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금융 챗봇 서비스의 사용 의도에 대한 질적 탐색

A Qualitative Exploration of Intentions of Financial Chatbot Service

  • Kim, Wonil (College of Business Administration, Chonnam National University) ;
  • Yoon, Hyun Shik (College of Business Administration, Chonnam National University)
  • 투고 : 2021.08.27
  • 심사 : 2021.11.20
  • 발행 : 2021.11.28

초록

최근 금융사는 영업점 축소와 비대면 서비스의 확대 추세와 맞물려 챗봇 서비스의 활성화를 추진하고 있다. 그러나 기술적 한계와 이를 둘러싼 내·외부 환경의 제약이 존재하는 상황에서 일시에 챗봇 서비스를 확대하기는 어렵다. 따라서 챗봇 서비스의 제반 상황을 분석하여 단계별로 발생 가능한 문제를 선제적으로 확인하고 해결방안을 모색할 필요가 있다. 이에, 본 연구는 금융 챗봇 서비스 사용자의 사용 의도 및 행동을 고찰하기 위해 현장 실무자 및 연구자 12명을 대상으로 인터뷰를 진행하고, 이를 계획된 행동이론(Theory of Planned Behaviors, 이하 TPB)으로 해석하였다. 연구 결과, 사용자들은 챗봇 사용 경험을 통해 갖게 된 편리함이나 불편함 등의 '감정 및 태도', 군중 심리나 타인의 공감을 갈망하는 심리 등의 '주관적 규범', 챗봇 사용 과정의 어려움이나 편리함에 대한 인식에 따른 '행동 통제' 등의 특성이 드러났다. 이를 통해 이 특성이 사용자의 챗봇 서비스에 대한 지속적 사용 의도와 실제 행동에 영향을 미칠 수 있음을 알 수 있었다. 후속연구에서는 실제 사용자를 대상으로 하여 구체적인 사용 의도와 영향 요인을 실증적으로 연구해 볼 필요가 있다.

Recently, financial companies are promoting chatbot services in line with the reduction of branches and the expansion of non-face-to-face services. However, it is difficult to expand the chatbot services at once in the presence of technical limitations and constraints of internal and external environment. Therefore, it is necessary to analyze the various situations of chatbot service to preemptively identify problems that can occur in stages and seek solutions. This study conducted interviews with 12 field practitioners and researchers to examine the intentions and behaviors of financial chatbot service users and interpreted them using TPB. The study revealed the characteristics of 'feelings and attitudes' such as convenience or inconvenience from the chatbot experience, 'subjective norms' such as herd behavior or the yearning for empathy of others, and 'behavioral control' according to the recognition of difficulty or convenience of chatbot use process. This study shows that this characteristic can affect the intention and actual behavior of users to use chatbot service continuously. In the future research, it is necessary to empirically study specific intentions and influence factors for actual users.

키워드

참고문헌

  1. K. R. Seo. (2018). Trends and Prospects of Chatbot Services Based on Artificial Intelligence at home and abroad. [D.gov Trend&Future 2018-2]. Daegu : National Information Society Agency.
  2. J. Weizenbaum. (1966). ELIZA-a computer program for the study of natural language communication between man and machine. Communications of the ACM, 9(1), 36-45. DOI : 10.1145/365153.365168
  3. J. Weizenbaum. (1995). Kurs auf den Eisberg: die Verantwortung des einzelnen und die Diktatur der Technik. (M. Lee, Translator). Seoul : MyungKyung.(Original work published 1988)
  4. Mordor Intelligence. (2020). Chatbot Market - Growth, Trends, Covid-19 Impact, and Forecasts(2021-206). mordorintelligence.com. https://www.mordorintelligence.com/industry-reports/chatbot-market
  5. Financial Supervisory Service. (2018). Financial company's AI-enabled chatbot operation status and inspection results. Seoul : FSS
  6. K. H. Kim et al. (2021). Establishment of Mid-to-Long-Term Roadmaps for Securing National Competitiveness in Artificail Intelligence. Seoul : National Research Council for Economics, Humantites and Social Sciences.
  7. BRN.AI company. (2019.4.19.). Chatbot Report 2019: Global Trends and Analysis. Chatbots Magazine. https://chatbotsmagazine.com/chatbot-report-2019-global-trends-and-analysis-a487afec05b
  8. T. Kim, H. S. Cha, C. Park & J. H. Wi. (2020). Identifying Factors Affecting Chatbot Use Intention of Online Shopping Mall Users. Knowledge Management Review 21(4), 211-225. DOI : 10.15813/kmr.2020.21.4.011
  9. S. J. Chung. (2015. Dec.). A company that offers 'good experience' rather than 'good product' succeeds. Dong-A Business Review, 190. https://dbr.donga.com/article/view/1203/article_no/7343/ac/magazine
  10. Y. H. Park. (2021.1.19.). Insurance companies start a dedicated AI chatbot team to prevent wrong data learning. Korea IT News. https://www.etnews.com/20210119000146#
  11. N. R. Kim. (2021.4.26.). In the non-face-to-face era, the choice of banknotes is 'AI' service. Cheonji-ilbo. http://www.newscj.com/news/articleView.html?idxno=853135
  12. J. T. Yu & J. H. Cho. (2017). The factors of continuous use for mobile payment service under the concept of Fintech. Journal of information systems, 26(3), 25-46. DOI : 10.5859/KAIS.2017.26.3.25
  13. P. B. Brandtzaeg & A. Folstad. (2017. Nov.). Why people use chatbots. The 4th International Conference on Internet Science.(pp. 377-392). Thessaloniki : Greece DOI : 10.1007/978-3-319-70284-1_30
  14. M. Ashfaq, J. Yun, S. Yu & S. Loureiro. (2020). I, Chatbot: Modeling the Determinants of Users' Satisfaction and Continuance Intention of AI-Powered Service Agents. Telematics and Informatics, 54, 101473. DOI : 10.1016/j.tele.2020.101473.
  15. S. J. Min, H. J. Kim & G. H. Song. (2017). An Exploratory Study on the Determinants of Chatbot Acceptance Using the Unified Technology Acceptance Theory(UTAUT). Proceedings of the Korea Technology Innovation Society Conference. (pp. 623-643). Seoul : Korea Technology Innovation Society.
  16. D. Y. Jang & C. K. Lee. (2019). A Study of Use Intention of Chatbot Using the Extended Theory of Planned Behavior: Focusing on the Role of Interaction. Journal of Tourism and Leisure Research, 31(8), 433-454. https://doi.org/10.31336/jtlr.2019.8.31.8.433
  17. J. W. Kim, H. I. Jo, & B. G. Lee. (2019). The Study on the Factors Influencing on the Behavioral Intention of Chatbot Service for the Financial Sector: Focusing on the UTAUT Model. Journal of Digital Contents Society, 20(1), 2019b, 41-50. DOI : 10.9728/dcs.2019.20.1.41
  18. M. K. Lee & H. J. Park. (2019). Exploring Factors Influencing Usage Intention of Chatbot - Chatbot in Financial Service. Journal of Korean Society for Quality Management, 47(4), 755-765. DOI : 10.7469/JKSQM.2019.47.4.755
  19. S. H. Byun & C. H. Cho. (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
  20. G. K. Cho & J. Y. Yun. (2019). UX Evaluation of Financial Service Chatbot Interactions. Jourmal of the HCI Society of Korea, 14(2), 61-69. DOI : 10.17210/jhsk.2019.05.14.2.61
  21. S. G. Kim & J. Y. Yun. (2020). Multidisciplinary User Experience Research on Task-oriented chatbot Images. The Korean Society of Science & Art, 38(2), 33-43. DOI : 10.17548/ksaf.2020.03.30.33
  22. Y. J. Song & S. J. Choi. (2020). The Effects of Chatbots' Anthropomorphism and Self-disclosure on Consumers' Perceptions of and Attitude toward the Chatbots. Jourmal of the HCI Society of Korea, 15(1), 17-28. DOI : 10.17210/jhsk.2020.03.15.1.17
  23. M. Minsky. (1991). Society of mind - A response to four reviews. Artificial intelligence, 48(3), 371-396. DOI : 10.1016/0004-3702(91)90036-J.
  24. J. Y. Seo. (2017). Natural Language Processing and Artificial Intelligence Technology in the Implementation of Dialogue Interface. Easily Learning Artificial Intelligence Part 2(Machine Translation and Chatbot Technology) Presentation Materials, Seoul : KIISE Artificial Intelligence Society. https://sigai.or.kr/workshop/AI-for-everyone/2017/
  25. H. S. Lim & Korea University NLP Labs. (2019). Natural Language Processing Bible. Seoul : Human Science.
  26. Editorial Department. (2018). Technology trend and market driving of AI chatbot. In-Cheon : Hayeon
  27. Koreapost Information Center. (2020). Post office Next generation Comprehensive System Construction Terms of Reference. Korea On-line E-Procurement System Bid Announcement 20200800095-8211, Ministry of Science and Technology Information and Communication.
  28. J. H. Seo & B. Y. Lee. (2020). 2030 Vision and Tasks of the Korean Financial Industry : Banks. Korea Institute Of Finance research general document, 2020(2), 1-221.
  29. H. R. Kim. (2020). A Study on the Use of Chatbot in Postal Finance. Koreapost Information, 120, 79-90.
  30. S. O. Yoon. (2018). Issues of Public Service Using Artificial Intelligence: Focused on Chatbot Service. Korean Public Management Review, 32(2), 83-104. DOI : 10.24210/kapm.201 8.32.2.004
  31. K. Dow & R. Leitch. (2007). Confidence in the Implementation Process of a New Information System. Journal of Emerging Technologies in Accounting - J Emerg Tech Account, 4, 139-159. DOI : 10.2308/jeta.2007.4.1.139.
  32. H. Sun. (2013). A Longitudinal Study of Herd Behavior in the Adoption and Continued Use of Technology. MIS Quarterly, 37(4), 1013-1041. https://doi.org/10.25300/MISQ/2013/37.4.02
  33. M. Turpin & N. D. Plooy. (2004). Decision-making Biases and Information Systems. Decision Support in an Uncertain and Complex World: The IFIP TC8/WG8.3 International Conference. (pp. 782-792). Melbourne Vic Australia : Monash University Publishing.
  34. J. T. Harvey. (1998). Heuristic Judgment Theory. Journal of Economic Issues, 32(1), 47-63. https://doi.org/10.1080/00213624.1998.11506010
  35. M. Fishbein & I. Ajzen. (1975). Beliefs, attitude, intention, and behavior. An introduction to Theory and Research. MA : Addison-Wesley.
  36. I. Ajzen. (1991). Organizational Behavior and Human Decision Process, 50, 179-211. https://doi.org/10.1016/0749-5978(91)90020-t
  37. W. Boulding, A. Kalra & V. A. Zeithaml. (1993). A dynamic process model of service quality: From expectation to behavioral intentions. Journal of Marketing Research, 30(1), 7-27. https://doi.org/10.2307/3172510
  38. Y. K. Sohn & B. G. Lee. (2012). An Efficacy of Social Cognitive Behavior Model based on the Theory of Planned Behavior : A Meta-Analytic Review. Korean Journal of Journalism & Communication Studies, 56(6), 127-161.
  39. M. Bayer, G. Ortner, O. Stern, A. Kuther, A. A. Gorbunov, A. Forchel, P. Hawrylak, S. Fafard, K. Hinzer, T. L. Reinecke, S. N. Walck, J. P. Reithmaier, F. Klopf, & F. Schafer. (2002). Fine structure of neutral and charged excitons in self-assembled In (Ga) As/(Al) GaAs quantum dots. Physical Review B, 65(19). 195315. DOI : 10.1103/PhysRevB.65.195315
  40. J. Doll & I. Ajzen. (1992). Accessibility and stability of predictors in the theory of planned behavior. Journal of Personality and Social Psychology, 63(5), 754-765. DOI : 10.1037/0022-3514.63.5.754
  41. I. Ajzen. (2011). The theory of planned behaviour: reactions and reflections. Psychology & Health, 26, 1113-1127. https://doi.org/10.1080/08870446.2011.613995
  42. D. Montano & D. Kasprzyk. (2008). Theory of reasoned action, theory of planned behavior, and the integrated behavioral model. In K. Glanz, B. K. Rimer, & K. Viswanath (Eds.), Health behavior and health education: Theory, research, and practice. (pp. 67- 96). Jossey-Bass.
  43. M. Conner & P. Sparks. (2005). Theory of Planned Behavior and Health Behaviour. Predicting health behavior: Research and Practice with Soical Congnitive Models. (pp. 170-222). New York : Two Penn Plaza.
  44. C. J. Armitage & M. Conner. (2001). Efficacy of the theory of planned behaviour: A meta-analytic review. Br J Soc Psychol., 40, 471-499. DOI : 10.1348/014466601164939
  45. J. Zoellner, E. Krzeski, S. Harden, E. Cook, K. Allen & P. A. Estabrooks. (2012). Qualitative Application of the Theory of Planned Behavior to Understand Beverage Consumption Behaviors among Adults. Journal of the Academy of Nutrition and Dietetics. 112(11), 1774-1784. DOI : 10.1016/j.jand.2012.06.368
  46. A. P. Silva, I. Figueiredo, T. Hogg & M. Sottomayor. (2014). Young adults and wine consumption a qualitative application of the theory of planned behavior. British Food Journal. 116(5), 832-848. DOI : 10.1108/BFJ-05-2012-0114
  47. J. H. Jung, S. H. Hong & S. M. Jang. (2017). A Systematic Literature Review of the Studies on Alcohol Use and Smoking Behaviors Applying the Theory of Planned Behavior, Health and Social Welfare Review, 38(4), 367-397. DOI : 10.15709/hswr.2018.38.4.367
  48. M. Q. Patton. (1990). Qualitative Evaluation Methods, 2nd ed. CA : Sage.
  49. I. Seidman. (2006) Interviewing as qualitative research: A guide for researchers in education and the social sciences. New York : Teachers College Press.
  50. S. Brinkmann & S. Kvale. (2015). InterViews: Learning the craft of qualitative research interviewing. Los Angeles : Sage Publications.
  51. T. B. Im. (2009). Qualitative Methodology: Approach and Application. Journal of Governmental Studies, 15(1), 155-188.
  52. W. J. Potter. (1996) An Analysis of Thinking and Research about Qualitative Methods. NJ : Lawrence Erlbaum.
  53. A. Y. Kim, J. E. Cha, C. H. Lee, J. E. Ju & E. Y. Lim. (2016). Solo research methodology, Seoul : Hajisa.
  54. J. Creswell. (1998). Qualitative inquiry and research design: Crossing among five traditions. Thousand Oaks, CA : Sage.
  55. J. A. Maxwell. (1992). Understanding and Validity in Qualitative Research. Harvard Educational Review, 62(3), 279-300. https://doi.org/10.17763/haer.62.3.8323320856251826
  56. H. W. Nam. (2020). Companies that introduced AI are 'sick' because they are ineffective. ZDNet Korea. https://zdnet.co.kr/view/?no=20201111161552
  57. J. Francis, M. P. Eccles, M. Johnston, A. E. Walker, J. M. Grimshaw, R. Foy, E. F. S. Kaner, L. Smith, & D. Bonetti. (2004). Constructing questionnaires based on the theory of planned behaviour: A manual for health services researchers. Newcastle upon Tyne, UK : Centre for Health Services Research, University of Newcastle upon Tyne.