Author
Abstract
The relationship between trust in AI services and the growing fear of job displacement is an increasingly important issue as AI continues to reshape industries. This study addresses two primary research questions: what factors influence trust in AI services, and is there a correlation between trust in AI and the fear of job displacement? Using a quantitative approach, a structured survey was administered to 137 participants, collecting data on their trust in AI, fears of job loss, and demographic characteristics. In order to assess the internal consistency of the survey items, a Cronbach alpha measure was developed. This research developed an econometric model based on a standard Ordinary Least Squares (OLS) equation. To increase the robustness of the model and confirm the results obtained with the OLS approach, a Weighted Least Squares (WLS) model was developed and later complemented with the Bootstrap method. The findings revealed that, with the most robust model, trust in AI was influenced by job displacement fear, personal adaptability and institutional trust. The results showed a negative coefficient between fear of job displacement and trust in AI, suggesting that as concerns about automation increase, trust in AI systems decreases. Personal adaptability emerged as a significant predictor of trust, with individuals who believe in their ability to adapt to technological changes reporting higher trust in AI services. This study also highlighted the need for businesses to address both the technical and social dimensions of AI adoption by fostering trust through transparency and communication while addressing fears about job displacement. The implications for policymakers and industry leaders underscore the importance of reskilling initiatives and proactive engagement to mitigate the social impact of AI integration.
Suggested Citation
José Campino & Evdokia Asvestopoulou, 2025.
"AI revolution: trust and the perceived threat to job security,"
SN Business & Economics, Springer, vol. 5(12), pages 1-31, December.
Handle:
RePEc:spr:snbeco:v:5:y:2025:i:12:d:10.1007_s43546-025-00963-z
DOI: 10.1007/s43546-025-00963-z
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