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Factors driving Fashion Chatbot Reliability -Focusing on the Mediating Effect of Perceived Intelligence and Positive Cognition-

패션상품 챗봇에 대한 신뢰 형성 요인 - 지각된 지능과 긍정적 인지의 매개효과를 중심으로 -

  • Lee, Ha Kyung (Dept. of Clothing & Textiles, Chungnam National University) ;
  • Yoon, Namhee (Human Ecology Research Center, Korea University)
  • 이하경 (충남대학교 의류학과) ;
  • 윤남희 (고려대학교 생활과학연구소)
  • Received : 2022.03.24
  • Accepted : 2022.04.27
  • Published : 2022.04.30

Abstract

This study explores the effect of anthropomorphism on fashion chatbot reliability, mediated by perceived intelligence and cognitive evaluation. The moderating effects of individuals' need for human interaction between chatbot anthropomorphism and perceived intelligence, cognitive evaluation, and chatbot reliability are also explored. Participants, who were recruited through the online research firm, responded to questions after watching a video clip showing a conversation with a fashion chatbot on a mobile screen. The data were collected through Mturk, a crowdsourcing platform with an online research panel. All responses (N = 212) were analyzed using SPSS 26.0 for the descriptive statistics, frequency analysis, reliability analysis, exploratory factor analysis, and PROCESS procedure. The results demonstrate that chatbot anthropomorphism increases chatbot reliability, and this is mediated by chatbot intelligence. Although chatbot anthropomorphism increases cognitive evaluation, the effect of cognitive evaluation on chatbot reliability is not significant; thereby, the effect of chatbot anthropomorphism on chatbot reliability is not mediated by the cognitive evaluation. The direct effect of anthropomorphism on chatbot reliability is also moderated by individuals' need for human interaction. For participants with a high need for human interaction, chatbot anthropomorphism increases chatbot reliability; however, anthropomorphism does not significantly affect chatbot reliability for participants with a low need for human interaction. The study's findings contribute to expanding the literature on consumers' new technology acceptance by testing the antecedents affecting service reliability.

Keywords

Acknowledgement

이 논문은 2020년 대한민국 교육부와 한국연구재단의 지원을 받아 수행된 연구임 (NRF-2020S1A5B5A17091064).

References

  1. Anselmsson, J. (2001). Customer-perceived service quality and technology-based self-service. Unpublished doctoral dissertation, Lurid University, Lund
  2. Baron, R. M., & Kenny, D. A. (1986). The moderator-mediator variable distinction in social psychological research - Conceptual, strategic, and statistical considerations. Journal of Personality and Social Psychology, 51(6), 1173-1182. doi:10.1037/0022-3514.51.6.1173
  3. Bartneck, C., Kulic, D., Croft, E., & Zoghbi, S. (2009). Measurement instruments for the anthropomorphism, animacy, likeability, perceived intelligence, and perceived safety of robots. International Journal of Social Robotics, 1(1), 71-81. doi:10.1007/s12369-008-0001-3
  4. Bitner, M. J., Booms, B. H., & Tetreault, M. S. (1990). The service encounter - Diagnosing favorable and unfavorable incidents. Journal of Marketing, 54(1), 71-84. doi:10.1177/002224299005400105
  5. Blut, M., Wang, C., & Schoefer, K. (2016). Factors influencing the acceptance of self-service technologies - A meta-analysis. Journal of Service Research, 19(4), 396-416. doi:10.1177/1094670516662352
  6. Blut, M., Wang, C., Wunderlich, N. V., & Brock, C. (2021). Understanding anthropomorphism in service provision - A meta-analysis of physical robots, chatbots, and other AI. Journal of the Academy of Marketing Science, 49(4), 632-658. doi:10.1007/s11747-020-00762-y
  7. Bohner, G. & Wanke, M. (2002), Attitudes and Attitude Change. New York, NY: Psychology Press Ltd.
  8. Bolton, R. N., McColl-Kennedy, J. R., Cheung, L., Gallan, A., Orsingher, C., Witell, L., & Zaki, M. (2018). Customer experience challenges. Journal of Service Management, 29(5), 776-808. doi:10.1108/JOSM-04-2018-0113
  9. Breakwell, G. M., Fife-Schaw, C., Lee, T., & Spencer, J. (1986). Attitudes to new technology in relation to social beliefs and group memberships - A preliminary investigation. Current Psychological Research & Reviews, 5(1), 34-47. https://doi.org/10.1007/BF02686595
  10. Broadbent, E., Jayawardena, C., Kerse, N., Stafford, R.Q., & MacDonald, B.A. (2011, August). Human-robot interaction research to improve quality of life in elder care - An approach and issues. Paper presented at Workshops at the Twenty-Fifth AAAI Conference on Artificial Intelligence, San Francisco, CA, pp. 13-19
  11. Butler, B. S., & Gray, P. H. (2006). Reliability, mindfulness, and information systems. MIS Quarterly, 30(2), 211-224. doi:10.2307/25148728
  12. Byun, S. H., & Cho, C. H. (2020). The effect of the anthropomorphism level and personalization level on AI financial chatbot recommendation messages on customer response. The Korean Journal of Advertising and Public Relations, 22(2), 466-502. doi:10.16914/kadpr.2020.22.2.466
  13. Canning, C., Donahue, T. J., & Scheutz, M. (2014). Investigating human perceptions of robot capabilities in remote human-robot team tasks based on first-person robot video feeds. Proceedings of 2014 IEEE/RSJ International Conference on Intelligent Robots and Systems, USA, pp.4354-4361. doi:10.1109/IROS.2014.6943178.
  14. Cho, G., & Yun, J. Y. (2019). UX evaluation of financial service chatbot interactions. Journal of the HCI Society of Korea, 14(2), 61-69. doi:10.17210/jhsk.2019.05.14.2.61
  15. Choi, M. Y. (2021). The effect of personalized product recommendation service of online fashion shopping mall on service use behaviors through cognitive attitude and emotional attachment. Fashion and Textile Research Journal, 23(5), 586-597. doi.org/10.5805/SFTI.2021.23.5.586
  16. Collier, J. E. & Kimes, S. E. (2013). Only if it is convenient: Understanding how convenience influences self-service technology evaluation. Journal of Service Research, 16(1), 39-51. doi:10.1177/1094670512458454
  17. Dabholkar, P. A. (1996). Consumer evaluations of new technology-based self-service options - An investigation of alternative models of service quality. International Journal of Research in Marketing, 13(1), 29-51. doi:10.1016/0167-8116(95)00027-5
  18. Davis, F. D., Bagozzi, R. P., & Warshaw, P. R. (1989). User acceptance of computer technology: A comparison of two theoretical models. Management Science, 35(8), 982-1003. doi: 10.1287/mnsc.35.8.982
  19. DiSalvo, C. F., Gemperle, F., Forlizzi, J., & Kiesler, S. (2002). All robots are not created equal - The design and perception of humanoid robot heads. Proceedings of the 4th Conference on Designing Interactive Systems: Processes, Practices, Methods, and Techniques, England, pp.321-326. doi:10.1145/778712.778756
  20. Epley, N., Waytz, A., & Cacioppo, J. T. (2007). On seeing human: A three-factor theory of anthropomorphism. Psychological Review, 114(4), 864-886. doi:10.1037/0033-295X.114.4.864
  21. Erebak, S., & Turgut, T. (2019). Caregivers' attitudes toward potential robot coworkers in elder care. Cognition, Technology and Work, 21(2), 327-336. doi:10.1007/s10111-018-0512-0
  22. Evans, K.R., & Brown, S.W. (1988). Strategic options for service delivery systems. In C.A. Ingene & G.L. Frazier (Eds.), Proceedings of the AMA Summer Educators' Conference (pp. 207-212). Chicago, IL: American Marketing Association. doi:10.1007/978-3-642-36172-2_200957.
  23. Evanschitzky, H., Iyer, G. R., Pillai, K. G., Kenning, P., & Schutte, R. (2015). Consumer trial, continuous use, and economic benefits of a retail service innovation - The case of the personal shopping assistant. Journal of Product Innovation Management, 32(3), 459-475. doi:10.1111/jpim.12241
  24. Fernandes, T., & Pedroso, R. (2017). The effect of selfcheckout quality on customer satisfaction and repatronage in a retail context. Service Business, 11(1), 69-92. doi:10.1007/s11628-016-0302-9
  25. Fishbein, M. (1963). An investigation of the relationships between beliefs about an object and the attitude toward that object. Human Relations, 16(3), 233-239. https://doi.org/10.1177/001872676301600302
  26. Frijda, N. H. (1993). Moods, emotion episodes, and emotions. In M. Lewis & J. M. Haviland (Eds.), Handbook of Emotions (pp. 381-403). New York, NY: The Guilford Press.
  27. Gardner, L., & Leshner, G. (2016). The role of narrative and other-referencing in attenuating psychological reactance to diabetes self-care messages. Health Communication, 31(6), 738-751. doi:10.1080/10410236.2014.993498
  28. Gardner, M. P. (1985). Mood states and consumer behavior - A critical review. Journal of Consumer Research, 12(3), 281-300. doi:10.1086/208516
  29. Hayes, A. F. (2021). Introduction to mediation, moderation, and conditional process analysis - A regression-based approach (3rd ed.). New York, NY: Guilford Publications.
  30. Hirschman, E. C., & Holbrook, M. B. (1982). Hedonic consumption - Emerging concepts, methods and propositions. Journal of Marketing, 46(3), 92-101. doi:10.1177/002224298204600314
  31. Huang, M. H., & Rust, R. T. (2018). Artificial intelligence in service. Journal of Service Research, 21(2), 155-172. doi:10.1177/1094670517752459
  32. Hwang, M. H., Lee, W. S., Hwang, H., Park, Y. S., Lim, Y. K., & Jeon, B. Y. (2021). Designing and validating chatbot counseling algorithms to alleviate smartphone addiction among adolescents. Journal of the HCI Society of Korea, 16(4), 33-42. doi:10.17210/jhsk.2021.12.16.4.33
  33. Jeong, S. W., & Park, J. S. (2020). Impacts of technology anxiety and perceived productivity on attitude toward self-service technology - The moderating role of need for interaction. The Research Journal of the Costume Culture, 28(4), 480-491. doi:10.29049/rjcc.2020.28.4.480
  34. Jeong, S. G., Hur, H. J., & Choo, H. J. (2020). The effect of fashion shopping chatbot characteristics on service acceptance intention - Focusing on anthropomorphism and personalization-. Journal of the Korean Society of Clothing and Textiles, 44(4), 573-593. doi:10.5850/JKSCT.2020.44.4.573
  35. Katsyri, J., Forger, K., Makarainen, M., & Takala, T. (2015). A review of empirical evidence on different uncanny valley hypotheses. Frontiers in Psychology, 6, 1-16. doi:10.3389/fpsyg.2015.00390
  36. Kim, A., Cho, M., Ahn, J., & Sung, Y. (2019). Effects of gender and relationship type on the response to artificial intelligence. Cyberpsychology, Behavior and Social Networking, 22(4), 249-253. doi:10.1089/cyber.2018.0581
  37. Kang, S. Hyun, B. E., & Kim, G. S. (2020). A study on the integration process between chatbot builder and online shopping mall for big data search - Focused on Kakao AI platform. Journal of East and Central Asian Studies, 31(3), 31-46.
  38. Kim, M., Yeom, J. Y., Hung, H., & Lim, C. I. (2021a). A review of research on artificial intelligence chatbot in education through the lens of activity theory. The Journal of Educational Information and Media, 27(2), 699-721. doi:10.15833/KAFEIAM.27.2.699
  39. Kim, O. K., & Yun, J. Y. (2019). A convergence study on the chatbot (voice-based/messenger-based) in mobile shopping and user experience in app services. The Korean Society of Science & Art, 37(2), 47-59. doi:10.17548/ksaf.2019.03.30.47
  40. Kim, T., Cha, H. S., Park, C., & Wi. J. H. (2020). Identifying factors affecting chatbot use intention of online shopping mall users. Knowledge Management Research, 21(4), 211-225. doi:10.15813/kmr.2020.21.4.011
  41. Kim, T. M., Jo, J. I., & Kim, J. G. (2021b). A cloud-based ordering chatbot for retail stores. Journal of Information Technology and Architecture, 18(2), 137-146. doi: 10.22865/jita.2021.18.2.137
  42. Korolov, M. (2021, October 12). "2028년까지 연 35%씩 성장"... 꼭 알아야 할 'AI 챗봇' 상식 ["Growth 35% per year by 2028" ... common sense of 'AI Chatbot' that you must know]. CIO Korea. Retrieved March 12, 2022, from https://www.ciokorea.com/news/210374
  43. Ledingham, J. A. (1984). Are consumers ready for the information age. Journal of Advertising Research, 24(4), 31-37.
  44. Lee, H. (2018). A study on the optimal interaction for robot personification - Focusing on home service robots. Unpublished master's thesis, Ewha Womans University, Seoul.
  45. Lee, H., & Leonas, K. K. (2021). Millennials' intention to use self-checkout technology in different fashion retail formats - Perceived benefits and risks. Clothing and Textiles Research Journal, 39(4), 264-280. doi:10.1177/0887302X20926577
  46. Lee, H. J., Fairhurst, A., & Cho, H. J. (2013). Gender differences in consumer evaluations of service quality - Self-service kiosks in retail. The Service Industries Journal, 33(2), 248-265. doi:10.1080/02642069.2011.614346
  47. Lee, J. M., Jung, M., Lee, J., Kim, Y. E., & An, C. (2019). Consumer perception and adoption intention of artificial intelligent speaker - Non-users perspective. Journal of Consumer Studies, 30(2), 193-213. doi:10.35736/JCS.30.2.9
  48. Lee, S. K., & Yun, J. Y. (2019). A convergence study on chatbot persona and user experience of financial service - Focused on loan service. The Korean society of Science & Art, 37(4), 257-267. doi:10.17548/ksaf.2019.09.30.257
  49. McDuff, D., & Czerwinski, M. (2018). Designing emotionally sentient agents. Communications of the ACM, 61(12), 74-83. doi:10.1145/3186591
  50. Mende, M., Scott, M. L., van Doorn, J., Grewal, D., & Shanks, I. (2019). Service robots rising - How humanoid robots influence service experiences and elicit compensatory consumer responses. Journal of Marketing Research, 56(4), 535-556. doi:10.1177/0022243718822827
  51. Meuter, M. L., Ostrom, A. L., Bitner, M. J., & Roundtree, R. (2003). The influence of technology anxiety on consumer use and experiences with self-service technologies. Journal of Business Research, 56(11), 899-906. doi:10.1016/S0148-2963(01)00276-4
  52. Meyer-Waarden, L., Pavone, G., Poocharoentou, T., Prayatsup, P., Ratinaud, M., Tison, A., & Torne, S. (2020). How service quality influences customer acceptance and usage of chatbots. SMR-Journal of Service Management Research, 4(1), 35-51. doi:10.15358/2511-8676-2020-1-35
  53. Miao, F., Kozlenkova, I. V., Wang, H., Xie, T., & Palmatier, R. W. (2022). An emerging theory of avatar marketing. Journal of Marketing, 86(1), 67-90. doi:10.1177/0022242921996646
  54. Mori, M. (1970). The uncanny valley. Energy, 7(4), 33-35.
  55. Novak, T. P., & Hoffman, D. L. (2019). Relationship journeys in the internet of things - A new framework for understanding interactions between consumers and smart objects. Journal of the Academy of Marketing Science, 47(2), 216-237. doi:10.1007/s11747-018-0608-3
  56. Pan, L. Y., & Chiou, J. S. (2011). How much can you trust online information? Cues for perceived trustworthiness of consumer-generated online information. Journal of Interactive Marketing, 25(2), 67-74. doi:10.1016/j.intmar.2011.01.002
  57. Parasuraman, A., Zeithaml, V. A., & Berry, L. L. (1985). A conceptual model of service quality and its implications for future research. Journal of Marketing, 49(4), 41-50. doi:10.1177/002224298504900403
  58. Parasuraman, A., Zeithaml, V. A., & Berry, L. (1988). SERVQUAL - A multiple-item scale for measuring consumer perceptions of service quality. In J. Dawson, A. Findlay, & L. Sparks (Eds), The Retailing Reader (pp. 12-40). New York, NY: Routledge.
  59. Park, H. J. (2020a). A study on the effectiveness of chat-bot service on service value and service acceptance attitude - Case study of "D" Airlines. International Journal of Tourism and Hospitality Research, 34(11), 111-124. doi:10.21298/IJTHR.2020.11.34.11.11
  60. Park, J. (2020b, July 14). "조건 갖춰졌다", 지금이 챗봇 도입 적기 ["The conditions are met". This is the right time to introduce a chatbot]. IT Daily. Retrieved March 2, 2022, from http://www.itdaily.kr/news/articleView.html?idxno=101902
  61. Park, J. H., Yun G. I., & Min, S. T. (2019). Trends in artificial intelligence-based chatbot system technology. Korea Information Processing Society Review, 26(2), 39-46.
  62. Park, M. J. (2013). The effect of information quality on consumers' cognitive, emotional and behavioral responses in group-buying social commerce -focused on technology acceptance model-. Journal of the Korean Society of Design Culture, 19(3), 293-303.
  63. Preacher, K. J., & Hayes, A. F. (2004). SPSS and SAS procedures for estimating indirect effects in simple mediation models. Behavior Research Methods, Instruments, & Computers, 36(4), 717-731. doi:10.3758/BF03206553
  64. Qiu, H., Li, M., Shu, B., & Bai, B. (2020). Enhancing hospitality experience with service robots. Journal of Hospitality Marketing and Management, 29(3), 247-268. doi:10.1080/19368623.2019.1645073
  65. Seo, J. P. (2019, February 15). 개인 패션 코디네이터 '챗봇'이 온다 [Personal fashion coordinator "Chatbot" is coming]. Fashion Insight. Retrieved March 10, 2022, from http://www.fi.co.kr/main/view.asp?idx=65190
  66. Sobel, M. E. (1982). Asymptotic confidence intervals for indirect effects in structural equation models. Sociological Methodology, 13, 290-312. doi:10.2307/270723
  67. Solomon, M. R., Surprenant, C., Czepiel, J. A., & Gutman, E. G. (1985). A role theory perspective on dyadic interactions - The service encounter. Journal of Marketing, 49(1), 99-111. doi:10.1177/002224298504900110
  68. Song, Y. J., & Choi, S. M. (2020). The effects of chatbots' anthropomorphism and self-disclosure on consumers' perceptions of and attitude toward the chatbots. Journal of the HCI Society of Korea, 15(1), 17-28. doi:10.17210/jhsk.2020.03.15.1.17
  69. Stroessner, S. J., & Benitez, J. (2019). The social perception of humanoid and non-humanoid robots - Effects of gendered and machinelike features. International Journal of Social Robotics, 11(2), 305-315. doi:10.1007/s12369-018-0502-7
  70. Sung, Y. S., & Park, E. (1995). 광고에 대한 감정의 유형화: 유발된 감정과 느낀 감정 [Types of emotions about advertising - Triggered and natural emotions], The Korean Journal of Advertising, 6(2), 7-49.
  71. van Doorn, J., Mende, M., Noble, S. M., Hulland, J., Ostrom, A. L., Grewal, D., & Petersen, J. A. (2017). Domo arigato Mr. Roboto - Emergence of automated social presence in organizational frontlines and customers' service experiences. Journal of Service Research, 20(1), 43-58. doi:10.1177/1094670516679272
  72. van Pinxteren, M. M. E., Ruud, W. H., Wetzels, J. R., Pluymaekers, M., & Wetzels, M. (2019). Trust in humanoid robots: implications for services marketing. Journal of Services Marketing, 33(4), 507-518. doi:10.1007/s11747-020-00762-y
  73. Wilson, E. J., & Sherrell, D. L. (1993). Source effects in communication and persuasion research: A meta-analysis of effect size. Journal of the Academy of Marketing Science, 21(2), 101-112. doi:10.1007/BF02894421
  74. Wirtz, J., Patterson, P. G., Kunz, W. H., Gruber, T., Lu, V. N., Paluch, S., & Martins, A. (2018). Brave new world - Service robots in the frontline. Journal of Service Management, 29(50), 907-931. doi:10.1108/JOSM-04-2018-0119
  75. Wright, P. L. (1973). The cognitive processes mediating acceptance of advertising. Journal of Marketing Research, 10(1), 53-62. doi:10.2307/3149409
  76. Yoo, H., & Lee, J. (2019). A study on the development of interaction design framework based on personality of customized chatbot design. Journal of Integrated Design Research, 18(1), 77-94. doi:10.21195/jidr.2019.18.1.005
  77. Yoo, J. (2020). Design and implementation of library chatbot for non-face-to-face reference services. Korean Society for Information Management, 37(4), 151-179. doi:10.3743/KOSIM.2020.37.4.151
  78. Yoon, Y. (2021). Prospects of using AI chabots in teaching speaking in primary English - With special reference to dialogflow. The Journal of Korea Elementary Education, 32, 15-28. doi:10.20972/kjee.32..202107.15
  79. Youn, S., & Kim, S. (2019). Understanding ad avoidance on Facebook - Antecedents and outcomes of psychological reactance. Computers in Human Behavior, 98, 232-244. doi:10.1016/j.chb.2019.04.025
  80. Zajonc, R. B. (1980). Feeling and thinking - Preferences need no inferences. American Psychologist, 35(2), 151-175. doi: 10.1037/0003-066X.35.2.151
  81. Zeithaml, V. A., & Gilly, M. C. (1987). Characteristics affecting the acceptance of retailing technologies - A comparison of elderly and nonelderly consumers. Journal of Retailing, 63(1), 49-68.
  82. Zeithaml, V. A., Parasuraman, A., & Malhotra, A. (2000). A conceptual framework for understanding e-service quality - Implications for future research and managerial practice(Vol. 115). Cambridge, MA: Marketing Science Institute.