Using the Default Option Bias to Influence Decision Making While Driving

Abstract

Gaining a better understanding of human–computer interaction in multiple-goal environments, such as driving, is critical as people increasingly use information technology to accomplish multiple tasks simultaneously. Extensive research shows that decision biases can be utilized as effective cues to guide user interaction in single-goal environments. This article is a first step toward understanding the effect of decision biases in multiple-goal environments. This study analyzed data from a field experiment during which a comparison was made between drivers’ decisions on parking lots in a single-goal environment and drivers’ decisions in a multiple-goal environment when being exposed to the default option bias. The article shows that the default option bias is effective in multiple-goal environments. The results have important implications for the design of human–computer interaction in multiple-goal environments.

Additional information

Funding

This research was partially supported by Technische Universität München (TUM) through the project “mobility lab: create, innovate & change the world – together.” The TUM Graduate School and the TUM Center for Doctoral Studies in Informatics and its Applications (CeDoSiA) also provided partial support for this research. We thank Carol Krcmar for editorial assistance.

Notes on contributors

Klaus Goffart

Klaus Goffart received his degree in Computer Science in 2008 from the University of Bonn. Afterward, he completed a trainee program at MTU Friedrichshafen, and since 2012 he has been a PhD student working for the BMW Group. His research interests include decisions biases, in-car recommendations, and multiple-goal environments.

Michael Schermann

Michael Schermann is a postdoctoral researcher at Technische Universität München (TUM), Germany. Michael’s research contributes to understanding how humans make judgments related to the perceived characteristics and severity of risks. His work has appeared in Journal of Information Technology, ACM Transactions on Computer-Human Interaction, and Business Information Systems & Engineering.

Christopher Kohl

Christopher Kohl received his degree in Computer Science in 2014 from Technische Universität München (TUM). He is a PhD student and research associate at the Chair for Information Systems at TUM. His research interests include highly automated driving, social network analysis, and the influence of social networking sites on the perception of risks.

Jörg Preißinger

Jörg Preißinger received his degree in computer science in 2004 and obtained his doctorate in natural science in 2008 at TUM. Since then he has been working as research engineer and project manager at the BMW Group in the fields of mobility services, Internet of things, and augmented reality.

Helmut Krcmar

Helmut Krcmar is full professor of Information Systems at Technische Universität München since 2002. His research interests include information and knowledge management; service management; innovative information systems in health and electronic government; and, at present, leadership in the Digital Transformation.