Identifying Causal Conditions Affecting Artificial Intelligence-Based Digital Entrepreneurship in Teaching Hospitals of Mazandaran University of Medical Sciences

Abstract

Artificial intelligence-based digital entrepreneurship, as an innovative approach, can provide an effective solution for addressing existing challenges in hospitals and improving the quality of healthcare services. Therefore, the main objective of the present study was to identify the causal conditions affecting artificial intelligence-based digital entrepreneurship in the teaching hospitals of Mazandaran University of Medical Sciences. This study was applied in terms of purpose and employed a mixed-methods design (qualitative–quantitative) with an exploratory sequential approach. In the qualitative phase, grounded theory methodology was used, while in the quantitative phase, a descriptive–survey method was applied. The statistical population in the qualitative section consisted of 20 experts in AI-based digital entrepreneurship and faculty members in entrepreneurship and management, while in the quantitative section, it included all clinical faculty members and middle managers working in teaching hospitals affiliated with Mazandaran University of Medical Sciences (N = 680). In the qualitative phase, 12 participants were selected through snowball sampling based on the principle of theoretical saturation, and in the quantitative phase, 330 participants were selected using stratified random sampling according to the Krejcie and Morgan table. Data were collected using semi-structured interviews in the qualitative phase and a researcher-developed questionnaire with 21 items in the quantitative phase. The validity and reliability of the instruments were confirmed. Data analysis was conducted using structural equation modeling. In the qualitative phase, 45 open codes were extracted and organized into four main categories, including “internal factors,” “hospital performance improvement,” “enhancement of healthcare and educational services,” and “external factors,” with “AI-based digital entrepreneurship” identified as the causal condition. The results of the quantitative phase indicated that the component of “enhancement of healthcare and educational services,” with a path coefficient of 0.858 and an R² value of 0.743, was the strongest and most influential predictor among the causal conditions. Enhancing healthcare and educational services is one of the most important causal conditions affecting AI-based digital entrepreneurship in the teaching hospitals of Mazandaran University of Medical Sciences, and therefore, hospital administrators and policymakers should pay particular attention to this component.