This paper proposes a model of technology acceptance that is specifically developed to test the acceptance of assistive social agents by elderly users. The research in this paper develops and tests an adaptation and theoretical extension of the Unified Theory of Acceptance and Use of Technology (UTAUT) by explaining intent to use not only in terms of variables related to functional evaluation like perceived usefulness and perceived ease of use, but also variables that relate to social interaction. The new model was tested using controlled experiment and longitudinal data collected regarding three different social agents at elderly care facilities and at the homes of older adults. The model was strongly supported accounting for 59-79% of the variance in usage intentions and 49-59% of the variance in actual use. These findings contribute to our understanding of how elderly users accept assistive social agents.

The increasing availability of (digital) cultural heritage artefacts offers great potential for increased access to art content, but also necessitates tools to help users deal with such abundance of information. User-adaptive art recommender systems aim to present their users with art content tailored to their interests. These systems try to adapt to the user based on feedback from the user on which artworks he or she finds interesting. Users need to be able to depend on the system to competently adapt to their feedback and find the artworks that are most interesting to them. This paper investigates the influence of transparency on user trust in and acceptance of content-based recommender systems. A between-subject experiment (N = 60) evaluated interaction with three versions of a content-based art recommender in the cultural heritage domain. This recommender system provides users with artworks that are of interest to them, based on their ratings of other artworks. Version 1 was not transparent, version 2 explained to the user why a recommendation had been made and version 3 showed a rating of how certain the system was that a recommendation would be of interest to the user. Results show that explaining to the user why a recommendation was made increased acceptance of the recommendations. Trust in the system itself was not improved by transparency. Showing how certain the system was of a recommendation did not influence trust and acceptance. A number of guidelines for design of recommender systems in the cultural heritage domain have been derived from the study's results.

The human robot interaction community is multidisciplinary by nature and has members from social science to engineering backgrounds. In this paper we aim to provide human robot developers with a straightforward toolkit to evaluate users' acceptance of assistive social robots they are designing or developing for elderly care environments. We will explain how we developed the measures for this analysis, provide do's and don'ts in designing the experiments, demonstrate the application of the measures we have developed for this purpose and the analysis and interpretation of the data. As such we hope to engage human robot interaction developers in evaluating the acceptability of their own robot to inform the development process and improve the final robot's design. SI Use PEOU FC PU ANX PENJ ATT Trust ITU PAD PS SP

Abstract-If robotic companions are to be used in the near future by aging adults, they have to be accepted by them. In the process of developing a methodology to measure, predict and explain acceptance of robotic companions, we researched the influence of social abilities, social presence and perceived enjoyment. After an experiment (n=30) that included collecting usage data and a second experiment (n=40) with a robot in a more and less sociable condition we were able to confirm the relevance of these concepts. Results suggest that social abilities contribute to the sense of social presence when interacting with a robotic companion and this leads, through higher enjoyment to a higher acceptance score.

We present an ample description of a socially compliant mobile robotic platform, which is developed in the EU-funded project SPENCER. The purpose of this robot is to assist, inform and guide passengers in large and busy airports. One particular aim is to bring travellers of connecting flights conveniently and efficiently from their arrival gate to the passport control. The uniqueness of the project stems from the strong demand of service robots for this application with a large potential impact for the aviation industry on one side, and on the other side from the scientific advancements in social robotics, brought forward and achieved in SPENCER. The main contributions of SPENCER are novel methods to perceive, learn, and model human social behavior and to use this knowledge to plan appropriate actions in realtime for mobile platforms. In this paper, we describe how the project advances the fields of detection and tracking of individuals and groups, recognition of human social relations and activities, normative human behavior learning, socially-aware task and motion planning, learning socially annotated maps, and conducting empirical experiments to assess socio-psychological effects of normative robot behaviors.