TÜKETİCİLERİN YAPAY ZEKÂ KAYGI DÜZEYİNİN ÇEVRİMİÇİ SATIN ALMA NİYETİNE ETKİSİNDE ALGILANAN RİSKİN ARACILIK ROLÜ

Yıl 2026, Cilt: 22 Sayı: 1, 178 - 215, 26.03.2026

Arzu Şeker , Kazım Kılınç

https://doi.org/10.17130/ijmeb.1674204

https://izlik.org/JA48GR79AB

Öz

Günümüzde yapay zekâ (YZ) destekli sistemler, e-ticaret platformlarında yaygın şekilde kullanılmakta olup tüketicilerin alışveriş deneyimlerini doğrudan etkilemektedir. Ancak, YZ’nin veri gizliliği, güvenlik ve etik konularındaki belirsizlikleri, tüketicilerde çeşitli kaygılara yol açabilmektedir. Bu çalışma, tüketicilerin YZ teknolojilerine yönelik kaygı düzeylerinin çevrimiçi satın alma niyetleri üzerindeki etkisini incelemekte ve bu ilişkide algılanan riskin aracılık rolünü araştırmaktadır. Nicel yöntemin kullanıldığı araştırmada YZ teknolojileri kullanan, çevrimiçi alışveriş yapan, 18 yaş ve üstü tüm tüketicilerin oluşturduğu evrende, tesadüfi olmayan örnekleme yöntemlerinden kolayda örnekleme yöntemiyle 427 katılımcıya ulaşılmıştır. Veriler çevrimiçi anket yoluyla toplanmış ve SmartPLS 4 istatistik programı kullanılarak yapısal eşitlik modellemesi ile analiz edilmiştir. Bulgular, yapay zekâ kaygısının çevrim içi satın alma niyeti üzerinde yalnızca “öğrenme kaygısı” boyutuyla anlamlı ve ters yönlü bir etkisinin olduğunu; diğer boyutların ise istatistiksel olarak anlamlı bir etkide bulunmadığını ortaya koymuştur. Yapay zekâ kaygısının tüketicinin algıladığı riski arttırmadığı ve algılanan riskin çevrim içi satın alma niyeti üzerinde anlamlı bir negatif etkiye sahip olmadığı belirlenmiştir. Ayrıca YZ kaygısı ve çevrim içi satın alma niyeti ilişkisinde algılanan riskin bir aracılık rolünün olmadığı tespit edilmiştir. Bu sonuçlar, tüketicilerin YZ destekli dijital alışveriş platformlarına genel olarak alışkın hâle geldiği ve YZ destekli sistemleri bir engel veya kaygı unsuru olarak görmedikleri şeklinde değerlendirilebilir.

Anahtar Kelimeler

Yapay Zekâ , Yapay Zekâ Kaygısı , Çevrim İçi Satın Alma Niyeti , Algılanan Risk , Tüketici Davranışı

THE MEDIATING ROLE OF PERCEIVED RISK IN THE RELATIONSHIP BETWEEN CONSUMERS’ ARTIFICIAL INTELLIGENCE ANXIETY AND THEIR ONLINE PURCHASE INTENTION

https://izlik.org/JA48GR79AB

Öz

Today, artificial intelligence (AI)-supported systems are widely utilized in e-commerce platforms and directly shape consumers’ shopping experiences. However, uncertainties related to data privacy, security, and ethical considerations may trigger various types of anxiety among consumers. This study investigates the influence of consumers’ anxiety levels toward AI technologies on their online purchase intention, while examining the mediating role of perceived risk in this relationship. Using a quantitative method, 427 participants were reached through convenience sampling and an online survey. Data were analyzed using structural equation modeling with the SmartPLS 4 program. The findings revealed that AI anxiety has a significant and inverse effect on purchase intention with respect to the “learning anxiety” dimension; other dimensions did not have a statistically significant effect. It was determined that AI anxiety does not increase the consumer’s perceived risk, and perceived risk does not have a significant negative effect on online purchase intention. Furthermore, it was found that perceived risk does not play a mediating role in the relationship between AI anxiety and online purchase intention. These results can be interpreted as indicating that consumers have generally become accustomed to AI-powered digital shopping platforms and do not perceive AI-powered systems as a cause for concern.

Anahtar Kelimeler

Artificial Intelligence , Artificial Intelligence Anxiety , Online Purchase Intention , Perceived Risk , Consumer Behavior

Kaynakça

• Acquisti, A., Brandimarte, L., & Loewenstein, G. (2015). Privacy and human behavior in the age of information. Science, 347(6221), 509-514. DOI: 10.1126/science.aaa1465
• Ajzen, I. (1991). The theory of planned behavior. Organizational Behavior and Human Decision Processes, 50(2), 179-211. https://doi.org/10.1016/0749-5978(91)90020-T
• Akkaya, B., Özkan, A., & Özkan, H. (2021). Yapay zekâ kaygı (YZK) ölçeği: Türkçeye uyarlama, geçerlik ve güvenirlik çalışması. Alanya Akademik Bakış, 5(2), 1125-1146. https://doi.org/10.29023/alanyaakademik.833668
• Alkaddour, M. (2022). Pazarlamada yapay zekâ kullanımı. İşletme ve Girişimcilik Araş. Dergisi. (1), 48-66. https://izlik.org/JA47WX75XX
• Altunısık, R., Coskun, R., Bayraktaroğlu, S., & Yıldırım, E. (2012). Sosyal bilimlerde araştırma yöntemleri SPSS uygulamalı. Sakarya: Sakarya Kitabevi.
• Ameen, N., Tarhini, A., Reppel, A., & Anand, A. (2021). Customer experiences in the age of artificial intelligence. Computers in Human Behavior, 114, 106548. https://doi.org/10.1016/j.chb.2020.106548
• American Psychiatric Association, D. (2013). Diagnostic and statistical manual of mental disorders: DSM-5 (Vol. 5, No. 5). Washington, DC: American psychiatric association.
• Arshad, A., Zafar, M., Fatima, I., & Khan, S. K. (2015). The impact of perceived risk on online buying behavior. International Journal of New Technology and Research, 1(8), 13-18.
• Aslam, U. (2023). Understanding the usability of retail fashion brand chatbots: Evidence from customer expectations and experiences. Journal of Retailing and Consumer Services, 74, 103377. https://doi.org/10.1016/j.jretconser.2023.103377
• Bagozzi, R. P., & Warshaw, P. R. (1990). Trying to consume. Journal of Consumer Research, 17(2), 127-140. https://doi.org/10.1086/208543
• Bauer, H. H., Falk, T. ve Hammerschmidt, M. (2006). eTransQual: A transaction process-based approach for capturing service quality in online shopping. Journal of Business Research, 59(7), 866-875. https://doi.org/10.1016/j.jbusres.2006.01.021
• Bayuk, M. N., & Demir, B. N. (2019). Endüstri 4.0 kapsaminda yapay zekâ ve pazarlamanin geleceği. International Journal Of Social, Humanities And Administrative Sciences, 5(19), 781-799. http://dx.doi.org/10.31589/JOSHAS.163
• Beck, A. T., Emery, G., & Greenberg, R. L. (2005). Anxiety disorders and phobias: A cognitive perspective. Basic Books/Hachette Book Group
• Bhagat, R., Chauhan, V. & Bhagat, P. (2023). Investigating the impact of artificial intelligence on consumer’s purchase intention in e-retailing. Foresight, 25 (2), 249-263. https://doi.org/10.1108/FS-10-2021-0218
• Bhuiyan, M. S. (2024). The role of AI-Enhanced personalization in customer experiences. Journal of Computer Science and Technology Studies, 6(1), 162-169. https://doi.org/10.32996/jcsts.2024.6.1.17
• Binns, R. (2018). Fairness in machine learning: Lessons from political philosophy. In Conference on fairness, accountability and transparency (pp. 149-159). PMLR.
• Bostrom, N. & Yudkowsky, E. (2018). The ethics of artificial intelligence. In Artificial intelligence safety and security (pp. 57-69). Chapman and Hall/CRC.
• Bozpolat, C. (2024). Yapay zekâ pazarlama teknolojisinin çevrim içi satın alma niyetine etkisinde algılanan risk ve güvenin rolü. Afyon Kocatepe Üniversitesi İktisadi ve İdari Bilimler Fakültesi Dergisi, 26(Özel Sayı), 1-16. https://doi.org/10.33707/akuiibfd.1403109
• Bronzin, T., Prole, B., Stipic, A., & Pap, K. (2021, September). Artificial Intelligence (AI) Brings Enhanced Personalized User Experience. In MIPRO (pp. 1070-1075).
• Calo, R. (2014). Digital market manipulation. The George Washington Law Review, 82(4), 995-1051. Castells, P. & Jannach, D. (2023). Recommender systems: A primer. arXiv preprint arXiv:2302.02579. https://doi.org/10.48550/arXiv.2302.02579
• Cave, S. & Dihal, K. (2019). Hopes and fears for intelligent machines in fiction and reality. Nature Machine Intelligence, 1(2), 74-78.
• Chang, P. C., Zhang, W., Cai, Q., & Guo, H. (2024). Does AI-driven technostress promote or hinder employees’ artificial intelligence adoption intention? A moderated mediation model of affective reactions and technical self-efficacy. Psychology Research and Behavior Management, 413-427. https://doi.org/10.2147/PRBM.S441444
• Chen, R., & F. He (2003). Examination of brand knowledge, perceived risk and consumers, intention to adopt an online retailer. Tom & Business Excellence, 40(6), 677-693. https://doi.org/10.1080/1478336032000053825
• Chung, M., Ko, E., Joung, H. & Kim, S. J. (2020). Chatbot e-service and customer satisfaction regarding luxury brands. Journal of Business Research, 117, 587-595. https://doi.org/10.1016/j.jbusres.2018.10.004
• Cintamür, İ. G. (2024). Acceptance of artificial intelligence devices in banking services: moderation role of technology anxiety and risk aversion. International Journal of Bank Marketing. (2024) 42 (7): 2143–2176. https://doi.org/10.1108/IJBM-10-2023-0563
• Cohen, J. (1988). Statistical power analysis for the behavioral sciences. New York. NY: Academic, 54, 77-155.
• Crawford, K., & Calo, R. (2016). There is a blind spot in AI research. Nature, 538(7625), 311-313.
• Cunningham, L. F., Gerlach, J. H., Harper, M. D., & Young,C. E. (2005). Perceived risk and the consumer buyingprocess: internet airline reservations. International Journal of Service Industry Management, 16(4), 357-372. https://doi.org/10.1108/09564230510614004
• Çicek, M., Gursoy, D., & Lu, L. (2025). Adverse impacts of revealing the presence of “Artificial Intelligence (AI)” technology in product and service descriptions on purchase intentions: the mediating role of emotional trust and the moderating role of perceived risk. Journal of Hospitality Marketing & Management, 34(1), 1-23. https://doi.org/10.1080/19368623.2024.2368040
• Çimen, M. (2025). Müşteri deneyiminin ıyileştirilmesinde yapay zekâ uygulamalarının kabulü ve riskin kalite algısı üzerindeki etkisi (Yayımlanmamış Yüksek Lisans Tezi). Necmettin Erbakan Universitesi, Sosyal Bilimler Enstitüsü, Konya.
• Davenport, T., Guha, A., Grewal, D., & Bressgott, T. (2020). How artificial intelligence will change the future of marketing. Journal of the Academy of Marketing Science, 48, 24-42. https://doi.org/10.1007/s11747-019-00696-0
• Davis, F. D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Quarterly, 13 (3), 319-340. https://doi.org/10.2307/249008
• Davis, F. D. (1993). User acceptance of information technology: system characteristics, user perceptions and behavioral impacts. International Journal of Man-Machine Studies, 38(3), 475-487. https://doi.org/10.1006/imms.1993.1022
• Demir, M. Ö. (2011). Risk algisinin marka sadakatine etkisi: Cep telefonlari kategorisinde bir uygulama/The effects of perceived risk on brand loyalty: A study on mobile phones. Ege Akademik Bakış, 11(2), 267.
• Deniz, N. (2024). Yapay zekânın sürdürülebilirliği: Sorumlu yapay zekâ. Dijital Teknolojiler ve Eğitim Dergisi, 3(1), 69-79. https://doi.org/10.5281/zenodo.12637303
• Dereli, T. (2020). Yapay zekâ ve insanlık. İçinde M. Şeker, Y. Bulduklu, C. Korkut. & M. Doğrul (Ed.). Bilişim Teknolojileri ve İletişim: Birey ve Toplum Güvenliği (s. 93-106). Ankara: Berk Grup.
• De Spiegeleire, S., Maas, M., & Sweijs, T. (2017). Front matter. In artifıcial intelligence and the future of defense: Strategıc implications for small- and medium-Sızed Force Provıders (pp. 2–5). Hague Centre for Strategic Studies. http://www.jstor.org/stable/resrep12564.1
• Doğan, M. (2017). Zihne ‘Zihinsel Özne’ ile bakmanın imkânı üzerine, Asobid, 1 (2), 2017, ss. 35-53.
• Dolu, Z. T. & Marangoz, M. (2023). Yapay zekâ uygulamalarının tüketicilerin satın alma niyeti üzerine etkisi: Büyük Hazine filmi üzerine bir analiz. Pamukkale Üniversitesi İşletme Araştırmaları Dergisi, 10(2), 525-549. https://doi.org/10.47097/piar.1271887
• Dowling, G.R., & R. Staelin (1994). A model of perceived risk and ıntended risk-handling activity. The Journal of Consumer Research, 21(1), 119-134. https://doi.org/10.1086/209386
• Ersoy, Ç. (2018). Robotlar, yapay zekâ ve hukuk. On İki Levha Yayıncılık. İstanbul: 2018.
• Eubanks, V. (2018). Automating inequality: How high-tech tools profile, police, and punish the poor. St. Martin's Press. Macmillan+ ORM.
• Featherman, M. S., & Pavlou, P. A. (2003). Predicting e-services adoption: a perceived risk facets perspective. International Journal of Human-Computer Studies, 59(4), 451-474. https://doi.org/10.1016/S1071-5819(03)00111-3
• Frenkenberg, A., & Hochman, G. (2025). It’s scary to use it, it’s scary to refuse it: the psychological dimensions of AI adoption—anxiety, motives, and dependency. Systems, 13(2), 82. https://doi.org/10.3390/systems13020082
• Frey, C. B., & Osborne, M. A. (2017). The future of employment: How susceptible are jobs to computerisation? Technological Forecasting and Social Change, 114, 254-280. https://doi.org/10.1016/j.techfore.2016.08.019
• Gantumur, K. (2025). The impact of artificial intelligence on online shopping. ESI Preprints (European Scientific Journal, ESJ, 21(4), 1-1. https://doi.org/10.19044/esj.2025.v21n4p1
• Gao, Y., & Liu, H. (2023). Artificial intelligence-enabled personalization in interactive marketing: A customer journey perspective. Journal of Research in Interactive Marketing, 17(5), 663-680. https://doi.org/10.1108/JRIM-01-2022-0023
• Garbarino, E., & Strahilevitz, M. (2004). Gender differencesin the perceived risk of buying online and the effects ofreceiving a site recommendation. Journal of Business Research, 57(7), 768-775. https://doi.org/10.1016/S0148-2963(02)00363-6
• Good, I. J. (1966). Speculations concerning the first ultraintelligent machine. In Advances In Computers (Vol. 6, pp. 31-88). Elsevier. https://doi.org/10.1016/S0065-2458(08)60418-0
• Graham, B. M., & Milad, M. R. (2011). The study of fear extinction: implications for anxiety disorders. American Journal of Psychiatry, 168(12), 1255-1265. https://doi.org/10.1176/appi.ajp.2011.11040557
• Grillon, C., Lissek, S., Rabin, S., McDowell, D., Dvir, S., & Pine, D. S. (2008). Increased anxiety during anticipation of unpredictable but not predictable aversive stimuli as a psychophysiologic marker of panic disorder. American Journal of Psychiatry, 165(7), 898-904. https://doi.org/10.1176/appi.ajp.2007.07101581
• Guo, Tang, Wang, Jia & Wang (2024). Artificial intelligent housekeeper based on consumer purchase decision: A case study of online E-commerce. Industrial Management & Data Systems, 124(8) 2588-2612. https://doi.org/10.1108/IMDS-12-2023-0905
• Gursoy, D., Chi, O. H., Lu, L., & Nunkoo, R. (2019). Consumers acceptance of artificially intelligent (AI) device use in service delivery. International Journal of Information Management, 49, 157-169. https://doi.org/10.1016/j.ijinfomgt.2019.03.008
• Gür, Y. E. (2022). Yapay zekâ ve pazarlama ilişkisi. Fırat Üniversitesi Uluslararası İktisadi ve İdari Bilimler Dergisi, 6(2), 131-148. https://izlik.org/JA48SR98JH
• Hair, J. F., Black, W. C., Babin, B. J., & Anderson, R. E. (2022). Multivariate data analysis. Cengage Learning.
• Hair, J. F., Hult, G. T. M., Ringle, C. M., Sarstedt, M., Danks, N. P., & Ray, S. (2021). Partial least squares structural equation modeling (PLS-SEM) using R: A workbook. Springer international publishing.
• Hasan, R., Shams, R., & Rahman, M. (2021). Consumer trust and perceived risk for voice-controlled artificial intelligence: The case of Siri. Journal of Business Research, 131, 591-597. https://doi.org/10.1016/j.jbusres.2020.12.012
• Hawking, S. (2017). Hawking: Yapay zekâ insanlığın sonunu getirebilir. https://www.bbc.com/turkce/haberler/2014/12/141202_hawking_yapay_zeka sayfasından erişilmiştir. Erişim Tarihi:10.01.2025
• Henseler, J., Ringle, C. M., & Sarstedt, M. (2015). A new criterion for assessing discriminant validity in varience-based structural equation modelling, Journal of the Academy of Marketing Science, 43, 115-135. https://doi.org/10.1007/s11747-014-0403-8
• Hong, Z., & Yi, L. (2012). Research on the influence ofperceived risk in consumer on-line purchasing decision. Physics Procedia, 24, 1304-1310. https://doi.org/10.1016/j.phpro.2012.02.195
• Huang, M.-H., & Rust, R. T. (2021). Engaged to a robot? The role of AI in service. Journal of Service Research, 24(1), 30-41. https://doi.org/10.1177/1094670520902266
• IBM (2024). What İs AI? https://www.ibm.com/search?lang=en&cc=us&q=what%20is%20AI sayfasından erişilmiştir. Erişim: 17.10.2024.
• Ifekanandu, C. C., Anene, J. N., Iloka, C. B., & Ewuzie, C. O. (2023). Influence of artificial intelligence (AI) on customer experience and loyalty: Mediating role of personalization. Journal of Data Acquisition and Processing, 38(3), 1936. https://sjcjycl.cn/ DOI: 10.5281/zenodo.98549423
• Işık, O. (2022). Kişisel verilerin korunması kanunu kapsamında veri sorumlusu olarak sosyal güvenlik kurumu. Erciyes Üniversitesi Hukuk Fakültesi Dergisi, 17(2), 263-362. https://izlik.org/JA89KS72XY
• İşgüzar, S. (2021). Çelik İrade: Güven bağlamında yapay zekâ üzerine bir değerlendirme. https://www.muharrembalci.com/hukukdunyasi/alintilar/2086.pdf sayfasından erişilmiştir. Erişim Tarihi: 30.11.2024.
• Johnson, D. G., & Verdicchio, M. (2017). AI anxiety. Journal of the Association for Information Science and Technology, 68(9), 2267-2270. https://doi.org/10.1002/asi.23867
• Jiang, Y., Li, X., Luo, H., Yin, S., & Kaynak, O. (2022). Quo vadis artificial intelligence? Discover Artificial Intelligence, 2(1), 4. https://doi.org/10.1007/s44163-022-00022-8
• Kamalul Ariffin, S., Mohan, T., & Goh, Y. N. (2018). Influence of consumers’ perceived risk on consumers online purchase intention. Journal of research in Interactive Marketing, 12(3), 309-327. https://doi.org/10.1108/JRIM-11-2017-0100
• Kaplan, L. B., Szybillo, G. J., & Jacoby, J. (1974). Components of perceived risk in product purchase: A cross-validation. Journal of Applied Psychology, 59(3), 287–291. https://doi.org/10.1037/h0036657
• Kaplan, A., & Haenlein, M. (2019). Siri, Siri, in my hand: Who’s the smartest in the land? On the interpretation, illustration, and implication of artificial intelligence. Business Horizons, 62(1), 15-25. https://doi.org/10.1016/j.bushor.2018.08.004
• Karaman, Ö. (2021). Yapay zekâ destekli kişiselleştirme algoritmalarının tüketici zihninde filtre balonu yaratma etkisinin incelenmesi. Süleyman Demirel Üniversitesi Vizyoner Dergisi, 12(32), 1339-1351. https://doi.org/10.21076/vizyoner.896059
• Kaushik, A. K., & Rahman, Z. (2014). Perspectives and dimensions of consumer innovativeness: A literature review and future agenda. Journal of International Consumer Marketing, 26(3), 239-263. https://doi.org/10.1080/08961530.2014.893150
• Keh, H. T., & J. Sun (2008). The complexities of perceived risk in cross-cultural services marketing. Journal of International Marketing, 16(1), 120-146. https://doi.org/10.1509/jimk.16.1.120
• Kessler, S. K., & Martin, M. (2017). How do potential users perceive the adoption of new technologies within the field of Artificial Intelligence and Internet-of-Things?-a revision of the UTAUT 2 model using voice assistants. https://lup.lub.lu.se/student-papers/search/publication/8909840 sayfasından erişildi. Erişim Tarihi: 30.11.2024.
• Khan, S. I. (2022). Impact of artificial intelligence on consumer buying behaviors: Study about the online retail purchase. International Journal of Health Sciences, (II), 8121-8129. https://doi.org/10.53730/ijhs.v6nS2.7025 Khorasani, E. S. (2008). Artificial intelligence: Structures and strategies for complex problem solving. Scalable Computing: Practice and Experience, 9(3).
• Kim, H.L., Qu, H., & D. J. Kim (2009). A Study of perceived risk and risk reduction of purchasing air-tickets online. Journal of Travel and Tourism Marketing, 26(3), 203-224. https://doi.org/10.1080/10548400902925031
• Kim, J., & Forsythe, S. (2008). Adoption of virtual try-on technology for online apparel shopping. Journal of Interactive Marketing, 22(2), 45-59. https://doi.org/10.1002/dir.20113
• Korkmaz, İ. (2024). Yapay zekâya yönelik tutumun, kolaylığın, yenilikçiliğin ve güvenin satın alma niyeti üzerine etkisi. Afyon Kocatepe Üniversitesi İktisadi ve İdari Bilimler Fakültesi Dergisi, 26(2), 318-339. https://doi.org/10.33707/akuiibfd.1522892
• Köroglu, Y. (2017). Yapay zekâ’nın teorik ve pratik sınırları. Bogaziçi Üniversitesi Yayınevi, 1-10.
• Kwong, K., & Wong, K. (2013). Partial least squares structural equation modeling (PLS-SEM) techniques using smartPLS. Marketing Bulletin, 24(Technical Note 1).
• Lin, P. J., Jones, E., & westwood, s. (2009). Perceived risk and risk-relievers in online travel purchase intentions. Journal of Hospitality Marketing & Management, 18(8), 782–810. https://doi.org/10.1080/19368620903235803
• Liu, Y., Park, Y., & Wang, H. (2025). The mediating effect of user satisfaction and the moderated mediating effect of AI anxiety on the relationship between perceived usefulness and subscription payment intention. Journal of Retailing and Consumer Services, 84, 104176. https://doi.org/10.1016/j.jretconser.2024.104176
• Lopez, A., & Garza, R. (2023). Consumer bias against evaluations received by artificial intelligence: the mediation effect of lack of transparency anxiety. Journal of Research in Interactive Marketing, 17(6), 831-847. https://doi.org/10.1108/JRIM-07-2021-0192
• Luo, X., Tong, S., Fang, Z., & Qu, Z. (2019). Frontiers: Machines vs. humans: The impact of artificial intelligence chatbot disclosure on customer purchases. Marketing Science, 38(6), 937-947. https://doi.org/10.1287/mksc.2019.1192
• Masoud, E. Y. (2013). The effect of perceived risk on onlineshopping in Jordan. European Journal of Business and Management, 5(6), 76-87.
• Maziriri, E. T., & Chuchu, T. (2017). The conception of consumer perceived risk towards online purchases of apparel and an idiosyncratic scrutiny of perceived social risk: a review of literature. International Review of Management and Marketing, 7(3), 257-265. https://izlik.org/JA66JF32HF
• Mendoza, C. (2022). The Age of Surveillance Capitalism: The fight for a human future at the new frontier of power: By Shoshana Zuboff, New York, Public Affairs, 2019, 704 pp., ISBN 978-1610395694. Church, Communication and Culture, 7(2), 452–455. https://doi.org/10.1080/23753234.2022.2086891
• McCarthy, J. (2004). What is artificial intelligence? https://www-formal.stanford.edu/jmc/whatisai/node1.html sayfasından erişildi. Erişim: 14.10.2024.
• Merhi, M. I. (2023). An evaluation of the critical success factors impacting artificial intelligence implementation. International Journal of Information Management, 69, 102545. https://doi.org/10.1016/j.ijinfomgt.2022.102545
• Mitchell, V. W. (1999). Consumer perceived risk: Conceptualisations and models. European Journal of Marketing, 33(1/2), 163-195. https://doi.org/10.1108/03090569910249229
• Mitchell, V. W., & Greatorex, M. (1993). Riskperception and reduction in the purchase ofconsumer services. The Service Industries Journal,13(4), 179-200. ttps://doi.org/10.1080/02642069300000068
• Miyazaki, AD., & Fernandez, A. (2000). Internet privacy and security: An examination of online retailer disclosures. Journal Of Public Policy & Marketing, 19(1), 54-61. https://doi.org/10.1509/jppm.19.1.54.16942
• Moon, W. K., Wei, X., Overton, H., & Kim, J. K. (2025). Between innovation and caution: How consumers’ risk perception shapes AI product decisions. Journal of Current Issues & Research in Advertising, 1-23. https://doi.org/10.1080/10641734.2025.2468474
• Murugan, T. S., & Kumar, C. M. (2024). Unveiling the evolution: Impact of artificial ıntelligence on consumer buying behaviors in online retail purchase. Educational Administration: Theory and Practice, 30(5), 1072-1078. Doi: 10.53555/kuey.v30i5.3011
• Ng, A. (2023). ‘Do we think the world is better off with more or less intelligence?’ https://www.ft.com/content/2dc07f9e-d2a9-4d98-b746-b051f9352be3 sayfasından erişildi. Erişim Tarihi:01.12.2024.
• Narcı, M. T. (2023). Understanding effect of consumers' perceived risk on online purchasing behavior: cosmetic product example. JOEEP: Journal of Emerging Economies and Policy, 8(2), 239-246. https://izlik.org/JA79RF95PE
• Nilsson, N. J. (1998). Artificial intelligence: A new synthesis. San Francisco, CA: Morgan Kaufmann. Ntoutsi, E., Fafalios, P., Gadiraju, U., Iosifidis, V., Nejdl, W., Vidal, M. E., ... Staab, S. (2020). Bias in data‐driven artificial intelligence systems—An introductory survey. Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery, 10(3), e1356. https://doi.org/10.1002/widm.1356
• Nguyen, C.T.K., Nguyen, Y.T.N., & Bui, A.N.T. (2024). Analyzing the Influence of Artificial Intelligence on Consumers’ Online Purchase Intentions. In: Tung, L.T., Sinh, N.H., Ha, P. (eds) Disruptive Technology and Business Continuity. Springer, Singapore. https://doi.org/10.1007/978-981-97-5452-6_5
• Obschonka, M., & Audretsch, D. B. (2020). Artificial ıntelligence and big data entrepreneurship: A new era has begun. Small Business Economics, 55, 529-539. https://doi:10.1007/s11187-019-00202-4 Oner N., & Le Compte A. (1985). Süreksiz durumluk- sürekli kaygı envanteri el kitabı, 20. Basım İstanbul: Boğaziçi Üniversitesi Yayınları.
• Öztürk, M. (2024). Tüketici davranışında yapay zekâ uygulamaları. İçinde Davut Karaman (Ed). Tüketici Davranışları 4-Güncel Akademik Çalışmalar, İstanbul: Eğitim Yayınevi.
• Paschen, J., Wilson, M., & Ferreira, J. J. (2020). Collaborative intelligence: How human and artificial intelligence create value along the B2B sales funnel. Business Horizons, 63(3), 403-414. https://doi.org/10.1016/j.bushor.2020.01.003
• Pasquale, F. (2015). The black box society: The secret algorithms that control money and information. Harvard University Press.
• Pavlou, P. A. (2003). Consumer acceptance of electronic commerce: Integrating trust and risk with the technology acceptance model. International Journal Of Electronic Commerce, 7(3), 101-134. https://doi.org/10.1080/10864415.2003.11044275
• Peter, J. P., & Ryan, M. J. (1976). An investigation of perceived risk at the brand level. Journal of Marketing Research, 13(2), 184-188. https://doi.org/10.1177/002224377601300210
• Phamthi, V. A., Nagy, Á., & Ngo, T. M. (2024). The influence of perceived risk on purchase intention in e‐commerce—Systematic review and research agenda. International Journal of Consumer Studies, 48(4), e13067. https://doi.org/10.1111/ijcs.13067
• Pillai, R., Sivathanu, B., & Dwivedi, Y. K. (2020). Shopping intention at AI-powered automated retail stores (AIPARS). Journal of Retailing and Consumer Services, 57, 102207. https://doi.org/10.1016/j.jretconser.2020.102207
• Rahma, S. J. A. (2024). Enhancing online purchase intention through artificial intelligence: Transformation to digital customer experience. In Proceedings of the International Conference on Business, Economics, Social Sciences, and Humanities-Economics, Business and Management Track (ICOBEST-EBM 2024) (p. 120). Springer Nature.
• Rich, E., & Knight, K. (2010). Artificial Intelligence. 3rd ed. New Delhi: McGraw Hill. https://github.com/saranshbht/msc-books/blob/master/M.Sc.%20CS%20Sem-1/Artificial%20Intelligence/Kevin%20Knight,%20Elaine%20Rich,%20B.%20Nair%20-%20Artificial%20Intelligence%20(2010,%20Tata%20McGraw-Hill%20Education%20Pvt.%20Ltd.)%20-%20libgen.lc.pdf sayfasından erişildi. Erişim: 14.10.2024.
• Roselius, T. (1971). Consumer rankings of risk reduction methods. Journal of Marketing, 35(1), 56-61. https://doi.org/10.1177/002224297103500110
• Russell, S., Norvig, P., & Intelligence, A. (1995). A modern approach. Artificial Intelligence. Prentice-Hall, Egnlewood Cliffs, 25(27), 79-80.
• Russell, S., & Norvig, P. (2016). Artificial Intelligence: A modern approach (2nd ed.). Prentice Hall. Salisbury, W. D., Pearson, R. A., Pearson, A. W., &Miller, D. W. (2001). Perceived security and world wide web purchase intention. Industrial Management & Data Systems, 101(3–4), 165-176. https://doi.org/10.1108/02635570110390071
• Sarstedt, M., Ringle, C. M., & Hair, J. F. (2021). Partial least squares structural equation modeling. In Handbook of market research (pp. 587-632). Cham: Springer International Publishing.
• Schiffman, L., O'Cass, A., Paladino, A., & Carlson, J. (2013). Consumer behaviour. Pearson Higher Education AU. Shin, D. (2020). User perceptions of algorithmic decisions in the personalized AI system: Perceptual evaluation of fairness, accountability, transparency, and explainability, Journal of Broadcasting & Electronic Media, 64:4, 541-565. https://doi.org/10.1080/08838151.2020.1843357
• Shin, D. (2021). The effects of explainability and causability on perception, trust, and acceptance: Implications for explainable AI. International journal of human-computer studies, 146, 102551. https://doi.org/10.1016/j.ijhcs.2020.102551
• Simpson, L., & Lakner, H. B. (1993). Perceived risk and mail order shopping for apparel. Journal of Consumer Studies & Home Economics, 17(4), 377-389. https://doi.org/10.1111/j.1470-6431.1993.tb00180.x
• Solomon, M., Russell-Bennett, R., & Previte, J. (2012). Consumer behaviour. Pearson Higher Education AU. Spielberger, C.D. (1972). Anxiety: Current trends in theory and research. Academic Press. https://books.google.com.tr/books? id=UmhaBQAAQBAJ&pg=PA322&hl=tr&source=gbs_selected_pages&cad=1#v=onepage&q&f=false sayfasından erişildi. Erişim tarihi: 10.01.2025
• Stone, R. N., & Grønhaug, K. (1993). Perceived risk: Further considerations for the marketing discipline. European Journal of Marketing, 27(3), 39-50. https://doi.org/10.1108/03090569310026637
• Süne, M., Akgün, L., & Armutcu, B. (2024). Yapay zekânın satın alma davranışı üzerine etkisi. Bingöl 6. Akademik Çalışmalar Kongresinde sunulmuş bildiri. Bingöl Üniversitesi.
• Şenyapar, H. N. D. (2024). Üretken yapay zekâ ve pazarlama stratejileri: SWOT analizi perspektifi. R&S-Research Studies Anatolia Journal, 7(1), 72-96. https://doi.org/10.33723/rs.1418098
• Taddeo, M., & Floridi, L. (2018). How AI can be a force for good. Science, 361(6404), 751-752. DOI: 10.1126/science.aat5991
• Temiz, S., Sönmez, A. T., & Kurtoğlu, R. (2025). Yapay zekâ kaygısı, memnuniyet ile marka itibarı arasındaki ilişkiyi değiştirir mi? İşletmelerin sunduğu chatbot (sanal asistan) hizmetleri üzerine bir araştırma. Pazarlama ve Pazarlama Araştırmaları Dergisi, 18(1), 243-276. https://izlik.org/JA45AE69MM
• Tham, K. W., Dastane, O., Johari, Z., & Ismail, N. B. (2019). Perceived risk factors affecting consumers' online shopping behaviour. The Journal of Asian Finance, Economics and Business, 6(4), 249-260. https://doi.org/10.13106/jafeb.2019.vol6.no4.249
• Thamma, N., Anywatnapong, W., Tunpornchai, W., & Saetang, C. (2024). Transforming e-commerce: Artificial intelligence effect on purchase decision and happiness. PSAKU International Journal of Interdisciplinary Research (forthcoming). Asian Administration and Management Review, 7(1). http://dx.doi.org/10.2139/ssrn.4803985
• Qin, M., Zhu, W., Zhao, S., & Zhao, Y. (2022). Is artificial intelligence better than manpower? The effects of different types of online customer services on customer purchase intentions. Sustainability, 14(7), 3974. https://doi.org/10.3390/su14073974
• Venkatesh, V., & Bala, H. (2008). Technology acceptance model 3 and a research agenda on interventions. Decision sciences, 39(2), 273-315. https://doi.org/10.1111/j.1540-5915.2008.00192.x
• Vergaray, A. D., Robles, W. F. P., & Jiménez, J. A. S. (2023). The impact of chatbots on customer satisfaction: A systematic literature review. TEM Journal, 12(3), 1407. DOI: 10.18421/TEM123-21
• Wang, W., He, H., Sahadev, S., & Song, W. (2018). UK consumers' perceived risk of buying products fromemerging economies: A moderated mediation model. Journal of Consumer Behaviour, 17(3), 326-339. https://doi.org/10.1002/cb.1714
• Wang, Y. Y., & Wang, Y. S. (2019). Development and validation of an artificial intelligence anxiety scale: an initial application in predicting motivated learning behavior. Interactive Learning Environments, 1–16. https://doi.org/10.1080/10494820.2019.1674887
• What is AI anxiety? 5 tips to help you deal with the fear (2024). Retrieved from https://www.calm.com/blog/ai-anxiety-tips Accessed 15.12.2024.
• Yeung, R. M., & Morris, J. (2006). An empirical study ofthe impact of consumer perceived risk on purchaselikelihood: a modelling approach. International Journalof Consumer Studies, 30(3), 294-305. https://doi.org/10.1111/j.1470-6431.2006.00493.x
• Yin, J., & Qiu, X. (2021). AI technology and online purchase intention: Structural equation model based on perceived value. Sustainability, 13(10), 5671. https://doi.org/10.3390/su13105671
• Yu, S. Y. (2021). Research on user experience and brand attitudes of chatbots. International Journal of Humanities and Social Sciences, 15(8), 698-704.
• Yuan, C., Zhang, C., & Wang, S. (2022). Social anxiety as a moderator in consumer willingness to accept AI assistants based on utilitarian and hedonic values. Journal of Retailing and Consumer Services, 65, 102878. https://doi.org/10.1016/j.jretconser.2021.102878
• Zeithaml, V.A. (1988). Consumer perceptions of price, quality, and value: a means-end model and synthesis of evidence. Journal of Marketing, 52: 2-22. https://doi.org/10.1177/002224298805200302
• Zhang, C., Zhang, C., Li, C., Qiao, Y., Zheng, S., Dam, S. K., ... Choi, J. (2023). One small step for generative ai, one giant leap for agi: A complete survey on chatgpt in aigc era. 1,1,29. https://doi.org/10.48550/arXiv.2304.06488
• Zhao, X, Lynch, J.G., & Chen, Q. (2010). Reconsidering baron and kenny: Myths and truths about mediation analysis, Journal of Consumer Research Inc., Vol. 37, No. 2, 197-206. https://doi.org/10.1086/651257
• Zhu, W., Huang, L., Zhou, X., Li, X., Shi, G., Ying, J., & Wang, C. (2024). Could AI ethical anxiety, perceived ethical risks and ethical awareness about AI influence university students’ use of generative AI products? An ethical perspective. International Journal of Human–Computer Interaction, 41(1), 742–764. https://doi.org/10.1080/10447318.2024.2323277
• Zuboff, S. (2024). The age of surveillance capitalism: The fight for a human future at the new frontier of power. Journal of Information Ethics, 33(1), 84-85.