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

Author

Listed:
  • Nor Ratna Masrom

    (Fakulti Pengurusan Teknologi dan Teknousahawanan, Universiti Teknikal Malaysia Melaka)

  • Wan Hasrulnizzam Wan Mahmood

    (Fakulti Teknologi dan Kejuruteraan Industri dan Pembuatan, Universiti Teknikal Malaysia Melaka)

  • Al Amin Mohamed Sultan

    (Fakulti Teknologi dan Kejuruteraan Industri dan Pembuatan, Universiti Teknikal Malaysia Melaka)

Abstract

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.

Suggested Citation

  • Nor Ratna Masrom & Wan Hasrulnizzam Wan Mahmood & Al Amin Mohamed Sultan, 2026. "Drivers of AI Adoption in Smart Supply Chains: An Empirical Study of Readiness, Trust, and the Insignificance of Perceived Risk," International Journal of Research and Innovation in Social Science, International Journal of Research and Innovation in Social Science (IJRISS), vol. 10(2), pages 4723-4732, February.
    • Handle: RePEc:bcp:journl:v:10:y:2026:i:2:p:4723-4732
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    References listed on IDEAS

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    1. Amit Kumar Bhardwaj & Arunesh Garg & Yuvraj Gajpal, 2021. "Determinants of Blockchain Technology Adoption in Supply Chains by Small and Medium Enterprises (SMEs) in India," Mathematical Problems in Engineering, Hindawi, vol. 2021, pages 1-14, June.
    2. Giua, Carlo & Materia, Valentina Cristiana & Camanzi, Luca, 2022. "Smart farming technologies adoption: Which factors play a role in the digital transition?," Technology in Society, Elsevier, vol. 68(C).
    3. Blandine Ageron & Omar Bentahar & Angappa Gunasekaran, 2020. "Digital Supply chain: challenges and future directions," Post-Print hal-03021364, HAL.
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