Artificial Intelligence and Behavioral Engineering: Hidden Societal Costs and Design Challenges in Consumer Contexts

Öz

Artificial Intelligence (AI) is no longer just optimizing consumer experiences—it is actively engineering behavioral environments through algorithmic design. This qualitative exploratory analysis examines the hidden societal costs and design challenges of AI-driven consumer systems by synthesizing insights from psychology, digital ethics, behavioral economics, and human–AI interaction. The study identifies ten interrelated domains—such as hyper-personalization, emotional AI, dark nudging, neuromarketing, and synthetic influencers—that reveal how AI systems reshape consumer autonomy, trust, and decision-making. A novel conceptual framework and Theme–Implication Matrix are developed to link AI’s behavioral mechanisms with design-level ethical risks and governance needs. Findings emphasize the need for transparent, human-centered AI architectures that balance personalization with cognitive integrity. The study contributes to socio-technical system design by framing AI as a behavioral engineering force with profound implications for consumer agency, digital equity, and sustainable innovation.

Anahtar Kelimeler

• Artificial Intelligence (AI) in consumer systems
• Behavioral engineering and AI design
• Algorithmic personalization and decision-making
• Dark nudging and interface manipulation
• Emotion AI and neuromarketing ethics
• Digital autonomy and cognitive integrity
• Ethical AI and socio-technical risk.

Yapay Zekâ ve Davranışsal Mühendislik: Tüketici Bağlamlarında Gizli Toplumsal Maliyetler ve Tasarım Zorlukları

Öz

Yapay zekâ (YZ), yalnızca tüketici deneyimlerini optimize eden bir araç olmanın ötesine geçerek, davranışsal ortamları algoritmik tasarım yoluyla yeniden şekillendiren bir mühendislik gücüne dönüşmüştür. Bu niteliksel ve keşifsel analiz, YZ’nin tüketici sistemlerindeki gizli toplumsal maliyetlerini ve tasarım temelli risklerini psikoloji, dijital etik, davranışsal iktisat ve insan–YZ etkileşimi alanlarından elde edilen bulgular ışığında bütüncül bir şekilde analiz etmektedir. Çalışmada, aşırı kişiselleştirme, duygusal yapay zekâ, karanlık yönlendirme, nöropazarlama ve sentetik etkileyiciler gibi on tematik alan belirlenmiştir. Bu alanlar, YZ’nin tüketici özerkliği, güveni ve karar alma süreçleri üzerinde nasıl hem açık hem örtük etkiler yarattığını ortaya koymaktadır. Araştırma, davranışsal mekanizmalar ile etik, politika ve tasarım düzeyinde müdahale alanlarını ilişkilendiren kavramsal bir çerçeve ve Tema–Sonuç Matrisi geliştirmiştir. Bulgular, YZ’nin sadece işlevsel değil; aynı zamanda davranışsal ve toplumsal yönleriyle de değerlendirilmesi gerektiğini savunmakta, insan merkezli, şeffaf ve etik temelli YZ sistemlerinin önemine dikkat çekmektedir.

Anahtar Kelimeler

• Tüketici sistemlerinde Yapay Zekâ (YZ)
• Davranışsal mühendislik ve YZ tasarımı
• Algoritmik kişiselleştirme ve karar verme süreçleri
• Karanlık yönlendirme ve arayüz manipülasyonu
• Duygu temelli YZ ve nöropazarlama etiği
• Dijital özerklik ve bilişsel bütünlük
• Etik yapay zekâ ve sosyo-teknik riskler

Kaynakça

1. [1] L. Abrardi, C. Cambini, and L. Rondi, “Artificial intelligence, firms and consumer behavior: A survey,” Journal of Economic Surveys, vol. 36, no. 4, pp. 969–991, 2022, doi: 10.1111/joes.12455.
2. [2] Mustafa Ayobami Raji, Hameedat Bukola Olodo, Timothy Tolulope Oke, Wilhelmina Afua Addy, Onyeka Chrisanctus Ofodile, and Adedoyin Tolulope Oyewole, “E-commerce and consumer behavior: A review of AI-powered personalization and market trends,” GSC Adv. Res. Rev., vol. 18, no. 3, pp. 066–077, Mar. 2024, doi: 10.30574/gscarr.2024.18.3.0090.
3. [3] M. Song, X. Xing, Y. Duan, J. Cohen, and J. Mou, “Will artificial intelligence replace human customer service? The impact of communication quality and privacy risks on adoption intention,” Journal of Retailing and Consumer Services, vol. 66, p. 102900, May 2022, doi: 10.1016/j.jretconser.2021.102900.
4. [4] V. Buhas, I. Ponomarenko, O. Kazak, and N. Korshun, “AI-Driven Sentiment Analysis in Social Media Content,” Digital Economy Concepts and Technologies Workshop 2024, vol. 3665, pp. 12–21, 2024.
5. [5] A. A. Huertos, I. P. Garb, and A. A. Sanchez-Ruiz, “Explanations on Smart Stores,” pp. 16–27, 2021.
6. [6] M. Mariani, R. Perez-Vega, and J. Wirtz, “AI in marketing, consumer research and psychology: A systematic literature review and research agenda,” Psychology & Marketing, vol. 39, no. 4, pp. 755–776, 2022, doi: 10.1002/mar.21619.
7. [7] D. Patil, “Artificial Intelligence For Personalized Marketing And Consumer Behaviour Analysis: Enhancing Engagement And Conversion Rates,” Nov. 2024, doi: 10.2139/ssrn.5057436.
8. [8] A. B. Sussman, H. E. Hershfield, and O. Netzer, “Consumer Financial Decision Making: Where We’ve Been and Where We’re Going,” Journal of the Association for Consumer Research, vol. 8, no. 4, pp. 365–372, Oct. 2023, doi: 10.1086/727194.

9. [9] S. Noranee and A. K. Bin Othman, “Understanding Consumer Sentiments: Exploring the Role of Artificial Intelligence in Marketing,” JMM17, vol. 10, no. 1, pp. 15–23, Jun. 2023, doi: 10.30996/jmm17.v10i1.8690.
10. [10] P. Jindal and H. Gouri, “AI-Personalization Paradox: Navigating Consumer Behavior in a Filter Bubble Era,” in AI Impacts in Digital Consumer Behavior, IGI Global Scientific Publishing, 2024, pp. 82–111. Accessed: Mar. 04, 2025. [Online]. Available: https://www.igi-global.com/chapter/ai-personalization-paradox/www.igi-global.com/chapter/ai-personalization-paradox/341042
11. [11] A. Lopez and R. Garza, “Consumer bias against evaluations received by artificial intelligence: the mediation effect of lack of transparency anxiety,” Journal of Research in Interactive Marketing, vol. 17, no. 6, pp. 831–847, Feb. 2023, doi: 10.1108/JRIM-07-2021-0192.
12. [12] M. Ienca, “On Artificial Intelligence and Manipulation,” Topoi, vol. 42, no. 3, pp. 833–842, Jul. 2023, doi: 10.1007/s11245-023-09940-3.
13. [13] E. Astuti, I. Harsono, S. Uhai, H. N. Muthmainah, and A. Y. Vandika, “Application of Artificial Intelligence Technology in Customer Service in the Hospitality Industry in Indonesia: A Literature Review on Improving Efficiency and User Experience,” Sciences du Nord Nature Science and Technology, vol. 1, no. 01, pp. 28–36, Feb. 2024, doi: 10.58812/snst.v1i1.15.
14. [14] X. Wang, Z. Zhang, and Q. Jiang, “The effectiveness of human vs. AI voice-over in short video advertisements: A cognitive load theory perspective,” Journal of Retailing and Consumer Services, vol. 81, p. 104005, Nov. 2024, doi: 10.1016/j.jretconser.2024.104005.
15. [15] J. M. Echterhoff, A. Melkote, S. Kancherla, and J. McAuley, “Avoiding Decision Fatigue with AI-Assisted Decision-Making,” in Proceedings of the 32nd ACM Conference on User Modeling, Adaptation and Personalization, in UMAP ’24. New York, NY, USA: Association for Computing Machinery, Jun. 2024, pp. 1–11. doi: 10.1145/3627043.3659569.
16. [16] J. Li and J. Kang, “Does Sophisticated AI Ease or Inflame Luxury Decision Fatigue and Purchase Postponement? The Moderated Serial Mediation Effect of Service Embarrassment,” International Textile and Apparel Association Annual Conference Proceedings, vol. 81, no. 1, Jan. 2025, doi: 10.31274/itaa.18820.
17. [17] R. Hasan, R. Shams, and M. Rahman, “Consumer trust and perceived risk for voice-controlled artificial intelligence: The case of Siri,” Journal of Business Research, vol. 131, pp. 591–597, Jul. 2021, doi: 10.1016/j.jbusres.2020.12.012.
18. [18] D. Kim et al., “How should the results of artificial intelligence be explained to users? - Research on consumer preferences in user-centered explainable artificial intelligence,” Technological Forecasting and Social Change, vol. 188, p. 122343, Mar. 2023, doi: 10.1016/j.techfore.2023.122343.
19. [19] R. Machlev et al., “Explainable Artificial Intelligence (XAI) techniques for energy and power systems: Review, challenges and opportunities,” Energy and AI, vol. 9, p. 100169, Aug. 2022, doi: 10.1016/j.egyai.2022.100169.
20. [20] S. Papadimitriou and M. Virvou, “General Data Protection Regulation and Adaptive Educational Games,” in Artificial Intelligence—Based Games as Novel Holistic Educational Environments to Teach 21st Century Skills, S. Papadimitriou and M. Virvou, Eds., Cham: Springer Nature Switzerland, 2025, pp. 253–275. Accessed: Mar. 04, 2025. [Online]. Available: https://doi.org/10.1007/978-3-031-77464-5_9
21. [21] A. Kjellberg and V. Daiwei Olsén, I, Skeptical? A quantitative study of Consumer Resistance to innovations in services in the Context of Autonomous Public Transport. 2025. Accessed: Mar. 04, 2025. [Online]. Available: https://urn.kb.se/resolve?urn=urn:nbn:se:su:diva-239293
22. [22] D. Desai, “Hyper-Personalization: An AI-Enabled Personalization for Customer-Centric Marketing,” in Adoption and Implementation of AI in Customer Relationship Management, IGI Global Scientific Publishing, 2022, pp. 40–53. Accessed: Mar. 04, 2025. [Online]. Available: https://www.igi-global.com/chapter/hyper-personalization/www.igi-global.com/chapter/hyper-personalization/289446
23. [23] L. Florido-Benítez, “Generative artificial intelligence: a proactive and creative tool to achieve hyper-segmentation and hyper-personalization in the tourism industry,” International Journal of Tourism Cities, vol. ahead-of-print, no. ahead-of-print, Oct. 2024, doi: 10.1108/IJTC-05-2024-0111.
24. [24] T. Guendouz, “Artificial Intelligence-Powered Customer Experience Management (Moving from Mass to Hyper-Personalization in light of Relationship Marketing),” IJSR, vol. 3, no. 6, pp. 247–306, Jun. 2024, doi: 10.59992/IJSR.2024.v3n6p9.
25. [25] F. Morton, T. T. Benavides, and E. González-Treviño, “Taking Customer-Centricity to New Heights: Exploring the Intersection of AI, Hyper-Personalization, and Customer-Centricity in Organizations,” in Smart Engineering Management, C. Machado and J. P. Davim, Eds., Cham: Springer International Publishing, 2024, pp. 23–41. Accessed: Mar. 04, 2025. [Online]. Available: https://doi.org/10.1007/978-3-031-52990-0_2
26. [26] R. Brooks et al., “Use of artificial intelligence to enable dark nudges by transnational food and beverage companies: analysis of company documents,” Public Health Nutrition, vol. 25, no. 5, pp. 1291–1299, May 2022, doi: 10.1017/S1368980022000490.
27. [27] A. Kitkowska, “Chapter 19: The AI-empowered dark patterns,” 2025. Accessed: Mar. 04, 2025. [Online]. Available: https://www.elgaronline.com/edcollchap/book/9781035300198/book-part-9781035300198-28.xml
28. [28] M. Grochowski, A. Jabłonowska, F. Lagioia, and G. Sartor, “Algorithmic Price Discrimination and Consumer Protection,” 2022, doi: 10.26116/techreg.2022.004.
29. [29] Z. Wu, Y. Yang, J. Zhao, and Y. Wu, “The Impact of Algorithmic Price Discrimination on Consumers’ Perceived Betrayal,” Front. Psychol., vol. 13, May 2022, doi: 10.3389/fpsyg.2022.825420.
30. [30] T. W. Kim and A. Duhachek, “Artificial Intelligence and Persuasion: A Construal-Level Account,” Psychol Sci, vol. 31, no. 4, pp. 363–380, Apr. 2020, doi: 10.1177/0956797620904985.
31. [31] G. Sebastian, A. George, and G. J. Jr, “Persuading Patients Using Rhetoric to Improve Artificial Intelligence Adoption: Experimental Study,” Journal of Medical Internet Research, vol. 25, no. 1, p. e41430, Mar. 2023, doi: 10.2196/41430.
32. [32] W. Pfoertsch and K. Sulaj, “Integrating Artificial Intelligence with Customer Experience in Banking: An Empirical Study on how Chatbots and Virtual Assistants Enhance Empathy,” in 2023 International Conference on Computing, Networking, Telecommunications & Engineering Sciences Applications (CoNTESA), Dec. 2023, pp. 69–74. doi: 10.1109/CoNTESA61248.2023.10384979.
33. [33] Y. Kyrylenko, “Chatbots and virtual assistants vs. human agents in IT customer support,” 2024, Accessed: Mar. 04, 2025. [Online]. Available: https://lauda.ulapland.fi/handle/10024/66071
34. [34] C. Liu, J. Jiang, C. Xiong, Y. Yang, and J. Ye, “Towards Building an Intelligent Chatbot for Customer Service: Learning to Respond at the Appropriate Time,” in Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, in KDD ’20. New York, NY, USA: Association for Computing Machinery, Aug. 2020, pp. 3377–3385. doi: 10.1145/3394486.3403390.
35. [35] L. Jenneboer, C. Herrando, and E. Constantinides, “The Impact of Chatbots on Customer Loyalty: A Systematic Literature Review,” Journal of Theoretical and Applied Electronic Commerce Research, vol. 17, no. 1, pp. 212–229, Mar. 2022, doi: 10.3390/jtaer17010011.
36. [36] B. C. Vinaykarthik and Mohana, “Design of Artificial Intelligence (AI) based User Experience Websites for E-commerce Application and Future of Digital Marketing,” in 2022 3rd International Conference on Smart Electronics and Communication (ICOSEC), Oct. 2022, pp. 1023–1029. doi: 10.1109/ICOSEC54921.2022.9952005.
37. [37] A. Novák, D. Bennett, and T. Klieštik, “Product Decision-Making Information Systems, Real-Time Sensor Networks, and Artificial Intelligence-driven Big Data Analytics in Sustainable Industry 4.0,” Economics, Management, and Financial Markets, vol. 16, no. 2, pp. 62–72, 2021.
38. [38] A. Haleem, M. Javaid, M. Asim Qadri, R. Pratap Singh, and R. Suman, “Artificial intelligence (AI) applications for marketing: A literature-based study,” International Journal of Intelligent Networks, vol. 3, pp. 119–132, Jan. 2022, doi: 10.1016/j.ijin.2022.08.005.
39. [39] R. Poscher, “Artificial Intelligence and the Right to Data Protection,” Jan. 2021, doi: 10.2139/ssrn.3769159.
40. [40] B. C. Newell, N. Purtova, Y. E. Moon, and H. J. Paterson III, “Regulating the Data Market: The Material Scope of American Consumer Data Privacy Law,” Apr. 2024, Accessed: Mar. 04, 2025. [Online]. Available: https://papers.ssrn.com/abstract=4338049
41. [41] D. Luckey, H. Fritz, D. Legatiuk, K. Dragos, and K. Smarsly, “Artificial Intelligence Techniques for Smart City Applications,” in Proceedings of the 18th International Conference on Computing in Civil and Building Engineering, E. Toledo Santos and S. Scheer, Eds., Cham: Springer International Publishing, 2021, pp. 3–15. doi: 10.1007/978-3-030-51295-8_1.
42. [42] K. Ahmad, M. Maabreh, M. Ghaly, K. Khan, J. Qadir, and A. Al-Fuqaha, “Developing future human-centered smart cities: Critical analysis of smart city security, Data management, and Ethical challenges,” Computer Science Review, vol. 43, p. 100452, Feb. 2022, doi: 10.1016/j.cosrev.2021.100452.
43. [43] H. M. K. K. M. B. Herath and M. Mittal, “Adoption of artificial intelligence in smart cities: A comprehensive review,” International Journal of Information Management Data Insights, vol. 2, no. 1, p. 100076, Apr. 2022, doi: 10.1016/j.jjimei.2022.100076.
44. [44] L. Bjørlo, Ø. Moen, and M. Pasquine, “The Role of Consumer Autonomy in Developing Sustainable AI: A Conceptual Framework,” Sustainability, vol. 13, no. 4, p. 2332, Jan. 2021, doi: 10.3390/su13042332.
45. [45] C. Campbell, K. Plangger, S. Sands, J. Kietzmann, and K. Bates, “How Deepfakes and Artificial Intelligence Could Reshape the Advertising Industry,” Journal of Advertising Research, vol. 62, no. 3, pp. 241–251, Sep. 2022, doi: 10.2501/JAR-2022-017.
46. [46] N. Hajli, U. Saeed, M. Tajvidi, and F. Shirazi, “Social Bots and the Spread of Disinformation in Social Media: The Challenges of Artificial Intelligence,” British Journal of Management, vol. 33, no. 3, pp. 1238–1253, 2022, doi: 10.1111/1467-8551.12554.
47. [47] V. R, “Comparative Analysis for Personality Prediction by Digital Footprints in Social Media,” JITDW, vol. 3, no. 2, pp. 77–91, May 2021, doi: 10.36548/jitdw.2021.2.002.
48. [48] J.-J. Rocha-Salazar, M.-J. Segovia-Vargas, J.-J. Rocha-Salazar, and M.-J. Segovia-Vargas, “Money Laundering in the Age of Cybercrime and Emerging Technologies,” in Corruption, Bribery, and Money Laundering - Global Issues, IntechOpen, 2024. Accessed: Mar. 04, 2025. [Online]. Available: https://www.intechopen.com/chapters/1172956
49. [49] T. S. Dinesh, K. Ammaiyappan, G. Janav, S. Gopikumar, R. S. Krishnan, and A. Srinivasan, “A Novel AI-Driven Recommendation System for Eco-Conscious Consumers,” in 2024 8th International Conference on Inventive Systems and Control (ICISC), Jul. 2024, pp. 376–382. doi: 10.1109/ICISC62624.2024.00071.
50. [50] L. Waltersmann, S. Kiemel, J. Stuhlsatz, A. Sauer, and R. Miehe, “Artificial Intelligence Applications for Increasing Resource Efficiency in Manufacturing Companies—A Comprehensive Review,” Sustainability, vol. 13, no. 12, p. 6689, Jan. 2021, doi: 10.3390/su13126689.
51. [51] H. Onyeaka et al., “Using Artificial Intelligence to Tackle Food Waste and Enhance the Circular Economy: Maximising Resource Efficiency and Minimising Environmental Impact: A Review,” Sustainability, vol. 15, no. 13, p. 10482, Jan. 2023, doi: 10.3390/su151310482.
52. [52] J. Aguilar, A. Garces-Jimenez, M. D. R-Moreno, and R. García, “A systematic literature review on the use of artificial intelligence in energy self-management in smart buildings,” Renewable and Sustainable Energy Reviews, vol. 151, p. 111530, Nov. 2021, doi: 10.1016/j.rser.2021.111530.
53. [53] J. Li, M. S. Herdem, J. Nathwani, and J. Z. Wen, “Methods and applications for Artificial Intelligence, Big Data, Internet of Things, and Blockchain in smart energy management,” Energy and AI, vol. 11, p. 100208, Jan. 2023, doi: 10.1016/j.egyai.2022.100208.
54. [54] W. Moodaley and A. Telukdarie, “Greenwashing, Sustainability Reporting, and Artificial Intelligence: A Systematic Literature Review,” Sustainability, vol. 15, no. 2, p. 1481, Jan. 2023, doi: 10.3390/su15021481.
55. [55] D. Zhang, “The pathway to curb greenwashing in sustainable growth: The role of artificial intelligence,” Energy Economics, vol. 133, p. 107562, May 2024, doi: 10.1016/j.eneco.2024.107562.
56. [56] S. Angioni, S. Consoli, D. Dessì, F. Osborne, D. Reforgiato Recupero, and A. Salatino, “Exploring Environmental, Social, and Governance (ESG) Discourse in News: An AI-Powered Investigation Through Knowledge Graph Analysis,” IEEE Access, vol. 12, pp. 77269–77283, 2024, doi: 10.1109/ACCESS.2024.3407188.
57. [57] N. Rane, S. Choudhary, and J. Rane, “Artificial intelligence driven approaches to strengthening Environmental, Social, and Governance (ESG) criteria in sustainable business practices: a review,” May 2024, doi: 10.2139/ssrn.4843215.
58. [58] B. P. Sah, S. Begum, M. R. Bhuiyan, and M. Shahjalal, “The Role of AI in Promotıng Sustaınabılıty wıthın the Manufacturıng Supply Chain Achievıng Lean and Green Objectıves,” Academic Journal on Business Administration, Innovation & Sustainability, vol. 4, no. 3, pp. 79–93, Aug. 2024, doi: 10.69593/ajbais.v4i3.97.
59. [59] A. Sofia Lesiv, “How Much Energy Will It Take To Power AI? | Contrary.” [Online]. Available: https://www.contrary.com/foundations-and-frontiers/ai-inference
60. [60] IEA, “What the data centre and AI boom could mean for the energy sector – Analysis,” IEA. [Online]. Available: https://www.iea.org/commentaries/what-the-data-centre-and-ai-boom-could-mean-for-the-energy-sector
61. [61] I. M. D. Andrade and C. Tumelero, “Increasing customer service efficiency through artificial intelligence chatbot,” Revista de Gestão, vol. 29, no. 3, pp. 238–251, May 2022, doi: 10.1108/REGE-07-2021-0120.
62. [62] D. Buhalis and I. Moldavska, “Voice assistants in hospitality: using artificial intelligence for customer service,” Journal of Hospitality and Tourism Technology, vol. 13, no. 3, pp. 386–403, Dec. 2021, doi: 10.1108/JHTT-03-2021-0104.
63. [63] A. Köchling, M. C. Wehner, and J. Warkocz, “Can I show my skills? Affective responses to artificial intelligence in the recruitment process,” Rev Manag Sci, vol. 17, no. 6, pp. 2109–2138, Aug. 2023, doi: 10.1007/s11846-021-00514-4.
64. [64] E. Pantano and D. Scarpi, “I, Robot, You, Consumer: Measuring Artificial Intelligence Types and their Effect on Consumers Emotions in Service,” Journal of Service Research, vol. 25, no. 4, pp. 583–600, Nov. 2022, doi: 10.1177/10946705221103538.
65. [65] R. Zimmermann et al., “Enhancing brick-and-mortar store shopping experience with an augmented reality shopping assistant application using personalized recommendations and explainable artificial intelligence,” Journal of Research in Interactive Marketing, vol. 17, no. 2, pp. 273–298, Apr. 2022, doi: 10.1108/JRIM-09-2021-0237.
66. [66] S. Chandra, A. Shirish, and S. C. Srivastava, “To Be or Not to Be …Human? Theorizing the Role of Human-Like Competencies in Conversational Artificial Intelligence Agents,” Journal of Management Information Systems, vol. 39, no. 4, pp. 969–1005, Oct. 2022, doi: 10.1080/07421222.2022.2127441.
67. [67] L. D. Hollebeek, C. Menidjel, M. Sarstedt, J. Jansson, and S. Urbonavicius, “Engaging consumers through artificially intelligent technologies: Systematic review, conceptual model, and further research,” Psychology & Marketing, vol. 41, no. 4, pp. 880–898, 2024, doi: 10.1002/mar.21957.
68. [68] S. Hlee, J. Park, H. Park, C. Koo, and Y. Chang, “Understanding customer’s meaningful engagement with AI-powered service robots,” Information Technology & People, vol. 36, no. 3, pp. 1020–1047, Apr. 2022, doi: 10.1108/ITP-10-2020-0740.
69. [69] S. M. Noble and M. Mende, “The future of artificial intelligence and robotics in the retail and service sector: Sketching the field of consumer-robot-experiences,” J. of the Acad. Mark. Sci., vol. 51, no. 4, pp. 747–756, Jul. 2023, doi: 10.1007/s11747-023-00948-0.
70. [70] C. Lou, S. T. J. Kiew, T. Chen, T. Y. M. Lee, J. E. C. Ong, and Z. Phua, “Authentically Fake? How Consumers Respond to the Influence of Virtual Influencers,” Journal of Advertising, vol. 52, no. 4, pp. 540–557, Aug. 2023, doi: 10.1080/00913367.2022.2149641.
71. [71] Lisa, “Top 10 Virtual Influencers in 2025,” Storyclash. [Online]. Available: https://www.storyclash.com/blog/en/virtual-influencers/
72. [72] R. Chaturvedi, S. Verma, R. Das, and Y. K. Dwivedi, “Social companionship with artificial intelligence: Recent trends and future avenues,” Technological Forecasting and Social Change, vol. 193, p. 122634, Aug. 2023, doi: 10.1016/j.techfore.2023.122634.
73. [73] B. Li, R. Yao, and Y. Nan, “How do friendship artificial intelligence chatbots (FAIC) benefit the continuance using intention and customer engagement?,” Journal of Consumer Behaviour, vol. 22, no. 6, pp. 1376–1398, 2023, doi: 10.1002/cb.2218.
74. [74] E. Hermann, “Artificial intelligence and mass personalization of communication content—An ethical and literacy perspective,” New Media & Society, vol. 24, no. 5, pp. 1258–1277, May 2022, doi: 10.1177/14614448211022702.
75. [75] H. Kang and C. Lou, “AI agency vs. human agency: understanding human–AI interactions on TikTok and their implications for user engagement,” Journal of Computer-Mediated Communication, vol. 27, no. 5, p. zmac014, Sep. 2022, doi: 10.1093/jcmc/zmac014.
76. [76] Z. Zhou, “Emotional thinking as the foundation of consciousness in artificial intelligence,” Cultures of Science, vol. 4, no. 3, pp. 112–123, Sep. 2021, doi: 10.1177/20966083211052651.
77. [77] M. S. Castellano et al., “Empowering human anatomy education through gamification and artificial intelligence: An innovative approach to knowledge appropriation,” Clinical Anatomy, vol. 37, no. 1, pp. 12–24, 2024, doi: 10.1002/ca.24074.
78. [78] D. A. Tajik, “Gamified and Non-Gamified AI Tools in Enhancing EFL Listening Comprehension: An Analysis of Duolingo and Replika’s Impact on Engagement, Motivation, and Learning Outcomes,” Feb. 2025, doi: 10.21203/rs.3.rs-6032009/v1.
79. [79] R. Dwi Santy and M. Iffan, “The Effect of Artifıcial Intelligence and Gamifıcation on Online Purchase Intention Mediated By Customer Experıence: Study On Indonesian Marketplace Users,” Mix: Jurnal Ilmiah Manajemen, vol. 13, no. 1, 2023, Accessed: Mar. 04, 2025. [Online]. Available: https://publikasi.mercubuana.ac.id/index.php/Jurnal_Mix/article/view/18246
80. [80] A. Förster, “How do competitiveness and self- construal character types influence the effects of social gamification features? An investigation based on the example of strava,” Jun. 2024, Accessed: Mar. 04, 2025. [Online]. Available: https://matheo.uliege.be/handle/2268.2/20172
81. [81] J. M. Lopes, L. F. Silva, I. Massano-Cardoso, and A. Galhardo, “Running Towards a Better Brand Attitude: How Gamification in Nike Run Club Can Help?,” J Knowl Econ, Dec. 2024, doi: 10.1007/s13132-024-02398-7.
82. [82] D. Mhlanga, “Financial Inclusion in Emerging Economies: The Application of Machine Learning and Artificial Intelligence in Credit Risk Assessment,” International Journal of Financial Studies, vol. 9, no. 3, p. 39, Sep. 2021, doi: 10.3390/ijfs9030039.
83. [83] H. Sadok, F. Sakka, and M. E. H. El Maknouzi, “Artificial intelligence and bank credit analysis: A review,” Cogent Economics & Finance, vol. 10, no. 1, p. 2023262, Dec. 2022, doi: 10.1080/23322039.2021.2023262.
84. [84] G. Northey, V. Hunter, R. Mulcahy, K. Choong, and M. Mehmet, “Man vs machine: how artificial intelligence in banking influences consumer belief in financial advice,” International Journal of Bank Marketing, vol. 40, no. 6, pp. 1182–1199, May 2022, doi: 10.1108/IJBM-09-2021-0439.
85. [85] P. G. Thirumagal, S. Vaddepalli, T. Das, S. Das, S. Madem, and P. S. Immaculate, “AI-Enhanced IoT Data Analytics for Risk Management in Banking Operations,” in 2024 5th International Conference on Recent Trends in Computer Science and Technology (ICRTCST), Apr. 2024, pp. 177–181. doi: 10.1109/ICRTCST61793.2024.10578533.
86. [86] YNAB, “YNAB.” [Online]. Available: https://www.ynab.com
87. [87] U. Noreen, A. Shafique, Z. Ahmed, and M. Ashfaq, “Banking 4.0: Artificial Intelligence (AI) in Banking Industry & Consumer’s Perspective,” Sustainability, vol. 15, no. 4, p. 3682, Jan. 2023, doi: 10.3390/su15043682.
88. [88] P. Ghosh, “Artificial Intelligence (AI) Aided Decision-Making: Understanding Consumer Adoption of AI-Based Financial Products,” doctoral, City, University of London, 2023. Accessed: Mar. 04, 2025. [Online]. Available: https://openaccess.city.ac.uk/id/eprint/31725/
89. [89] A. A. Sabir, I. Ahmad, H. Ahmad, M. Rafiq, M. A. Khan, and N. Noreen, “Consumer Acceptance and Adoption of AI Robo-Advisors in Fintech Industry,” Mathematics, vol. 11, no. 6, p. 1311, Jan. 2023, doi: 10.3390/math11061311.
90. [90] E. Btoush, X. Zhou, R. Gururaian, K. Chan, and X. Tao, “A Survey on Credit Card Fraud Detection Techniques in Banking Industry for Cyber Security,” in 2021 8th International Conference on Behavioral and Social Computing (BESC), Oct. 2021, pp. 1–7. doi: 10.1109/BESC53957.2021.9635559.
91. [91] B. Narsimha, C. V. Raghavendran, P. Rajyalakshmi, G. K. Reddy, M. Bhargavi, and P. Naresh, “Cyber Defense in the Age of Artificial Intelligence and Machine Learning for Financial Fraud Detection Application,” International Journal of Electrical and Electronics Research, vol. 10, no. 2, pp. 87–92, Jun. 2022, doi: 10.37391/ijeer.100206.
92. [92] N. Tyagi, A. Rana, S. Awasthi, and L. K. Tyagi, “Data Science: Concern for Credit Card Scam with Artificial Intelligence,” in Cyber Security in Intelligent Computing and Communications, R. Agrawal, J. He, E. Shubhakar Pilli, and S. Kumar, Eds., Singapore: Springer, 2022, pp. 115–128. Accessed: Mar. 04, 2025. [Online]. Available: https://doi.org/10.1007/978-981-16-8012-0_10
93. [93] Y. K. Yee and M. Raheem, “Predicting Music Popularity Using Spotify and YouTube Features,” IJST, vol. 15, no. 36, pp. 1786–1799, Sep. 2022, doi: 10.17485/IJST/v15i36.2332.
94. [94] M. Flayelle, V. Sigre-Leirós, L. Ferrecchia, S. M. Gainsbury, and J. Billieux, “How does on-demand streaming technology promote binge-watching? An exploration and classification of streaming platform design features,” Psychology of Popular Media, p. No-Pagination Specified-No Pagination Specified, 2025, doi: 10.1037/ppm0000579.
95. [95] K. McTigue, Disney+ and Machine Learning in the Streaming Age. London: Kellogg School of Management, 2024. Accessed: Mar. 04, 2025. [Online]. Available: https://sk.sagepub.com/cases/disney-and-machine-learning-in-the-streaming-age
96. [96] M. Reddy and M. Basha, “DALL-E: Creating Images from Text,” Dogo Rangsang Research Journal, vol. 8, no. 14, 2021, [Online]. Available: https://www.journal-dogorangsang.in/no_1_NECG_21/14.pdf
97. [97] M. Cheong et al., “Investigating Gender and Racial Biases in DALL-E Mini Images,” ACM J. Responsib. Comput., vol. 1, no. 2, p. 13:1-13:20, Jun. 2024, doi: 10.1145/3649883.
98. [98] M. Arora, K. K. Mishra, M. Singh, P. Singh, and R. Tripathi, “Deepfake Technology and Its Implications for Influencer Marketing,” in Navigating the World of Deepfake Technology, IGI Global Scientific Publishing, 2024, pp. 66–90. Accessed: Mar. 04, 2025. [Online]. Available: https://www.igi-global.com/chapter/deepfake-technology-and-its-implications-for-influencer-marketing/www.igi-global.com/chapter/deepfake-technology-and-its-implications-for-influencer-marketing/353614
99. [99] F. Sardana, K. K. Mishra, A. Singh, and N. Saini, “Transforming Social Media Marketing Through Deepfake Technology,” in Navigating the World of Deepfake Technology, IGI Global Scientific Publishing, 2024, pp. 431–453. Accessed: Mar. 04, 2025. [Online]. Available: https://www.igi-global.com/chapter/transforming-social-media-marketing-through-deepfake-technology/www.igi-global.com/chapter/transforming-social-media-marketing-through-deepfake-technology/353631
100. [100] B. Sivathanu, R. Pillai, and B. Metri, “Customers’ online shopping intention by watching AI-based deepfake advertisements,” International Journal of Retail & Distribution Management, vol. 51, no. 1, pp. 124–145, Sep. 2022, doi: 10.1108/IJRDM-12-2021-0583.
101. [101] Q. Huang and J. Maracic, Consumer perception of Deepfake Technology in Marketing : An abductive study on consumer attitude, trust and brand authenticity. 2024. Accessed: Mar. 04, 2025. [Online]. Available: https://urn.kb.se/resolve?urn=urn:nbn:se:lnu:diva-129799
102. [102] N. de Marcellis-Warin, F. Marty, E. Thelisson, and T. Warin, “Artificial intelligence and consumer manipulations: from consumer’s counter algorithms to firm’s self-regulation tools,” AI Ethics, vol. 2, no. 2, pp. 259–268, May 2022, doi: 10.1007/s43681-022-00149-5.
103. [103] C. Snijders, R. Conijn, E. de Fouw, and K. van Berlo, “Humans and Algorithms Detecting Fake News: Effects of Individual and Contextual Confidence on Trust in Algorithmic Advice,” International Journal of Human–Computer Interaction, vol. 39, no. 7, pp. 1483–1494, Apr. 2023, doi: 10.1080/10447318.2022.2097601.
104. [104] D. Shin, “Misinformation and Algorithmic Bias,” in Artificial Misinformation: Exploring Human-Algorithm Interaction Online, D. Shin, Ed., Cham: Springer Nature Switzerland, 2024, pp. 15–47. Accessed: Mar. 04, 2025. [Online]. Available: https://doi.org/10.1007/978-3-031-52569-8_2
105. [105] S. Ben Jabeur, H. Ballouk, W. Ben Arfi, and J.-M. Sahut, “Artificial intelligence applications in fake review detection: Bibliometric analysis and future avenues for research,” Journal of Business Research, vol. 158, p. 113631, Mar. 2023, doi: 10.1016/j.jbusres.2022.113631.
106. [106] S. He, B. Hollenbeck, and D. Proserpio, “The Market for Fake Reviews,” Marketing Science, vol. 41, no. 5, pp. 896–921, Sep. 2022, doi: 10.1287/mksc.2022.1353.
107. [107] J. Salminen, C. Kandpal, A. M. Kamel, S. Jung, and B. J. Jansen, “Creating and detecting fake reviews of online products,” Journal of Retailing and Consumer Services, vol. 64, p. 102771, Jan. 2022, doi: 10.1016/j.jretconser.2021.102771.
108. [108] U. Bakır, Z. Akyol, and S. Bazarcı, “‘Is It a Machine Influencing Me?’: An Analysis on Hyper-Realistic AI Influencers,” in New Trends in Marketing and Consumer Science, IGI Global Scientific Publishing, 2024, pp. 19–45. Accessed: Mar. 04, 2025. [Online]. Available: https://www.igi-global.com/chapter/is-it-a-machine-influencing-me/www.igi-global.com/chapter/is-it-a-machine-influencing-me/347605
109. [109] B. Isem Cáceres, “Just as Advertised: The Perception and Effectiveness of Artificial Intelligence in Digital Advertising,” CMC Senior Theses, Jan. 2025, [Online]. Available: https://scholarship.claremont.edu/cmc_theses/3820
110. [110] R. J. Neuwirth, The EU Artificial Intelligence Act: Regulating Subliminal AI Systems. London: Routledge, 2022.
111. [111] T. Cohen, “Regulating Manipulative Artificial Intelligence,” SCRIPTed, vol. 20, p. 203, 2023.
112. [112] J. P. Bermúdez et al., “What Is a Subliminal Technique? An Ethical Perspective on AI-Driven Influence,” in 2023 IEEE International Symposium on Ethics in Engineering, Science, and Technology (ETHICS), May 2023, pp. 1–10. doi: 10.1109/ETHICS57328.2023.10155039.
113. [113] M.-H. Huang and R. T. Rust, “A strategic framework for artificial intelligence in marketing,” J. of the Acad. Mark. Sci., vol. 49, no. 1, pp. 30–50, Jan. 2021, doi: 10.1007/s11747-020-00749-9.
114. [114] S. Monteith, T. Glenn, J. Geddes, P. C. Whybrow, and M. Bauer, “Commercial Use of Emotion Artificial Intelligence (AI): Implications for Psychiatry,” Curr Psychiatry Rep, vol. 24, no. 3, pp. 203–211, Mar. 2022, doi: 10.1007/s11920-022-01330-7.
115. [115] B. Singh and C. Kaunert, “Future of Digital Marketing: Hyper-Personalized Customer Dynamic Experience with AI-Based Predictive Models,” in Revolutionizing the AI-Digital Landscape, Productivity Press, 2024.
116. [116] O. Adeola, O. Evans, J. Ndubuisi Edeh, and I. Adisa, “The Future of Marketing: Artificial Intelligence, Virtual Reality, and Neuromarketing,” in Marketing Communications and Brand Development in Emerging Economies Volume I: Contemporary and Future Perspectives, O. Adeola, R. E. Hinson, and A. M. Sakkthivel, Eds., Cham: Springer International Publishing, 2022, pp. 253–280. Accessed: Mar. 04, 2025. [Online]. Available: https://doi.org/10.1007/978-3-030-88678-3_12
117. [117] R. R. Ahmed, D. Streimikiene, Z. A. Channar, H. A. Soomro, J. Streimikis, and G. L. Kyriakopoulos, “The Neuromarketing Concept in Artificial Neural Networks: A Case of Forecasting and Simulation from the Advertising Industry,” Sustainability, vol. 14, no. 14, p. 8546, Jan. 2022, doi: 10.3390/su14148546.
118. [118] S. Bansal and M. Gupta, “Towards Using Artificial Intelligence in Neuromarketing,” in Promoting Consumer Engagement Through Emotional Branding and Sensory Marketing, IGI Global Scientific Publishing, 2023, pp. 16–23. Accessed: Mar. 04, 2025. [Online]. Available: https://www.igi-global.com/chapter/towards-using-artificial-intelligence-in-neuromarketing/www.igi-global.com/chapter/towards-using-artificial-intelligence-in-neuromarketing/314500
119. [119] M. Aldayel, M. Ykhlef, and A. Al-Nafjan, “Consumers’ Preference Recognition Based on Brain–Computer Interfaces: Advances, Trends, and Applications,” Arab J Sci Eng, vol. 46, no. 9, pp. 8983–8997, Sep. 2021, doi: 10.1007/s13369-021-05695-4.
120. [120] A. Byrne, E. Bonfiglio, C. Rigby, and N. Edelstyn, “A systematic review of the prediction of consumer preference using EEG measures and machine-learning in neuromarketing research,” Brain Inf., vol. 9, no. 1, p. 27, Nov. 2022, doi: 10.1186/s40708-022-00175-3.
121. [121] M. Hamad1 and S. Kurnaz2, “Improving Repeat Purchase Prediction in Digital Marketing Using Machine Learning,” Humanities & Natural Sciences Journal, vol. 6, no. 1, pp. 91–100, Jan. 2025, doi: 10.53796/hnsj61/7.
122. [122] M.-H. Huang and R. T. Rust, “A Framework for Collaborative Artificial Intelligence in Marketing,” Journal of Retailing, vol. 98, no. 2, pp. 209–223, Jun. 2022, doi: 10.1016/j.jretai.2021.03.001.
123. [123] B. G. C. Dellaert et al., “Consumer decisions with artificially intelligent voice assistants,” Mark Lett, vol. 31, no. 4, pp. 335–347, Dec. 2020, doi: 10.1007/s11002-020-09537-5.
124. [124] S. Malodia, N. Islam, P. Kaur, and A. Dhir, “Why Do People Use Artificial Intelligence (AI)-Enabled Voice Assistants?,” IEEE Transactions on Engineering Management, vol. 71, pp. 491–505, 2024, doi: 10.1109/TEM.2021.3117884.