Human vs. Algorithmic Recommendations in Big Data and the Role of Amiguity

Fuchs, C; Matt, C; Hess, T; Hoerndlein, C (2016). Human vs. Algorithmic Recommendations in Big Data and the Role of Amiguity. In: Twenty-second Americas Conference on Information Systems. San Diego - USA. 11.-14.08.2016.

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Based on big data, decisions can increasingly be drawn from data-driven analytics and algorithmic decision support. However, it remains unclear whether recommendations issued by computer algorithms are equally accepted by individuals as human advices. This is particularly intriguing given that big data entails various forms of ambiguous decision situations in which individuals cannot assess the underlying database or possible consequences. We conceptually introduce ambiguity in the light of big data and conduct an experiment to identify whether individuals prefer algorithmic to human recommendations and how outcome and data ambiguity affect individuals’ adoption behavior of algorithmic decision support. We find a preference for human recommendations, independently from the level of inherent ambiguity. However, areas that are more data-driven have a higher potential to overcome resistance to algorithmic decision support. Our results imply that developers of algorithmic decision support should provide high levels of transparency in areas which currently lack support.

Item Type:

Conference or Workshop Item (Paper)

Division/Institute:

03 Faculty of Business, Economics and Social Sciences > Department of Business Management > Institute of Information Systems > Information Management
03 Faculty of Business, Economics and Social Sciences > Department of Business Management > Institute of Information Systems

UniBE Contributor:

Matt, Christian

Subjects:

000 Computer science, knowledge & systems
300 Social sciences, sociology & anthropology > 330 Economics

Projects:

[852] Digitale Empfehlungen Official URL

Language:

English

Submitter:

Patrick Cédric Munz

Date Deposited:

12 Mar 2018 11:39

Last Modified:

18 Oct 2018 15:49

BORIS DOI:

10.7892/boris.105395

URI:

https://boris.unibe.ch/id/eprint/105395

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