03/25/2026
By Nabi Ebrahimi

The Manning School of Business, Department of Management, invites you to attend a doctoral dissertation defense by Nabi Ebrahimi on “AI System Characteristics, Stress-Related Outcomes, and Algorithmic Resistance: The Roles of Trust and Felt Trust.”

Candidate Name: Nabi Ebrahimi
Degree: Doctoral
Defense Date: Tuesday, April 7, 2026
Time: 2-4 p.m.
Location: PTB 205, Pulichino Tong Business Center, North Campus
Thesis/Dissertation Title: AI System Characteristics, Stress-Related Outcomes, and Algorithmic Resistance: The Roles of Trust and Felt Trust

Committee:

  • Advisor: Tamara Montag-Smit, Ph.D., Department of Management, Manning School of Business, UMass Lowell
  • Kimberly Merriman, Ph.D., Department of Management, Manning School of Business, UMass Lowell
  • Amy Chen, Ph.D., Department of Marketing, Manning School of Business, UMass Lowell

Brief Abstract:
As artificial intelligence (AI) systems become increasingly embedded in organizations, employees increasingly work in AI-mediated environments in which tasks, decisions, and daily work experiences are shaped by AI systems. Although prior research suggests that AI may be associated with employee anxiety, insecurity, and resistance, less is known about how AI becomes psychologically consequential for employees, why some AI-mediated work environments may be more stressful than others, and under what conditions these effects may be attenuated. Drawing on transactional stress theory, this dissertation develops and tests a process model in which three structural characteristics of AI systems—opacity, autonomy, and dynamism—generate proximal stressful work conditions in the form of traditional and AI-specific technostressors. These technostressors, in turn, predict two focal stress-related outcomes, job insecurity and psychological strain, which may further spill over into a downstream coping-related behavioral response in the form of algorithmic resistance. The dissertation also examines whether trust in top management and felt trust by top management shape different stages of this broader process as distinct relational resources.
Across two studies, the findings provide partial support for this framework. Study 1 establishes the baseline stress relevance of AI system characteristics by showing that AI systems characterized by high opacity, autonomy, and dynamism elicit higher levels of job insecurity than traditional workplace technologies. Building on this baseline comparison, Study 2 examines the broader process model in organizational settings. The results show that perceived AI opacity, autonomy, and dynamism were positively associated with both traditional and AI-specific technostressors. In turn, technostressors predicted psychological strain and job insecurity and mediated the relationship between AI system characteristics and these stress-related outcomes. Contrary to expectations, these stress-related outcomes did not predict algorithmic resistance; instead, algorithmic resistance was more strongly associated with employees’ affective orientation toward AI and AI use frequency. Trust in top management attenuated the relationship between technostressors and stress-related outcomes, whereas felt trust by top management did not moderate the relationship between stress-related outcomes and algorithmic resistance. Taken together, this dissertation provides a more precise explanation of how AI systems become psychologically consequential for employees by showing not only that AI systems differ from traditional workplace technologies in their stress relevance, but also how variation in AI system characteristics gives rise to stressful work conditions and stress-related outcomes. It also contributes to the trust literature by examining trust in top management and felt trust by top management as distinct relational resources within the broader stress process.