Big Data Analytics and Green Core Competencies: Important Role of Data-Driven Decision-Making Culture and Leader Conscientiousness

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Authors: Muhammad Afdhal Usman, Nadia Zahoor, Erhan Boğan, Muhammad Waheed Akhtar, Bekir Bora Dedeoğlu
Cited by: 1
References: 78

Abstract

Given the growing emphasis on business sustainability and environmental management aimed at protecting the natural environment, this study, drawing insights from organizational information processing theory, investigates the direct association of artificial intelligence-supported big data analytics (AI-BDA) with organizations' green core competencies as well as the indirect association through green data-driven decision-making culture (GDDC). Additionally, the role of leader conscientiousness as an important boundary condition is examined. Data collected from 339 managers is analyzed using structural equation modeling in Mplus (8.8). Our findings indicate that AI-BDA has both direct and indirect positive relationships with green core competencies. Moreover, leader conscientiousness moderated the direct impact of AI-BDA on GDDC. Supplementary semi-structured interviews with 12 senior managers provide contextual validation and illustrate how AI-BDA, green data-driven culture, and leader conscientiousness jointly shape green core competencies in practice. Our research provides actionable insights that empower organizations to develop green core competencies, thereby enhancing their impact on initiatives aimed at preserving the natural environment. By integrating technological capabilities with leadership traits, this study highlights a pathway for organizations to align digital transformation with environmental sustainability goals, thereby advancing their corporate responses to environmental challenges.

Fields of Study & Subjects

AI and HR Technologies Environmental Sustainability in Business Big Data and Business Intelligence

Publication details

Journal / source
IEEE Transactions on Engineering Management
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Volume
73
Pages
1363–1375
Publication date
1 January 2026
Language
EN