Unveiling the impact of the congruence between artificial intelligence and explorative learning on supply chain resilience
International Journal of Operations & Production Management
ISSN: 0144-3577
Article publication date: 6 August 2024
Abstract
Purpose
Drawing upon socio-technical system theory, this study intends to investigate the effects of the congruence and incongruence between artificial intelligence (AI) and explorative learning on supply chain resilience as well as the moderating role of organizational inertia.
Design/methodology/approach
Using survey data collected from 170 Chinese manufacturing firms, we performed polynomial regression and response surface analyses to test our hypotheses.
Findings
We find that the congruence between AI and explorative learning enhances firms’ supply chain resilience, while the incongruence between these two factors impairs their supply chain resilience. In addition, compared with low–low congruence, high–high congruence between AI and explorative learning improves supply chain resilience to a greater extent. Moreover, organizational inertia attenuates the positive influence of the congruence between AI and explorative learning on supply chain resilience, while it aggravates the negative influence of the incongruence between these two factors on supply chain resilience.
Originality/value
Our study expands the literature on supply chain resilience by demonstrating that the congruence between a firm’s AI (i.e. technical aspect) and explorative learning (i.e. social aspect) boosts its supply chain resilience. More importantly, our study sheds new light on the role of organizational inertia in moderating the congruent effect of AI and explorative learning, thereby extending the boundary condition for socio-technical system theory in the supply chain resilience literature.
Keywords
Acknowledgements
This work was supported by the National Natural Science Foundation of China [grant numbers 72202193 and 72202112] and the Fundamental Research Funds for the Central Universities [grant number ZK1164].
Citation
Dai, J., Geng, R., Xu, D., Shangguan, W. and Shao, J. (2024), "Unveiling the impact of the congruence between artificial intelligence and explorative learning on supply chain resilience", International Journal of Operations & Production Management, Vol. ahead-of-print No. ahead-of-print. https://doi.org/10.1108/IJOPM-12-2023-0990
Publisher
:Emerald Publishing Limited
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