Paper Number

1533

Paper Type

CRP

Abstract

Advances in big data analytics and machine learning facilitate the emergence of process mining tools to analyze business processes. Despite the potential for increased efficiency, mixed evidence on user aversion to or appreciation of intelligent systems prevails. Conflicting results raise doubts about users’ reliance on algorithmic advice. Regarding process analysis, aversion behaviors may stem from skepticism towards advice from external sources, possibly linked to the not-invented-here syndrome. In this experimental study, we manipulate the source of process advice (human vs. automated) and its origin (internal vs. external) in the context of process analysis, i.e., conformance checking. The results indicate increased trust and reliance on automated advice with external origin. Our findings contribute to theory by identifying external origin as beneficial in process advice. Furthermore, we add to literature on algorithm appreciation and aversion by showing that people readily rely on algorithmic support in process optimization but exhibit human aversion.

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Jun 14th, 12:00 AM

Better Not Invented Here: Investigating Algorithm Appreciation in Process Mining

Advances in big data analytics and machine learning facilitate the emergence of process mining tools to analyze business processes. Despite the potential for increased efficiency, mixed evidence on user aversion to or appreciation of intelligent systems prevails. Conflicting results raise doubts about users’ reliance on algorithmic advice. Regarding process analysis, aversion behaviors may stem from skepticism towards advice from external sources, possibly linked to the not-invented-here syndrome. In this experimental study, we manipulate the source of process advice (human vs. automated) and its origin (internal vs. external) in the context of process analysis, i.e., conformance checking. The results indicate increased trust and reliance on automated advice with external origin. Our findings contribute to theory by identifying external origin as beneficial in process advice. Furthermore, we add to literature on algorithm appreciation and aversion by showing that people readily rely on algorithmic support in process optimization but exhibit human aversion.

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