Artificial Intelligence-Enabled Logistics Resources and Customer Satisfaction Under Global Trade Disruptions: An Integrated RBV, EDT, and TCE Perspective

Abstract

The purpose of this study is to investigate the role of a firm’s artificial intelligence-enabled valuable, rare, inimitable, and non-substitutable (AI-VRIN) based logistics resources in mitigating the effects of global trade disruptions on customer satisfaction. The study conducted conventional (inductive) qualitative content analysis of authoritative multilateral economic and strategic institutional reports to validate the proposed relationships and identify recurring disruption performance patterns. The reports used in the study are from World Trade Organisation (WTO), United Nations Conference on Trade and Development (UNCTAD), World Bank, International Monetary Fund (IMF), and DHL–New York University Stern School of Business. Based on the analysis, a moderated serial mediation model has been proposed that explains how global trade disruption influences transaction cost and logistics process performance, ultimately affecting customer satisfaction. Additionally, AI-VRIN-based logistics resources moderate the relationship between transaction cost and logistics process performance, thereby mitigating the cascading impact of global trade disruption on customer satisfaction. The findings of the study can be used by international supply chain, operations managers and strategic decision makers as the proposed model provides a strategic lens that may guide firms in sustaining customer satisfaction in highly dynamic and evolving global markets.