AI Literacy and Sustainable Competitive Advantage of Chinese Corporations: Mediating Role of Innovation Consciousness
The rapid advancement of Artificial Intelligence (AI) technologies, exemplified by Chat GPT, has significantly transformed the global business landscape, raising critical questions about the role of AI literacy among managers, their innovation consciousness, and the sustainable competitive advantage (SCA) of Chinese companies. This study investigates the impact of AI literacy on the SCA of Chinese corporations in Africa and explores the mediating role of innovation consciousness. AI literacy is decomposed into four dimensions: AI awareness (AWN), AI empowerment (EMP), AI evaluation capacity (EVC), and AI ethical cognition (ECG). Using structural equation modeling (SEM) techniques, data from 330 individuals in Chinese firms operating in Africa were analyzed through SPSS 27.0 and AMOS 24.0. The results reveal that AI AWN and AI EMP significantly enhance SCA directly and through innovation consciousness. Conversely, AI EVC does not significantly influence SCA or innovation consciousness, while AI ECG has a weaker direct impact on SCA but significantly influences it through innovation consciousness. These findings underscore the critical role of AI literacy in fostering innovation and competitive advantage. The study contributes to the literature by providing a comprehensive analysis of AI literacy's direct and indirect effects on SCA, particularly in the context of Chinese corporations in Africa. It highlights the importance of fostering innovation consciousness to leverage AI literacy for strategic success. The study's implications for strategic management practices and AI literacy development policies are discussed, emphasizing the need for a nuanced approach to integrating AI literacy into corporate strategies to sustain competitive advantage.
Authors
- Chan-Li Lin
- Zhanglan You (ORCID: https://orcid.org/0009-0008-2406-4339)
- Qi Kai
- Ming Guo
Institutions
- Fujian Business University (CN)
Publication Details
- Published
- 2024-12-27
- DOI
- https://doi.org/10.1145/3718491.3718524
- Primary Topic
- Innovation, Sustainability, Human-Machine Systems
- Type
- article
- Field-Weighted Citation Impact
- 0.00