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Exploring psychosocial predictors of AI use intention: the moderating role of organisational culture in diverse workplaces

João M. Lopes et al.

Behaviour and Information Technology2026https://doi.org/10.1080/0144929x.2026.2626743article
AJG 2ABDC A
Weight
0.50

Abstract

Artificial intelligence (AI) is transforming workplaces and shaping how employees perceive and interact with technology. Drawing on the Technology Acceptance Model (TAM) and Organisational Culture Theory, this study examines how psychosocial predictors, promotion focus, positive reinterpretation and technostress influence workers' perceived usefulness of AI and their intention to use it. It also tests whether an adaptable organisational culture strengthens the relationship between perceived usefulness and intention to use AI. Data were collected from 422 Portuguese respondents and analysed using partial least squares. Results show that both promotion focus and positive reinterpretation positively affect perceived usefulness and the intention to use AI at work. Technostress negatively affects perceived usefulness but does not significantly impact intention to use AI. Perceived usefulness mediates the effects of psychosocial predictors on intention: the indirect effects are positive for promotion focus and positive reinterpretation and negative for technostress. Moreover, a highly adaptable organisational culture amplifies the positive link between perceived usefulness and employees' intention to adopt AI. The study extends TAM by incorporating psychosocial antecedents and provides empirical support for the moderating role of cultural adaptability, offering insights for fostering responsible and sustainable AI adoption across diverse organisational contexts.

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https://doi.org/https://doi.org/10.1080/0144929x.2026.2626743

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@article{joão2026,
  title        = {{Exploring psychosocial predictors of AI use intention: the moderating role of organisational culture in diverse workplaces}},
  author       = {João M. Lopes et al.},
  journal      = {Behaviour and Information Technology},
  year         = {2026},
  doi          = {https://doi.org/https://doi.org/10.1080/0144929x.2026.2626743},
}

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Evidence weight

0.50

Balanced mode · F 0.40 / M 0.15 / V 0.05 / R 0.40

F · citation impact0.50 × 0.4 = 0.20
M · momentum0.50 × 0.15 = 0.07
V · venue signal0.50 × 0.05 = 0.03
R · text relevance †0.50 × 0.4 = 0.20

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