Psychology Ph.D. Dissertations

Dispositional Algorithm Aversion: A Criterion-Related Validity Study

Date of Award

2021

Document Type

Dissertation

Degree Name

Doctor of Philosophy (Ph.D.)

Department

Psychology/Industrial-Organizational

First Advisor

Scott Highhouse (Committee Chair)

Second Advisor

Eric Dubow (Committee Member)

Third Advisor

Margaret Brooks (Committee Member)

Fourth Advisor

Jill Zeilstra-Ryalls (Committee Member)

Abstract

Dispositional Algorithm Aversion (DAA) is presented as the preference for expert judgment over mechanical judgment. Previous research suggests that the DAA measure is a reliable and potentially valid measure of this disposition (Melick, 2020). The present research examines the criterion-related validity of the DAA measure for predicting algorithm averse behavior. Participants (N = 500) were presented with self-report measures of DAA, personality, risk propensity, and decision style as well as basic demographic questions. Two weeks later, they were asked to engage in a college grade point average (GPA) prediction task for which they may tie their incentive to predictions made by either an algorithm or an admissions officer. Results indicate that DAA was positively related to preference for the admissions advisor over the algorithm (r = .14) and that DAA predicted algorithm-averse behavior over and above personality, risk propensity, and decision style.

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