Gizem Yalçın will be giving a research seminar in our department seminar series. She is currently a PhD candidate in Marketing at Rotterdam School of Management, Erasmus University, Netherlands with expected completion in 2022. She has completed her MPhil at the same university. She is a graduate of Bilkent University Management Department. She has also been a visiting scholar at SC Johnson College of Business, Cornell University and Saauder School of Business, University of British Columbia. Her research explores how consumers process and react to the information (e.g., recommendations, decision outcomes) provided by algorithms or humans. In addition to this line of inquiry, she studies prosocial behavior, and work on how consumers decide where to donate to and how to motivate consumers to make more effective donations. She employs a mix of methods to address the research questions, including lab/online panel studies, field experiments, secondary data analysis, content analysis, and meta-analysis.

Her research topic and abstract are as follows.

Title: Thumbs Up of Down: Consumer Reactions to Decisions by Algorithms versus Humans

Abstract: Although companies increasingly are adopting algorithms for consumer-facing tasks (e.g., application evaluations), little research has compared consumers' reactions to favorable decisions (e.g., acceptances) versus unfavorable decisions (e.g., rejections) about themselves that are made by an algorithm versus a human. Ten studies reveal that, in contrast to managers' predictions, consumers react less positively when a favorable decision is made by an algorithmic (vs. a human) decision maker, whereas this difference is mitigated for an unfavorable decision. The effect is driven by distinct attribution processes: It is easier for consumers to internalize a favorable decision outcome that is rendered by a human (vs. an algorithm), while it is easy to externalize an unfavorable decision outcome regardless of the decision maker type. The authors conclude by advising managers on how to limit the likelihood of less positive reactions toward algorithmic (vs. human) acceptances.

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