Publication Date
2021
Document Type
Dissertation
Committee Members
Ion Juvina, Ph.D. (Advisor); Debra Steele-Johnson, Ph.D. (Committee Member); Valerie L. Shalin, Ph.D. (Committee Member); Joseph W. Houpt, Ph.D. (Committee Member)
Degree Name
Doctor of Philosophy (PhD)
Abstract
While many researchers have investigated the performance consequences of automated recommender systems, little research that has explored how these systems impact the decision making process. The purpose of this dissertation is to examine how people process information from an automated recommender system and raw information from the en- vironment using Systems Factorial Technology (SFT). Participants completed a speeded length judgment task with a reliable but imperfect aid. Experiment 1 focused on whether people process all the available information or are selective in their information search under certain circumstances (e.g., with performance incentives and with more experience with automation failures in training). Results indicate that participants likely use only one source of information, alternating between the automated aid and the environmental information. Additionally, performance incentives and less experience with automation failures can lead to slower but not necessarily more accurate performance with an automated aid. Experiment 2 focused on whether display design (e.g, proximity of information and density of information) can encourage serial or parallel processing of information. Unsurprisingly, the results indicate that integrating information on the display allows participants to process information more efficiently. Implications of this research not only sheds light on how people gather and process information with an automation aid but also how we might design systems to improve decision performance.
Page Count
152
Department or Program
Department of Psychology
Year Degree Awarded
2021
Copyright
Copyright 2021, all rights reserved. My ETD will be available under the "Fair Use" terms of copyright law.
