Artificial Intelligence and Information Management for Data-Driven Decision-Making: A Multi-Group Analysis Across Managers and Non-Managers

Abstract

Artificial intelligence (AI) is increasingly embedded in manufacturing operations; however, its organizational value may vary according to employees' managerial responsibilities. This study applies Multi-Group Analysis (PLS-MGA) to compare managers and non-managers in terms of AI capability, information management capability (IMC), data-driven decision-making (DDDM), and decision-making effectiveness. The findings confirm that AI capability strengthens IMC, which subsequently facilitates DDDM and improves decision-making effectiveness. IMC also serves as the mechanism through which AI capability contributes to data-driven organizational decisions. The multi-group findings indicate that managers obtain significantly greater benefits from AI capability in developing IMC and from DDDM in improving decision-making effectiveness than non-managers. In contrast, IMC supports DDDM similarly across both groups. These findings identify managerial position as a critical organizational boundary condition in AI-enabled manufacturing environments and suggest that organizations should adopt role-specific AI implementation, information management, and decision-support strategies to maximize organizational effectiveness and digital transformation outcomes.