The impact of task-technology fit in generative AI on utilisation and employee output

Lim, Qi Fei (2025) The impact of task-technology fit in generative AI on utilisation and employee output. Final Year Project, UTAR.

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Abstract

Generative AI (GenAI) is transforming workplace dynamics by enabling enhanced creativity, efficiency, and productivity. This study explores the impact of Generative AI on employee output, focusing on how task characteristics, technology characteristics, task-technology fit, supervisory support, and utilisation interact to influence performance and satisfaction. While GenAI promises increased efficiency and quality of work, concerns about cognitive overload and uneven productivity outcomes remain. Grounded in the Task-Technology Fit Theory and Social Learning Theory, this research develops a conceptual framework to investigate these dynamics. A quantitative approach was adopted, involving a survey of full-time employees in Malaysian organisations. The findings are expected to reveal the relationships among the independent variables (task and technology characteristics, supervisory support), both dependent and independent variables (task-technology fit and utilisation), and the dependent variable (employee output). Results aim to offer actionable insights for business leaders to optimize GenAI integration and enhance employee output. By bridging gaps in current literature and addressing practical challenges, this study contributes to both academic discourse and strategic decision-making for organizational growth in the digital age. Keywords: Generative AI, employee performance, task-technology fit, supervisory support, employee satisfaction

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