Barriers to Big Data Analytics Adoption in Supply Chain Operations: Evidence from Sri Lankan Manufacturing Sector.

Abstract

The rise in technology in the various sectors of the industry has caught the attention of organizations. Among the numerous developments, big data (BD) has also become a great field of study. Despite the increasing global emphasis on big data analytics (BDA) in optimizing supply chain operations, there remains a significant gap in understanding the specific barriers to its adoption in developing economies, particularly in Sri Lanka. While previous research has emphasized the performance benefits of BDA, limited empirical work addresses the challenges that hinder its implementation. This study addresses that gap by empirically investigating the technological, organizational, and environmental barriers to BDA adoption in the Sri Lankan manufacturing sector. This study follows a quantitative approach to explore the barriers and adoption level of BDA in supply chain operations within the manufacturing sector in Sri Lanka. Using the Technology–Organization–Environment (TOE) framework and an ordered probit regression model, we analyze data from 164 supply chain professionals across multiple Sri Lankan manufacturing sector sub-sectors. According to the findings of this study, the hypotheses, data quality, technological infrastructure, data security, organizational culture, financial constraints, talent management, regulatory support, and competitive pressure have been accepted. Theoretically, this research extends the TOE framework by validating its applicability in an underexplored context. It provides actionable insights for policymakers and practitioners aiming to foster data-driven transformation in emerging markets.