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Cited by 12AI in Service InteractionsSocial Robot Interaction and HRIInnovative Human-Technology Interaction

Exploring the Impact of Anthropomorphism in Role-Playing AI Chatbots on Media Dependency: A Case Study of Xuanhe AI

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Yu, Qiufang, Lan, Xingyu·2024·Proceedings of the Twelfth International Symposium of Chinese CHI
DOI 10.1145/3758871.3758884
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Abstract

Powered by large language models, the conversational capabilities of AI have seen significant improvements. In this context, a series of role-playing AI chatbots have emerged, exhibiting a strong tendency toward anthropomorphism, such as conversing like humans, possessing personalities, and fulfilling social and companionship functions. Informed by media dependency theory in communication studies, this work hypothesizes that a higher level of anthropomorphism of the role-playing chatbots will increase users’ media dependency (i.e., people will depend on media that meets their needs and goals). Specifically, we conducted a user study on a Chinese role-playing chatbot platform, Xuanhe AI, selecting four representative chatbots as research targets. We invited 149 users to interact with these chatbots over a period. A questionnaire survey revealed a significant positive correlation between the degree of anthropomorphism in role-playing chatbots and users’ media dependency, with user satisfaction mediating this relationship. Next, based on the quantitative results, we conducted semi-structured interviews with ten users to further understand the factors that deterred them from depending on anthropomorphic chatbots. In conclusion, this work has provided empirical insights for the design of role-playing AI chatbots and deepened the understanding of how users engage with conversational AI over a longer period.

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  • Self-developed measures may focus on symptoms of behavioral addiction adapted to LLM-CBs (e.g., craving, withdrawal, tolerance, loss of interests, negative impact on other aspects of life), potentially confounding emotional and functional dependence.

    “Self-developed measures may focus on symptoms of behavioral addiction adapted to LLM-CBs (e.g., craving, withdrawal, tolerance, loss of interests, negative impact on other aspects of life)103,106, potentially confounding emotional and functional dependence;”

    From A scoping review on the mental health harms of LLM-based chatbots (2026) · cites this paper as [106]

    Split into 2 atoms — 0 confirmed, 2 partially supported, 0 disputed, 0 misattributed, 0 unverifiable. Expand the claim to see per-atom verdicts.

    Experimental — accuracy not yet measured

  • Anthropomorphizing chatbots predicts LLM-CB use.

    “Yu et al. found that anthropomorphizing chatbots predicted LLM-CB use in a mixed-methods analysis106.”

    From A scoping review on the mental health harms of LLM-based chatbots (2026) · cites this paper as [106]

    Section 3.4.1 reports a regression in which anthropomorphism predicted users’ media dependency on a role-playing chatbot, and Section 4.2 describes follow-up interviews. But the measured outcome was dependency, not LLM-chatbot use generally; the Introduction also says the positive relationship was significant for only three of four chatbots studied. (LLM, conf=0.87)

    Experimental — accuracy not yet measured

  • Measures of AI dependence consist either of adaptations from other scales of behavioral addiction to technologies (e.g., social media, smartphone, or internet addiction) or self-developed measures.

    “Measures of “AI dependence” consist either of adaptations from other scales of behavioral addiction to technologies (e.g., social media, smartphone, or internet addiction)100–102, or self-developed measures103–106.”

    From A scoping review on the mental health harms of LLM-based chatbots (2026) · cites this paper as [106]

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Research works printed but not linked: 46

  1. Eleni Adamopoulou and Lefteris Moussiades. 2020. An overview of chatbot technology. In IFIP international conference on artificial intelligence applications and innovations. Springer, 373-383.
  2. Muhammad Ashfaq, Jiang Yun, Shubin Yu, and Sandra Maria Correia Loureiro. 2020. I, Chatbot: Modeling the determinants of users' satisfaction and continuance intention of AI-powered service agents. Telematics and Informatics 54 (2020), 101473.
  3. Christoph Bartneck, Dana Kulić, Elizabeth Croft, and Susana Zoghbi. 2009. Mea- surement instruments for the anthropomorphism, animacy, likeability, perceived intelligence, and perceived safety of robots. International journal of social robotics 1 (2009), 71-81.
  4. Markus Blut, Cheng Wang, Nancy V Wünderlich, and Christian Brock. 2021. Un- derstanding anthropomorphism in service provision: a meta-analysis of physical robots, chatbots, and other AI. Journal of the Academy of Marketing Science 49 (2021), 632-658.
  5. Petter Bae Brandtzaeg and Asbjørn Følstad. 2017. Why people use chatbots. In Internet Science: 4th International Conference. Springer, 377-392.
  6. Justine Cassell. 2000. Embodied conversational interface agents. Commun. ACM 43, 4 (2000), 70-78.
  7. Younghoon Chang, Seongyong Lee, Siew Fan Wong, and Seon-phil Jeong. 2022. AI-powered learning application use and gratification: an integrative model. Information Technology & People 35, 7 (2022), 2115-2139.
  8. Oscar Hengxuan Chi, Dogan Gursoy, and Christina G Chi. 2022. Tourists' at- titudes toward the use of artificially intelligent (AI) devices in tourism service delivery: moderating role of service value seeking. Journal of Travel Research 61, 1 (2022), 170-185.
  9. Lara Christoforakos and Sarah Diefenbach. 2023. Technology as a social com- panion? An exploration of individual and product-related factors of anthropo- morphism. Social Science Computer Review 41, 3 (2023), 1039-1062.
  10. Pedro Costa. 2018. Conversing with personal digital assistants: On gender and artificial intelligence. Journal of Science and Technology of the Arts 10, 3 (2018), 59-72.
  11. Amber L Ferris and Erin E Hollenbaugh. 2018. A uses and gratifications approach to exploring antecedents to Facebook dependency. Journal of Broadcasting & Electronic Media 62, 1 (2018), 51-70.
  12. James J Gross. 2008. Emotion regulation. Handbook of emotions 3, 3 (2008), 497-513.
  13. Chin-Chang Ho and Karl F MacDorman. 2010. Revisiting the uncanny valley the- ory: Developing and validating an alternative to the Godspeed indices. Computers in Human Behavior 26, 6 (2010), 1508-1518.
  14. Eva Hudlicka. 2003. To feel or not to feel: The role of affect in human-computer interaction. International journal of human-computer studies 59, 1-2 (2003), 1-32.
  15. Seo Young Kim, Bernd H Schmitt, and Nadia M Thalmann. 2019. Eliza in the uncanny valley: Anthropomorphizing consumer robots increases their perceived warmth but decreases liking. Marketing letters 30 (2019), 1-12.
  16. Mohammad Amin Kuhail, Mohamed Bahja, Ons Al-Shamaileh, Justin Thomas, Amina Alkazemi, and Joao Negreiros. 2024. Assessing the Impact of Chatbot- Human Personality Congruence on User Behavior: A Chatbot-based Advising System Case. IEEE Access 12 (2024), 71761-71782.
  17. Anastasia Kuzminykh, Jenny Sun, Nivetha Govindaraju, Jeff Avery, and Edward Lank. 2020. Genie in the bottle: Anthropomorphized perceptions of conversa- tional agents. In Proceedings of the CHI Conference on Human Factors in Computing Systems. 1-13.
  18. Mengjun Li and Ayoung Suh. 2022. Anthropomorphism in AI-enabled technology: A literature review. Electronic Markets 32, 4 (2022), 2245-2275.
  19. Xinge Li and Yongjun Sung. 2021. Anthropomorphism brings us closer: The medi- ating role of psychological distance in User-AI assistant interactions. Computers in Human Behavior 118 (2021), 106680.
  20. Jindong Liu. 2021. Social Robots as the bride?: Understanding the construction of gender in a Japanese social robot product. Human-Machine Communication 2 (2021), 105-120.
  21. Kate Loveys, Gabrielle Sebaratnam, Mark Sagar, and Elizabeth Broadbent. 2020. The effect of design features on relationship quality with embodied conversational agents: a systematic review. International Journal of Social Robotics 12, 6 (2020), 1293-1312.
  22. Bei Luo, Raymond YK Lau, Chunping Li, and Yain-Whar Si. 2022. A critical review of state-of-the-art chatbot designs and applications. Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery 12, 1 (2022), e1434.
  23. Masahiro Mori, Karl F MacDorman, and Norri Kageki. 2012. The uncanny valley [from the field]. IEEE Robotics & automation magazine 19, 2 (2012), 98-100.
  24. Emi Moriuchi. 2021. An empirical study on anthropomorphism and engagement with disembodied AIs and consumers' re-use behavior. Psychology & Marketing 38, 1 (2021), 21-42.
  25. Sara Moussawi and Marios Koufaris. 2019. Perceived intelligence and perceived anthropomorphism of personal intelligent agents: Scale development and val- idation. In Proceedings of the 52nd Hawaii International Conference on System Sciences. 115-124.
  26. Dongfang Niu, Jacques Terken, and Berry Eggen. 2018. Anthropomorphizing information to enhance trust in autonomous vehicles. Human Factors and Er- gonomics in Manufacturing & Service Industries 28, 6 (2018), 352-359.
  27. Chinedu Wilfred Okonkwo and Abejide Ade-Ibijola. 2021. Chatbots applications in education: A systematic review. Computers and Education: Artificial Intelligence 2 (2021), 100033.
  28. Julianne S Oktay. 2012. Grounded theory. Oxford University Press.
  29. Dong-Min Park, Seong-Soo Jeong, and Yeong-Seok Seo. 2022. Systematic review on chatbot techniques and applications. Journal of Information Processing Systems 18, 1 (2022), 26-47.
  30. Cailian Press. 2023. Sci-Tech Innovation Board Daily: First-week downloads crush ChatGPT! Another AI app goes viral, founded by ex-Google employees with a team of just 30 people. https://www.cls.cn/detail/1366590. Last accessed: Sept 15, 2024.
  31. Simon Provoost, Ho Ming Lau, Jeroen Ruwaard, and Heleen Riper. 2017. Embodied conversational agents in clinical psychology: a scoping review. Journal of medical Internet research 19, 5 (2017), e151.
  32. Mashrur Rashik, Mahmood Jasim, Kostiantyn Kucher, Ali Sarvghad, and Narges Mahyar. 2024. Beyond Text and Speech in Conversational Agents: Mapping the Design Space of Avatars. In Proceedings of the CHI Conference on Human Factors in Computing Systems. 1875-1894.
  33. Carla Ruiz Mafé and Silvia Sanz Blas. 2008. The impact of television dependency on teleshopping adoption. Direct Marketing: An International Journal 2, 1 (2008), 5-19.
  34. Ameneh Shamekhi, Q Vera Liao, Dakuo Wang, Rachel KE Bellamy, and Thomas Erickson. 2018. Face Value? Exploring the effects of embodiment for a group facilitation agent. In Proceedings of the CHI Conference on Human Factors in Computing Systems. 1-13.
  35. Hyejo Hailey Shin and Miyoung Jeong. 2020. Guests' perceptions of robot concierge and their adoption intentions. International Journal of Contemporary Hospitality Management 32, 8 (2020), 2613-2633.
  36. Nicolas Spatola, Barbara Kühnlenz, and Gordon Cheng. 2021. Perception and eval- uation in human-robot interaction: The Human-Robot Interaction Evaluation Scale (HRIES)-A multicomponent approach of anthropomorphism. International Journal of Social Robotics 13, 7 (2021), 1517-1539.
  37. Sinarwati Mohamad Suhaili, Naomie Salim, and Mohamad Nazim Jambli. 2021. Service chatbots: A systematic review. Expert Systems with Applications 184 (2021), 115461.
  38. Shaojing Sun, Alan M Rubin, and Paul M Haridakis. 2008. The role of moti- vation and media involvement in explaining internet dependency. Journal of Broadcasting & Electronic Media 52, 3 (2008), 408-431.
  39. Keith S Taber. 2018. The use of Cronbach's alpha when developing and reporting research instruments in science education. Research in science education 48 (2018), 1273-1296.
  40. Stefano Valtolina and Mattia Marchionna. 2021. Design of a chatbot to assist the elderly. In International Symposium on End User Development. Springer, 153-168.
  41. Tianling Xie, Iryna Pentina, and Tyler Hancock. 2023. Friend, mentor, lover: does chatbot?engagement lead to psychological dependence? Journal of service management: JOSM (2023).
  42. Anbang Xu, Zhe Liu, Yufan Guo, Vibha Sinha, and Rama Akkiraju. 2017. A new chatbot for customer service on social media. In Proceedings of the CHI Conference on Human Factors in Computing Systems. 3506-3510.
  43. Hui-Jen Yang and Yun-Long Lay. 2011. The effects of service utility, security, trust and satisfaction on weblog social site dependency for young adults in Taiwan. International Journal of Information and Communication Technology 3, 4 (2011), 324-338.
  44. Xi Yang and Marco Aurisicchio. 2021. Designing conversational agents: A self- determination theory approach. In Proceedings of the CHI Conference on Human Factors in Computing Systems. 1-16.
  45. Xi Yang, Marco Aurisicchio, and Weston Baxter. 2019. Understanding affective experiences with conversational agents. In Proceedings of the CHI Conference on Human Factors in Computing Systems. 1-12.
  46. Jakub Złotowski, Diane Proudfoot, Kumar Yogeeswaran, and Christoph Bart- neck. 2015. Anthropomorphism: opportunities and challenges in human-robot interaction. International journal of social robotics 7 (2015), 347-360.

Cited by

Sorted by citations

Authors
Yu, Q., & Lan, X.
Affiliations
Fudan University (CN)
ORCID
Venue
Proceedings of the Twelfth International Symposium of Chinese CHI
Publisher
ACM
Published
2024-11-22
Pages
170-181
DOI
10.1145/3758871.3758884
OpenAlex
W4415685782
Cited by
12
References
12
Citations by year
2026: 7 · 2025: 5
FWCI
3.55
Type
conference paper
Language
en
Open access
Licence
cc-by
Locations
  • International Symposium of Chinese CHI (Chinese CHI) (publishedVersion, cc-by)
  • arXiv (Cornell University) (submittedVersion, cc-by)
  • arXiv (Cornell University) (cc-by)
Funders
National Natural Science Foundation of China (62402121)
Topics
  • Physical Sciences › Computer Science › Artificial Intelligence › AI in Service Interactions
  • Social Sciences › Psychology › Social Psychology › Social Robot Interaction and HRI
  • Physical Sciences › Computer Science › Human-Computer Interaction › Innovative Human-Technology Interaction
Keywords
anthropomorphism, media dependence, user satisfaction, conversational AI, large language models, chatbot personality

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