Dergiler / Uluslararası Yönetim İktisat ve İşletme Dergisi / 2026 / Cilt: 22 Sayı: 1

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

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

Kazım Kılınç(BATMAN ÜNİVERSİTESİ)0000-0003-4154-5245
Sayfa
178–215
Yayın Tarihi
2026-03-26
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Yapay Zeka Hızlı Bakış & Çıkarımlar

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⚡ TL;DR(1 Cümlede Hızlı Çıkarım)

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.

📌 3 Maddede Bu Makale

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Amaç & Hipotez:Günümüzde yapay zekâ (YZ) destekli sistemler, e-ticaret platformlarında yaygın şekilde kullanılmakta olup tüketicilerin alışveriş deneyimlerini doğrudan etkilemektedir.
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Temel Bulgular & Veri:Bu çalışma, tüketicilerin YZ teknolojilerine yönelik kaygı düzeylerinin çevrimiçi satın alma niyetleri üzerindeki etkisini incelemekte ve bu ilişkide algılanan riskin aracılık rolünü araştırmaktadır.
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Sonuç & Katkı: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.
🔬Tür:Uygulamalı Anket & Alan Araştırması
📍Kapsam:427 katılımcı
⚙️Değişkenler:Yapay Zekâ · Yapay Zekâ Kaygısı · Çevrim İçi Satın Alma Niyeti

Özet

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.

Abstract

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.

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Bu Makaleye Atıf Yap

Şeker, A., Kılınç, K. (2026). TÜKETİCİLERİN YAPAY ZEKÂ KAYGI DÜZEYİNİN ÇEVRİMİÇİ SATIN ALMA NİYETİNE ETKİSİNDE ALGILANAN RİSKİN ARACILIK ROLÜ. Uluslararası Yönetim İktisat ve İşletme Dergisi, Cilt: 22 Sayı: 1, 178–215. https://doi.org/10.17130/ijmeb.1674204

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Kaynakça (140)

  1. 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
  2. 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
  3. 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
  4. Alkaddour, M. (2022). Pazarlamada yapay zekâ kullanımı. İşletme ve Girişimcilik Araş. Dergisi. (1), 48-66. https://izlik.org/JA47WX75XX
  5. Altunısık, R., Coskun, R., Bayraktaroğlu, S., & Yıldırım, E. (2012). Sosyal bilimlerde araştırma yöntemleri SPSS uygulamalı. Sakarya: Sakarya Kitabevi.
  6. 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
  7. American Psychiatric Association, D. (2013). Diagnostic and statistical manual of mental disorders: DSM-5 (Vol. 5, No. 5). Washington, DC: American psychiatric association.
  8. 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.
  9. 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
  10. Bagozzi, R. P., & Warshaw, P. R. (1990). Trying to consume. Journal of Consumer Research, 17(2), 127-140. https://doi.org/10.1086/208543
  11. 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
  12. 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
  13. Beck, A. T., Emery, G., & Greenberg, R. L. (2005). Anxiety disorders and phobias: A cognitive perspective. Basic Books/Hachette Book Group
  14. 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
  15. 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
  16. Binns, R. (2018). Fairness in machine learning: Lessons from political philosophy. In Conference on fairness, accountability and transparency (pp. 149-159). PMLR.
  17. Bostrom, N. & Yudkowsky, E. (2018). The ethics of artificial intelligence. In Artificial intelligence safety and security (pp. 57-69). Chapman and Hall/CRC.
  18. 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
  19. Bronzin, T., Prole, B., Stipic, A., & Pap, K. (2021, September). Artificial Intelligence (AI) Brings Enhanced Personalized User Experience. In MIPRO (pp. 1070-1075).
  20. 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
  21. Cave, S. & Dihal, K. (2019). Hopes and fears for intelligent machines in fiction and reality. Nature Machine Intelligence, 1(2), 74-78.
  22. 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
  23. 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
  24. 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
  25. 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
  26. Cohen, J. (1988). Statistical power analysis for the behavioral sciences. New York. NY: Academic, 54, 77-155.
  27. Crawford, K., & Calo, R. (2016). There is a blind spot in AI research. Nature, 538(7625), 311-313.
  28. 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
  29. 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
  30. 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.
  31. 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
  32. 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
  33. 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
  34. 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.
  35. 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
  36. 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.
  37. 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
  38. Doğan, M. (2017). Zihne ‘Zihinsel Özne’ ile bakmanın imkânı üzerine, Asobid, 1 (2), 2017, ss. 35-53.
  39. 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
  40. 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
  41. Ersoy, Ç. (2018). Robotlar, yapay zekâ ve hukuk. On İki Levha Yayıncılık. İstanbul: 2018.
  42. Eubanks, V. (2018). Automating inequality: How high-tech tools profile, police, and punish the poor. St. Martin's Press. Macmillan+ ORM.
  43. 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
  44. 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
  45. 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
  46. 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
  47. 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
  48. 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
  49. 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
  50. 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
  51. 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
  52. 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
  53. 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
  54. 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
  55. Hair, J. F., Black, W. C., Babin, B. J., & Anderson, R. E. (2022). Multivariate data analysis. Cengage Learning.
  56. 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.
  57. 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
  58. 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
  59. 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
  60. 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
  61. 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
  62. IBM (2024). What İs AI? https://www.ibm.com/search?lang=en&cc=us&q=what%20is%20AI sayfasından erişilmiştir. Erişim: 17.10.2024.
  63. 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
  64. 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
  65. 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.
  66. 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
  67. 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
  68. 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
  69. 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
  70. 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
  71. 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
  72. 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
  73. 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
  74. 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.
  75. 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).
  76. 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
  77. 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
  78. 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
  79. Köroglu, Y. (2017). Yapay zekâ’nın teorik ve pratik sınırları. Bogaziçi Üniversitesi Yayınevi, 1-10.
  80. Kwong, K., & Wong, K. (2013). Partial least squares structural equation modeling (PLS-SEM) techniques using smartPLS. Marketing Bulletin, 24(Technical Note 1).
  81. 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
  82. 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
  83. 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
  84. 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
  85. Masoud, E. Y. (2013). The effect of perceived risk on onlineshopping in Jordan. European Journal of Business and Management, 5(6), 76-87.
  86. 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
  87. 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
  88. McCarthy, J. (2004). What is artificial intelligence? https://www-formal.stanford.edu/jmc/whatisai/node1.html sayfasından erişildi. Erişim: 14.10.2024.
  89. 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
  90. 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
  91. 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
  92. 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
  93. 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
  94. 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
  95. 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.
  96. 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
  97. 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
  98. 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
  99. 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.
  100. Öztürk, M. (2024). Tüketici davranışında yapay zekâ uygulamaları. İçinde Davut Karaman (Ed). Tüketici Davranışları 4-Güncel Akademik Çalışmalar, İstanbul: Eğitim Yayınevi.
  101. 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
  102. Pasquale, F. (2015). The black box society: The secret algorithms that control money and information. Harvard University Press.
  103. 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
  104. 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
  105. 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
  106. 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
  107. 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.
  108. 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.
  109. Roselius, T. (1971). Consumer rankings of risk reduction methods. Journal of Marketing, 35(1), 56-61. https://doi.org/10.1177/002224297103500110
  110. Russell, S., Norvig, P., & Intelligence, A. (1995). A modern approach. Artificial Intelligence. Prentice-Hall, Egnlewood Cliffs, 25(27), 79-80.
  111. 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
  112. 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.
  113. 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
  114. 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
  115. 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
  116. 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
  117. 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
  118. Süne, M., Akgün, L., & Armutcu, B. (2024). Yapay zekânın satın alma davranışı üzerine etkisi. Bingöl 6. Akademik Çalışmalar Kongresinde sunulmuş bildiri. Bingöl Üniversitesi.
  119. 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
  120. Taddeo, M., & Floridi, L. (2018). How AI can be a force for good. Science, 361(6404), 751-752. DOI: 10.1126/science.aat5991
  121. 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
  122. 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
  123. 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
  124. 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
  125. 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
  126. 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
  127. 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
  128. 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
  129. 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.
  130. 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
  131. 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
  132. Yu, S. Y. (2021). Research on user experience and brand attitudes of chatbots. International Journal of Humanities and Social Sciences, 15(8), 698-704.
  133. 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
  134. 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
  135. 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
  136. 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
  137. 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
  138. 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.
  139. 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
  140. 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