Abstract

Occupational stress and deteriorating mental health constitute an increasing issue in the global community, and the problem of how job demands, personal resources, and cognitive processes interrelate to determine employee well-being is hardly known. This dissertation will study these dynamics by incorporating theory-driven Structural Equation Modeling (SEM) with data-driven Machine Learning (ML) methods that offer explanatory and predictive information. The study examines the influences of job demands, personal resources, effort–reward imbalance (ERI), emotional labor, cognitive appraisal, and social support on the stress and mental health outcomes of a sample of 5,000 employees working in various industries and regions of the world. The strong psychometric validity was confirmed by a comprehensive measurement model in which all the factor loadings were greater than 0.50, satisfactory reliability, and reasonable discriminant validity. Patterns of correlations demonstrated theoretically consistent correlations between job demands, cognitive appraisal, resources, and well-being measures. The structural model showed a very good fit and presented a number of interesting significant pathways: job demands, ERI/overcommitment, emotional labor, and conservation of resources were found to increase stress, whereas job resources, personal resources, and personal environment fit decreased it significantly. Stress had a serious, negative impact on cognitive appraisal (β = -.55) and a more moderate, direct, negative impact on mental health ( ( β = -.07). Notably, the immense indirect effect was significant when the researchers discovered that the negative impact of stress on mental health was reflected in the fact that it weakens the capacity of the employees to make an adaptive assessment of work demands. The results were that the cognitive appraisal had a significant beneficial effect on mental health (β = 0.26) although social support was found as a significant moderator that buffered the effect of stress on mental health to a significant degree ( β = -0.09). All these results are solid evidence in favor of Job Demands Resources and Effort Reward Imbalance models. Five machine learning models, including Decision Tree, Random Forest, K-Nearest Neighbors, Support Vector Machine, and Gradient Boosting, were trained to predict mental health outcomes in order to supplement the SEM analysis. The Support Vector Machine and the Gradient Boosting models had the best predictive results (R2 = 0.52, RMSE = 0.57), which indicates that it is possible to develop data-driven methods to estimate mental health risks. In all cases, cognitive appraisal, job demands, emotional labor, social support, self-determination, and access to mental health resources all proved to be the strongest predictors. These findings demonstrate a high similarity between causal pathways based on SEM and causal pathways based on ML, which confirms the validity of the conclusions reached in the study. In general, the dissertation has a theoretical value because it explains stress as a mediator between work conditions and mental health, and also a practical value in terms of the identification of crucial targets in the workplace with the purpose of interventions. The inclusion of SEM and ML also proves the importance of integrating both explanatory and predictive analytics in the research of organizations. The results provide a basis on which to create scalable survey-based mental health assessment instruments that could help detect early and intervene in the right place to prevent mental health issues in the workplace.

Library of Congress Subject Headings

Job stress--Psychological aspects; Quality of life; Medical economics; Psychology, Industrial

Publication Date

12-2025

Document Type

Thesis

Student Type

Graduate

Degree Name

Professional Studies (MS)

Department, Program, or Center

Graduate Programs & Research

Advisor

Sanjay Modak

Advisor/Committee Member

Ioannis Karamitsos

Campus

RIT Dubai

Plan Codes

PROFST-MS

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