26 Apr 2021
10:15  - 12:00

Location:
online Zoom meeting

Guest lecture / Talk

What Makes Robots Persuasive?

Prof. Dr. Kerstin Fischer (Syddansk Universitet, Sønderborg)

Robots have been shown to be interacted with in social ways, sometimes even as if they were humans, which suggests that they may generally be able to persuade others just like people (e.g. Fogg & Nass 1997; Ham et al. 2015; Fischer 2016). For instance, in an elderly care facility, it may be useful to have a robot serve water to the residents and to persuade them to drink more since we tend to lose our sense for thirst when we get older. In order to determine what makes a robot persuasive, we have carried out lab studies in which we tried out Cialdini's (1984) strategies of influence, such as social proof and appeals to authority (cf. also Winkle 2019). The results show that these strategies of influence are indeed persuasive, even if used by a robot, and as far as the principle of social proof is concerned, it is more effective if the appeal is personalized (cf. Goldstein et al. 1990).

In another experiment, we took these findings into a field study where we found that the sequential placement of the social proof utterances matters, as well as other context features (Jensen 2018). Furthermore, the interactions show that the robot's other behaviors, such as its eye gaze and body orientation, contribute considerably to how its utterances are received in general (Fischer et al. 2020a). This is in line with our findings from another study in which we found that if the robot referred to previous discourse, the situational context and the user's behavior, its advice to drink more water was taken into account much more than if it didn't mention aspects of the context. These results together suggest that the robot's situatedness have a large effect on its persuasiveness.

In another set of experiments, we tested the effects of the robot's speaking style and found that the robot's speech melody also has a significant effect on the persuasiveness of robots (Fischer et al. 2020b). Thus, on top of the robot's situatedness, also the personality it projects has a significant effect on how persuasive it is.

If you would like to listen to this guest lecture, please contact Dr. Daria Dayter to obtain the necessary Zoom link.

References

  • Cialdini, R.B. (2007). Influence: the psychology of persuasion (Revision Edition).
  • Fischer, Kerstin (2016): Designing Speech for a Recipient: The Roles of Partner Modeling, Alignment and Feedback in So-Called ‘Simplified Registers’. Amsterdam: John Benjamins.
  • Fischer, Kerstin, Rosalyn Langedijk, Lotte Damsgaard Nissen, Eduardo Ruiz Ramirez & Oskar Palinko (2020). Gaze-Speech Coordination Inuences the Persuasiveness of Human-Robot Dialog in the Wild". In: International Conference on Social Robotics. Springer. 2020, pp. 157-169.
  • Fischer, Kerstin, Niebuhr, Oliver, Jensen, Lars C. and Bodenhagen, Leon (2020b): Speech Melody Matters – How robots can profit from using charismatic speech. ACM Transactions in Human-Robot Interaction 9, 1, Article 4: 1-21.
  • Fogg, B.J. and Clifford Nass (1997). How users reciprocate to computers: an experiment that demonstrates behavior change". In: CHI'97 extended abstracts on Human factors in computing systems. 1997, pp. 331-332.
  • Goldstein, Noah, J., Robert B Cialdini, and Vladas Griskevicius (2008). A room with a viewpoint: Using social norms to motivate environmental conservation in hotels". In: Journal of consumer Research 35.3 (2008), pp. 472-482.
  • Ham, Jaap, Raymond H Cuijpers, and John-John Cabibihan (2015). Combining robotic persuasive strategies: The persuasive power of a storytelling robot that uses gazing and gestures". In: International Journal of Social Robotics 7.4 (2015), pp. 479-487.
  • Jensen, Lars C. (2018): Effects of Contingent Robot Response to the Situatedness of Human-Robot Interactions. Doktorarbeit, University of Southern Denmark.
  • Katie Winkle et al. (2019). Effective persuasion strategies for socially assistive robots". In: 2019 14th ACM/IEEE International Conference on Human-Robot Interaction (HRI). IEEE. 2019, pp. 277-285.


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