC Medical Informatics and Decision Making, 20(1). https://doi.org/10.1186/s12911-020-01191-1
Finck, D., & Tillmann, P. (2023). The Macroeconomic Effects of Global Supply Chain Disruptions. SSRN Electronic Journal. https://doi.org/10.2139/ssrn.4349825
Guntuka, L., Corsi, T. M., & Cantor, D. E. (2023). Recovery From Plant-Level Supply Chain Disruptions: Supply Chain Complexity and Business Continuity Management. International Journal of Operations & Production Management, 44(1), 1–31. https://doi.org/10.1108/ijopm-09-2022-0611
Heikkinen, H. (2024). Exploring How Retail and Logistics Service Provider Managers Make Sense of Sustainability in Last Mile Delivery. International Journal of Physical Distribution & Logistics Management, 54(2), 139–162. https://doi.org/10.1108/ijpdlm-02-2023-0073
Herold, D. M., & Marzantowicz, Ł. (2023). Supply Chain Responses to Global Disruptions and Its Ripple Effects: An Institutional Complexity Perspective. Operations Management Research, 16(4), 2213–2224. https://doi.org/10.1007/s12063-023-00404-w
Iskandar, W., & Putri, F. A. (2020). Persepsi Kepala Madrasah Ibtidaiyah Terhadap Kinerja Supervisi Manajerial Pengawas Madrasah Ibtidaiyah Kota Yogyakarta. Dirasat Jurnal Manajemen Dan Pendidikan Islam, 6(2), 158–181. https://doi.org/10.26594/dirasat.v6i2.2210
Ivanov, D. (2021). Exiting the COVID-19 Pandemic: After-Shock Risks and Avoidance of Disruption Tails in Supply Chains. Annals of Operations Research, 335(3), 1627–1644. https://doi.org/10.1007/s10479-021-04047-7
Katsaliaki, K., Galetsi, P., & Kumar, S. (2021). Supply Chain Disruptions and Resilience: A Major Review and Future Research Agenda. Annals of Operations Research, 319(1), 965–1002. https://doi.org/10.1007/s10479-020-03912-1
Keller, T. E., Drew, A. L., Clark‐Shim, H., Spencer, R., & Herrera, C. (2020). It’s About Time: Staff Support Contacts and Mentor Volunteer Experiences. Journal of Youth Development, 15(4), 145–161. https://doi.org/10.5195/jyd.2020.879
Kessler, M., Rosca, E., & Arlinghaus, J. C. (2023). Risk Management Behaviour in Digital Factories: The Influence of Technology and Task Uncertainty on Managerial Risk Responses. Supply Chain Management an International Journal, 29(2), 297–314. https://doi.org/10.1108/scm-06-2023-0296
Li, L., Gong, Y., Wang, Z., & Liu, S. (2022). Big Data and Big Disaster: A mechanism of Supply Chain Risk management in Global Logistics Industry. International Journal of Operations & Production Management, 43(2), 274–307. https://doi.org/10.1108/ijopm-04-2022-0266
Lima, N., Groenewald, E., Hamidi, I., & Garg, A. (2024). Optimizing Supply Chain Processes Through Deep Learning Algorithms: A Managerial Approach. https://doi.org/10.52783/jier.v4i1.567
Manuj, I., Herburger, M., & Adana, S. (2024). Supply Chain Resilience Capabilities in Automotive and Other Industries: A Mixed Method Approach. Journal of Business and Industrial Marketing, 39(6), 1311–1336. https://doi.org/10.1108/jbim-07-2022-0355
Nayal, K., Raut, R. D., Priyadarshinee, P., Narkhede, B. E., Kazançoğlu, Y., & Narwane, V. S. (2021). Exploring the Role of Artificial Intelligence in Managing Agricultural Supply Chain Risk to Counter the Impacts of the COVID-19 Pandemic. The International Journal of Logistics Management, 33(3), 744–772. https://doi.org/10.1108/ijlm-12-2020-0493
Nikookar, E., Stevenson, M., & Varsei, M. (2024). Building an Antifragile Supply Chain: A Capability Blueprint for Resilience and Post‐disruption Growth. Journal of Supply Chain Management, 60(1), 13–31. https://doi.org/10.1111/jscm.12313
Odulaja, B. A., Oke, T. T., Eleogu, T., Abdul, A. A., & Daraojimba, H. O. (2023). Resilience in the Face of Uncertainty: A Review on the Impact of Supply Chain Volatility Amid Ongoing Geopolitical Disruptions. International Journal of Applied Research in Social Sciences, 5(10), 463–486. https://doi.org/10.51594/ijarss.v5i10.634
Okafor, U. G., Olaleye, M. A., Asobara, H. C., & Umeodinka, E. F. (2021). Global Impact of COVID-19 Pandemic on Public Health Supply Chains. https://doi.org/10.5772/intechopen.97454
Pelegrina-Romero, M. P., Vargas-Muñoz, J. C., Palacios-Argüello, L., Ríos, R., & Jaimes, W. A. (2024). Taxonomy of Resilience Strategies for Addressing the Impacts of COVID-19 Pandemic on Food Supply Chains. Clío América, 18(36), 170–188. https://doi.org/10.21676/23897848.6059
Revilla, E., & Sáenz, M. J. (2017). The Impact of Risk Management on the Frequency of Supply Chain Disruptions. International Journal of Operations & Production Management, 37(5), 557–576. https://doi.org/10.1108/ijopm-03-2016-0129
Roberts, M. D., Douglas, M. A., & Overstreet, R. E. (2024). Safety in Context: Routines and the Effect of a Balanced Safety and Operations Focus on Worker Perceptions and Performance. International Journal of Physical Distribution & Logistics Management, 54(4), 333–354. https://doi.org/10.1108/ijpdlm-08-2023-0293
Shalini, T., & Varghese, B. M. (2024). Industry Skill Gap Addressed Towards Managerial Skills in Graduates Using Logistic Regression. Ictact Journal on Management Studies, 10(1), 1901–1907. https://doi.org/10.21917/ijms.2024.0291
Singh, C. S., Soni, G., & Badhotiya, G. K. (2019). Performance Indicators for Supply Chain Resilience: Review and Conceptual Framework. Journal of Industrial Engineering International, 15(S1), 105–117. https://doi.org/10.1007/s40092-019-00322-2
Sodiya, E. O., Jacks, B. S., Ugwuanyi, E. D., Adeyinka, M. A., Umoga, U. J., Daraojimba, A. I., & Lottu, O. A. (2024). Reviewing the Role of AI and Machine Learning in Supply Chain Analytics. GSC Advanced Research and Reviews, 18(2), 312–320. https://doi.org/10.30574/gscarr.2024.18.2.0069
Tu, M. (2018). An Exploratory Study of Internet of Things (IoT) Adoption Intention in Logistics and Supply Chain Management. The International Journal of Logistics Management, 29(1), 131–151. https://doi.org/10.1108/ijlm-11-2016-0274
Widodo, A., & Sundari, S. (2024). Management Visit Evaluation to Improve the Quality of Ponek Services at Bhakti Wira Tamtama Hospital Semarang. Jurnal Promkes, 13(SI1), 155–162. https://doi.org/10.20473/jpk.v13.isi1.2025.155-162
Ye, Y., Suleiman, M. A., & Huo, B. (2022). Impact of Just-in-Time (JIT) on Supply Chain Disruption Risk: The Moderating Role of Supply Chain Centralization. Industrial Management & Data Systems, 122(7), 1665–1685. https://doi.org/10.1108/imds-09-2021-0552
Younis, H., Sundarakani, B., & Alsharairi, M. (2021). Applications of Artificial Intelligence and Machine Learning Within Supply Chains:systematic Review and Future Research Directions. Journal of Modelling in Management, 17(3), 916–940. https://doi.org/10.1108/jm2-12-2020-0322
Zavala, A., Nowicki, D., & Ramírez-Márquez, J. E. (2018). Quantitative Metrics to Analyze Supply Chain Resilience and Associated Costs. Proceedings of the Institution of Mechanical Engineers Part O Journal of Risk and Reliability, 233(2), 186–199. https://doi.org/10.1177/1748006x18766738
Zhao, N., Hong, J., & Lau, K. H. (2023). Impact of Supply Chain Digitalization on Supply Chain Resilience and Performance: A Multi-Mediation Model. International Journal of Production Economics, 259. https://doi.org/10.1016/j.ijpe.2023.108817
