Date of Award
2026
Document Type
Dissertation
Degree Name
Doctor of Business Administration (DBA)
Department
School of Business
First Advisor
Tim Veach, DBA
Second Advisor
Dr. Kara Grant
Third Advisor
Dr. Yeongjoon Yoon
Abstract
Performance evaluation systems increasingly incorporate artificial intelligence, yet employee acceptance remains limited. This experimental study examined how explanation quality and evaluator type influenced fairness perceptions and trust in AI-based performance evaluations among 275 U.S. working adults. Using a 2 × 2 vignette design, results demonstrated that explanation quality produced large effects on perceived fairness (d = 1.79) and trust, while evaluator type—human versus AI—had negligible independent effects (d = −0.08) when explanations were high quality. Fairness mediated the explanation-trust relationship (indirect effect = 1.95, 95% CI [1.65, 2.26]), with inconsistent mediation indicated by a negative direct effect (b = −0.81, p < .001) when fairness was controlled. Findings suggest organizations should prioritize explanation quality over evaluator source when implementing AI evaluation systems.
Recommended Citation
McGraw, Darren, "Beyond Algorithm Aversion: How Explanation Quality Enables Fairness and Trust in AI-Based Performance Evaluations" (2026). Doctor of Business Administration (DBA). 82.
https://digitalcommons.georgefox.edu/dbadmin/82