Does the adoption of emerging technologies improve technical efficiency? Evidence from Korean manufacturing SMEs

The Impact of Artificial Intelligence on Firm Performance: An Application of the Resource-Based View to e-Commerce Firms

The application of artificial intelligence (AI) technology has evolved into an influential endeavor to improve firm performance, but little research considers the relationship among artificial intelligence capability (AIC), management (AIM), driven decision making (AIDDM), and firm performance. Based on the resource-based view (RBV) and existing findings, this paper constructs a higher-order model of AIC and suggests a research model of e-commerce firm AIC and firm performance. We collected 394 valid questionnaires and conducted data analysis using partial least squares structural equation modeling (PLS-SEM). As a second-order variable, AIC was formed by three first-order variables: basic, proclivity, and skills. AIC indirectly affects firm performance through creativity, AIM, and AI-driven decision making. Firm creativity, AIM, and AIDDM are essential variables between AIC and firm performance. Innovation culture (IC) positive moderates the relationship between firm creativity and AIDDM as well as the relationship between AIDDM and firm performance. Environmental dynamism (ED) positive mediates the connection between AIM and AIDDM. Among the control variables, firm age negatively affects firm performance, and employee size does not. This study helps enterprises leverage AI to improve firm performance, achieve a competitive advantage, and contribute to theory and management practice.

Big Data in Entrepreneurship

Entrepreneurship research is paying increasing attention to big data. However, there is only a fragmented understanding on how big data influences entrepreneurial activities. To review previous research systematically and quantitatively, the authors use bibliometrics method to analyze 164 research articles on big data in entrepreneurship. They visualize the landscape of these studies, such as publication year, country, and research area. They then use VOSviewer to conduct theme clustering analysis, finding four themes, namely the COVID-19 pandemic and small medium enterprise (SME) digitization, application of big data analytics to decision making, application of big data in platform, and the effects of big data on enterprises. In addition, they construct an integrated framework that integrates the antecedents of big data adoption and influence mechanism of big data on entrepreneurial activities.