What should an intelligent in-vehicle assistant (IVA) look like, and how should it behave to truly enhance the in-car experience? We present a large-scale video-based online experiment (n = 1238) exploring how IVA design factors influence user perceptions. Participants evaluated two scenarios (adjusting temperature, adjusting seat position) across 32 conditions varying in autonomy (user-initiated, system-initiated, autonomous with explanation, autonom- ous without explanation), embodiment (abstract virtual agent, humanlike virtual agent, abstract robot, humanoid robot), and conversational style (formal, informal). Contrary to prevailing academic trends, our findings reveal a clear preference against robotic embodiments and high levels of autonomy, sometimes even when explainable. Instead, participants favored proactivity with lower system autonomy and less anthropomorphic designs. We discuss how these insights challenge current design assumptions and offer concrete guidelines for shaping IVAs that align with driver expectations and comfort. This work contributes an empirically grounded understanding of IVA appearance, behavior, and communication style to inform future human-centered automotive interaction design.

Favorable assessments of social robots are addressed in several research and development attempts because positive attitudes and intentions towards technology are regarded as a necessary prerequisite for usage. To predict a favorable evaluation, it is inevitable to understand the appraisal process and determine crucial variables that affect the evaluative and behavioral consequences of HRI. Robotic morphology has been identified as one of these variables. In the present work, we expand previous work by demonstrating that capability attributions associated with robots’ morphological features explain variations in evaluations. Based on two large picture-based online studies (Study 1, n = 673; Study 2, n = 586) we show that robots with similar morphological features (e.g., robots with arms and grippers) can be clustered along their assigned capabilities, and that these capabilities (e.g., to manipulate objects) explain evaluations of the robots in terms of acceptance and social attributes (i.e., warmth, competence, and discomfort). We discuss whether these initial assessments are relevant to live interactions and how our results can inform robot design.

We propose a new theoretical framework assuming that embodiment effects in HAI and HRI are mediated by users' perceptions of an artificial entity's body-related capabilities. To enable the application of our framework to foster more theoretical-driven research, we developed a new self-report measurement that assesses bodilyrelated perceptions of the embodiment and corporeality - which we reveal as not being a binary characteristic of artificial entities. For the development and validation of the new scale we conducted two surveys and one video-based experiment. Exploratory factor analysis reveal a four-factorial solution with good reliability (Study 2, n = 442), which was confirmed via confirmatory factor analysis (Study 3, n = 260). In addition, we present first insights into the explanatory power of the scale: We reveal that humans? perceptions of an artificial entity's capabilities vary between virtual and physical embodiments, and that the evaluation of the artificial counterpart can be explained through the perceived capabilities. Practical applications and future research lines are discussed.

This methodological article discusses the influence of individuals’ beliefs about their abilities to use and control robotic technologies on their evaluation of human-robot-interaction (HRI). We conducted three surveys to develop and validate a new measure of Self-Efficacy in HRI. Exploratory factor analysis revealed a two-factorial (factors perceived self-efficacy and loss of control) solution with good reliability (Study 1, n = 201). Confirmatory factor analysis did not confirm the two-factorial structure. Instead, it revealed a better model fit for a one-factorial solution for a German (Study 2, n = 450) and an English version (Study 3, n = 209) of the scale with good indices for convergent and divergent validity. The final questionnaire with 18 items was used in two experimental studies (Study 4, n = 120). We found that interacting with a robot increased self-efficacy and that individual changes in self-efficacy predict more positive evaluations within a student sample, but not a sample of seniors. Interviews with seniors from this study suggested shortening the scale, and revising the instructions and answering scheme. The revised scale was again subject to confirmatory factor analysis (Study 5, n = 198), confirming the one-factorial solution for the German and the English version of the scale. We discuss potential use cases for the scale in HRI research.

The goal of this work is to explore the influence of do-it-yourself customization of a robot on technologically experienced students and unexperienced elderly users' perceived self-efficacy in HRI, uncertainty, and evaluation of the robot and interaction. We introduce the Self-Efficacy in HRI Scale and present two experimental studies. In study 1 (students, n=60) we found that any interaction with the robot increased self-efficacy, regardless of whether this interaction involves customization or not. Moreover, individual increases in self-efficacy predict more positive evaluations. In a second study with elderly users (n=60) we could not replicate the general positive effect of the interaction on self-efficacy. Again, we did not find the hypothesized stronger effect of customization on self-efficacy, nor did we find that relationship between self-efficacy increase and evaluation. We discuss limitations of the setting and for questionnaire design for elderly participants.