Leveraging Generative AI for Managerial Performance: A Technology-to-Performance Chain Perspective
DOI:
https://doi.org/10.26905/jp.v23i1.17334Keywords:
Generative AI, Middle Manager Performance, Task-Technology Fit, Technology-to-Performance Chain, UtilizationAbstract
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.
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