The influence of AI capability on enterprise competitive advantage: the mediating effect of business model innovation
Sunan Shao et al.
Abstract
Purpose This study seeks to examine the relationships among artificial intelligence capability (AIC), business model innovation (BMI), and the competitive advantage of enterprises (CAE) within the framework of dynamic capabilities theory. It specifically focuses on how small and medium-sized (SMEs) enterprises utilise artificial intelligence capability to foster business model innovation in a digital context, thereby attaining a sustainable competitive advantage. Design/methodology/approach This study utilises a questionnaire survey to gather empirical data from 546 SMEs in China. Structural equation modelling was employed for quantitative analysis to examine the direct effect of artificial intelligence capabilities on competitive advantage, alongside the mediating role of business model innovation. Findings Research indicates that three primary components of artificial intelligence capabilities, tangible resources, intangible resources, and skill resources, exert a significant positive influence on a company's competitive advantage. At the same time, business model innovation serves as a mediating factor within this relationship. Moreover, the findings underscore the necessity for firms to proactively adapt to technological advancements and to foster the synergistic development of artificial intelligence capabilities alongside business model innovation to enhance their competitiveness in a rapidly evolving environment. Originality/value This study extends the dynamic capabilities theory from the perspective of artificial intelligence, proposing AI capability as a systemic and multidimensional dynamic capability, emphasising its transformative role in the ways small and medium-sized enterprises create and capture value. The research not only enriches the theoretical understanding in the field of artificial intelligence but also offers practical insights and policy recommendations for SMEs on how to achieve a competitive advantage through the development of AI capabilities.
1 citation
Evidence weight
Balanced mode · F 0.40 / M 0.15 / V 0.05 / R 0.40
| F · citation impact | 0.16 × 0.4 = 0.06 |
| M · momentum | 0.53 × 0.15 = 0.08 |
| V · venue signal | 0.50 × 0.05 = 0.03 |
| R · text relevance † | 0.50 × 0.4 = 0.20 |
† Text relevance is estimated at 0.50 on the detail page — for your query’s actual relevance score, open this paper from a search result.