Automotive Emotions: An Investigation of Their Natures, Frequencies of Occurrence and Causes

Technological and sociological developments in the automotive sector are shifting the focus of design towards developing a better understanding of driver needs, desires and emotions. Human centred design methods are being more frequently applied to automotive research, including the use of systems to detect human emotions in real-time. One method for a non-contact measurement of emotion with low intrusiveness is Facial-Expression Analysis (FEA). This paper describes a research study investigating emotional responses of 22 participants in a naturalistic driving environment by applying a multi-method approach. The research explored the possibility to investigate emotional responses and their frequencies during naturalistic driving through real-time FEA. Observational analysis was conducted to assign causes to the collected emotional responses. In total, 730 emotional responses were measured in the collective study time of 440 minutes. Causes were assigned to 92% of the measured emotional responses. This research establishes and validates a methodology for the study of emotions and their causes in the driving environment through which systems and factors causing positive and negative emotional effects can be identified.

The Influence of User Involvement and Personal Innovativeness on User Behavior

The search for factors that influence user behavior has remained an important theme for both the academic and practitioner Information Systems Communities. In this paper we examine relevant user behaviors in the phase after adoption and investigate two factors that are expected to influence such behaviors, namely User Involvement (UI) and Personal Innovativeness in IT (PIIT). We conduct a field study to examine how these factors influence postadoption behavior and how they are interrelated. Building on theoretical premises and prior empirical findings, we propose and test two alternative models of the relationship between these factors. Our results reveal that the best explanation of post-adoption behavior is provided by the model where UI and PIIT independently influence post-adoption behavior. Our findings have important implications for research and practice. To that end, we offer directions for future research.