The Impact of Generative AI on Corporate Decision Making and Innovation Performance - Nirmala T.P., Ernest; Qurrota A'yun, Annisa (Inovasi Analisis Data, 2025)
Purpose: This paper aims to illuminate the main predictors of innovation performance through exploring a set of direct i. e. generative AI adoption, data-driven decision making, knowledge management systems and leadership support and indirect paths testing an organizational learning mediating role suited for these relations.Method: The research is a quantitative type with cross-sectional survey design. Structural equation modeling was used to test direct and mediated relationships in the proposed theoretical model.Findings: The results reveal that the four antecedent factors significantly contribute to innovation performance, and organizational learning surface as a pivotal mediator. Precisely, organizational learning completely mediates the relationship between KM and innovation besides partially mediating the remaining three relationships implying its pivotal function of translating organizational inputs into attaining innovation.Novelty: This study makes an original contribution by bringing together several theoretical perspectives to explain the sovereign role of organizational learning in connecting technological capital with innovation performance. The paper contributes to a line of research unifying the technological adoption, and organizational capabilities literatures.Implications: The results indicate that companies need to accompany their investments in technology by a learning-oriented culture if they are to realise the potential of innovation. At the theoretical level, the study contributes by illuminating organisation learning as a key dynamic capability, which processes resource to create performance.