Interdependence theory in humans’ interaction with automated vehicles: The impact of perceived situational factors on trust and cooperation

Abstract

With the progressive development of automated vehicles (AVs), drivers and AVs need to share driving tasks as the AVs are yet unable to cope with every traffic situation. As these systems continue to evolve, AVs are increasingly perceived as social interaction partners that drivers must trust and with which they must be willing to cooperate. The presented study investigates how drivers’ social evaluation of situational characteristics affects their willingness to cooperate with and exhibit trust toward AVs. The study (n = 82) was conducted online using the random situation design in which participants imagine, describe, and evaluate an interaction with an AV. To evaluate the self-imagined situation, the interdependence theory and its dimensions measured by the Human–Machine-Interaction-Interdependence (HMII) questionnaire (conflict, power, mutual dependence, information certainty, and future interdependence) were used. The results show that 42% of the variance of trust and 35% of the willingness to cooperate could be explained by conflict, information certainty, power, and mutual dependence. Moreover, the findings imply that the interdependence theory can provide a framework needed in driver–vehicle research that can uniformly describe the influence of the social evaluation of the interaction with AVs on trust and willingness to cooperate. The study provides the first evidence that the interdependence theory is a fundamental social cognitive evaluation strategy humans use for interaction with AVs and automated systems in general.