YAPAY ZEKA TABANLI CHATBOT HİZMETİNİN KULLANICI ALIŞKANLIK VE DAVRANIŞLARI ÜZERİNE ETKİLERİ VE BİR UYGULAMA

Year 2024, , 20 - 43, 27.06.2024

Yasemin Doğu Yıldıran , Şakir Erdem

https://doi.org/10.14780/muiibd.1381666

Abstract

Günümüzde yapay zeka kullanan sistemlerin yaygınlaşması ve daha fazla kullanıcının günlük yaşantısında farklı alanlarda yer bulmasıyla tüketici davranışları üzerinde etkileri artmaktadır. Dijital kullanıcılar, daha fazla kontrol sahibi olduğu ve ihtiyaç duyduğu bilgiye hızlı ulaştığı sistemleri tercih etmektedir. Bu da sorunsuz etkileşim ve kişiselleştirme olanağı sunan chatbotların kullanımını arttırmıştır. Bu bağlamda bu araştırma çalışmasının asıl amacı; kavramsal açıdan Kişilerarası Davranış Teorisi ve E-S-QUAL ölçeğine dayanarak kullanıcıların chatbot kullanım niyeti ve alışkanlıklarını incelemek, onlarda kullanım davranışı oluşup oluşmadığını gözlemlemek ve kullanım niyeti oluşturan etmenleri ortaya koymaktır. Araştırma örneği için Türkiye’de çok kullanılan, yapay zeka tabanlı chatbot hizmeti sunan, bir e-ticaret platformundan alışveriş yapmış kullanıcılara erişilerek çevrimiçi anket yapılmış ve toplanan 319 geçerli anket analize dahil edilmiştir. Yapılan analizlerin sonucunda “Göreceli Avantaj” faktörünün “Kullanım Niyet”ini, “Kullanım Niyeti” ve “Alışkanlık” faktörlerinin “Davranış”ı etkilediği görülmüş ayrıca aracılık analizlerinde de ilişki tespit edilmiştir.

Keywords

Yapay zeka, Chatbot, Kullanıcı Davranışı, Kullanıcı Alışkanlıkları, Kişilerarası Davranış Teorisi

Ethical Statement

Bu çalışmanın, özgün bir çalışma olduğunu; çalışmanın hazırlık, veri toplama, analiz ve bilgilerin sunumu olmak üzere tüm aşamalarından bilimsel etik ilke ve kurallarına uygun davrandığımı; bu çalışma kapsamında elde edilmeyen tüm veri ve bilgiler için kaynak gösterdiğimi ve bu kaynaklara kaynakçada yer verdiğimi etik görev ve sorumluluklara riayet ettiğimi beyan ederim.

Supporting Institution

Marmara Üniversitesi

Thanks

Çalışma boyunca her daim yanımda olan değerli hocalarım Prof. Dr. Şakir Erdem ve Prof. Dr. Beril Durmuş'a teşekkür ve saygılarımla...

References

• Ajzen, I., (1985), From Intentions to Actions: A Theory of Planned Behavior, Action Control, From Cognition to Behaviour, Springer-Verlag Berlin Heidelberg, 11-39.
• Ajzen, I. (1991), The theory of planned behavior, Organ. Behav. Hum. Decis. Process. 50, 179–211
• Ajzen, I., Fishbein, M. (1980), Understanding Attitudes and Predicting Social Behavior, Prentice-Hall, Eglewood Cliffs, NJ.
• Awad, N. F., Krishnan, M. S. (2006). The personalization privacy paradox: an empirical evaluation of information transparency and the willingness to be profiled online for personalization, MIS Quarterly, 30(1), 13-28
• Ball, D., Coelho, P. S., Vilares, M. J. (2006), Service personalization and loyalty, Journal of services marketing, 20(6), 391-403
• Cabrera, A., Collins, W. C., Salgado, J. F. (2006), Determinants of individual engagement in knowledge sharing, International J. of Human Resource Management, 17(2), 245–264
• Chang, H. S., Fu, M. C., Hu, J., Marcus, S. I. (2016), Google DeepMind's AlphaGo:operations research's unheralded role in path-breaking achievement. Or/Ms Today, 43(5), 24-30.
• Chellappa, R. K., Sin, R. G. (2005), Personalization versus privacy: an empirical examination of the online consumer’s dilemma, Inf. Technology and Management, 6(2/3), 181-202
• Chowdhury, G. (2003), Natural language processing, Annual Review of Information Science and Technology, 37. 51-89.
• Clark, L., Doyle, P., Garaialde, D., Gilmartin, E., Schlogl, S., Edlund, J., vd. (2019), The state of speech in HCI: Trends, themes and challenges, Interacting with Computers, 31 (4), 349–371.
• Colby, K. M., Hilf, F. D., Weber, S., Kraemer, H. C. (1972), Turing-like indistinguishability tests for the validation of a computer simulation of paranoid processes, Artificial Intelligence, 3, 199–221.
• Colby, K. M., Weber, S., Hilf, F. D. (1971), Artificial paranoia, Artificial Int., 2(1), 1–25
• Dahiya, M. (2017). A tool of conversation: Chatbot. International journal of computer sciences and engineering, 5(5), 158-161.
• Dale, R. (2016), The return of the chatbots, Natural Language Engineering, 22, 811–817
• Durmuş, B., Yurtkoru, E.S., Çinko, M. (2013), Sosyal Bilimlerde SPSS’le Veri Analizi. Bata Basım A.Ş. (İstanbul-Türkiye). 5. Baskı.
• Ehrenberg, A., Juckes, S., White, K.M., Walsh, S.P. (2008), Personality and self-esteem as predictors of young people’s technology use, Cyberpsychology & Beh., 11(6), 739-741
• Fornell, C., Larcker, D. F. (1981), “Evaluating Structural Equation Models with Unobservable Variables and Measurement Error”, Journal of Marketing Research, 18, 39-50
• Gaskin, J. (2016), Validitymaster, stats tools package, Sem, gaskination's statwiki, (Çevrimiçi) http://statwiki.kolobkreations.com, 23 Ağustos 2017. Goldberg, L. R. (1993),The structure of phenotypic personality traits,American Psyc, 48, 26–34.
• Gruen, T.W., Osmonbekov, T., Czaplewski, A.J. (2006), Ewom: the impact of customer-tocustomer online know-how exchange on customer value and loyalty, J of Business Research 59 (4), 449–456.
• Gunther, O., Spiekermann, S. (2005), RFID and the perception of control: the consumer’s view, Communications of the ACM, 48(9), 73-76
• Guzman, A. L. (2019), Voices in and of the machine: Source orientation toward mobile virtual assistants, Computers in Human Behavior, 90, 343–350.
• Hair, J., Black, W., Babin, B., Anderson, R. (2010), Multivariate data analysis 7th edition, Prentice Hall.
• Hebb D. O. (1949), The Organization of Behavior: A Neuropsychological Theory, New York, John Wiley, 1949 and Sons.
• Hermida, R. (2015), The Problem of Allowing Correlated Errors in Structural Equation Modeling: Concerns and Considerations. Comp. Methods in Social Sciences, 3(1), 5-17
• Hien, H.T., Cuong, P.-N., Nam, L.N.H., Nhung, H.L.T.K., Thang, L.D. (2018), Intelligent assistants in higher-education environments: the FIT-EBot, a chatbot for adm. and learning support, Proceedings of the 9th Int Symposium on Inf and Comm Tech, 69–76.
• Ho, S., Kwok, S. (2003), The attraction of personalized service for users in mobile commerce: an empirical study, ACM SIGecom Exchanges, 3(4), 10-18
• Hoffman, D.L., Novak, T.P. (2018), Consumer and object experience in the internet of things: an assemblage theory approach, Journal of Consumer Research, 44 (6), 1178-1204.
• Hossain, M. A., Kim, M. (2018), Does multidimensional service quality generate sustainable use intention for Facebook?, Sustainability, 10(7), 2283.
• Hoy, M. (2018), Alexa, siri, cortana, and more: An introduction to voice assistants, Medical Reference Services Quarterly, 37, 81–88.
• Hoyer, W.D., Kroschke, M., Schmitt, B., Kraume, K., Shankar, V. (2020), Transforming the customer experience through new technologies, J. Interact. Market. 51 (1), 57–71.
• Issock, P.B.I., Roberts-Lombard, M., Mpinganjira, M. (2020), Understanding household waste separation in South Africa: an empirical study based on an extended theory of interpersonal behaviour, Manag. Environ. Qual. 31 (3), 530–547.
• John, O.P., Srivastava, S. (1999), The Big Five trait taxonomy: history, measurement, and theoretical perspectives, Handbook of Personality: Theory and Res., 2 (1), 102-138
• Jordan, M. I., Mitchell, T. M. (2015). Machine learning: Trends, perspectives, and prospects, Science, 349(6245), 255–260.
• Kaplan, A. M., Haenlein, M. (2019). Siri, Siri, in my hand: Who’s the fairest in the land? On the interpretations, illustrations, and implications of artificial intelligence, Business Horizons, 62(1), 15–25.
• Kar, R., Haldar, R. (2016), Applying chatbots to the internet of things: Opportunities and architectural elements”, Int. J. of Advanced Computer Science and App., 7, 1-9,
• Kushwaha, A.K., Kar, A.K., Dwivedi, Y.K. (2021), Applications of big data in emerging management disciplines: a literature review using text mining, Int. J. Informat.Manag. Data Insights 1 (2), 100017, 1-17.
• LeCun, Y., Bengio, Y., Hinton, G. (2015). Deep learning. Nature, 521(7553), 436–444
• Lertwongsatien, C., Wongpinunwatana, N. (2003), E-commerce adoption in Thailand: An empirical study of SMEs, J of Global Information Techn Management, 6(3), 67-83.
• Makanyeza, C. (2017), Determinants of consumers’ intention to adopt mobile banking services in Zimbabwe, International Journal of Bank Marketing, 35( 6), 997-1017.
• Manning, C. D., Schutze, H. (1999), Foundations of statistical natural lang processing, MIT Press
• Marbach, J., Lages, C.R., Nunan, D. (2016), Who are you and what do you value? Investigating the role of personality traits and customer-perceived value in online customer engagement, Journal of Marketing Management, Vol. 32 No 5/6, 502-525.
• Marney, Jo (1995), Selling in Tongues, Marketing Magazine, 100 (38), 14.
• Mauldin, M. L. (1994), CHATTERBOTS, TINYMUDS, and The Turing Test: entering the Loebner prize competition, AAAI-94, 16-21.
• McKenna, K.Y., Bargh, J.A. (2000), Plan 9 from cyberspace: the implications of the internet for personality and social psychology”, Personality&Social Psychology Rev., 4 (1), 57-75.
• Molnár, G., Zoltán, S. (2018), The role of chatbots in formal education, Conference: IEEE 16th International Symposium on Intelligent Systems and Informatic, 197-201.
• Moore, G. C., Benbasat, I. (1991), Development of an instrument to measure the perceptions of adopting an information techn innovation, Information systems research, 2(3), 192-222.
• Parasuraman, A., Berry, L.L., Zeithaml, V.A. (1991), Understanding customer expectations of service, Sloan Manag. Rev. 32(3), 39–48.
• Parasuraman, A., Zeithaml, V., Berry, L.L. (1998), SERVQUAL: a multiple-item scale for measuring consumer perceptions of service quality, J. Retail. 64 (1), 12–40.
• Parasuraman, A., Zeithaml, V.A., Malhotra, A., (2005), ESQUAL: a multiple-item scale for assessing electronic service quality, J. Service Res. 7 (3), 213–233.
• Payne, E.M., Peltier, J.W., Barger, V.A. (2018), Mobile banking and AI-enabled mobile banking: the differential effects of technological and non-technological factors on dig. natives’ perceptions and behavior, J. of Research in Interactive Mark., 12 (3), 328-346.
• Pee, L.G., Woon, I.M.Y., Kankanhalli, A. (2008), Explaining non-work-related computing in the workplace: a comparison of alternative models, Inf. Manag. 45, 120–130.
• Quah, J.T.S., Chua, Y.W. (2019), Chatbot assisted marketing in financial service industry, Services Computing – SCC, 107-114.
• Rajaobelina, L., Brun, I., Kilani, N., Ricard, L. (2022), Examining emotions linked to live chat services: The role of e-service quality and impact on word of mouth, Journal of Financial Services Marketing, 27(3), 232-249.
• Rimol, M. (2022, 31 Ağustos), Gartner Predicts Conversational AI Will Reduce Contact Center Agent Labor Costs by $80 Billion in 2026. https://www.gartner.com/en/newsroom/press-releases/2022-08-31-gartner-predicts-conversational-ai-will-reduce-contac adresinden alındı
• Rogers, E. M. (1962), Diffusion of innovations (1st ed.). New York: Free Press.
• Rogers, E. M. (1993), Diffusion of innovations (4th ed.). New York: Free Press.
• Roussos, G., Peterson, D., Patel, U. (2003), Mobile identity management: an enacted view, International Journal of Electronic Commerce, 8(12), 81-100.
• Russell, S. J., Norvig, P. (2021). Artificial intelligence: A modern approach (4th ed.), Pearson
• Ryan, T., Xenos, S. (2011), Who uses Facebook? An investigation into the relationship between the Big Five, shyness, narcissism, loneliness, and Facebook usage, Computers in Human Behavior, Vol. 27 No. 5, 1658-1664.
• Schermelleh-Engel, K., Moosbrugger, H., Müller, H. (2003), Evaluating the fit of structural equation models: Tests of significance and descriptive goodness-of-fit measures, Methods of psychological research online, 8(2), 23-74.
• Sharma, S., Sharma, M. (2019), Examining the role of trust and quality dimensions in the actual usage of mobile banking services:An empirical investigation, Int J of Inf Mng 44.65-75.
• Shavelson, R.J., Hubner, J.J., Stanton, G.C. (1976), Self-concept: validation of construct interpretations, Rev. Educ. Res. 46 (3), 407–441.
• Sheng, H., Nah, F. (2008), An experimental study on U-commerce adoption: impact of personalization and privacy concerns, J. of the Ass. for Inf Systems, 9(6), 344-376
• Shinde P. P., Shah S. (2018), A Review of Machine Learning and Deep Learning Applications, 4th International Conference on Computing Communication Control&Automation, 1-6
• Silverman, G. (2001), The Power of Word of Mouth, Direct Marketing, 64(5), 47-52.
• Silverman, George (1997), “Harvesting the Power of Word of Mouth,” Potentials in Marketing, 30 (9), 14-16.
• Soldz, S., Vaillant, G. E. (1999), The Big Five personality traits and the life course: A 50-year longitudinal study, Journal of Research in Personality, 33, 208–232. Triandis, H.C. (1977), Interpersonal Behavior. Brooks/Cole, Monterey, CA
• Triandis, H.C. (1980), Values, attitudes, and interpersonal behavior, Howe, H.E., Page, M.M. (Eds.), Nebraska Sym on Motivation 1979. Uni of Nebraska Press, Lincoln, 195–259.
• Turing, A.M. (1950), Computing Machinery and Intelligence, Mind, Oxford University Press 59(236), 433-460.
• Venkatesh, V., Morris, M.G., Davis, G.B., Davis, F.D. (2003), User acceptance of information technology: toward a unified view, MIS Quarterly, Vol. 27 No. 3, 425-478.
• Venkatesh, V., Thong, J. Y., Xu, X. (2012), Consumer acceptance and use of inf tech: extending the unified theory of acceptance and use of technology, MIS quarterly 36(1), 157-178.
• Verkijika, S.F., De Wet, L. (2019), Understanding WOM intentions of mobile app users: The role of simplicity and emotions during the first interaction, Tel and Inf, 41, 218-228.
• Wallace, R.S. (2009). The Anatomy of A.L.I.C.E. Epstein, R., Roberts, G., Beber, G. (eds) Parsing the Turing Test. Springer, Dordrecht.
• Weizenbaum, J. (1966), ELIZA-A computer program for the study of natural language communication between man and machine, Commun. ACM, 9(1), 36–45.
• Wirtz, J., den Ambtman, A., Bloemer, J., Horváth, C., Ramaseshan, B., van de Klundert, J., Gurhan Canli, Z., Kandampully, J. (2013), Managing brands and customer engagement in online brand communities, Journal of Service Management, Vol. 24 No. 3, 223-244.
• Wolfinbarger, M., Gilly, M.C. (2001), Shopping online for freedom, control, and fun, California Management Review, Vol. 43 No. 2, 34-55.
• Yoo, K.H., Gretzel, U. (2011), Influence of personality on travel-related consumer-generated media creation, Computers in Human Behavior 27, 609-621.
• Yun, J., Park, J. (2022), The Effects of Chatbot Service Recovery with Emotion Words on Customer Satisfaction, Repurchase Intention, and Positive Word-Of-Mouth, Frontiers in psychology, 13, 922503-922503.
• Zaltman, Gerald, Christian R. A. Pinson, and Reinhard Angelmar (1973), Metatheory and Consumer Research. New York: Holt, Rinehart, and Winston
• Zeithaml, V. A., Parasuraman, A., Berry, L. L. (2000), Delivering Quality Serv., Free Press, NY.
• Zemčík, T. (2019), A brief history of chatbots. DEStech Transactions on Computer Science and Eng., International Conference on Artificial Intelligence, Control&Autom. Eng., 14-18
• Zha, X., Zhang, J., Yan, Y., Xiao, Z. (2014), User perceptions of e-quality of and affinity with virtual comm.: the effect of ind. differences, Comp. in Human Behavior,38(1), 185-195.