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The impact of AI technology adoption on operational decision-making in competitive heterogeneous ports☆

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  • Xu, Haonan
  • Liu, Jiaguo
  • Xu, Xiaofeng
  • Chen, Jihong
  • Yue, Xiaohang

Abstract

Artificial Intelligence (AI) technology is changing the industrial paradigm and has been widely adopted in port operations. Although AI technology can improve the efficiency of port operations and service quality, ports need to bear some costs. Discussing the role of applying AI technology to ports in complex competitive environments has become an important issue in the operations of ports and shipping. In this study, we construct a game-theoretic model of competitive heterogeneous ports. The research conclusions indicate that the adoption of AI technology by heterogeneous ports can enhance port profits. Unfortunately, simultaneous adoption exacerbates homogenized competition, posing a threat to profit realization. Furthermore, while the hub port can leverage AI-empowered capabilities to strengthen own competitiveness, it can undermine the performance of competitors and society at large. Surprisingly, the adoption of AI technology by feeder port is more advantageous in achieving social welfare and achieving multiple benefits such as carbon reduction.

Suggested Citation

  • Xu, Haonan & Liu, Jiaguo & Xu, Xiaofeng & Chen, Jihong & Yue, Xiaohang, 2024. "The impact of AI technology adoption on operational decision-making in competitive heterogeneous ports☆," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 183(C).
    • Handle: RePEc:eee:transe:v:183:y:2024:i:c:s1366554524000188
    DOI: 10.1016/j.tre.2024.103428
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    1. Li, Diansheng & Qu, Yuanyuan & Ma, Yanhong, 2020. "Study on the impact of subsidies for overlapping hinterland shippers on port competition," Transportation Research Part A: Policy and Practice, Elsevier, vol. 135(C), pages 24-37.
    2. Ivanov, Dmitry, 2023. "Intelligent digital twin (iDT) for supply chain stress-testing, resilience, and viability," International Journal of Production Economics, Elsevier, vol. 263(C).
    3. Lang Xu & Fengjue Xie & Chuanxu Wang, 2022. "Passive or proactive capacity sharing? A perspective of cooperation and competition between two regional ports," Maritime Policy & Management, Taylor & Francis Journals, vol. 49(4), pages 492-509, May.
    4. Wang, Junjin & Liu, Jiaguo & Wang, Fan & Yue, Xiaohang, 2021. "Blockchain technology for port logistics capability: Exclusive or sharing," Transportation Research Part B: Methodological, Elsevier, vol. 149(C), pages 347-392.
    5. Talley, Wayne K. & Ng, ManWo & Marsillac, Erika, 2014. "Port service chains and port performance evaluation," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 69(C), pages 236-247.
    6. Song, Zhuzhu & Tang, Wansheng & Zhao, Ruiqing, 2020. "A simple game theoretical analysis for incentivizing multi-modal transportation in freight supply chains," European Journal of Operational Research, Elsevier, vol. 283(1), pages 152-165.
    7. Tiwari, Sunil & Sharma, Pankaj & Choi, Tsan-Ming & Lim, Andrew, 2023. "Blockchain and third-party logistics for global supply chain operations: Stakeholders’ perspectives and decision roadmap," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 170(C).
    8. Li, Guo & Xue, Jing & Li, Na & Ivanov, Dmitry, 2022. "Blockchain-supported business model design, supply chain resilience, and firm performance," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 163(C).
    9. Huber, Martin & Meier, Jonas & Wallimann, Hannes, 2022. "Business analytics meets artificial intelligence: Assessing the demand effects of discounts on Swiss train tickets," Transportation Research Part B: Methodological, Elsevier, vol. 163(C), pages 22-39.
    10. Dong, Shuang & Qin, Zhongfeng & Yan, Yingchen, 2022. "Effects of online-to-offline spillovers on pricing and quality strategies of competing firms," International Journal of Production Economics, Elsevier, vol. 244(C).
    11. Wang, Qiang & Ji, Xiang & Zhao, Nenggui, 2024. "Embracing the power of AI in retail platform operations: Considering the showrooming effect and consumer returns," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 182(C).
    12. Wu, Lingxiao & Wang, Shuaian, 2020. "The shore power deployment problem for maritime transportation," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 135(C).
    13. Qiaoyu Peng & Chuanxu Wang, 2022. "Ship space sharing strategies with different rental modes: How does NVOCCs cooperate with booking platform?," Operational Research, Springer, vol. 22(3), pages 3003-3035, July.
    14. Yu, Yugang & Luo, Yifei & Shi, Ye, 2022. "Adoption of blockchain technology in a two-stage supply chain: Spillover effect on workforce," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 161(C).
    15. Gupta, Shivam & Modgil, Sachin & Choi, Tsan-Ming & Kumar, Ajay & Antony, Jiju, 2023. "Influences of artificial intelligence and blockchain technology on financial resilience of supply chains," International Journal of Production Economics, Elsevier, vol. 261(C).
    16. Filom, Siyavash & Amiri, Amir M. & Razavi, Saiedeh, 2022. "Applications of machine learning methods in port operations – A systematic literature review," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 161(C).
    17. Song, Zhuzhu & Tang, Wansheng & Zhao, Ruiqing & Zhang, Guoqing, 2022. "Implications of government subsidies on shipping companies’ shore power usage strategies in port," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 165(C).
    18. Zheng, Shiyuan & Jiang, Changmin & Fu, Xiaowen, 2021. "Investment competition on dedicated terminals under demand ambiguity," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 150(C).
    19. Song, Zhuzhu & Tang, Wansheng & Zhao, Ruiqing, 2021. "Liner alliances with heterogeneous price level and service competition: Partial vs. full," Omega, Elsevier, vol. 103(C).
    20. Yang, Wenjuan & Zhang, Jiantong & Yan, Hong, 2022. "Promotions of online reviews from a channel perspective," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 161(C).
    21. Yan, Ran & Wang, Shuaian & Psaraftis, Harilaos N., 2021. "Data analytics for fuel consumption management in maritime transportation: Status and perspectives," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 155(C).
    22. Liu, Shan & Zhang, Ya & Wang, Zhengli & Gu, Shiyi, 2023. "AdaBoost-Bagging deep inverse reinforcement learning for autonomous taxi cruising route and speed planning," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 177(C).
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