Exploring The Barriers to Artificial Intelligence Integration in Green Supply Chain Using Interpretive Structural Modeling Approach: Insights from Bangladesh's Multi-Stakeholder Perspective

Abstract

In recent years, Artificial Intelligence (AI) integration to Green Supply Chains (GSC) has been identified as a vital organizational practice for achieving profitability by reducing both environmental and social risks.  So far, no study has explored the barriers to implementing AI integrate GSC. The current research will analyze the barriers to adopting the concept of AI in GSC through multi-stakeholder aspect with regard to opinions provided by industry practitioners, government, and academics. The study utilized a thorough literature analysis, and consultations with experts resulted in a list of 11 barriers to AI implementation in GSC. These are the low top management commitment, the high cost of investing in AI, low digital infrastructure etc. This study used Interpretive Structural Modeling (ISM) to represent the contextual relationships between the barriers, resulting in a hierarchical model. The results of the study show that obstacles are categorized into six hierarchic levels in the model with the top management commitment and Government support are identified as the leading drivers. Addressing the main cause of the problem, which is improving infrastructure, raising awareness, and developing professional ability, is critical to encouraging AI adoption. One limitation is that the study is based on the opinions of experts limited to Bangladesh, which may reduce generalizability. The implications include the recommendation that policymakers and supply chain managers focus on initiatives to address the driving barriers and develop supporting policies to assist AI-enabled practices in the sustainable process of resource-constrained situations.