Alvaro Chacon , Edgar E. Kausel , Tomas Reyes , Stefan Trautmann
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引用次数: 0
Abstract
As algorithms often outperform humans in prediction, algorithm aversion is economically harmful. To enhance algorithm utilization, we suggest emphasizing their learning capabilities, i.e., their increasing predictive precision over time, through the explicit addition of a “learning” label. We conducted five incentivized studies in which 1,167 participants may prefer algorithms or take up algorithmic advice in a financial or healthcare related task. Our results suggest that people use algorithms with a learning label to a greater extent than algorithms without such a label. As the accuracy of advice improves beyond a threshold, the use of algorithms with a learning label increases more than algorithms without a label. Thus, we show that a salient learning attribute can positively affect algorithm use in both the financial and health domain.
期刊介绍：
The Journal of Business Research aims to publish research that is rigorous, relevant, and potentially impactful. It examines a wide variety of business decision contexts, processes, and activities, developing insights that are meaningful for theory, practice, and/or society at large. The research is intended to generate meaningful debates in academia and practice, that are thought provoking and have the potential to make a difference to conceptual thinking and/or practice. The Journal is published for a broad range of stakeholders, including scholars, researchers, executives, and policy makers. It aids the application of its research to practical situations and theoretical findings to the reality of the business world as well as to society. The Journal is abstracted and indexed in several databases, including Social Sciences Citation Index, ANBAR, Current Contents, Management Contents, Management Literature in Brief, PsycINFO, Information Service, RePEc, Academic Journal Guide, ABI/Inform, INSPEC, etc.
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