The main aim of the study was to empirically investigate the mediating role of organizational commitment (OC) and person–organization fit (POF) between the causal relationship of supportive work environment (SWE) and employee retention (ER). One thousand questionnaires were sent to the targeted population included employees of all chains of multinational fast-food brands (restaurants) in Lahore, Pakistan. The restaurants were selected from clusters by using a cluster sampling technique. Questionnaires were comprised of multiple items adopted from former studies to obtain responses using quantitative methodology. For statistical analysis and to test the proposed hypothesis, the partial least squares (PLS) structural equation modeling (SEM) technique was employed through Smart PLS 2.0 M3 software. The study’s findings elucidated that SWE has a positive and significant association with ER. In addition, OC and POF acted as mediators between the relationship of a SWE and ER. This study presented implications for human resource (HR) practitioners that they should endure developing mechanisms for imparting a SWE to foster healthy exchange relationships with people, which in turn will result in ER. This article significantly contributed to the extant literature on the relationship of the SWE and ER while highlighting the critical factors to be noticed for retaining key employees. This study also explicated the limitations and scope for further research.

Purpose: This study is undertaken to examine the antecedents and role of big data decision-making capabilities toward decision-making quality and environmental performance among the Chinese public and private hospitals. It also examined the moderating effect of big data governance that was almost ignored in previous studies.Design/methodology/approach: The target population consisted of managerial employees (IT experts and executives) in hospitals. Data collected using a survey questionnaire from 752 respondents (374 respondents from public hospitals and 378 respondents from private hospitals) was subjected to PLS-SEM for analysis.Findings: Findings revealed that data management challenges (leadership focus, talent management, technology and organizational culture for big data) are significant antecedents for big data decision-making capabilities in both public and private hospitals. Moreover, it was also found that big data decision-making capabilities played a key role to improve the decision-making quality (effectiveness and efficiency), which positively contribute toward environmental performance in public and private hospitals of China. Public hospitals are playing greater attention to big data management for the sake of quality decision-making and environmental performance than private hospitals.Practical implications: This study provides guidelines required by hospitals to strengthen their big data capabilities to improve decision-making quality and environmental performance.Originality/value: The proposed model provides an insight look at the dynamic capabilities theory in the domain of big data management to tackle the environmental issues in hospitals. The current study is the novel addition in the literature, and it identifies that big data capabilities are envisioned to be a game-changer player in effective decision-making and to improve the environmental performance in health sector.