Generative Artificial Intelligence Adoption and Business Decision Quality: The Mediating Role of Organizational Learning Capability  in Digital Enterprises

Teti Safari; Hani Krisnawati; Rita Andini

doi:10.61132/jpbi.v2i1.1597

Authors

Teti Safari ITB Semarang
Hani Krisnawati ITB Semarang
Rita Andini ITB Semarang

DOI:

https://doi.org/10.61132/jpbi.v2i1.1597

Keywords:

Business Decision Quality, Digital Enterprises, Generative AI, Organizational Learning Capability, Technology Adoption

Abstract

Generative Artificial Intelligence (GenAI) is increasingly adopted by digital enterprises to support data analysis, planning, information synthesis, and managerial decision-making. However, the organizational value of GenAI depends on the ability of enterprises to transform AI-generated insights into actionable knowledge and high-quality decisions. This study aimed to examine the direct effect of GenAI adoption on business decision quality and the mediating role of organizational learning capability. A quantitative explanatory design was employed involving 250 managers, supervisors, and professionals with experience using GenAI for organizational tasks. Data were collected using a five-point Likert scale questionnaire and analyzed through Structural Equation Modeling with Partial Least Squares. The results confirmed that the measurement model fulfilled the criteria for validity and reliability. GenAI adoption had a positive and significant effect on organizational learning capability and business decision quality. Organizational learning capability also positively and significantly influenced business decision quality and partially mediated the relationship between GenAI adoption and business decision quality. The study concludes that GenAI improves decision quality through direct analytical support and organizational learning processes that facilitate knowledge acquisition, sharing, interpretation, and utilization. Digital enterprises should therefore combine GenAI implementation with employee training, collaborative learning, knowledge-sharing mechanisms, reliable data governance, and systematic verification of AI-generated outputs to support accurate, timely, relevant, and strategically appropriate business decisions.

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