Digital maturity, green HR analytics, and sustainable logistics performance in agri-food supply chains

Introduction Digital and environmental transformation increasingly converge in agri-food cold-chain logistics, yet how digital capabilities relate to frontline loss-prevention practices remains unclear. Drawing on the Twin Transition perspective, this study examined the associations among digital maturity, green human resource analytics (GHRA), employee green loss-prevention behavior, and supervisor-rated sustainable logistics performance. It also examined whether the association between GHRA and employee behavior varied with perceived algorithmic pressure. Methods This cross-sectional study analyzed matched questionnaire data from 96 supervisors and 545 employees across 96 Chinese agri-food cold-chain firms or departments. The primary analysis used a pooled single-level structural equation model. Sensitivity analyses used composite-score mixed models and unit-level cluster bootstrapping to assess the influence of clustering. Results Digital maturity was positively associated with GHRA, which in turn was positively associated with employee green loss-prevention behavior. This behavior was positively associated with supervisor-rated sustainable logistics performance. The direct association between GHRA and performance was statistically nonsignificant. Positive indirect estimates were consistent with the proposed pathways linking GHRA to performance through employee behavior and linking digital maturity to performance through GHRA and employee behavior in sequence. The positive association between GHRA and employee behavior was weaker at higher levels of perceived algorithmic pressure. The sensitivity analyses supported the overall pattern of associations. Discussion The findings extend the Twin Transition perspective by specifying how digital capability, analytics applied to green employee management, and frontline loss-prevention practices are linked in cold-chain operations. They also highlight the importance of how employees experience algorithmic oversight when organizations use analytics to support loss prevention.