AI Adoption Impacts Employee Well-Being Through Job Stress

By Soham NandiReviewed by Joel ScanlonNov 20 2024

Discover how AI adoption reshapes workplace dynamics, driving stress and burnout, and why self-efficacy might be the ultimate shield for employee resilience.

Study: The mental health implications of artificial intelligence adoption: the crucial role of self-efficacy. Image Credit: PeopleImages.com - Yuri A / Shutterstock

In an article published in the journal Nature, researchers in South Korea explored how artificial intelligence (AI) adoption affected employee well-being, focusing on job stress, burnout, and self-efficacy in AI learning. Using data from a diverse sample of 416 South Korean professionals, the study found AI adoption indirectly increased burnout via job stress, while self-efficacy moderated stress levels. It highlighted the need for organizations to manage stress, address role ambiguity, and boost self-efficacy, emphasizing a human-centric approach to AI adoption that balanced technological advancements with employee well-being.

Background

The integration of AI in organizations has reshaped work processes, offering advantages like automation and improved decision-making. However, its adoption raises concerns about employee well-being, particularly burnout, characterized by emotional exhaustion and reduced achievement. Although many studies focus on operational benefits, research often overlooks the psychological effects of AI adoption, particularly its impacts on mental health. Limited studies explore how AI-induced job stress and personal factors like self-efficacy influence burnout.

This paper bridged these gaps using the job demands-resources (JD-R) model and other frameworks, such as the conservation of resources (COR) theory and the transactional model of stress and coping (TMSC). It investigated job stress as a mediator between AI adoption and burnout and self-efficacy in AI learning as a mitigating factor. By analyzing these dynamics, the study offered a nuanced understanding of how AI adoption impacted employee well-being and proposed strategies to foster resilience, contributing to organizational psychology and informing human-centric approaches to AI deployment.

Theory and Hypotheses

Job stress mediated the relationship between AI adoption and burnout by amplifying demands and reducing resources. According to the JD-R model, AI adoption increased role ambiguity, workload, and other job demands while limiting resources, leading to burnout when demands outweighed available support. The study also incorporated COR theory, suggesting AI adoption could threaten critical resources, such as job stability and confidence, resulting in emotional exhaustion.

Self-efficacy in AI learning, or confidence in one’s ability to learn and use AI, moderated the effect of AI adoption on job stress. Employees with high self-efficacy viewed AI as an opportunity for growth, engaged in proactive learning, and adapted to new technologies, reducing stress. Conversely, those with low self-efficacy perceived AI as a threat, resisted learning, and experienced greater stress due to fears of job insecurity and failure. This aligns with both SCT and TMSC frameworks, which emphasize that confidence shapes motivation, resilience, and perceptions of challenges.

Research Design and Methodology

The researchers utilized a three-wave time-lagged design with working adults in South Korea to investigate the relationships between AI adoption, job stress, and burnout. Data were collected at three intervals (five to six weeks apart) from 803 participants in the first wave, 571 in the second, and 418 in the third, with 416 completing all surveys. Stratified random sampling ensured a representative sample based on demographic and occupational factors. Surveys were conducted online, with measures to maintain data integrity, such as geolocation by internet protocol (Geo-IP) traps and participation monitoring. Financial incentives were provided, and ethical guidelines were followed.

The authors measured AI adoption, self-efficacy in AI learning, job stress, and burnout using validated Likert-scale instruments. AI adoption was assessed through five items reflecting its use in organizational systems, while self-efficacy in AI learning relied on a four-item scale. Job stress and burnout were measured using six-item scales adapted from established sources, with Cronbach’s alpha values exceeding 0.89 for all measures. Control variables included gender, tenure, position, and education. The data were analyzed using statistical methods such as confirmatory factor analysis (CFA) and structural equation modeling (SEM) to ensure robustness.

Results and Discussion

The authors found that job stress mediated the relationship between AI adoption and burnout, with self-efficacy moderating the impact of AI adoption on job stress. The results confirmed that AI adoption increased job stress, which in turn led to burnout, while self-efficacy in AI learning reduced stress. Notably, AI adoption did not directly increase burnout but did so indirectly through job stress, highlighting the non-significance of a direct relationship. These findings suggested organizations should focus on fostering self-efficacy through training and addressing job demands like workload and role ambiguity to mitigate negative outcomes.

The theoretical implications included combining the JD-R model with SCT, where AI adoption was conceptualized as a job demand that added complexity and uncertainty. The researchers also emphasized that self-efficacy could reduce stress, making it a key personal resource. This research extended the literature on job demands and personal resources, providing empirical evidence linking AI adoption to burnout, with job stress as a mediator.

Practical implications stressed the importance of addressing the psychological effects of AI adoption by supporting employee well-being through stress management resources, AI training, and involving employees in AI-related decisions. Transparency in the AI adoption process and employee participation in decision-making were also recommended to reduce uncertainty and foster trust. AI systems should complement human skills, and ongoing assessments of AI’s impact on employee well-being are necessary.

Conclusion

In conclusion, the researchers examined the impact of AI adoption on employee well-being, focusing on job stress, burnout, and self-efficacy in AI learning. They found that AI adoption indirectly increased burnout through job stress, while self-efficacy moderated stress levels. The research emphasized the need for organizations to focus on fostering self-efficacy through training programs to mitigate stress and burnout. The study highlighted that AI adoption should be accompanied by strategies to address job demands, role ambiguity, and emotional resource depletion, suggesting that employee well-being is crucial for successful AI integration.