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Misbakhul Mustofin
University of Merdeka Malang
Economics, Econometrics & Finance Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Social Sciences

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Leveraging Generative AI for Managerial Performance: A Technology-to-Performance Chain Perspective Misbakhul Mustofin; Syarif Hidayatullah; Mardiana Andarwati
Jurnal Penelitian Vol. 23 No. 1 (2026): June 2026
Publisher : University of Merdeka Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26905/jp.v23i1.17334

Abstract

This study aims to analyze the effect of Generative Artificial Intelligence (AI) utilization on middle manager performance in the digital creative industry of Malang City using the Technology-to-Performance Chain (TPC) approach. Specifically, this study examines how Task Characteristics and Technology Characteristics influence Task-Technology Fit (TTF), and their subsequent impact on manager performance through Utilization as a mediating variable. The research method employed is quantitative cross-sectional. Data were collected via questionnaires from 182 middle managers and analyzed using Partial Least Squares Structural Equation Modeling (PLS-SEM). The results indicate that complex task characteristics and reliable AI technology characteristics significantly influence the formation of Task-Technology Fit. Furthermore, TTF is proven to have a positive and significant effect on middle manager performance, both directly and indirectly through utilization as a mediator. These findings indicate that the fit between Generative AI capabilities and managerial task requirements drives more intensive technology utilization, which ultimately enhances efficiency and decision-making quality.
Co-Authors Mardiana Andarwati Syarif Hidayatullah