CoChat
Sign Up
Cited by 244AI in Service InteractionsSocial Robot Interaction and HRITechnology Adoption and User Behaviour

Uncanny Valley Effects on Chatbot Trust, Purchase Intention, and Adoption Intention in the Context of E-Commerce: The Moderating Role of Avatar Familiarity

Open access
Song, Stephen Wonchul, Shin, Mincheol·2024·International Journal of Human–Computer Interaction
DOI 10.1080/10447318.2022.2121038
Open this paper in CoChat — free account

Abstract

This study investigates the effect of chatbot humanization on the perception of eeriness, trust, and users’ behavioral intention. Specifically, this study employed a 2 (humanization of chatbot agent avatar: hyperrealistic-animated vs. cartoonish-still) × 2 (avatar familiarity: celebrity avatar vs. non-celebrity avatar) between-subjects experiment (N = 185), in which participants were asked to purchase a laptop from an e-commerce vendor by interacting with a chatbot agent. Based on predictions from the uncanny valley effect hypothesis (UVE), enhancing the human likeness of a chatbot agent through visual realism and animacy was predicted to negatively influence users’ trust in the chatbot agent and behavioral intention as a consequence of the activation of a negative affective state (i.e., a feeling of eeriness). Consistent with our predictions, the results from PLS-SEM showed that (a) enhancing the human likeness of a chatbot agent significantly increased users’ feeling of eeriness, (b) the feeling of eeriness negatively influenced users’ trust in the chatbot agent, c) trust, determined by the feeling of eeriness, significantly affected users’ purchase intention and willingness to reuse the chatbot, and d) the relationship between humanization and eeriness significantly moderated by the familiarity of the chatbot avatar. We discuss the theoretical implications of the current study on UVE as well as its practical implications for the implementation of anthropomorphized chatbot agents in the e-commerce context.

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Integrity signals

Registry

Crossref · 10.1080/10447318.2022.2121038

Cites

16 of 16 references verified0 citation errors · 0 not checkable

Statistics

Claims the paper makes

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

  • Design and channel factors (voice vs. text; initiation mode; anthropomorphic cues) influence perceived control and social presence, occasionally altering the direction of effects through reactance or satisfaction mechanisms.

    “Fourth, design and channel factors (voice vs. text; initiation mode; anthropomorphic cues) influence perceived control and social presence, occasionally altering the direction of effects through reactance or satisfaction mechanisms [7,24,31,32,52].”

    From From Recommendations to Delegation: A Systematic Review Mapping Agentic AI in E-Commerce and Its Consumer Effects (2026) · cites this paper as [32]

    The Literature review (§§2.1–2.3; page unavailable) discusses chatbot social presence and reports that anthropomorphic visual cues can have positive or negative effects, including uncanny-valley reactions. It does not establish effects of voice versus text or initiation mode, or explain changes in effect through perceived control, reactance, or satisfaction. (LLM, conf=0.86)

    Experimental — accuracy not yet measured

  • Purchase intention/adoption intention is mostly positive via utility/trust, but can decrease under high autonomy.

    “Mostly positive via utility/trust, but can decrease under high autonomy”

    From From Recommendations to Delegation: A Systematic Review Mapping Agentic AI in E-Commerce and Its Consumer Effects (2026) · cites this paper as [32]

    The Results (section 5.2; page unavailable) report that higher trust was associated with higher purchase intention and willingness to use the chatbot in the future. The paper also reports decreases linked to avatar-induced eeriness through trust, but the cited passages do not support utility as the pathway or high autonomy as the condition for a decrease. (LLM, conf=0.86)

    Experimental — accuracy not yet measured

  • The utility chain dominates: quality/usefulness leads to attitude/satisfaction, which leads to intention.

    “Utility chain dominates: quality/usefulness → at-titude/satisfaction → intention.”

    From From Recommendations to Delegation: A Systematic Review Mapping Agentic AI in E-Commerce and Its Consumer Effects (2026) · cites this paper as [32]

    Section 3.4 describes a beliefs → attitudes → behavioral-intention chain, and Section 6.1 reports that chatbot trust was related to purchase and reuse intentions. The paper’s tested pathway concerns eeriness and trust, not quality or usefulness leading to attitude or satisfaction; it also does not establish that a utility chain dominates. (LLM, conf=0.83)

    Experimental — accuracy not yet measured

  • High-autonomy cues can reduce intention via powerlessness/threat-to-freedom, unless urgency/scarcity raises benefit salience and attenuates resistance.

    “High-autonomy cues can reduce intention via powerless-raises benefit salience and attenuates resistance. ness/threat-to-freedom, unless urgency/scarcity”

    From From Recommendations to Delegation: A Systematic Review Mapping Agentic AI in E-Commerce and Its Consumer Effects (2026) · cites this paper as [32]

    The Discussion (section 6.1; page unavailable) links avatar eeriness to lower chatbot trust, and section 3.4 hypothesizes effects on purchase and reuse intentions. Neither passage addresses high-autonomy cues, powerlessness or threats to freedom, or urgency and scarcity attenuating resistance; searches for those terms found no supporting passage in our copy. (LLM, conf=0.76)

    Experimental — accuracy not yet measured

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The self-check needs the paper’s full text.

Cites

Flagged first, then by citations

Research works printed but not linked: 88

  1. Adamopoulou, E., & Moussiades, L. (2020). An overview of chatbot technology. In I. Maglogiannis, L. Iliadis, & E. Pimenidis (Eds.), FIP international conference on artificial intelligence applications and innovations (pp. 373–383). Springer International Publishing.
  2. Ajzen, I., & Fishbein, M. (1975). A Bayesian analysis of attribution processes. Psychological Bulletin, 82(2), 261–277. https://doi.org/10.1037/h0076477 · DOI · Paper page
  3. Akman, I., & Mishra, A. (2017). Factors influencing consumer intention in social commerce adoption. Information Technology & People, 30(2), 356–370. https://doi.org/10.1108/ITP-01-2016-0006 · DOI · Paper page
  4. Al-Natour, S., Benbasat, I., & Cenfetelli, R. (2011). The adoption of online shopping assistants: Perceived similarity as an antecedent to evaluative beliefs. Journal of the Association for Information Systems, 12(5), 347–374. https://doi.org/10.17705/1jais.00267 · DOI
  5. Amos, C., Holmes, G., & Strutton, D. (2008). Exploring the relationship between celebrity endorser effects and advertising effectiveness: A quantitative synthesis of effect size. International Journal of Advertising, 27(2), 209–234. https://doi.org/10.1080/02650487.2008.11073052 · DOI
  6. Araujo, T. (2018). Living up to the chatbot hype: The influence of anthropomorphic design cues and communicative agency framing on conversational agent and company perceptions. Computers in Human Behavior, 85, 183–189. https://doi.org/10.1016/j.chb.2018.03.051 · DOI · Paper page
  7. Ashfaq, M., Yun, J., Yu, S., & Loureiro, S. M. C. (2020). I, chatbot: Modeling the determinants of users’ satisfaction and continuance intention of AI-powered service agents. Telematics and Informatics, 54, 101473. https://doi.org/10.1016/j.tele.2020.101473 · DOI
  8. Bansal, H., & Khan, R. (2018). A review paper on human computer interaction. International Journal of Advanced Research in Computer Science and Software Engineering, 8(4), 53–56. https://doi.org/10.23956/ijarcsse.v8i4.630 · DOI
  9. Bergkvist, L., & Zhou, K. Q. (2016). Celebrity endorsements: A literature review and research agenda. International Journal of Advertising, 35(4), 642–663. https://doi.org/10.1080/02650487.2015.1137537 · DOI
  10. Bodenhausen, G. V., Sheppard, L. A., & Kramer, G. P. (1994). Negative affect and social judgment: The differential impact of anger and sadness. European Journal of Social Psychology, 24(1), 45–62. https://doi.org/10.1002/ejsp.2420240104 · DOI
  11. Brandtzaeg, P. B., & Følstad, A. (2017). Why people use chatbots. In I. Kompatsiaris, J. Cave, A. Satsiou, G. Carle, A. Passani, E. Kontopoulos, S. Diplaris, & D. McMillan (Eds.), International conference on internet science (pp. 377–392). Springer International Publishing.
  12. Buhr, T. A., Simpson, T. L., & Pryor, B. (1987). Celebrity endorsers’ expertise and perceptions of attractiveness, likability, and familiarity. Psychological Reports, 60(3_part_2), 1307–1309. https://doi.org/10.1177/0033294187060003-264.1 · DOI
  13. Burgoon, J. K. (1993). Interpersonal expectations, expectancy violations, and emotional communication. Journal of Language and Social Psychology, 12(1–2), 30–48. https://doi.org/10.1177/0261927X93121003 · DOI
  14. Burgoon, J. K., Bonito, J. A., Lowry, P. B., Humpherys, S. L., Moody, G. D., Gaskin, J. E., & Giboney, J. S. (2016). Application of expectancy violations theory to communication with and judgments about embodied agents during a decision-making task. International Journal of Human-Computer Studies, 91, 24–36. https://doi.org/10.1016/j.ijhcs.2016.02.002 · DOI
  15. Chai, J., Lin, J., Zadrozny, W., Ye, Y., Stys-Budzikowska, M., Horvath, V., Kambhatla, N., & Wolf, C. (2001). The role of a natural language conversational interface in online sales: A case study. International Journal of Speech Technology, 4(3/4), 285–295. https://doi.org/10.1023/A:1011316909641 · DOI
  16. Chaudhuri, A., & Holbrook, M. B. (2001). The chain of effects from brand trust and brand affect to brand performance: The role of brand loyalty. Journal of Marketing, 65(2), 81–93. https://doi.org/10.1509/jmkg.65.2.81.18255 · DOI · Paper page
  17. Chu, S. L., Fedorovskaya, E., Quek, F., & Snyder, J. (2013). The effect of familiarity on perceived interestingness of images [Paper presentation]. Proceedings of SPIE: The International Society for Optical Engineering, 8651, 86511C. https://doi.org/10.1117/12.2008551 · DOI
  18. Ciechanowski, L., Przegalinska, A., Magnuski, M., & Gloor, P. (2019). In the shades of the uncanny valley: An experimental study of human–chatbot interaction. Future Generation Computer Systems, 92, 539–548. https://doi.org/10.1016/j.future.2018.01.055 · DOI
  19. Ciechanowski, L., Przegalinska, A., & Wegner, K. (2017). The necessity of new paradigms in measuring human-chatbot interaction [Paper presentation]. International Conference on Applied Human Factors and Ergonomics.
  20. Clayton, R. B., & Leshner, G. (2015). The uncanny valley: The effects of rotoscope animation on motivational processing of depression drug messages. Journal of Broadcasting & Electronic Media, 59(1), 57–75. https://doi.org/10.1080/08838151.2014.998227 · DOI
  21. Cuomo, M. T., Foroudi, P., Tortora, D., Hussain, S., & Melewar, T. C. (2019). Celebrity endorsement and the attitude towards luxury brands for sustainable consumption. Sustainability, 11(23), 6791. https://doi.org/10.3390/su11236791 · DOI
  22. de Graaf, M. M., & Allouch, S. B. (2013). Exploring influencing variables for the acceptance of social robots. Robotics and Autonomous Systems, 61(12), 1476–1486. https://doi.org/10.1016/j.robot.2013.07.007 · DOI · Paper page
  23. Dehn, D. M., & Van Mulken, S. (2000). The impact of animated interface agents: A review of empirical research. International Journal of Human-Computer Studies, 52(1), 1–22. https://doi.org/10.1006/ijhc.1999.0325 · DOI
  24. Destephe, M., Brandao, M., Kishi, T., Zecca, M., Hashimoto, K., & Takanishi, A. (2015). Walking in the uncanny valley: Importance of the attractiveness on the acceptance of a robot as a working partner. Frontiers in Psychology, 6, 204. https://doi.org/10.3389/fpsyg.2015.00204 · DOI
  25. Deutsch, F. M., LeBaron, D., & Fryer, M. M. (1987). What is in a smile? Psychology of Women Quarterly, 11(3), 341–352. https://doi.org/10.1111/j.1471-6402.1987.tb00908.x · DOI
  26. Doney, P. M., & Cannon, J. P. (1997). An examination of the nature of trust in buyer–seller relationships. Journal of Marketing, 61(2), 35–51. https://doi.org/10.2307/1251829 · DOI · Paper page
  27. Ekman, P., & Friesen, W. V. (1969). Nonverbal leakage and clues to deception. Psychiatry, 32(1), 88–106.
  28. Erdogan, B. Z., Baker, M. J., & Tagg, S. (2001). Selecting celebrity endorsers: The practitioner’s perspective. Journal of Advertising Research, 41(3), 39–48. https://doi.org/10.2501/JAR-41-3-39-48 · DOI
  29. Ferrey, A. E., Burleigh, T. J., & Fenske, M. J. (2015). Stimulus-category competition, inhibition, and affective devaluation: A novel account of the uncanny valley. Frontiers in Psychology, 6, 249. https://doi.org/10.3389/fpsyg.2015.00249 · DOI
  30. Følstad, A., & Brandtzaeg, P. B. (2017). Chatbots and the new world of HCI. Interactions, 24(4), 38–42. https://doi.org/10.1145/3085558 · DOI
  31. Følstad, A., Nordheim, C. B., Bjørkli, C. A. (2018). What makes users trust a chatbot for customer service? An exploratory interview study. In S. S. Bodrunova (Ed.), International conference on internet science (pp. 194–208). Springer International Publishing.
  32. Følstad, A., & Skjuve, M. (2019). Chatbots for customer service: User experience and motivation [Paper presentation]. Proceedings of the 1st International Conference on Conversational User Interfaces, Dublin, Ireland. https://doi.org/10.1145/3342775.3342784 · DOI
  33. Garau, M., Slater, M., Vinayagamoorthy, V., Brogni, A., Steed, A., & Sasse, M. A. (2003). The impact of avatar realism and eye gaze control on perceived quality of communication in a shared immersive virtual environment [Paper presentation]. Proceedings of the SIGCHI Conference on Human Factors in Computing Systems (pp. 529–536). Association for Computing Machinery. https://doi.org/10.1145/642611.642703 · DOI · Paper page
  34. Garcia-Marques, T., & Mackie, D. M. (2001). The feeling of familiarity as a regulator of persuasive processing. Social Cognition, 19(1), 9–34. https://doi.org/10.1521/soco.19.1.9.18959 · DOI
  35. Gefen, D. (2000). E-commerce: The role of familiarity and trust. Omega, 28(6), 725–737. https://doi.org/10.1016/S0305-0483(00)00021-9 · DOI · Paper page
  36. Gnewuch, U., Morana, S., & Maedche, A. (2017). Towards designing cooperative and social conversational agents for customer service [Paper presentation]. International Conference on Information Systems, South Korea.
  37. Han, M. C. (2021). The impact of anthropomorphism on consumers’ purchase decision in chatbot commerce. Journal of Internet Commerce, 20(1), 46–65. https://doi.org/10.1080/15332861.2020.1863022 · DOI · Paper page
  38. Hassan, M., Iqbal, Z., & Khanum, B. (2018). The role of trust and social presence in social commerce purchase intention. Pakistan Journal of Commerce and Social Sciences (PJCSS), 12(1), 111–135.
  39. Hinds, P. J., Roberts, T. L., & Jones, H. (2004). Whose job is it anyway? A study of human-robot interaction in a collaborative task. Human-Computer Interaction, 19(1), 151–181. https://doi.org/10.1207/s15327051hci1901&2_7 · DOI
  40. Ho, C.-C., & MacDorman, K. F. (2010). Revisiting the uncanny valley theory: Developing and validating an alternative to the Godspeed indices. Computers in Human Behavior, 26(6), 1508–1518. https://doi.org/10.1016/j.chb.2010.05.015 · DOI
  41. Holzwarth, M., Janiszewski, C., & Neumann, M. M. (2006). The influence of avatars on online consumer shopping behavior. Journal of Marketing, 70(4), 19–36. https://doi.org/10.1509/jmkg.70.4.019 · DOI
  42. Hung-Chou, L., Te-Yung, C., & Su-Hui, K. (2018). Effects of social influence and system characteristics on traceable agriculture product reuse intention of elderly people: Integrating trust and attitude using the technology acceptance model. Journal of Research in Education Sciences, 63(3), 291. https://doi.org/10.6209/JORIES.201809_63 · DOI
  43. Jung, S. (2019). Semantic vector learning for natural language understanding. Computer Speech & Language, 56, 130–145. https://doi.org/10.1016/j.csl.2018.12.008 · DOI
  44. Kang, S.-H., & Watt, J. H. (2013). The impact of avatar realism and anonymity on effective communication via mobile devices. Computers in Human Behavior, 29(3), 1169–1181. https://doi.org/10.1016/j.chb.2012.10.010 · DOI
  45. Kennedy, M. S., Ferrell, L. K., & LeClair, D. T. (2001). Consumers’ trust of salesperson and manufacturer: An empirical study. Journal of Business Research, 51(1), 73–86. https://doi.org/10.1016/S0148-2963(99)00039-9 · DOI
  46. Kim, M., & Chang, B. (2020). The effect of service quality on the reuse intention of a chatbot: Focusing on user satisfaction, reliability, and Immersion. International Journal of Contents, 16(4), 1–15. https://doi.org/10.5392/IJoC.2020.16.4.001 · DOI
  47. Koda, T., Maes, P. (1996). Agents with faces: The effect of personification [Paper presentation]. Proceedings 5th IEEE International Workshop on Robot and Human Communication (pp. 189–194). IEEE.
  48. Komiak, S. X., & Benbasat, I. (2004). Understanding customer trust in agent-mediated electronic commerce, web-mediated electronic commerce, and traditional commerce. Information Technology and Management, 5(1/2), 181–207. https://doi.org/10.1023/B:ITEM.0000008081.55563.d4 · DOI
  49. Kruschwitz, U., De Roeck, A., Scott, P., Steel, S., Turner, R., Webb, N. (1999). Natural language access to yellow pages [Paper presentation]. Proceedings of 1999 Third International Conference on Knowledge-Based Intelligent Information Engineering Systems (pp. 34–37). IEEE.
  50. Laksmidewi, D., & Soelasih, Y. (2019). Anthropomorphic green advertising: How to enhance consumers’ environmental concern. DLSU Business & Economics Review, 29(1), 72–84.
  51. Lee, E.-J. (2010). The more humanlike, the better? How speech type and users’ cognitive style affect social responses to computers. Computers in Human Behavior, 26(4), 665–672. https://doi.org/10.1016/j.chb.2010.01.003 · DOI
  52. Lee, K. M., Park, N., & Song, H. (2005). Can a robot be perceived as a developing creature? Effects of a robot’s long‐term cognitive developments on its social presence and people’s social responses toward it. Human Communication Research, 31(4), 538–563. https://doi.org/10.1111/j.1468-2958.2005.tb00882.x · DOI
  53. Liebrecht, C., Sander, L., & Van Hooijdonk, C. (2020). Too informal? How a chatbot’s communication style affects brand attitude and quality of interaction. [Paper presentation]. Conversations 2020: 4th International Workshop on Chatbot Research, Netherlands.
  54. MacDorman, K. F., Srinivas, P., & Patel, H. (2013). The uncanny valley does not interfere with level 1 visual perspective taking. Computers in Human Behavior, 29(4), 1671–1685. https://doi.org/10.1016/j.chb.2013.01.051 · DOI · Paper page
  55. Mathur, M. B., & Reichling, D. B. (2009). An uncanny game of trust: Social trustworthiness of robots inferred from subtle anthropomorphic facial cues [Paper presentation]. 2009 4th ACM/IEEE International Conference on Human-Robot Interaction (HRI), La Jolla, CA.
  56. Matsuda, Y.-T., Okamoto, Y., Ida, M., Okanoya, K., & Myowa-Yamakoshi, M. (2012). Infants prefer the faces of strangers or mothers to morphed faces: An uncanny valley between social novelty and familiarity. Biology Letters, 8(5), 725–728. https://doi.org/10.1098/rsbl.2012.0346 · DOI
  57. McKnight, D. H., Choudhury, V., & Kacmar, C. (2002). Developing and validating trust measures for e-commerce: An integrative typology. Information Systems Research, 13(3), 334–359. https://doi.org/10.1287/isre.13.3.334.81 · DOI · Paper page
  58. Mende, M. A., Fischer, M. H., & Kühne, K. (2019). The use of social robots and the uncanny valley phenomenon. In Y. Zhou & M. H. Fischer (Eds.), AI love you: Developments in human-robot intimate relationships (pp. 41–73). Springer International Publishing.
  59. Moreland, R. L., & Zajonc, R. B. (1982). Exposure effects in person perception: Familiarity, similarity, and attraction. Journal of Experimental Social Psychology, 18(5), 395–415. https://doi.org/10.1016/0022-1031(82)90062-2 · DOI
  60. Mori, M., MacDorman, K. F., & Kageki, N. (2012). The uncanny valley [from the field]. IEEE Robotics & automation magazine, 19(2), 98–100.
  61. Mori, M. (1970). The uncanny valley. Energy, 7(4), 33–35.
  62. Morwitz, V. G., Steckel, J. H., & Gupta, A. (2007). When do purchase intentions predict sales? International Journal of Forecasting, 23(3), 347–364. https://doi.org/10.1016/j.ijforecast.2007.05.015 · DOI
  63. Nowak, K. L. (2006). The influence of anthropomorphism and agency on social judgment in virtual environments. Journal of Computer-Mediated Communication, 9(2), JCMC925. https://doi.org/10.1111/j.1083-6101.2004.tb00284.x · DOI
  64. Nowak, K. L. (2013). Choosing Buddy Icons that look like me or represent my personality: Using Buddy Icons for social presence. Computers in Human Behavior, 29(4), 1456–1464. https://doi.org/10.1016/j.chb.2013.01.027 · DOI
  65. Nowak, K. L., & Fox, J. (2018). Avatars and computer-mediated communication: A review of the definitions, uses, and effects of digital representations. Review of Communication Research, 6, 30–53. https://doi.org/10.12840/issn.2255-4165.2018.06.01.015 · DOI
  66. Nowak, K. L., Hamilton, M. A., & Hammond, C. C. (2009). The effect of image features on judgments of homophily, credibility, and intention to use as avatars in future interactions. Media Psychology, 12(1), 50–76. https://doi.org/10.1080/15213260802669433 · DOI
  67. Pan, Y., & Zinkhan, G. M. (2006). Determinants of retail patronage: A meta-analytical perspective. Journal of Retailing, 82(3), 229–243. https://doi.org/10.1016/j.jretai.2005.11.008 · DOI · Paper page
  68. Parise, S., Kiesler, S., Sproull, L., & Waters, K. (1999). Cooperating with life-like interface agents. Computers in Human Behavior, 15(2), 123–142. https://doi.org/10.1016/S0747-5632(98)00035-1 · DOI
  69. Pavlikova, L., Schmid, B. F., Maass, W., & MüLler, J. P. (2003). Editorial: Software agents. Electronic Markets, 13(1), 1–2. https://doi.org/10.1080/1019678032000062195 · DOI
  70. Pérez-Marín, D., & Pascual-Nieto, I. (2013). An exploratory study on how children interact with pedagogic conversational agents. Behaviour & Information Technology, 32(9), 955–964. https://doi.org/10.1080/0144929X.2012.687774 · DOI
  71. Rapp, A., Curti, L., & Boldi, A. (2021). The human side of human-chatbot interaction: A systematic literature review of ten years of research on text-based chatbots. International Journal of Human-Computer Studies, 151, 102630. https://doi.org/10.1016/j.ijhcs.2021.102630 · DOI
  72. Reeves, B., & Nass, C. (1996). The media equation: How people treat computers, television, and new media like real people. Cambridge university Press.
  73. Sansonnet, J.-P., Leray, D., & Martin, J.-C. (2006). Architecture of a framework for generic assisting conversational agents. In J. Gratch, M. Young, R. Aylett, D. Ballin, & P. Olivier (Eds.), Proceedings of the 6th international conference on intelligent virtual agents (pp. 145–156). Springer.
  74. Shawar, A. B., & Atwell, E. (2007). Chatbots: Are they really useful? LDV Forum, 22(1), 29–49.
  75. Shawar, A. B., & Atwell, E. (2015). ALICE chatbot: Trials and outputs. Computación y Sistemas, 19(4), 625–632. https://doi.org/10.13053/CyS-19-4-2326 · DOI
  76. Shin, M., Song, S. W., & Chock, T. M. (2019). Uncanny valley effects on friendship decisions in virtual social networking service. Cyberpsychology, Behavior, and Social Networking, 22(11), 700–705. https://doi.org/10.1089/cyber.2019.0122 · DOI
  77. Short, J., Williams, E., & Christie, B. (1976). The social psychology of telecommunications. Wiley.
  78. Sidaoui, K., Jaakkola, M., & Burton, J. (2020). AI feel you: Customer experience assessment via chatbot interviews. Journal of Service Management, 31(4), 745–766. https://doi.org/10.1108/JOSM-11-2019-0341 · DOI
  79. Sivaramakrishnan, S., Wan, F., & Tang, Z. (2007). Giving an “e‐human touch” to e‐tailing: The moderating roles of static information quantity and consumption motive in the effectiveness of an anthropomorphic information agent. Journal of Interactive Marketing, 21(1), 60–75. https://doi.org/10.1002/dir.20075 · DOI
  80. Sundar, S. S. (2008). The MAIN model: A heuristic approach to understanding technology effects on credibility. In M. J. Metzger & A. J. Flanagin (Eds.), Digital media, youth, and credibility (pp. 73–100) The MIT Press.
  81. Torre, I., Carrigan, E., McDonnell, R., Domijan, K., McCabe, K., Harte, N. (2019). The effect of multimodal emotional expression and agent appearance on trust in human-agent interaction [Paper presentation]. Proceedings of the 2019 Motion, Interaction and Games (pp. 1–6). Association for Computing Machinery.
  82. Traylor, M. B. (1981). Product involvement and brand commitment. Journal of Advertising Research, 21(6), 51–56.
  83. Voth, D. (2005). Practical agents help out [virtual agent system. IEEE Intelligent Systems, 20(2), 4–6. https://doi.org/10.1109/MIS.2005.35 · DOI
  84. Wang, S., & Rochat, P. (2017). Human perception of animacy in light of the uncanny valley phenomenon. Perception, 46(12), 1386–1411.
  85. Wang, Y. D., & Emurian, H. H. (2005). An overview of online trust: Concepts, elements, and implications. Computers in Human Behavior, 21(1), 105–125. https://doi.org/10.1016/j.chb.2003.11.008 · DOI
  86. Weber, G. M. (2005). Data representation and algorithms for biomedical informatics applications [Ph.D. thesis]. ProQuest Dissertations & Theses Global. Harvard University.
  87. Weisman, W. D., & Peña, J. F. (2021). Face the uncanny: The effects of doppelganger talking head avatars on affect-based trust toward artificial intelligence technology are mediated by uncanny valley perceptions. Cyberpsychology, Behavior, and Social Networking, 24(3), 182–187. https://doi.org/10.1089/cyber.2020.0175 · DOI
  88. Yuan, L., & Dennis, A. R. (2019). Acting like humans? Anthropomorphism and consumer’s willingness to pay in electronic commerce. Journal of Management Information Systems, 36(2), 450–477. https://doi.org/10.1080/07421222.2019.1598691 · DOI

Cited by

Sorted by citations

Authors
Song, S. W., & Shin, M.
Affiliations
  • University of South Florida St. Petersburg (US)
  • Tilburg University (NL)
Corresponding author
Shin, Mincheol
ORCID
Venue
International Journal of Human–Computer Interaction
Publisher
Informa UK Limited
Published
2022-09-14
Volume
40
Issue
2
Pages
441-456
ISSN
  • 1044-7318 (ISSN-L)
  • 1532-7590
DOI
10.1080/10447318.2022.2121038
OpenAlex
W4295665710
Cited by
244
References
16
Citations by year
2026: 81 · 2025: 85 · 2024: 54 · 2023: 24
FWCI
13.41
Type
journal article
Language
en
Open access
Licence
Locations
  • International Journal of Human-Computer Interaction (publishedVersion, cc-by-nc-nd)
  • Research portal (Tilburg University) (publishedVersion, cc-by)
  • Data Archiving and Networked Services (DANS) (submittedVersion, cc-by)
Funders
Tilburg University
Topics
  • Physical Sciences › Computer Science › Artificial Intelligence › AI in Service Interactions
  • Social Sciences › Psychology › Social Psychology › Social Robot Interaction and HRI
  • Social Sciences › Decision Sciences › Information Systems and Management › Technology Adoption and User Behaviour
Keywords
uncanny valley, purchase intention, animated avatars, eeriness, anthropomorphism, e-commerce chatbots

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review