The Impact of Digital Assistants on Customer Satisfaction: Artificial Intelligence Applications Turkiye Case

Year 2023, Volume: 4 Issue: 2, 225 - 248, 31.12.2023

Maryam Mohammadabbasi , Anıl Değermen

https://doi.org/10.62001/gsijses.1371527

Abstract

Digital assistants are sophisticated AI-based technologies. Although businesses have begun to integrate this technology into their operations with the expectation of increasing their efficiency, there are few empirical studies on the impact of digital assistants on customer satisfaction. In this context, the aim of the study is to determine the effects of digital assistants on customer satisfaction in Istanbul, Ankara and Izmir. This study used the AMOS program to evaluate 454 questionnaire responses taken from digital assistant consumers. The research findings determined that this model significantly explains customer satisfaction with digital assistants. This research provides recommendations that allow businesses to prioritize management and marketing tasks. These priorities define areas that are of high importance for customer satisfaction.

Keywords

Artificial Intelligence, Digital Assistant, Marketing, Customer Satisfaction

Dijital Asistanların Müşteri Memnuniyeti Üzerindeki Etkisi: Yapay Zekâ Uygulamaları Türkiye Örneği1

Abstract

Dijital asistanlar, gelişmiş yapay zekâ tabanlı teknolojilerdir. İşletmeler, verimliliklerini artırma beklentisiyle bu teknolojiyi operasyonlarına entegre etmeye başlamışlarsa da dijital asistanların müşteri memnuniyetine etkisine yönelik çok az ampirik çalışma bulunmaktadır. Bu bağlamda çalışmanın amacı, İstanbul, Ankara ve İzmir'de kullanılan dijital asistanların müşteri memnuniyeti üzerindeki etkilerini tespit etmektir. Bu çalışmada dijital asistan kullanıcısı tüketicilerden alınan 454 anket yanıtını analiz etmek için AMOS programı kullanılmıştır. Çalışma sonuçları, modelin dijital asistanların müşteri memnuniyeti üzerinde etkili olduğunu ortaya koymaktadır. Bu araştırma, işletmelerin yönetim ve pazarlama görevlerine öncelik vermelerine olanak tanıyan öneriler sunmaktadır. Bu öncelikler, müşteri memnuniyeti için yüksek öneme sahip olan alanları tanımlamaktadır.

Keywords

Yapay Zekâ, Dijital Asistan, Pazarlama, Müşteri Memnuniyeti

Supporting Institution

İstanbul Üniversitesi Bilimsel Arastırma Projeleri Birimi ve İstanbul Üniversitesi İstatistik Uygulama ve Araştırma Merkezi

References

• Ab Hamid, M. R., Sami, W., & Sidek, M. M. (2017, September). Discriminant validity assessment: Use of Fornell & Larcker criterion versus HTMT criterion. Journal of Physics: Conference Series (Vol. 890, No. 1, p. 012163). IOP Publishing.
• Acquisti, A., Brandimarte, L., & Loewenstein, G. (2015). Privacy and Human Behavior in the Age of Information. Science, 347(6221), 509-514.
• Ajzen, I. (2005), Attitudes, Personality and Behaviour. McGraw-Hill Education (UK).
• Alpaydın, E. (2014). Introduction to Machine Learning. MIT Press.
• Anderson, E. W., & Sullivan, M. W. (1993). The Antecedents and Consequences of Customer Satisfaction for Firms. Marketing Science, 12(2), 125-143.
• Aşkar, P., & Umay, A. (2001). Preservice Elementary Mathematics Teachers’ Computer Self-Efficacy, Attitudes Towards Computers, and Their Perceptions Of Computer-Enriched Learning Environments. in Society For Information Technology & Teacher Education International Conference (Pp. 2262-2263). Association For The Advancement Of Computing İn Education (AACE).
• Baier, D., Rese, A., & Röglinger. M. (2018). Conversational user interfaces for online shops? A categorization of use cases. Paper presented at the 39th International Conference on Information Systems (ICIS). San Francisco, USA.
• Bandura, A. (1982). Self-efficacy Mechanism in Human Agency. American Psychologist, 37(2), 122.
• Bandura, A. (1986). Social Foundations of Thought and Action. Englewood Cliffs, 23-28.
• Belanger, F., & Xu, H. (2015). The Role of Information Systems Research in Shaping the Future of Information Privacy. Information Systems Journal, 25(6), 573-578.
• Bhattacherjee, A. (2001), Understanding Information Systems Continuance: An Expectation Confirmation Model, MIS Quarterly, 25(3), 351-370.
• Brill, T.M., Munoz, L., & Miller, R. J. (2019). Siri, Alexa, And Other Digital Assistants: A Study Of Customer Satisfaction With Artificial İntelligence Applications. Journal Of Marketing Management, 35(15-16), 1401-1436.
• Canbek, N., & Mutlu, M. E. (2016). Sayısal Gelecekte Yeni Adım: Akıllı Kişisel Yardımcılar. Açıköğretim Uygulamaları ve Araştırmaları Dergisi, 2(1), 114-129.
• Cardozo, R. N. (1965). An Experimental Study of Customer Effort, Expectation, and Satisfaction. Journal of Marketing Research, 2(3), 244-249.
• Chien, T. K., Chang, T. H., & Su, C. T. (2003). Did Your Efforts Really Win Customers’ Satisfaction?. Industrial Management & Data Systems, 103(4), 253-262.
• Cohen, J. B., & Goldberg, M. E. (1970). The Dissonance Model in Post-Decision Product Evaluation. Journal of Marketing Research, 7(3), 315-321.
• Compeau, D. R., & Higgins, C. A. (1995). Application of Social Cognitive Theory to Training For Computer Skills. Information Systems Research, 6(2), 118-143.
• Compeau, D., Higgins, C. A., & Huff, S. (1999). Social Cognitive Theory And Individual Reactions To Computing Technology: A Longitudinal Study. MIS quarterly, 145-158.
• Festinger, L. (1957). A Theory of Cognitive Dissonanc. Stanford, CA: Stanford University.
• Fortin D.R., & Dholakia R.R. (2005). Interactivity And Vividness Effects On Social Presence And İnvolvement With A Web- Based Advertisement. Journal of Business Research, 58(3), 387–396.
• Guo, Y., Barnes, S., & Le-Nguyen, K. (2015). Consumer Acceptance IT Products: An Integrative Expectation-Confirmation Model. Paper presented at the Twenty-first Americas Conference on Information Systems. Puerto Rico.
• Hasan, U., & Nasreen, R. (2014). The Empirical Study of Relationship Between Post Purchase Dissonance And Consumer Behaviour. Journal of Marketing Management, 2(2), 65–77.
• Hauswald, J., Laurenzano, M. A., Zhang, Y., Li, C., Rovinski, A., Khurana, A., Dreslinski, R. G., Mudge, T., Petrucci, V., Tang, L., & Mars, J. (2015) . Sirius: An Open End-To-End Voice And Vision Per-Sonal Assistant And İts İmplications For Future Warehouse Scale Computers, In Proceedings of the Twetieth International Conference on Architectural Support for Programming Languages and Operating Systems, 223–238
• Holloway, R. J. (1967). An Experiment on Consumer Dissonance. The Journal of Marketing, 31(1), 39-43.
• Jin, X. L., Zhou, Z., Lee, M. K. O., & Cheung, C. M. K. (2013). Why Users Keep Answering Questions in Online Question Answering Communities: A Theoretical and Empirical Investigation. International Journal of Information Management, 33(1), 93-104.
• Johnson, R. D., & Marakas, G. M. (2000). The Role Of Behavioral Modeling in Computer Skills Acquisition: Toward Refinement Of The Model. Information Systems Research, 11(4), 402-417.
• Kehr, F., Kowatsch, T., Wentzel, D., & Fleisch, E. (2015), Blissfully Ignorant: The Effects of General Privacy Concerns, General Institutional Trust, And Affect in The Privacy Calculus. Information Systems Journal, 25(6), 607-635.
• Kim, D. J. (2012). An Investigation of The Effect Of Online Consumer Trust On Expectation, Satisfaction, And Post-Expectation. Information systems and e-business Management, 10, 219-240.
• Komiak, S. X., & Benbasat, I. (2006). The Effects of Personalization and Familiarity on Trust and Adoption Of Recommendation Agents, MIS Quarterly, 30(4), 941-960.
• Krejcie, R. V., & Morgan, D. W. (1970), Determining Sample Size for Research Activities. Educational and Psychological Measurement, 30(3), 607-610. https://doi.org/10.1177/001316447003000308
• Kumar, V., Dixit, A., Javalgi, R. R. G., & Dass, M. (2016). Research Framework, Strategies, And Applications of Intelligent Agent Technologies (Iats) in Marketing. Journal of the Academy of Marketing Science, 44(1), 24-45.
• Lankton, N., McKnight, D. H., & Thatcher, J. B. (2014). Incorporating trust-in-technology into Expectation Disconfirmation Theory. The Journal of Strategic Information Systems, 23(2), 128-145.
• Lankton, N. K., & McKnight, D. H. (2012). Examining Two Expectation Disconfirmation Theory Models: Assimilation and Asymmetry Effects. Journal of the Association for Information Systems, 13(2), 88-115.
• LaTour, Stephen A., & Nancy C. Peat. (1979). Conceptual and Methodological İssues İn Consumer Satisfaction Research. ACR North American Advances, 6(1), 431-437.
• Lin, X., Featherman, M., & Sarker, S. (2017). Understanding factors affecting users’ social networking site continuance: A gender difference perspective. Information & Management, 54(3), 383-395.
• Luhmann, N., & Schorr, K. E. (1979). Problems of Reflection in The Educational Syste. Suhrkamp.
• Malhotra, N. K., Kim, S. S., & Agarwal, J. (2004). Internet Users' Information Privacy Concerns (IUIPC): The Construct, The Scale, And A Causal Model”, Information Systems Research,15(4), 336-355.
• 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.
• McMillan, S. J. (2005). The Researchers And The Concept: Moving Beyond A Blind Examination Of İnteractivity. Journal Of Interactive Advertising, 5(2), 1-4.
• Milhorat, P., Schlögl, S., Chollet, G., Boudy, J., Esposito, A., Pelosi, G. (2014). Building The Next Generation Of Personal Digital Assistants. In 2014 1st İnternational Conference On Advanced Technologies For SignalAnd İmage Processing (Atsip), 458-463.
• Miltgen, C. L., Popovič, A., & Oliveira, T. (2013). Determinants Of End-User Acceptance Of Biometrics: Integrating The “Big 3” Of Technology Acceptance With Privacy Context. Decision Support Systems, 56, 103-114.
• Moar, J. (2019). The Digital Assistants of Tomorrow, Retrieved from,https://www.juniperresearch.com/document-library/white-papers/the-digital-assistants-of-tomorrow
• Oliver, R. L. (1980). A Cognitive Model Of The Antecedents And Consequences Of Satisfaction Decisions.Journal of Marketing Research, 17(4), 460-469.
• Oliver, R. L. (2014). Satisfaction: A Behavioral Perspective on the Consumer. New York, NY: Routledge.
• Oliver, R., Rust, R., Varki, S. (1997). Customer Delight: Foundations, Findings, And Managerial Insight. Journal of Retailing, 73(3), 311-336.
• Oliver, R. L., Balakrishnan, P. S., & Barry, B. (1994). Outcome Satisfaction İn Negotiation: A Test Of Expectancy Disconfirmation. Organizational Behavior And Human Decision Processes, 60(2), 252-275.
• Olshavsky, R. W., & Miller, J. A. (1972). Consumer Expectations, Product Performance, And Perceived Product Quality. Journal of Marketing Research, 9(1), 19-21.
• Peart, A. (2018). Conversational AI platforms demand is growing. Available at: https://blog.worldsummit.ai/ conversational-ai-platforms-demand-is-growing, accessed 04.02.2020.
• Purwanto, P., Kuswandi, K., & Fatmah, F. (2020). Interactive Applications With Artificial İntelligence: The Role Of Trust Among Digital Assistant Users. Форсайт, 14(2), 64-75.
• Rogers, R. W. (1975). A Protection Motivation Theory Of Fear Appeals And Attitude Change. The Journal of Psychology, 91(1), 93-114.
• Rust, R. T., Huang, M. H, 2014, "The Service Revolution And The Transformation Of Marketing Science," Marketing Science, 33/2, 206-221.
• Schoeman, F. (1984). Privacy: Philosophical Dimensions. American Philosophical Quarterly, 21(3), 199-213.
• Spreng, R. A., & Olshavsky, R. W. (1993). A Desires-As-Standard Model Of Consumer Satisfaction: Implications For Measuring Satisfaction. Journal Of The Academy Of Marketing Science, 21(3), 169–177.
• Spreng, R. A., & Page, T. J. (2003). A Test of Alternative Measures of Disconfirmation. Decision Sciences, 34(1), 31-62.
• Tse, D. K., & Wilton, P. C. (1988). Models Of Consumer Satisfaction Formation: An Extension. Journal of Marketing Research, 25(2), 204-212.
• Venkatesh, V., Morris, M. G., Davis, G. B., & Davis, F. D. (2003). User Acceptance Of İnformation Technology: Toward A Unified View. MIS Quarterly, 27(3), 425–478.
• Venkatesh, V., Thong, J. Y. L., Chan, F. K. Y., Hu, P. J. H., & Brown, S. A. 2011. Extending the Two-Stage Information Systems Continuance Model: Incorporating UTAUT Predictors And The Role Of Context. Information Systems Journal, 21(6), 527-555.
• Wise, J., VanBoskirk, S., & Liu, S. (2016). The Rise Of İntelligent Agents, Forrester.com, Retrieved from https://www.forrester.com/report/The+Rise+Of+Intelligent+Agents/-/E-RES128047#figure1
• Yi, Youjae. (1990). A Critical Review Of Consumer Satisfaction. Review of marketing, 4(1), 68-123.
• Yim, C. K., Chan, K. W., & Lam, S. S. (2012). Do Customers and Employees Enjoy Service Participation? Synergistic Effects of Self-And Other-Efficacy. Journal of Marketing, 76(6), 121-140.
• Yoo W.S., Yunjung L., & Jung K. P. (2010). The Role of Interactivity in E-Tailing: Creating Value and Increasing Satisfaction. Journal of Retailing and Consumer Services, 17, 89–96.
• Zeithaml, V. A., Berry, L. L., & Parasuraman, A. (1993). The Nature And Determinants Of Customer Expectations Of Service. Journal Of The Academy Of Marketing Science, 21, 1-12.