Work-related generative artificial intelligence adoption and employee-perceived organisational performance in Thailand's manufacturing industry

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dc.contributor.authorYudhistyra, Wecka Imam
dc.contributor.authorSrinuan, Chalita
dc.date.accessioned2026-08-06T10:56:21Z
dc.date.available2026-08-06T10:56:21Z
dc.date.issued2026-10-01
dc.description.abstractGenerative Artificial Intelligence (GenAI) is increasingly integrated into organisational work processes, yet its implications for manufacturing firms cannot be inferred from adoption intention or usage frequency alone. In contrast to deterministic workplace technologies, GenAI produces probabilistic and context-responsive outputs whose organisational value depends on employees' ability to evaluate, interpret, and apply generated content effectively. This research examines how work-related GenAI use is associated with employee perceptions of GenAI-enabled organisational performance in Thailand's manufacturing sector. Drawing on the Unified Theory of Acceptance and Use of Technology (UTAUT) framework, the Human-Centred Design paradigm, and Knowledge Management theory, the research develops a framework in which perceived usability, knowledge acquisition, knowledge application, and performance expectancy influence behavioural intention, which subsequently affects use behaviour and employee-perceived GenAI-enabled organisational performance. Survey data from 381 manufacturing employees with prior GenAI experience were analysed using Partial Least Squares Structural Equation Modelling. Bayesian Network (BN) algorithms were subsequently employed to refine the theoretical structure by identifying conditional dependencies beyond the hypothesised model, while Artificial Neural Networks and Importance-Performance Map Analysis provided complementary predictive and managerial insights. The results support all hypothesised relationships. The BN-refined model further identifies direct links between knowledge application, performance expectancy, and organisational performance, suggesting that, in addition to adoption and use, GenAI-derived value also depends on employees' capacity to translate AI-generated knowledge into workplace outcomes. The findings advance technology adoption research by developing and validating a GenAI-specific extension of the UTAUT that integrates human-centred design and knowledge management perspectives. The research further demonstrates that organisational value is realised through a sequential pathway linking adoption antecedents, behavioural intention, and use behaviour, while providing policy-relevant evidence on workforce-driven AI adoption in an emerging economy undergoing digital transformation.
dc.identifier.citationTelecommunications Policy, 50(9), 2026
dc.identifier.doi10.1016/j.telpol.2026.103292
dc.identifier.issn03085961
dc.identifier.other2-s2.0-105045073557
dc.identifier.urihttps://dspace.kmitl.ac.th/handle/123456789/18320
dc.sourceTelecommunications Policy
dc.subjectArtificial neural networks
dc.subjectBayesian networks
dc.subjectDigital transformation
dc.subjectGenerative artificial intelligence
dc.subjectManufacturing
dc.subjectThailand
dc.titleWork-related generative artificial intelligence adoption and employee-perceived organisational performance in Thailand's manufacturing industry
dc.typeArticle

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