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TRUVACE RECORD VERSION
record: TRV-2026-2697
version: 1
kind: certified
reason: Certified into the record
timestamp: 2026-11-12T17:02:08.736921Z
status: published
lens: g_space
sector: labor
headline: From AI use to positive functioning: The roles of trust and need satisfaction
dek: Abstract In this empirical contribution to Group. Interaction. Organization. (GIO), we examined how the incorporation of artificial intelligence (AI) into everyday work processes is associated with employee well-being and engagement focusing on the psychological mechanisms of trust in AI and basic need satisfaction. Drawing on Self-Determination Theory and trust-in-technology research, we investigated whether different AI use patterns are associated with workplace well-being and work engagement via trust in AI a…
gain_title: In a cross-sectional survey of 379 mostly early-career employees, longer time using AI at work was associated with higher work engagement (β = 0.12, p = 0.024), though not with well-being.
problem_title: (none)
trace_subject: (none)
gain_reading: In a cross-sectional survey of 379 mostly early-career employees, longer time using AI at work was associated with higher work engagement (β = 0.12, p = 0.024), though not with well-being.
gain_evidence: AI use duration was positively associated with work engagement (β = 0.12, p = 0.024)
problem_reading: (none)
problem_evidence: (none)
quick_read: A pre-registered online survey of 379 employees who use AI at work at least several times a month tested whether AI use is linked to well-being and engagement through trust in AI and basic psychological needs. Employees who had used AI for longer reported higher work engagement (β = 0.12, p = 0.024).

Trust in AI was strongly associated with feeling autonomous (β = 0.70), competent (β = 0.84) and connected to the technology (β = 0.52) during AI use. Only problem-solving use, such as decision support, was associated with higher trust (β = 0.22), while frequency of use and information-processing use were not.

The proposed chain from AI use to trust to need satisfaction to well-being got only partial support. None of the three need measures was significantly associated with workplace well-being or work engagement, so good experiences while using AI did not translate into broader gains at work in this sample.
limitation: The survey was cross-sectional, so the associations cannot show cause or order in time, and the sample was mostly young early-career employees, so findings may not apply to more experienced staff.
tag: Evidence-backed gain
key_points: The sample was 379 employees working at least 20 hours a week and using AI at work at least several times monthly; 69% were women and the average age was 25.5. | Problem-oriented AI use was linked to higher trust in AI (β = 0.22, p = 0.006); use intensity (β = 0.09) and duration (β = 0.04) were not. | Trust in AI was linked to autonomy (β = 0.70), competence (β = 0.84) and relatedness to technology (β = 0.52), all p < 0.001. | None of the three needs was significantly associated with workplace well-being or work engagement, for example competence and well-being (β = 2.06, p = 0.278). | Organizational support for AI use was positively associated with well-being (β = 0.13, p = 0.021). | Technology affinity (β = 0.33) and general disposition to trust technology (β = 0.30) were strongly associated with trust in AI. | Longer AI use duration was positively associated with work engagement (β = 0.12, p = 0.024) and showed a marginal link to well-being (β = 0.11, p = 0.053).
rundown: Data were collected through an online questionnaire over four weeks in October 2025, with participants recruited through professional networks and mailing lists. Of 457 initial respondents, 381 finished the survey and two were removed for being under 18. Finance (13%), services and trades (11%) and insurance and real estate (8%) were the largest sectors, and 21% were working students.

The authors split AI use into information processing (retrieving, summarising, translating) and problem-solving (recommendations, evaluating alternatives, decision support). Trust, need satisfaction, PERMA well-being and the UWES-9 engagement scale were measured with standard questionnaires, and the model was estimated with structural equation modelling in R. Model fit was acceptable (robust RMSEA 0.039, SRMR 0.058).

The chain from AI use through trust and need satisfaction to well-being was significant for well-being (β = 0.39, p = 0.015) but not for engagement (β = 0.21, p = 0.101). Exploratory models with the five PERMA sub-dimensions found the same pattern: no need was significantly linked to any of them.

The authors suggest organizations could build trust through transparency, training and explainable systems, and that AI may suit problem-solving tasks. They also note that AI-specific need satisfaction may be only a small part of overall work experience.
sources:
- peer_reviewed | Gruppe. Interaktion. Organisation. Zeitschrift für Angewandte Organisationspsychologie (GIO) | https://doi.org/10.1007/s11612-026-00886-9 | 2026-05-21
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