Exploring psychosocial predictors of AI use intention

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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Publication Details

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Repository of the University of Ljubljana (University of Ljubljana)
Published
2026-03-03
Primary Topic
Technostress in Professional Settings
Type
article
Field-Weighted Citation Impact
0.00

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article

Exploring psychosocial predictors of AI use intention

Elisabete Nogueira, Sofia Gomes, João M. Lopes, Marina Dabić et al.
Repository of the University of Ljubljana (University of Ljubljana)
Technostress in Professional Settings
article

Exploring psychosocial predictors of AI use intention

Elisabete Nogueira, Sofia Gomes, João M. Lopes, Marina Dabić, João J. M. Ferreira
article en

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.

Repository of the University of Ljubljana (University of Ljubljana)
Javna Agencija za Raziskovalno Dejavnost RS, NECE - Research Center in Business Sciences, University of Beira Interior, Fundação para a Ciência e a Tecnologia
Openalex Percentile: Top 41%
Technostress in Professional Settings
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Exploring psychosocial predictors of AI use intention — Elisabete Nogueira, Sofia Gomes, et al. · Repository of the University of Ljubljana (University of Ljubljana) (2026) | TGRS Research Map | TGRS