Drivers of AI Adoption in Smart Supply Chains: An Empirical Study of Readiness, Trust, and the Insignificance of Perceived Risk.

Artificial Intelligence (AI)-enabled Smart Supply Chain Management (SSCM) systems are increasingly implemented within manufacturing organizations to enhance operational efficiency and strategic decision-making. While perceived risk is widely recognized as a deterrent in technology adoption research, its explanatory role in institutionalized business-to-business (B2B) environments remains under-theorized. This study investigates the determinants of AI-enabled SSCM system usage among Malaysian manufacturing firms, focusing on Effort Expectancy, Technology Readiness, Trust, and Perceived Risk. Using survey data from 308 manufacturing professionals, Partial Least Squares Structural Equation Modelling (PLS-SEM) was employed to test the proposed relationships. To complement explanatory modelling, machine learning techniques including Extreme Gradient Boosting (XGBoost) and Artificial Neural Networks (ANN) were applied to assess predictive capability. The results indicate that Effort Expectancy, Technology Readiness, and Trust significantly influence SSCM usage behaviour, whereas Perceived Risk does not exhibit a significant effect. Drawing on institutional theory and organizational risk absorption perspectives, the findings suggest that governance mechanisms and formal safeguards attenuate the behavioural impact of perceived uncertainty in B2B contexts. This study contributes theoretically by identifying an institutional boundary condition of perceived risk in AI adoption and methodologically by integrating SEM with validated predictive AI modelling.

AI adoption and methodologically by integrating SEM with validated predictive AI modelling.

1. C. Cagliano, G. Mangano, and C. Rafele, "Determinants of digital technology adoption in supply chain. An exploratory analysis," Supply Chain Forum: An Int. J., vol. 22, no. 4, pp. 337–351, 2021, doi: 10.1080/16258312.2021.1973312. [Google Scholar] [Crossref]

2. Hasija and T. L. Esper, "In artificial intelligence (AI) we trust: A qualitative investigation of AI technology acceptance," J. Bus. Logist., vol. 43, no. 1, pp. 133–158, 2022, doi: 10.1111/jbl.12292. [Google Scholar] [Crossref]

3. K. Bhardwaj, A. Garg, and A. G. Garg, "Determinants of blockchain technology adoption in supply chains by small and medium enterprises (SMEs) in India," Math. Probl. Eng., vol. 2021, p. 5534035, 2021, doi: 10.1155/2021/5534035. [Google Scholar] [Crossref]

4. Rijanto, "Blockchain technology adoption in supply chain finance," J. Theor. Appl. Electron. Commer. Res., vol. 16, no. 7, pp. 3078–3098, 2021, doi: 10.3390/jtaer16070168. [Google Scholar] [Crossref]

5. Ageron, O. Bentahar, and R. Gunasekaran, "Digital supply chain: challenges and future directions," Supply Chain Forum: An Int. J., vol. 21, no. 3, pp. 133–138, 2020, doi: 10.1080/16258312.2020.1816361. [Google Scholar] [Crossref]

6. Giua, V. C. Materia, and L. Camanzi, "Smart farming technologies adoption: Which factors play a role in the digital transition?," Technol. Soc., vol. 68, p. 101869, 2022, doi: 10.1016/j.techsoc.2022.101869. [Google Scholar] [Crossref]

7. T. Nguyen, T. T. Nguyen, L. Cai, K. F. Yuen, and X. Wang, "A perception-based investigation on logistics robot adoption: from an integrated perspective of trust formation and technology acceptance," Int. J. Logist. Res. Appl., vol. 28, no. 1, pp. 89–112, 2025, doi: 10.1080/13675567.2024.2346265. [Google Scholar] [Crossref]

8. Loske and M. Klumpp, "Intelligent and efficient? An empirical analysis of human–AI collaboration for truck drivers in retail logistics," Int. J. Logist. Manag., vol. 32, no. 4, pp. 1344–1367, 2021, doi: 10.1108/IJLM-03-2020-0145. [Google Scholar] [Crossref]

9. D. Rico-Bautista, C. D. Guerrero, and C. A. Collazos-Morales, "Smart University: A vision of technology adoption," Revista Colombiana de Computación, vol. 22, no. 1, pp. 13–25, 2021, doi: 10.29375/25392115.4214. [Google Scholar] [Crossref]

10. Weisz, D. M. Herold, N. K. Ostern, R. Payne, and S. M. Wagner, "Artificial intelligence (AI) for supply chain collaboration: implications on information sharing and trust," Online Inf. Rev., vol. 49, no. 2, pp. 321–340, 2025, doi: 10.1108/OIR-04-2024-0205. [Google Scholar] [Crossref]

11. Jain, H. Singh, K. R. Chaturvedi, and S. Rakesh, "Blockchain in logistics industry: in fizz customer trust or not," J. Enterp. Inf. Manag., vol. 33, no. 3, pp. 541–558, 2020, doi: 10.1108/JEIM-01-2019-0004. [Google Scholar] [Crossref]

12. Malhotra and M. Kharub, "Elevating logistics performance: harnessing the power of artificial intelligence in e-commerce," Int. J. Logist. Manag., vol. 36, no. 1, pp. 234–256, 2025, doi: 10.1108/IJLM-05-2023-0201. [Google Scholar] [Crossref]

13. G. Zhang, Y. Yang, and G. Yang, "Smart supply chain management in Industry 4.0: the review, research agenda and strategies in North America," Ann. Oper. Res., vol. 322, pp. 1075–1117, 2023, doi: 10.1007/s10479-021-04490-6. [Google Scholar] [Crossref]

14. G. Sahebi, B. Masoomi, and S. Ghorbani, "Expert oriented approach for analyzing the blockchain adoption barriers in humanitarian supply chain," Technol. Soc., vol. 63, p. 101427, 2020, doi: 10.1016/j.techsoc.2020.101427. [Google Scholar] [Crossref]

15. Paliszkiewicz and J. Gołuchowski, "Trust and artificial intelligence," in Trust and Application of AI in Industry 5.0, Boca Raton, FL: CRC Press, 2024, pp. 1–16. [Google Scholar] [Crossref]

16. L. Lee, S. Y. Wong, H. M. Alzoubi, and M. Alshurideh, "Adopting smart supply chain and smart technologies to improve operational performance in manufacturing industry," Int. J. Innov. Sci., vol. 15, no. 5, pp. 837–857, 2023, doi: 10.1108/IJIS-07-2022-0125. [Google Scholar] [Crossref]

17. W. Wong, G. W. H. Tan, V. H. Lee, K. B. Ooi, and A. K. L. Soh, "Unearthing the determinants of Blockchain adoption in supply chain management," Int. J. Prod. Res., vol. 59, no. 11, pp. 3336–3353, 2021, doi: 10.1080/00207543.2020.1861317. [Google Scholar] [Crossref]

18. Alazab, S. Alhyari, A. Awajan, and A. B. Abdallah, "Blockchain technology in supply chain management: an empirical study of the factors affecting user adoption/acceptance," Cluster Comput., vol. 24, no. 1, pp. 83–101, 2021, doi: 10.1007/s10586-020-03200-4. [Google Scholar] [Crossref]

19. Jain, G. Soni, D. Verma, R. Baraiya, and B. Ramtiyal, "Selection of technology acceptance model for adoption of industry 4.0 technologies in agri-fresh supply chain," Sustainability, vol. 15, no. 4, p. 3769, 2023, doi: 10.3390/su15043769. [Google Scholar] [Crossref]

20. Ullah, W. M. Al-Rahmi, A. I. Alzahrani, O. Alfarraj, and F. Alblehai, "Blockchain technology adoption in smart learning environments," Sustainability, vol. 13, no. 4, p. 1801, 2021, doi: 10.3390/su13041801. [Google Scholar] [Crossref]

21. B. Babatunde, C. T. Okonji, and A. O. Adekunle, "Public-Private Partnerships in Advancing AI-Based Logistics," NIU J. Soc. Sci., vol. 1, no. 1, pp. 45–58, 2025. [Google Scholar] [Crossref]

22. Kahr, G. Rooks, C. Snijders, and M. van der Aalst, "Good Performance Isn't Enough to Trust AI: Lessons from Logistics Experts on their Long-Term Collaboration with an AI Planning System," in Proc. 2025 CHI Conf. Hum. Factors Comput. Syst., New York, NY, USA, 2025, pp. 1–15, doi: 10.1145/xxxxxxx.xxxxxxx. [Google Scholar] [Crossref]

23. D. Raut, A. Gotmare, B. E. Narkhede, S. J. Govindarajan, and P. R. J., "Enabling technologies for Industry 4.0 manufacturing and supply chain: concepts, current status, and adoption challenges," IEEE Eng. Manag. Rev., vol. 48, no. 2, pp. 83–102, 2020, doi: 10.1109/EMR.2020.2987874. [Google Scholar] [Crossref]

24. G. Richey Jr, S. Chowdhury, M. S. Davis-Sramek, A. Giannakis, and D. A. Whipple, "Artificial intelligence in logistics and supply chain management: A primer and roadmap for research," J. Bus. Logist., vol. 44, no. 4, pp. 532–549, 2023, doi: 10.1111/jbl.12364. [Google Scholar] [Crossref]

25. A. R. Khan, Z. Ahmad, A. A. Sheikh, and Y. Yu, "Digital transformation, smart technologies, and eco-innovation are paving the way toward sustainable supply chain performance," Sci. Prog., vol. 105, no. 3, p. 00368504221120147, 2022, doi: 10.1177/00368504221120147. [Google Scholar] [Crossref]

26. S. Bag, D. A. Viktorovich, A. K. Sahu, and M. Shankar, "Barriers to adoption of blockchain technology in green supply chain management," J. Glob. Oper. Strateg. Sourc., vol. 14, no. 1, pp. 104–133, 2021, doi: 10.1108/JGOSS-05-2020-0027. [Google Scholar] [Crossref]

27. S. Chowdhury, O. Rodriguez-Espindola, P. Dey, and S. K. Budhwar, "Blockchain technology adoption for managing risks in operations and supply chain management: evidence from the UK," Ann. Oper. Res., vol. 327, pp. 539–577, 2023, doi: 10.1007/s10479-021-04490-6. [Google Scholar] [Crossref]

28. S. Saurabh and K. Dey, "Blockchain technology adoption, architecture, and sustainable agri-food supply chains," J. Clean. Prod., vol. 284, p. 124731, 2021, doi: 10.1016/j.jclepro.2020.124731. [Google Scholar] [Crossref]

29. S. Yousefi and B. M. Tosarkani, "An analytical approach for evaluating the impact of blockchain technology on sustainable supply chain performance," Int. J. Prod. Econ., vol. 246, p. 108429, 2022, doi: 10.1016/j.ijpe.2022.108429. [Google Scholar] [Crossref]

30. Mitra, R. Kapoor, and N. Gupta, "Studying key antecedents of disruptive technology adoption in the digital supply chain: an Indian perspective," Int. J. Emerg. Mark., vol. 18, no. 12, pp. 5993–6015, 2023, doi: 10.1108/IJOEM-08-2021-1315. [Google Scholar] [Crossref]

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