Artificial Intelligence, Knowledge Diversity, and Sociopreneurship on Hospital Performance: Moderating Role of Service Quality in Central Java - Tri Darsono; Tulus Haryono; Ahmad Ikhwan Setiawan; Mugi Harsono; Heri Wijayanto; Rohwiyati Rohwiyati (Sekolah Tinggi Agama Islam Sabilul Muttaqin Mojokerto, 2027)
Objective: Grounded in the Resource-Based View (RBV) Theory, this study examines how technological interventions and medical staff capabilities interact to influence healthcare service performance in hospitals. Specifically, the study seeks to understand the relationships among these key resources within the context of healthcare service delivery. Method: A quantitative survey was conducted using descriptive statistics from Likert-scale questionnaires (1-5) distributed online to 102 respondents, including medical staff, managers, and administrators at type B hospitals in Central Java. Given the research objectives of prediction, exploration, or structural theory development, data were processed using SmartPLS 4.0. The independent variables in this study were Implementing Artificial Intelligence (AI), Knowledge Diversity, and Sociopreneur, while the dependent variable was Organizational Performance, with Service Quality as a moderating variable. Results: The findings indicate that each variable has a good level of validity (LF ≥ 0.70) and significance; the evaluation of discriminant validity using the Fornell-Larcker criterion, cross-loadings, and HTMT met the requirements; the f-test result of 0.037 (high) indicates the significant moderating effect of service quality on the relationship between AI implementation and organizational performance. Based on the model fit assessment, the influence of AI implementation has an R² of 0.787% (strong); the SRMR is 0.093, which is < 0.10, indicating that the model fits the empirical data well. Novelty: The implementation of AI can improve operational efficiency, diagnostic accuracy, and data management, thereby enhancing quality and service in hospitals. The follow-up to this research is the need to consider the infrastructure and medical staff expertise when implementing AI in type B hospitals in Central Java to improve healthcare services.