How big data analytics improves hospital environmental performance through supply chain innovation, decision quality, and risk taking

Despite big data analytics (BDA) capabilities have been increasingly recognized for their potential to improve sustainability, the underlying mechanisms by which BDA capabilities influence hospital environmental performance in the context of healthcare supply chains are not well understood. This paper aims to bridge this significant empirical void by examining the mediating effect of supply chain innovation, decision-making quality and risk-taking on the links between BDA capabilities and environmental performance for Chinese hospitals. Based on Stimulus-Organism-Response theory, the theoretical model depicts BDA capability as a key stimulus factor affecting the hospital sustainability outcomes. This research employed a quantitative research method, and a structured survey instrument was administered to 653 healthcare providers from various hospitals. The participants were recruited using a random sampling method to achieve broad representation. Variables in the survey include measures of big data analytics capability, supply chain innovation, quality of decision-making, risk-taking, and environmental performance. AMOS was used for Structural Equation Modeling (SEM) analysis to test the proposed relationships and mediation effects among the variables in due diligence. Empirical results support a positive relationship between hospitals' BDA capability and environmental performance, indicating that it is statistically significant. Crucially, this link is to some degree mediated by supply chain innovation, quality of decision-making and risk taking behaviour. In particular, hospitals with high analytical capability were more innovative in their supply chain production, had better decision-making structures, and showed a tendency to be more risk takers, leading to good environmental outcomes. This research limns the manner in which improving BDA capabilities can systematically contribute to hospital sustainability in innovative, informed, and strategically bold supply chain management practices; thereby new theoretical and practical aspects are provided. These results not only enrich current theoretical constructs but also give insights to healthcare managers and policy makers for harnessing the big data analytics to promote environmental sustainability and implement a real change in the healthcare performance.