Date of Award

8-2026

Document Type

Dissertation

Degree Name

Doctor of Philosophy (PhD)

Department

Human Factors Psychology

Committee Chair/Advisor

Richard Pak

Committee Member

Ericka Rovira

Committee Member

Patrick Rosopa

Committee Member

William Volante

Abstract

Trust is often treated as a key driver of automation use, with the assumption that higher trust leads to greater dependence. Yet findings have been inconsistent, with trust-dependence correlations ranging from negative to positive, and some studies finding no relationship at all. One factor that may explain this inconsistency is mental workload, which has received little attention as a moderator. This research examined whether workload moderates the trust-dependence relationship, and whether attention control, working memory, and fluid intelligence further shape this moderation.

Study 1 used a web-based AI chatbot quiz to test whether workload moderated the trust-dependence relationship. Trust was positively associated with dependence overall, but this relationship weakened as workload increased, providing initial evidence that workload influences how trust translates into behavior, though the correlational design limited causal conclusions.

Study 2 experimentally manipulated workload within a multitasking environment. Participants were randomly assigned to a low or high workload condition and could choose among manual, semi-automated, and fully automated modes for three subtasks. Dependence was measured as a time-weighted average of automation use, and the three cognitive abilities were assessed. Trust was again positively associated with dependence, but workload alone did not moderate this relationship. Rather, its moderating effect emerged only when cognitive abilities were considered: attention control and working memory each shaped the relationship differently, while fluid intelligence did not moderate it. Together, these studies suggest that workload and cognitive abilities shape trust calibration, extending trust frameworks that have not accounted for the cognitive resources calibration likely requires.

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