Integrating Trust and Feedback Intervention Theories to Predict Behavioral Change in Response to Algorithmic Feedback [research project]
Funding agency
National Science Foundation (August 15, 2020 - July 31, 2022)
Investigators
Richard Landers, Evan Suma Rosenberg (assistant professor)
Abstract
As organizations increasingly integrate algorithms into their decision-making, (such as by providing decision aids and algorithmically generated advice to employees), it has become evident that we lack a scientific understanding of when and why people heed such advice. In this project, an integrative theory linking the characteristics of algorithmic advice to advice-taking behavior is proposed and tested. To test this theory, we develop a web-based application that allows internet users to complete automated virtual job interviews and receive algorithmic feedback or human feedback on their performance. In doing so, we improve scientific understanding of how people respond to algorithmic feedback while simultaneously providing people with authentic feedback on their interview performance, a societal benefit. In this project, organizational trust theory, which specifies a theoretical structure for trust and likely consequences, has been integrated into feedback intervention theory, which describes the process by which people act upon received feedback, to better predict behavioral change in response to algorithmic feedback. This study thus fills theoretical gaps about the influence of feedback source on interview performance while also informing broader questions regarding algorithms, trust, and behavioral change. The study's core propositions will be tested with a between-subjects experimental design and authentic job seekers to maximize generalizability to the present-day workforce.
More information
Keywords
virtual reality