Exploring the influence of generative artificial intelligence on job satisfaction in SMEs: insights from managers
TL;DR
The findings revealed that security and privacy, ethical use, perceived GenAI value and available resources significantly enhance managerial job satisfaction, in contrast to ease of use, expectation confirmation and social influence, which were not statistically significant.
Abstract
This study aims to investigate the impact of “generative artificial intelligence (GenAI)”on managerial job satisfaction in “small and medium-sized manufacturing enterprises (SMEs)”, with a specific focus on work quality and decision-making effectiveness. Grounded in the “unified theory of acceptance and use of technology (UTAUT)” and the “expectation-confirmation model (ECM)”, this study also incorporates additional constructs such as ethical use, perceived GenAI value, and available resources. A survey of 163 SME managers was analyzed using “partial least squares structural equation modeling (PLS-SEM)”. The findings revealed that security and privacy, ethical use, perceived GenAI value and available resources significantly enhance managerial job satisfaction. In contrast, ease of use, expectation confirmation and social influence were not statistically significant. Additionally, job satisfaction positively influences both work quality and decision-making effectiveness. Results offer guidance for SME managers, developers and policymakers seeking to maximize the benefits of GenAI adoption and boost managerial job satisfaction. Organizations should strengthen data protection measures and establish ethical GenAI governance frameworks to build trust and accountability, while policymakers can support these efforts through clear national standards. Enhancing infrastructure, providing targeted training and ensuring equitable access to resources help managers integrate GenAI confidently. Aligning GenAI initiatives with employee well-being and professional development fosters engagement. Developers can embed secure, transparent and user-centered features to enhance reliability. Collectively, these actions support sustainable GenAI use that improves satisfaction and decision-making. This study is original in its focus on managerial-level outcomes of GenAI adoption within manufacturing SMEs a perspective largely overlooked in existing literature. Unlike prior research that centers on technological performance or firm-level benefits, this study investigates the individual manager's experience, integrating underexplored constructs such as ethical use and perceived GenAI value. It also offers novel theoretical empirical evidence to an emerging research area and provides a practical framework to inform GenAI adoption strategies in resource-constrained SME environments.