Modeling the nexus of data analytics, sustainability practices and quality management: Evidence of key enablers

Data-driven sustainable quality management (DDSQM) integrates data analytics, sustainable practices, and quality management to improve product quality, customer satisfaction, and positive environmental impact in manufacturing organizations. However, organizations in developing economies are lacking superior performance in terms of sustainability, quality, and competitiveness due to their unwillingness to adopt DDSQM practices. To encourage adoption, the nexus among quality management, sustainability, and data analytics needs to be explored. This paper pioneers the use of the intuitionistic fuzzy decision-making trial and evaluation laboratory (IF-DEMATEL) methodology to explore the enablers of DDSQM and analyze the causal links among the enablers, which enhance sustainable quality performance. The proposed methodology is tested with experienced academics and practitioners in emerging economies. The findings reveal that “Enthusiasm and commitment from top management”, “Crowdfunding”, “Application of advanced quality analytics” and “Implementation of data-driven lean and green initiatives” constitute the most crucial enablers of DDSQM. The findings may aid policymakers in emerging economies to adopt DDSQM. This research is one of few initial attempts to investigate the enablers of DDSQM practices through an IF-DEMATEL based methodological framework.