Sarah Theres Völkel
ABSTRACT
Recent research suggests that deliberately manipulating a chatbot’s personality and matching it to the user’s personality can positively impact the user experience. Yet, little is known about whether this similarity attraction effect also applies to the personality dimension agreeableness. In a lab experiment, 30 participants interacted with three versions of an agreeable chatbot (agreeable, neutral, and disagreeable). Whilst our results corroborate a similarity attraction effect between user agreeableness and their preference for the agreeable chatbot, we did not find a reversed relationship with a disagreeable chatbot. Our findings point to a need for moderate instead of extreme chatbot personalities.
- Sean Andrist, Bilge Mutlu, and Adriana Tapus. 2015. Look Like Me: Matching Robot Personality via Gaze to Increase Motivation. In Proceedings of the 33rd Annual ACM Conference on Human Factors in Computing Systems (Seoul, Republic of Korea) (CHI ’15). Association for Computing Machinery, New York, NY, USA, 3603–3612. https://doi.org/10.1145/2702123.2702592Google Scholar
Digital Library
- Jeromy Anglim, Sharon Horwood, Luke D Smillie, Rosario J Marrero, and Joshua K Wood. 2020. Predicting psychological and subjective well-being from personality: A meta-analysis.Psychological Bulletin 146, 4 (2020), 279–323. https://doi.org/10.1037/bul0000226Google Scholar
- Timothy W Bickmore and Rosalind W Picard. 2005. Establishing and maintaining long-term human-computer relationships. ACM Transactions on Computer-Human Interaction (TOCHI) 12, 2(2005), 293–327.Google ScholarDigital Library
- Donn Erwin Byrne. 1971. The attraction paradigm. Academic Press, Cambridge, MA, USA.Google Scholar
- Angelo Cafaro, Hannes Högni Vilhjálmsson, and Timothy Bickmore. 2016. First Impressions in Human–Agent Virtual Encounters. ACM Trans. Comput.-Hum. Interact. 23, 4, Article 24 (Aug. 2016), 40 pages. https://doi.org/10.1145/2940325Google ScholarDigital Library
- Anne Campbell and J. Philippe Rushton. 1978. Bodily communication and personality. British Journal of Social and Clinical Psychology 17, 1(1978), 31–36. https://doi.org/10.1111/j.2044-8260.1978.tb00893.xGoogle ScholarCross Ref
- DW Carment, CG Miles, and VB Cervin. 1965. Persuasiveness and persuasibility as related to intelligence and extraversion. British Journal of Social and Clinical Psychology 4, 1 (1965), 1–7. https://doi.org/10.1111/j.2044-8260.1965.tb00433.xGoogle ScholarCross Ref
- Daniel Danner, Beatrice Rammstedt, Matthias Bluemke, Lisa Treiber, Sabrina Berres, Christopher Soto, and Oliver John. 2016. Die deutsche Version des Big Five Inventory 2 (BFI-2). GESIS, Mannheim, Germany. https://doi.org/10.6102/zis247Google Scholar
- Jean-Marc Dewaele and Adrian Furnham. 2000. Personality and speech production: A pilot study of second language learners. Personality and Individual Differences 28, 2 (2000), 355–365. https://doi.org/10.1016/S0191-8869(99)00106-3Google ScholarCross Ref
- Patrick Ehrenbrink, Seif Osman, and Sebastian Möller. 2017. Google Now is for the Extraverted, Cortana for the Introverted: Investigating the Influence of Personality on IPA Preference. In Proceedings of the 29th Australian Conference on Computer-Human Interaction (Brisbane, Queensland, Australia) (OZCHI ’17). Association for Computing Machinery, New York, NY, USA, 257–265. https://doi.org/10.1145/3152771.3152799Google ScholarDigital Library
- Adrian Furnham. 1990. Language and Personality. In Handbook of language and social psychology, William Peter Robinsonand Howard Giles (Eds.). John Wiley & Sons, Chichester, UK, 73–95.Google Scholar
- Ulrich Gnewuch, Meng Yu, and Alexander Maedche. 2020. The Effect of Perceived Similarity in Dominance on Customer Self-Disclosure to Chatbots in Conversational Commerce. In Proceedings of the 28th European Conference on Information Systems (ECIS 2020). AIS, eLibrary (AISeL).Google Scholar
- William G Graziano, Meara M Habashi, Brad E Sheese, and Renée M Tobin. 2007. Agreeableness, empathy, and helping: A person × situation perspective.Journal of Personality and Social Psychology 93, 4(2007), 583–599. https://doi.org/10.1037/0022-3514.93.4.583Google Scholar
- Jia He and Fons J.R. van de Vijver. 2013. A general response style factor: Evidence from a multi-ethnic study in the Netherlands. Personality and Individual Differences 55, 7 (2013), 794–800. https://doi.org/10.1016/j.paid.2013.06.017Google ScholarCross Ref
- Jacob B. Hirsh, Colin G. DeYoung, and Jordan B. Peterson. 2009. Metatraits of the Big Five Differentially Predict Engagement and Restraint of Behavior. Journal of Personality 77, 4 (2009), 1085–1102. https://doi.org/10.1111/j.1467-6494.2009.00575.xGoogle ScholarCross Ref
- Thomas Holtgraves. 2011. Text messaging, personality, and the social context. Journal of Research in Personality 45, 1 (2011), 92 – 99. https://doi.org/10.1016/j.jrp.2010.11.015Google ScholarCross Ref
- Kwan Min Lee and Clifford Nass. 2003. Designing Social Presence of Social Actors in Human Computer Interaction. In Proceedings of the SIGCHI Conference on Human Factors in Computing Systems (Ft. Lauderdale, Florida, USA) (CHI ’03). Association for Computing Machinery, New York, NY, USA, 289–296. https://doi.org/10.1145/642611.642662Google ScholarDigital Library
- Jan-Erik Lönnqvist, Sampo Paunonen, Markku Verkasalo, Sointu Leikas, Annamari Tuulioâ-Henriksson, and Jouko Lönnqvist. 2007. Personality characteristics of research volunteers. European Journal of Personality 21, 8 (2007), 1017–1030. https://doi.org/10.1002/per.655Google ScholarCross Ref
- Davide Marengo, Fabrizia Giannotta, and Michele Settanni. 2017. Assessing personality using emoji: An exploratory study. Personality and Individual Differences 112 (2017), 74 – 78. https://doi.org/10.1016/j.paid.2017.02.037Google ScholarCross Ref
- Robert R. McCrae and Paul T. Costa. 2008. A five-factor theory of personality. In Handbook of Personality: Theory and Research, O.P. John, R.W. Robins, and L.A. Pervin (Eds.). Vol. 3. The Guilford Press, New York, NY, USA, 159–181.Google Scholar
- Matthias R Mehl, Samuel D Gosling, and James W Pennebaker. 2006. Personality in its natural habitat: Manifestations and implicit folk theories of personality in daily life.Journal of Personality and Social Psychology 90, 5(2006), 862–877. https://doi.org/10.1037/0022-3514.90.5.862Google Scholar
- Clifford Nass and Scott Brave. 2005. Wired for speech: How voice activates and advances the human-computer relationship. MIT press, Cambridge, MA, USA.Google ScholarDigital Library
- Clifford Nass and Kwan Min Lee. 2001. Does computer-synthesized speech manifest personality? Experimental tests of recognition, similarity-attraction, and consistency-attraction.Journal of Experimental Psychology: Applied 7, 3 (2001), 171. https://doi.org/10.1037/1076-898X.7.3.171Google Scholar
- Clifford Nass, Jonathan Steuer, and Ellen R. Tauber. 1994. Computers Are Social Actors. In Proceedings of the SIGCHI Conference on Human Factors in Computing Systems (Boston, Massachusetts, USA) (CHI ’94). Association for Computing Machinery, New York, NY, USA, 72–78. https://doi.org/10.1145/191666.191703Google ScholarDigital Library
- Jon Oberlander and Alastair J. Gill. 2004. Individual differences and implicit language: personality, parts-of-speech and pervasiveness. Proceedings of the Annual Meeting of the Cognitive Science Society 26 (2004), 1035–1040.Google Scholar
- M. Patterson and D.S. Holmes. 1966. Social Interaction Correlates of the MMPI Extraversion Introversion Scale. American Psychologist 21(1966), 724–25.Google Scholar
- James W. Pennebaker and Laura A. King. 1999. Linguistic styles: Language use as an individual difference.Journal of Personality and Social Psychology 77, 6(1999), 1296–1312. https://doi.org/10.1037/0022-3514.77.6.1296Google Scholar
- Byron Reeves and Clifford Ivar Nass. 1996. The media equation: How people treat computers, television, and new media like real people and places.Cambridge University Press, Cambridge, UK.Google ScholarDigital Library
- Elayne Ruane, Sinead Farrell, and Anthony Ventresque. 2021. User Perception of Text-Based Chatbot Personality. In Chatbot Research and Design. Springer International Publishing, Cham, Switzerland, 32–47. https://doi.org/10.1007/978-3-030-68288-0_3Google Scholar
- D. R. Rutter, Ian E. Morley, and Jane C. Graham. 1972. Visual interaction in a group of introverts and extraverts. European Journal of Social Psychology 2, 4 (1972), 371–384. https://doi.org/10.1002/ejsp.2420020403Google ScholarCross Ref
- Klaus Rainer Scherer. 1979. Personality markers in speech. In Social Markers in Speech, Klaur Rainer Scherer and Howard Giles (Eds.). Cambridge University Press, Cambridge, UK.Google Scholar
- Michael Schmitz, Antonio Krüger, and Sarah Schmidt. 2007. Modelling Personality in Voices of Talking Products through Prosodic Parameters. In Proceedings of the 12th International Conference on Intelligent User Interfaces (Honolulu, Hawaii, USA) (IUI ’07). Association for Computing Machinery, New York, NY, USA, 313–316. https://doi.org/10.1145/1216295.1216355Google ScholarDigital Library
- Michael Shumanov and Lester Johnson. 2021. Making conversations with chatbots more personalized. Computers in Human Behavior 117 (2021), 106627. https://doi.org/10.1016/j.chb.2020.106627Google ScholarCross Ref
- Christopher J. Soto and Oliver P. John. 2017. The next Big Five Inventory (BFI-2): Developing and assessing a hierarchical model with 15 facets to enhance bandwidth, fidelity, and predictive power.Journal of Personality and Social Psychology 113, 1(2017), 117 – 143. https://doi.org/10.1037/pspp0000096Google Scholar
- Jürgen Trouvain, Sarah Schmidt, Marc Schröder, Michael Schmitz, and William J. Barry. 2006. Modelling personality features by changing prosody in synthetic speech. In Proceedings of the 3rd International Conference on Speech Prosody. TUDpress, Dresden, Germany, 4 pages. https://doi.org/10.22028/D291-25920Google Scholar
- Greet Van Hoye and Daniel B. Turban. 2015. Applicant-Employee Fit in Personality: Testing predictions from similarity-attraction theory and trait activation theory. International Journal of Selection and Assessment 23, 3(2015), 210–223. https://doi.org/10.1111/ijsa.12109Google ScholarCross Ref
- Sarah Theres Völkel, Daniel Buschek, Malin Eiband, Benjamin R. Cowan, and Heinrich Hussmann. 2021. Eliciting and Analysing Users’ Envisioned Dialogues with Perfect Voice Assistants. In Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems (Yokohama, Japan) (CHI ’21). Association for Computing Machinery, New York, NY, USA, Article 254, 15 pages. https://doi.org/10.1145/3411764.3445536Google ScholarDigital Library
- Sarah Theres Völkel, Daniel Buschek, Jelena Pranjic, and Heinrich Hussmann. 2019. Understanding Emoji Interpretation through User Personality and Message Context. In Proceedings of the 21st International Conference on Human-Computer Interaction with Mobile Devices and Services (Taipei, Taiwan) (MobileHCI ’19). Association for Computing Machinery, New York, NY, USA, Article 3, 12 pages. https://doi.org/10.1145/3338286.3340114Google ScholarDigital Library
- Sarah Theres Völkel, Ramona Schödel, Daniel Buschek, Clemens Stachl, Verena Winterhalter, Markus Bühner, and Heinrich Hussmann. 2020. Developing a Personality Model for Speech-Based Conversational Agents Using the Psycholexical Approach. In Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems (Honolulu, HI, USA) (CHI ’20). Association for Computing Machinery, New York, NY, USA, 1–14. https://doi.org/10.1145/3313831.3376210Google ScholarDigital Library
- Tal Yarkoni. 2010. Personality in 100,000 Words: A large-scale analysis of personality and word use among bloggers. Journal of Research in Personality 44, 3 (2010), 363 – 373. https://doi.org/10.1016/j.jrp.2010.04.001Google ScholarCross Ref
- Michelle X. Zhou, Gloria Mark, Jingyi Li, and Huahai Yang. 2019. Trusting Virtual Agents: The Effect of Personality. ACM Trans. Interact. Intell. Syst. 9, 2-3, Article 10 (March 2019), 36 pages. https://doi.org/10.1145/3232077Google ScholarDigital Library
Recommendations
User Perceptions of Extraversion in Chatbots after Repeated Use
CHI '22: Proceedings of the 2022 CHI Conference on Human Factors in Computing SystemsWhilst imbuing robots and voice assistants with personality has been found to positively impact user experience, little is known about user perceptions of personality in purely text-based chatbots. In a within-subjects study, we asked N=34 participants ...
Manipulating and Evaluating Levels of Personality Perceptions of Voice Assistants through Enactment-Based Dialogue Design
CUI '21: Proceedings of the 3rd Conference on Conversational User InterfacesWe present an enactment-based dialogue design approach to imbue voice assistants with different levels of personality. In two focus groups, we asked amateur actors to animate different voice assistant personalities by writing and enacting dialogues ...
Chatbot Acceptance: A Latent Profile Analysis on Individuals' Trust in Conversational Agents
SIGMIS-CPR '19: Proceedings of the 2019 on Computers and People Research ConferenceAccording to industry reports, the lack of trust in non-human interaction prevents widespread Chatbot acceptance. Since the willingness and the ability to trust varies between individuals, this study examines to what extent the trust in Chatbots varies ...
Comments