Algorithms refer to the software programs designed to support problem solving in a wide range of decision domains. Given the Artificial Intelligence (AI) revolution, algorithms have become an integral part of our personal, social, and professional lives. As technology rapidly advances, these algorithms are not only becoming more capable but are also finding a growing array of applications in managerial and consumer decision support. Despite their increasing presence, reactions to algorithms are mixed. While some research highlights a preference for algorithms over human judgment ("algorithm appreciation"), other studies reveal a contrary preference ("algorithm aversion"), where people favor human expertise.
This research provides a conceptual framework and empirical evidence regarding factors that may influence preference for algorithmic versus human expert recommendations in business decision contexts. We use experimental psychological methods to investigate how algorithm characteristics, decision-maker psy / Doctor of Philosophy / Amid the AI revolution, algorithms have become central to our personal, social, and professional lives, evolving rapidly in both capability and application. Reactions to these algorithms are mixed: some studies show a preference for algorithms over human judgment, known as "algorithm appreciation," while others reveal a preference for human judgment, or "algorithm aversion." Understanding these preferences is essential.
Our research helps to clarify this issue by examining the factors that influence whether people prefer algorithms or human experts in business decisions. Using experimental methods, we explore how algorithm features, decision-maker psychology, and situational factors impact these preferences. We focus on scenarios where algorithms and human experts are presented as competing options rather than complementary ones. Our findings, detailed in two empirical essays, aim to advance marketing literature on algorithms and decision-making, identify future research opportunities, and offer insights for
| Identifer | oai:union.ndltd.org:VTETD/oai:vtechworks.lib.vt.edu:10919/121272 |
| Date | 04 October 2024 |
| Creators | Lyvers, Aaron Kenneth |
| Contributors | Business, Chakravarti, Dipankar, Herr, Paul Michael, Bagchi, Rajesh, Zhu, Meng |
| Publisher | Virginia Tech |
| Source Sets | Virginia Tech Theses and Dissertation |
| Language | English |
| Detected Language | English |
| Type | Dissertation |
| Format | ETD, application/pdf |
| Rights | In Copyright, http://rightsstatements.org/vocab/InC/1.0/ |
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