@proceedings{3,
  author = {Moojan Ghafurian and Kerstin Dautenhahn},
  title = {“Robot Like Me” Revisited-An Alternative Approach of Measuring Human and Agent Personalities and Its Impact on Reported Intention to Use},
  abstract = {<p>Past studies have emphasized the importance of adjusting agent personalities for improving users’ acceptance and engagement. However, it is not yet clear how agent personalities can be decided on, as preferences have highly varied in different studies and are task/context dependent. In this proof of concept study, we use Affect Control Theory (ACT) to evaluate perceived affective dimensions of personality (called identities thereafter) of 11 different social robots, and study how this perception affects participants’ interests in interacting with the robots. We ask whether ACT can be used as a novel approach to identify participants’ preferred identities for robots in health/therapy contexts. An online study with 95 participants (a total of 1045 robot ratings) was conducted. Our study supports the use of ACT for understanding users’ preferences for social robot identities measured through robot images: the closer the participants rated their own identity to a robot’s, the more interested they reported to be in using the robot in a health/well-being context. We also report on different factors that influenced rating of social robots as described by the participants, such as robot’s size, animal/human-likeness, and perceived friendliness and complexity. We finally discuss advantages of using ACT as an alternative method, compared to Big 5 dimensions, to assess user and agent/robot identities and to guide personalization.</p>
},
  year = {2023},
  journal = {Human-Agent Interaction Conference},
  month = {12/2023},
  publisher = {ACM Digital Library},
  address = {Gothenburg Sweden},
  isbn = {979-8-4007-0824-4},
  url = {https://dl.acm.org/doi/abs/10.1145/3623809.3623817},
  doi = {https://doi.org/10.1145/3623809.362381},
}