How to cite this paper
APA: Shrivastav, A & Joshi, J. (2027). Artificial intelligence adoption and maritime supply chain resilience among MSME exporters in the India–Thailand trade corridor: A PLS-SEM approach. Uncertain Supply Chain Management, 15(1), 31-44.
Chicago/Turabian: Shrivastav, A & Joshi, J. 2027. "Artificial intelligence adoption and maritime supply chain resilience among MSME exporters in the India–Thailand trade corridor: A PLS-SEM approach." Uncertain Supply Chain Management 15, no. 1 (2027): 31-44.
AMA: Shrivastav, A & Joshi, J. Artificial intelligence adoption and maritime supply chain resilience among MSME exporters in the India–Thailand trade corridor: A PLS-SEM approach. Uncertain Supply Chain Management. 2027;15(1):31-44.
References
Akter, S., Bandara, R. J., Hani, U., Wamba, S. F., Foropon, C., & Papadopoulos, T. (2022). Analytics-based decision-making for service systems: A qualitative study and agenda for future research. International Journal of Information Management, 48, 85–95. https://doi.org/10.1016/j.ijinfomgt.2019.01.020
Bai, C., & Sarkis, J. (2020). A supply chain transparency and sustainability technology appraisal model for blockchain technology. International Journal of Production Research, 58(7), 2142–2162. https://doi.org/10.1080/00207543.2019.1708989
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
Belhadi, A., Kamble, S., Gunasekaran, A., Mani, V., & Benkhati, I. (2021). Analyzing the mediating role of organizational ambidexterity and digital business transformation on industry 4.0 capabilities and sustainable supply chain performance. Supply Chain Management: An International Journal, 26(6), 696–711. https://doi.org/10.1108/SCM-08-2020-0352
Benzidia, S., Makaoui, N., & Bentahar, O. (2021). The impact of big data analytics and artificial intelligence on green supply chain process integration and hospital environmental performance. Technological Forecasting and Social Change, 165, 120557. https://doi.org/10.1016/j.techfore.2020.120557
Cao, G., Duan, Y., Edwards, J. S., & Dwivedi, Y. K. (2021). Understanding managers’ attitudes and behavioral intentions towards using artificial intelligence for organizational decision-making. Technovation, 106, 102312. https://doi.org/10.1016/j.technovation.2021.102312
Christopher, M., & Peck, H. (2004). Building the resilient supply chain. The International Journal of Logistics Management, 15(2), 1–14. https://doi.org/10.1108/09574090410700275
Davis, F. D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Quarterly, 13(3), 319–340. https://doi.org/10.2307/249008
Dolgui, A., Ivanov, D., & Sokolov, B. (2020). Reconfigurable supply chain: The X-network. International Journal of Production Research, 58(13), 4138–4163. https://doi.org/10.1080/00207543.2020.1774679
Dubey, R., Gunasekaran, A., Bryde, D. J., Dwivedi, Y. K., & Papadopoulos, T. (2020). Blockchain technology for enhancing swift-trust, collaboration and resilience within a humanitarian supply chain setting. International Journal of Production Research, 58(11), 3381–3398. https://doi.org/10.1080/00207543.2020.1722860
Dubey, R., Gunasekaran, A., Childe, S. J., Blome, C., & Papadopoulos, T. (2019). Big data and predictive analytics and manufacturing performance: Integrating institutional theory, resource-based view and big data culture. British Journal of Management, 30(2), 341–361. https://doi.org/10.1111/1467-8551.12355
Dwivedi, Y. K., Hughes, L., Ismagilova, E., Aarts, G., Coombs, C., Crick, T., Duan, Y., Dwivedi, R., Edwards, J., Eirug, A., Galanos, V., Ilavarasan, P., Janssen, M., Jones, P., Kar, A., Kizgin, H., Kronemann, B., Lal, B., Lucini, B., Medaglia, R., … Williams, M. D. (2021). Artificial intelligence (AI): Multidisciplinary perspectives on emerging challenges, opportunities, and agenda for research, practice and policy. International Journal of Information Management, 57, 101994. https://doi.org/10.1016/j.ijinfomgt.2019.08.002
Fatorachian, H., & Kazemi, H. (2021). Impact of industry 4.0 on supply chain performance. Production Planning & Control, 32(1), 63–81. https://doi.org/10.1080/09537287.2020.1712487
Fosso Wamba, S., Gunasekaran, A., Akter, S., Ren, S., Dubey, R., & Childe, S. J. (2017). Big data analytics and firm performance. Journal of Business Research, 70, 356–365. https://doi.org/10.1016/j.jbusres.2016.08.009
Ghobakhloo, M. (2020). Industry 4.0, digitization, and opportunities for sustainability. Journal of Cleaner Production, 252, 119869. https://doi.org/10.1016/j.jclepro.2019.119869
Gunasekaran, A., Subramanian, N., & Papadopoulos, T. (2017). Information technology for competitive advantage within logistics and supply chains: A review. Transportation Research Part E: Logistics and Transportation Review, 99, 14–33. https://doi.org/10.1016/j.tre.2016.12.008
Hair, J. F., Hult, G. T. M., Ringle, C. M., & Sarstedt, M. (2023). A primer on partial least squares structural equation modeling (PLS-SEM) (4th ed.). Sage Publications.
Hohenstein, N. O., Feisel, E., Hartmann, E., & Giunipero, L. (2015). Research on the phenomenon of supply chain resilience: A systematic review and paths for further investigation. International Journal of Physical Distribution & Logistics Management, 45(1/2), 90–117. https://doi.org/10.1108/IJPDLM-05-2013-0128
Ivanov, D. (2020). Predicting the impacts of epidemic outbreaks on global supply chains. Transportation Research Part E, 136, 101922. https://doi.org/10.1016/j.tre.2020.101922
Ivanov, D. (2021). Supply chain viability and the COVID-19 pandemic: A conceptual and formal generalization of four major adaptation strategies. International Journal of Production Research, 59(12), 3535–3552. https://doi.org/10.1080/00207543.2021.1890852
Ivanov, D., & Dolgui, A. (2020). Viability of intertwined supply networks: Extending the supply chain resilience angles towards survivability. International Journal of Production Research, 58(10), 2904–2915. https://doi.org/10.1080/00207543.2020.1750727
OECD. (2023). SME and entrepreneurship outlook 2023. OECD Publishing. https://doi.org/10.1787/34907e9c-en
Parasuraman, A., & Colby, C. L. (2015). An updated and streamlined technology readiness index. Journal of Service Research, 18(1), 59–74. https://doi.org/10.1177/1094670514539730
Richey, R. G., Chowdhury, S., Davis‐Sramek, B., Giannakis, M., & Dwivedi, Y. K. (2023). Artificial intelligence in logistics and supply chain management: A primer and roadmap for research. Journal of Business Logistics, 44(4), 532–549. https://doi.org/10.1111/jbl.12364
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
Tjahjono, B., Esplugues, C., Ares, E., & Pelaez, G. (2017). What does Industry 4.0 mean to supply chain? Procedia Manufacturing, 13, 1175–1182. https://doi.org/10.1016/j.promfg.2017.09.191
UNCTAD. (2023). Review of maritime transport 2023. United Nations Conference on Trade and Development.
Venkatesh, V., Morris, M. G., Davis, G. B., & Davis, F. D. (2003). User acceptance of information technology: Toward a unified view. MIS Quarterly, 27(3), 425–478. https://doi.org/10.2307/30036540
Wamba, S. F., Dubey, R., Gunasekaran, A., & Akter, S. (2020). The performance effects of big data analytics and supply chain ambidexterity: The moderating effect of environmental dynamism. International Journal of Production Economics, 222, 107498. https://doi.org/10.1016/j.ijpe.2019.09.019
Wieland, A., & Durach, C. F. (2021). Two perspectives on supply chain resilience. Journal of Business Logistics, 42(3), 315–322. https://doi.org/10.1111/jbl.12271
Bai, C., & Sarkis, J. (2020). A supply chain transparency and sustainability technology appraisal model for blockchain technology. International Journal of Production Research, 58(7), 2142–2162. https://doi.org/10.1080/00207543.2019.1708989
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
Belhadi, A., Kamble, S., Gunasekaran, A., Mani, V., & Benkhati, I. (2021). Analyzing the mediating role of organizational ambidexterity and digital business transformation on industry 4.0 capabilities and sustainable supply chain performance. Supply Chain Management: An International Journal, 26(6), 696–711. https://doi.org/10.1108/SCM-08-2020-0352
Benzidia, S., Makaoui, N., & Bentahar, O. (2021). The impact of big data analytics and artificial intelligence on green supply chain process integration and hospital environmental performance. Technological Forecasting and Social Change, 165, 120557. https://doi.org/10.1016/j.techfore.2020.120557
Cao, G., Duan, Y., Edwards, J. S., & Dwivedi, Y. K. (2021). Understanding managers’ attitudes and behavioral intentions towards using artificial intelligence for organizational decision-making. Technovation, 106, 102312. https://doi.org/10.1016/j.technovation.2021.102312
Christopher, M., & Peck, H. (2004). Building the resilient supply chain. The International Journal of Logistics Management, 15(2), 1–14. https://doi.org/10.1108/09574090410700275
Davis, F. D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Quarterly, 13(3), 319–340. https://doi.org/10.2307/249008
Dolgui, A., Ivanov, D., & Sokolov, B. (2020). Reconfigurable supply chain: The X-network. International Journal of Production Research, 58(13), 4138–4163. https://doi.org/10.1080/00207543.2020.1774679
Dubey, R., Gunasekaran, A., Bryde, D. J., Dwivedi, Y. K., & Papadopoulos, T. (2020). Blockchain technology for enhancing swift-trust, collaboration and resilience within a humanitarian supply chain setting. International Journal of Production Research, 58(11), 3381–3398. https://doi.org/10.1080/00207543.2020.1722860
Dubey, R., Gunasekaran, A., Childe, S. J., Blome, C., & Papadopoulos, T. (2019). Big data and predictive analytics and manufacturing performance: Integrating institutional theory, resource-based view and big data culture. British Journal of Management, 30(2), 341–361. https://doi.org/10.1111/1467-8551.12355
Dwivedi, Y. K., Hughes, L., Ismagilova, E., Aarts, G., Coombs, C., Crick, T., Duan, Y., Dwivedi, R., Edwards, J., Eirug, A., Galanos, V., Ilavarasan, P., Janssen, M., Jones, P., Kar, A., Kizgin, H., Kronemann, B., Lal, B., Lucini, B., Medaglia, R., … Williams, M. D. (2021). Artificial intelligence (AI): Multidisciplinary perspectives on emerging challenges, opportunities, and agenda for research, practice and policy. International Journal of Information Management, 57, 101994. https://doi.org/10.1016/j.ijinfomgt.2019.08.002
Fatorachian, H., & Kazemi, H. (2021). Impact of industry 4.0 on supply chain performance. Production Planning & Control, 32(1), 63–81. https://doi.org/10.1080/09537287.2020.1712487
Fosso Wamba, S., Gunasekaran, A., Akter, S., Ren, S., Dubey, R., & Childe, S. J. (2017). Big data analytics and firm performance. Journal of Business Research, 70, 356–365. https://doi.org/10.1016/j.jbusres.2016.08.009
Ghobakhloo, M. (2020). Industry 4.0, digitization, and opportunities for sustainability. Journal of Cleaner Production, 252, 119869. https://doi.org/10.1016/j.jclepro.2019.119869
Gunasekaran, A., Subramanian, N., & Papadopoulos, T. (2017). Information technology for competitive advantage within logistics and supply chains: A review. Transportation Research Part E: Logistics and Transportation Review, 99, 14–33. https://doi.org/10.1016/j.tre.2016.12.008
Hair, J. F., Hult, G. T. M., Ringle, C. M., & Sarstedt, M. (2023). A primer on partial least squares structural equation modeling (PLS-SEM) (4th ed.). Sage Publications.
Hohenstein, N. O., Feisel, E., Hartmann, E., & Giunipero, L. (2015). Research on the phenomenon of supply chain resilience: A systematic review and paths for further investigation. International Journal of Physical Distribution & Logistics Management, 45(1/2), 90–117. https://doi.org/10.1108/IJPDLM-05-2013-0128
Ivanov, D. (2020). Predicting the impacts of epidemic outbreaks on global supply chains. Transportation Research Part E, 136, 101922. https://doi.org/10.1016/j.tre.2020.101922
Ivanov, D. (2021). Supply chain viability and the COVID-19 pandemic: A conceptual and formal generalization of four major adaptation strategies. International Journal of Production Research, 59(12), 3535–3552. https://doi.org/10.1080/00207543.2021.1890852
Ivanov, D., & Dolgui, A. (2020). Viability of intertwined supply networks: Extending the supply chain resilience angles towards survivability. International Journal of Production Research, 58(10), 2904–2915. https://doi.org/10.1080/00207543.2020.1750727
OECD. (2023). SME and entrepreneurship outlook 2023. OECD Publishing. https://doi.org/10.1787/34907e9c-en
Parasuraman, A., & Colby, C. L. (2015). An updated and streamlined technology readiness index. Journal of Service Research, 18(1), 59–74. https://doi.org/10.1177/1094670514539730
Richey, R. G., Chowdhury, S., Davis‐Sramek, B., Giannakis, M., & Dwivedi, Y. K. (2023). Artificial intelligence in logistics and supply chain management: A primer and roadmap for research. Journal of Business Logistics, 44(4), 532–549. https://doi.org/10.1111/jbl.12364
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
Tjahjono, B., Esplugues, C., Ares, E., & Pelaez, G. (2017). What does Industry 4.0 mean to supply chain? Procedia Manufacturing, 13, 1175–1182. https://doi.org/10.1016/j.promfg.2017.09.191
UNCTAD. (2023). Review of maritime transport 2023. United Nations Conference on Trade and Development.
Venkatesh, V., Morris, M. G., Davis, G. B., & Davis, F. D. (2003). User acceptance of information technology: Toward a unified view. MIS Quarterly, 27(3), 425–478. https://doi.org/10.2307/30036540
Wamba, S. F., Dubey, R., Gunasekaran, A., & Akter, S. (2020). The performance effects of big data analytics and supply chain ambidexterity: The moderating effect of environmental dynamism. International Journal of Production Economics, 222, 107498. https://doi.org/10.1016/j.ijpe.2019.09.019
Wieland, A., & Durach, C. F. (2021). Two perspectives on supply chain resilience. Journal of Business Logistics, 42(3), 315–322. https://doi.org/10.1111/jbl.12271