Cognitive Mechanisms of Behavior Change in the Case of In-Vehicle Fuel Economy Feedback.

摘要

This paper presents results from a year-long study on driver feedback, driver attitudes, and the adoption of ecodriving behaviors. Narrowly defined, ecodriving represents only the set of behaviors that a driver can use to minimize the energy use of a trip after the trip has begun. The general ecodriving behaviors are moderating acceleration, top speed, and braking. Ecodriving has long been recognized as a potential source of reductions in transportation energy use, with reduction estimates ranging widely from less than 5% to over 20% depending on context. In-vehicle feedback is one way to motivate ecodriving by connecting drivers with salient information suited to their personal goals. Although many studies have tested unique feedback designs, little research has been conducted into the cognitive precursors to driver behavior change that may underlie the adoption or rejection of ecodriving practices, and therefore underlie the effectiveness of any feedback design. This study examines both precursor cognitive factors and driver behavior changes with the introduction of energy feedback, using a framework hypothesizing that attitudes, social norms, perceived control, and goals influence behavior and behavior change. The study finds that the introduction of a feedback interface can both activate these cognitive factors and result in behavior change. Furthermore, the study finds that there was an overall 4.4% reduction in fuel consumption due entirely to one group that showed increases in their knowledge of fuel economy and reported high levels of technical proficiency during the experiment. The second group made no improvement and may have been confused by the feedback. In addition, statistically significant relationships are found in the effective group between the magnitude of cognitive change and the magnitude of behavior change – supporting the theoretical framework. Finally, the baseline (prefeedback) performance of the drivers was an important model factor, indicating that drivers that already use highly efficient styles do not benefit much from feedback.