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Investigating AI Adoption, Knowledge Absorptive Capacity, and Open Innovation in Chinese Apparel MSMEs: An Extended TAM-TOE Model with PLS-SEM Analysis
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Investigating AI Adoption, Knowledge Absorptive Capacity, and Open Innovation in Chinese Apparel MSMEs: An Extended TAM-TOE Model with PLS-SEM Analysis
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Journal Article
Investigating AI Adoption, Knowledge Absorptive Capacity, and Open Innovation in Chinese Apparel MSMEs: An Extended TAM-TOE Model with PLS-SEM Analysis
2025
Overview
The rapid evolution of artificial intelligence (AI) has significantly transformed industries, positioning the fashion sector as a critical area of study due to its mass production and pressing sustainability challenges. As the world’s largest apparel producer, China faces unique hurdles in terms of integrating AI technologies, highlighting the intersection of technological innovation and sustainability within this industry. In this context, this study aims to provide the initial exploratory correlations between AI adoption and open innovation from apparel manufacturing micro-, small-, and medium-size enterprises (MSMEs) managers’ perspectives, identifying knowledge absorptive capacity (KACAP)’s significant impacts through an integrated and extended TAM-TOE model. We conducted PLS-SEM to empirically validate the antecedents of AI adoption and its consequential effects on KACAP and open innovation by collecting information from 269 of the apparel manufacturing MSMEs’ top managers. The results show that the TAM-TOE structural model explains 60.7% of the variance in AI adoption, 47.4% in KACAP, and 55.4% in open innovation, which suggests that the model has good explanatory capacity, and that all these Q2 values indicate a sizeable predictive accuracy threshold. Drawing on the proposed model, the study has identified technological (e.g., perceived usefulness) and environmental factors (e.g., competitive pressure, market uncertainty, and government support and policy) that significantly impact AI adoption, while organizational factors (e.g., organizational readiness) directly impact KACAP, and environmental factors (e.g., competitive pressure, supplier involvement, and market uncertainty) directly impact open innovation. Subsequently, the AI construct is having a significant influence on MSMEs’ open innovation through KACAP. This fills existing theoretical gaps by linking AI technology to organizational innovation processes and demonstrates the mediating influence of KACAP. Also, the proposed model provides a foundation for future research by exploring the intersection of AI and innovation in similar industries.
Publisher
MDPI AG
