In the near future, robots will function in social roles and attempt to influence the user’s behavior and / or thinking. The current contribution analyses how to influence robot influence: Persuasive robots can be personalized to make them more effective. We present an overview of (1) the user characteristics to which persuasive robots can be personalized, (2) considering the specific current situation of a user; and (3) the robot characteristics that can be personalized. Thereby, we give an overview of how the persuasive robot’s physical appearance, behavior, (perceived) cognition and affect can be influenced to characteristics of the user (personalized) in order to make the robot more persuasive and thereby to understand better how the persuasive power of an embodied artificial social entity can be influenced.
Abraham, C., & Michie, S. (2008). A taxonomy of behavior change techniques used in interventions. Health Psychology,
27
(3), 379–387.
Albert, S., & Dabbs, J. M., Jr. (1970). Physical distance and persuasion. Journal of Personality and Social Psychology,
15
(3), 265–270.
Allan, D. D., Vonasch, A., & Bartneck, C. (2021). The doors of social robot perception: The Influence of Implicit Self-theories. International Journal of Social Robotics, 1–14.
Andrist, S., Ziadee, M., Boukaram, H., Mutlu, B., & Sakr, M. (2015). Effects of Culture on the Credibility of Robot Speech: A Comparison between English and Arabic. ACM/IEEE International Conference on Human-Robot Interaction
. 2015.
Asch, S. E. (1946). Forming impressions of personality. The Journal of Abnormal and Social Psychology,
41
(3): 258–290.
Asch, S. E. (1951). Effects of group pressure upon the modification and distortion of judgment. In H. Guetzkow (ed.) Groups, Leadership and Men. Pittsburgh, PA: Carnegie Press.
Bailenson, J. & Yee, N. (2005). Digital chameleons: Automatic assimilation of nonverbal gestures in immersive virtual environments. Psychological Science,
16
1, 814–819.
Berdichevsky, D., & Neuschwander, E. (1999, May). Toward an ethics of persuasive technology. Communications of the ACM, 51–58.
Berkovsky, S., Freyne, J., & Oinas-Kukkonen, H. (2012). Influencing Individually: Fusing Personalization and Persuasion. ACM Transactions on Interactive Intelligent Systems (TiiS).
2
1.
Bernotat, J., & Eyssel, F., & Sachse, J. (2019). The (Fe)male Robot: How Robot Body Shape Impacts First Impressions and Trust Towards Robots. International Journal of Social Robotics.
Cacioppo, J. T. & Petty, R. E. (1982). “The need for cognition”. Journal of Personality and Social Psychology,
42
(1): 116–131.
Cacioppo, J. T., Petty, R. E., Kao, C. F., & Rodriguez, R. (1986). Central and peripheral routes to persuasion: An individual difference perspective. Journal of Personality and Social Psychology,
51
(5), 1032–1043.
Chaiken, S., & Trope, Y. (Eds.). (1999). Dual-process theories in social psychology. The Guilford Press.
Chidambaram, V., Chiang, Y-H., & Mutlu, B. (2012). Designing persuasive robots: how robots might persuade people using vocal and nonverbal cues. In Proceedings of the seventh annual ACM/IEEE international conference on Human-Robot Interaction (HRI ’12). Association for Computing Machinery, New York, NY, USA, 293–300.
Cialdini, R. B., & Trost, M. R. (1998). Social influence: Social norms, conformity and compliance. In D. T. Gilbert, S. T. Fiske, & G. Lindzey (Eds.), The handbook of social psychology (p. 151–192). McGraw-Hill.
Cialdini, R. B. (1984). Influence: the psychology of persuasion. William Morrow and Company, New York.
Compton, J. (2013). Inoculation theory. In J. P. Dillard & L. Shen (Eds.), The Sage handbook of persuasion: Developments in theory and practice (2nd ed.) (pp. 220–236). Thousand Oaks, CA: Sage.
Crano, W. & Prislin, R. (2006). Attitudes and Persuasion. Annual review of psychology. 571. 345–74.
Dijkstra, A. (2008). The Psychology of Tailoring-Ingredients in Computer-Tailored Persuasion. Social and Personality Psychology Compass,
2
1: 765–784.
Dijkstra, A., & Ballast, K. (2012). Personalization and perceived personal relevance in computer-tailored persuasion in smoking cessation. British Journal of Health Psychology, 171, 60–73.
Epley, N., Waytz, A. & Cacioppo, J. (2007). On Seeing Human: A Three-Factor Theory of Anthropomorphism. Psychological review,
114
1, 864–86.
Eyssel, F. A., & Pfundmair, M. (2015). Predictors of psychological anthropomorphization, mind perception, and the fulfillment of social needs: A case study with a zoomorphic robot. In: Proceedings of the 24th IEEE International Symposium on Robot and Human Interactive Communication (RO-MAN 2015): 827–832.
Eyssel, F. A., & Reich, N. (2013). Loneliness makes the heart grow fonder (of robots). On the effects of loneliness on psychological anthropomorphism. In: Kuzuoka, H., ed. Proceedings of the 8th ACM/IEEE International Conference on Human-Robot Interaction (HRI 2013). Piscataway, NJ: IEEE Press: 121–122.
Fischer, K., Jensen, L. C., & Zitzmann, N. (2022). Sharing the situational context influences a speaker’s (a robot’s) persuasiveness. Interaction Studies, in this volume.
Fogg, B. J. (2003). Persuasive technology: using computers to change what we think and do. Morgan Kaufmann, Amsterdam.
Fogg, B. J. (2009). A behavior model for persuasive design. In Proceedings of the 4th International Conference on Persuasive Technology (Persuasive ’09). Association for Computing Machinery, New York, NY, USA, Article 401, 1–7.
Fountoukidou, S., Ham, J., Matzat, U., & Midden, C. (2020). Persuasive design principles and user models for people with motor disabilities. In: Spiros Nikolopoulos, Chandan Kumar & Ioannis Kompatsiaris (Eds): Signal Processing to Drive Human-Computer Interaction: EEG and eye-controlled interfaces. The Institution of Engineering and Technology, ISBN: 978-1-78561-919-9.
Ghazali, A. (2019). Designing social cues for effective persuasive robots. Thesis published at Eindhoven University of Technology.
Ghazali, A. S., Ham, J., Barakova, E. I., & Markopoulos, P. (2018b). Poker face influence: Persuasive robot with minimal social cues triggers less psychological reactance. 27th IEEE International Symposium on Robot and Human Interactive Communication (IEEE RO-MAN 2018), 940–946.
Ghazali, A., Ham, J., Barakova, E., & Markopoulos, P. (2018a). Effects of robot facial characteristics and gender in persuasive human-robot interaction. Frontiers in Robotics and AI.
Ghazali, A., Ham, J., Barakova, E., & Markopoulous, P. (2020). Persuasive robot acceptance model (PRAM): Roles of social responses within the acceptance model of persuasive robots. International Journal of Social Robotics, 1–18.
Gibson, J. J. (1977). The theory of affordances. In Shaw, R., Bransford, J. (Eds.), Perceiving, acting, and knowing: Toward an ecological psychology (pp. 67–82). Hillsdale, NJ: Erlbaum.
Goldstein, N. J., Cialdini, R. B., Griskevicius, V. (2008). A Room with a Viewpoint: Using Social Norms to Motivate Environmental Conservation in Hotels. Journal of Consumer Research,
35
1, 31, p. 472–482.
Hall, C. S., Lindzey, G., & Campbell, J. B. (1998). Theories of personality, 4th ed. J. Wiley and Sons, New York.
Halttu, K., Oduor, M., Tikka, P., & Oinas-Kukkonen, H. (2015). About the Persuasion Context for BCSSs: Analyzing the Contextual Factors. BCSS@PERSUASIVE 2015: 43–50.
Ham, J., & Midden, C. (2014). A Persuasive Robot to Stimulate Energy Conservation: The Influence of Positive and Negative Social Feedback and Task Similarity on Energy Consumption Behavior. International Journal of Social Robotics.
Ham, J., & Spahn, A. (2015). Shall I show you some other shirts too? The ethics and psychology of social persuasive robots. In: Robert Trappl (Ed): A Construction Manual for Robot’s Ethical Systems, MIT Press 2015.
Ham, J., Cuijpers, R. H., & Cabibihan, J.-J. (2015). Combining robotic persuasive strategies: The persuasive power of a storytelling robot that uses gazing and gestures. International Journal of Social Robotics.
Ham, J., Esch, M. van, Limpens, Y., Pee, J. de, Cabibihan, J.-J., & Ge, S. S. (2012). The automaticity of social behavior towards robots: The influence of cognitive load on interpersonal distance to approachable versus less approachable robots. In S. S. Geet al. (Eds.): Proceedings of the International Conference on Social Robotics, 2012, LNAI 7621, pp. 15–25, 2012.
Ham, J., van Schendel, J., Koldijk, S., & Demerouti, E. (2016). Finding kairos: The influence of context-based timing on compliance with well-being triggers. Conference proceedings of Symbiotic 2016, Padua, Italy, September 2016.
Haugtvedt, C. P., Petty, R. E., Cacioppo, J. T. (1992). Need for cognition and advertising: understanding the role of personality variables in consumer behavior. Journal of Consumer Psychology,
1
(3), 239–260.
Hemminghaus, J., & Kopp, S. (2017). Towards adaptive social behavior generation for assistive robots using reinforcement learning. In: Proceedings of the 2017 ACM/IEEE international conference on human–robot interaction, ACM, pp 332–340.
Howard, J. A., Renfrow, D. G. (2006). Social Cognition. In: Delamater, J. (eds) Handbook of Social Psychology. Handbooks of Sociology and Social Research. Springer, Boston, MA.
Janssen, J. & Bailenson, J., Ijsselsteijn, W., & Westerink, J. (2010). Intimate Heartbeats: Opportunities for Affective Communication Technology. Affective Computing, IEEE Transactions,
1
1, 72–80.
Kaptein, M. C., Markopoulos, P., Ruyter de, B. E. R., & Aarts, E. H. L. (2015). Personalizing persuasive technologies: Explicit and implicit personalization using persuasion profiles. International Journal of Human Computer Studies, 771, 38–51.
Kaptein, M., de Ruyter, B., Markopoulos, P., & Aarts, E. (2012). Adaptive persuasive systems: a study of tailored persuasive text messages to reduce snacking. ACM Transactions on Interactive Intelligent Systems,
2
1.
Koranteng, F., Matzat, U., Ham, J., & Wiafe, I. (in press). The role of usability, aesthetics, usefulness and primary task support in predicting the perceived credibility of academic social networking sites. Behaviour & Information Technology.
Korn, O., Akalin, N., & Gouveia, R. (2021). Understanding Cultural Preferences for Social Robots: A Study in German and Arab Communities. ACM Transactions on Human-Robot Interaction,
10
1, 1–19.
Lee, I., Kim, J., Kim, J. (2005). Use contexts for the mobile internet: a longitudinal study monitoring actual use of mobile internet services. International Journal of Human-Computer Interaction, 18(3), 269–292.
Leite, I., Pereira, A., Castellano, G., Mascarenhas, S., Martinho, C., & Paiva, A. (2011). Modelling empathy in social robotic companions. In: International conference on user modeling, adaptation, and personalization. Springer, pp 135–147.
Lewin, K. (1951). Field theory in social science. New York: Harper.
Leyzberg, D., Spaulding, S., Scassellati, B. (2014). Personalizing robot tutors to individuals’ learning differences. In: Proceedings of the 2014 ACM/IEEE international conference on human–robot interaction. ACM, pp 423–430.
Maeda, R., Brščić, D., & Kanda, T. (2021). Influencing Moral Behavior Through Mere Observation of Robot Work: Video-based Survey on Littering Behavior. In Proceedings of the 2021 ACM/IEEE International Conference on Human-Robot Interaction (HRI ’21). Association for Computing Machinery, New York, NY, USA, 83–91.
Makenova, Regina & Karsybayeva, Raushan & Sandygulova, Anara. (2018). Exploring Cross-cultural Differences in Persuasive Robotics. 185–186.
Masthoff, J., Grasso, F., & Ham, J. (2014). Preface to the special issue on Personalisation and Behaviour Change. User Modeling and User-Adapted Interaction,
24
1.
McGuire, W. J. (1964). Inducing resistance to persuasion. In L. Berkowitz (Ed.), Advances in experimental social psychology,
1
1, pp. 191–229. New York: Academic Press.
Michie, S., van Stralen, M. M., & West, R. (2011). The behaviour change wheel: A new method for characterising and designing behaviour change interventions. Implementation Science,
6
(1).
Michie, S., West, R., Campbell, R., Brown, J., & Gainforth, H. (2014). ABC of Behaviour Change Theories. Silverback Publishing.
Mileounis, A., Cuijpers, R., & Barakova, E. (2015). Creating Robots with Personality: The Effect of Personality on Social Intelligence. WINAC 2015.
Milgram, S. (1974). Obedience to authority: An experimental view. New York: Harper & Row.
Moscovici, S. & Gabriel, M. & Avermaet, E. (1985). Perspectives on Minority Influence. Canadian Journal of Sociology,
11
1.
Norman, D. A. (1999). Affordance, conventions, and design. Interactions6
1, 31, 38–43.
Norman, D. A. (2008). Signifiers, not affordances. Interactions,
15
1, 18–19.
Oinas-Kukkonen, H., & Harjumaa, M. (2009). Persuasive Systems Design: Key Issues, Process Model, and System Features. Communications of the Association for Information Systems, 241.
Orji, R. (2014). Exploring the Persuasiveness of Behavior Change Support Strategies and Possible Gender Differences. Workshop on Behavior Change Support Systems at the International Conference on Persuasive Technology, 2014.
Orji, R., Kaptein, M., Ham, J., Oyibo, K., & Nwokeji, J. C. (2018). Proceedings of the Third International Workshop on Personalization in Persuasive Technology co-located with the 13th International Conference on Persuasive Technology, PPT@PERSUASIVE 2018, Waterloo, Canada, April 16, 2018. CEUR Workshop Proceedings 2089, CEUR-WS.org 2018.
Orji, R., Nacke, L. E., & Di Marco, C. (2017). Towards personality-driven persuasive health games and gamified systems. Proceedings of the 2017 CHI Conference on Human, 2017.
Orji, R., Vassileva, J., & Mandryk, R. L. (2014). Modeling the efficacy of persuasive strategies for different gamer types in serious games for health. User Modeling and User-Adapted Interaction,
24
(5), 453–498.
Oyibo, K., Orji, R., Ham, J., Nwokeji, J. C., & Ciocarlan, A. (2019). Special issue on Personalizing Persuasive Technologies. Information, 2019, 10.
Oyibo, K., Orji, R. & Vassileva, J. (2017). Investigation of the Persuasiveness of Social Influence in Persuasive Technology and the Effect of Age and Gender. Workshop on Personalizing Persuasive Technology at International Conference on Persuasive Technology, 32–44.
Paradeda, R. B., Martinho, C., Paiva, A. (2017). Persuasion based on personality traits: Using a social robot as storyteller. Proceedings of the Companion of the 2017 ACM/IEEE Human Robot Interaction conference.
Petty, R. E., & Cacioppo, J. T. (1986). The elaboration likelihood model of persuasion. Advances in Experimental Social Psychology,
19
(1), 123–205.
Petty, R. E., & Wegener, D. T. (1999). The elaboration likelihood model: current status and controversies. In: Chaiken, S., Trope, Y. (Eds.), Dual-process Theories in Social Psychology. Guilford Press, New York, pp. 41–72.
Rea, D. J., Schneider, S., & Kanda, T. (2021). “Is this all you can do? Harder!”: The Effects of (Im)Polite Robot Encouragement on Exercise Effort. In Proceedings of the 2021 ACM/IEEE International Conference on Human-Robot Interaction (HRI ’21). Association for Computing Machinery, New York, NY, USA, 225–233.
Reeves, B., & Nass, C. (1996). The Media Equation: How People Treat Computers, Television, and New Media Like Real People and Places. Cambridge University Press
Ripple, L. (1955). Motivation, Capacity, and Opportunity as Related to the Use of Casework Service: Theoretical Base and Plan of Study. Social Service Review, 29 (2), 172–193.
Ritschel, H., Baur, T., Andre, E. (2017). Adapting a robot’s linguistic style based on socially-aware reinforcement learning.
Rosenberg, M. J. and Hovland, C. I. (1960). Cognitive, Affective and Behavioral Components of Attitudes. In: Rosenberg, M. J. and Hovland, C. I., Eds., Attitude Organization and Change: An Analysis of Consistency among Attitude Components, Yale University Press, New Haven.
Roubroeks, M., Ham, J., & Midden, C. (2011). When artificial social agents try to persuade people: The role of social agency on the occurrence of psychological reactance. International Journal of Social Robotics, 31, 155–165.
Ruijten, P. (2020). The similarity-attraction paradigm in persuasive technology: effects of system and user personality on evaluations and persuasiveness of an interactive system. Behaviour & Information Technology, 1–13.
Ruijten, P. A. A., Midden, C. J. H., & Ham, J. (2015). Lonely and susceptible: The influence of social exclusion and gender on persuasion by an artificial agent. International Journal of Human-Computer Interaction.
Ruijten, P. A. M., Midden, C. J. H., & Ham, J. (2016). Ambiguous agents: The influence of consistency of an artificial agent’s social cues on emotion recognition, recall, and persuasiveness. International Journal of Human-Computer Interaction, 321, 734–744.
Rutherford, M. D., & Kuhlmeier, V. A. (Eds.). (2013). Social perception: Detection and interpretation of animacy, agency, and intention. MIT Press.
Salomons, N., van der Linden, M., Strohkorb Sebo, S., & Scassellati, B. (2018). Humans Conform to Robots: Disambiguating Trust, Truth, and Conformity. In Proceedings of the 2018 ACM/IEEE International Conference on Human-Robot Interaction (HRI ’18). Association for Computing Machinery, New York, NY, USA, 187–195.
Schneider, S. & Kummert, F. (2020). Comparing Robot and Human guided Personalization: Adaptive Exercise Robots are Perceived as more Competent and Trustworthy. International Journal of Social Robotics.
Siegel, M., Breazeal, C., & Norton, M. I. (2009). Persuasive robotics: the influence of robot gender on human behavior. In: IROS 2009, pp. 2563–2568.
Spahn, A. (2012). and Lead Us (Not) into Persuasion…? Persuasive Technology and the Ethics of Communication. Science and Engineering Ethics18
1, 633–650 (2012)
Spelt, H. A. A., Westerink, J. H. D. M., Ham, J., & IJsselsteijn, W. A. (2020). Persuasion-Induced Physiology Partly Predicts Persuasion Effectiveness. IEEE Transactions on Affective Computing.
Tapus, A., & Ţapus, C., & Mataric, M. (2008). User-Robot Personality Matching and Robot Behavior Adaptation for Post-Stroke Rehabilitation Therapy. Intelligent Service Robotics,
1
1, 169–183.
Trovato, G., Zecca, M., Sessa, S., Jamone, L., Ham, J., Hashimoto, K., & Takanishi, A. (2013). Cross-cultural study on human-robot greeting interaction: acceptance and discomfort by Egyptians and Japanese. Paladyn. Journal of Behavioral Robotics,
4
1, 83–93.
Tsiakas, K., Papakostas, M., Chebaa, B., Ebert, D., Karkaletsis, V., & Makedon, F. (2016). An interactive learning and adaptation framework for adaptive robot assisted therapy. PETRA.
Turner, J. & Oakes, P. (1986). The significance of the social identity concept for social psychology with reference to individualism, interactionism and social influence. British Journal of Social Psychology, 25 (3): 237–252.
Turner, J. C.; Reynolds, K. J. (2010). The story of social identity. In T. Postmes; N. Branscombe (eds.), Rediscovering Social Identity: Core Sources. Psychology Press.
Ullrich, D., Butz, A. & Diefenbach, S. (2018). Who Do You Follow?: Social Robots’ Impact on Human Judgment. Companion of the 2018 ACM/IEEE International Conference, 265–266.
United Nations (2015). Transforming our world: the 2030 Agenda for Sustainable Development. Retrieved from: [URL]
Verbeek, P.-P. (2006). Persuasive technology and moral responsibility: Toward an ethical framework for persuasive technologies. Persuasive,
6
1, 1–15.
Verberne, F. M. M., Ham, J., & Midden, C. (2012). Trust in smart systems: The influence of sharing user goals and information on trust in and acceptance of smart systems in cars. Human Factors,
54
1, 799–810.
Verberne, F., Ham, J., & Midden, C. (2013). The car that looks like me: Similarity cues can increase trust in the self-driving cars of the future. ERCIM News, 941, 23–24.
Verberne, M. F., Ham, J., & Midden, J. H. (2015). Trusting a virtual driver that looks, acts, and thinks like you. Human Factors.
Wykowska, A. & Chellali, R., Al-Amin, M., & Müller, H. (2014). Implications of Robot Actions for Human Perception. How Do We Represent Actions of the Observed Robots?. International Journal of Social Robotics,
6
1.
Wykowska, A. (2020). Social Robots to Test Flexibility of Human Social Cognition. International Journal of Social Robotics,
12
1, 1203–1211.
You, S., Robert, L., Alahmad, R., Esterwood, C., & Zhang, Q. (2020). A Review of Personality in Human-Robot Interactions. Foundations and Trends in Information Systems.
2023. Companion of the 2023 ACM/IEEE International Conference on Human-Robot Interaction, ► pp. 580 ff.
Oyibo, Kiemute, Kang Wang & Plinio Pelegrini Morita
2023. Using Smart Home Technologies to Promote Physical Activity Among the General and Aging Populations: Scoping Review. Journal of Medical Internet Research 25 ► pp. e41942 ff.
2023. Proceedings of the 16th International Conference on PErvasive Technologies Related to Assistive Environments, ► pp. 313 ff.
Williams, Tom
2023. Companion of the 2023 ACM/IEEE International Conference on Human-Robot Interaction, ► pp. 1 ff.
Williams, Tom
2024. Companion of the 2024 ACM/IEEE International Conference on Human-Robot Interaction, ► pp. 46 ff.
This list is based on CrossRef data as of 8 january 2025. Please note that it may not be complete. Sources presented here have been supplied by the respective publishers.
Any errors therein should be reported to them.