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AI FoMO (fear of missing out) in the workplace

Author

Listed:
  • Méndez-Suárez, Mariano
  • Ćukušić, Maja
  • Ninčević-Pašalić, Ivana

Abstract

While AI is credited with improving productivity, automating repetitive tasks, and fostering safer work environments, it also raises employee concerns about job security, reduced autonomy, and the perceived obsolescence of their skills. This study focuses on the fear of being left behind, or fear of missing out (FoMO), to understand employees' perceptions of AI adoption. Using data from the latest OECD study on AI in the workplace, specifically focusing on workers who had been using AI over an extended period of time, this article applies Fuzzy Set Qualitative Comparative Analysis (fsQCA) to examine the relationships between employee control over decision making, mental health impacts, concerns about AI oversight (“robo-boss”), and skill devaluation. Results reveal multiple causal pathways to FoMO, where combinations of perceived skill devaluation, lost autonomy, and concerns over AI supervision are key drivers of this anxiety. As the study reveals, employees who perceive AI to reduce their decision-making autonomy are significantly more likely to experience FoMO, amplifying the psychological impact of automation on job anxiety. Conversely, positive perceptions of AI's role in supporting well-being and maintaining decision-making authority mitigate FoMO. The study highlights the importance of promoting transparent communication, ongoing training, and inclusive AI implementation strategies to address emotional responses and improve workforce adaptability. To ensure a balanced transition to AI-enabled workplaces, organizations must integrate AI in a way that empowers employees, rather than exacerbating fears of obsolescence.

Suggested Citation

  • Méndez-Suárez, Mariano & Ćukušić, Maja & Ninčević-Pašalić, Ivana, 2026. "AI FoMO (fear of missing out) in the workplace," Technology in Society, Elsevier, vol. 84(C).
    • Handle: RePEc:eee:teinso:v:84:y:2026:i:c:s0160791x25002428
    DOI: 10.1016/j.techsoc.2025.103052
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