From experiential to computational trust: how data-driven decision-making enhances ESG performance in emerging market supply chains

Abstract

Emerging-market supply chains face growing ESG pressure, yet governance based on experiential trust often falls short in complex sustainability decisions. Drawing on the DECAS framework, this study examines how data-driven decision-making reshapes relational learning from experiential trust to computational trust and, in turn, improves ESG performance. Using panel data on Chinese A-share listed supply chain firms from 2015 to 2024, we find that DDDM significantly improves ESG performance. This effect works through two reinforcing channels: higher investment efficiency and stronger collaborative innovation. We also find that internationalisation strengthens the role of collaborative innovation. By introducing computational trust into the analysis, this study offers a clearer explanation of how data and analytics support sustainable supply chain governance. It extends the application of DECAS in ESG research and provides evidence that firms can improve sustainable performance by combining better resource allocation with stronger interorganizational innovation.