SavedAdd your journal

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

BIBLIOMETRIC ANALYSIS OF PUBLICATIONS IN THE FIELD OF ARTIFICIAL INTELLIGENCE AND CONSUMER BEHAVIOR

YAPAY ZEKÂ VE TÜKETİCİ DAVRANIŞI ALANINDAKİ YAYINLARIN BİBLİYOMETRİK ANALİZİ

Sinem Sargın(NUH NACİ YAZGAN ÜNİVERSİTESİ)0000-0002-7504-154X
Pages
1–42
Publication Date
2026-03-26
🏛️Bibliographic metadata and full text access for this article are provided via DergiPark.
DergiPark Record ↗
✨

AI Summary & Key Takeaways

AI Insight
⚡ TL;DR(Too Long; Didn't Read)

The findings indicate a significant increase in publication volume beginning in 2020.

📌 3 Key Takeaways

🎯
Objective & Hypothesis:This study examines academic publications in the field of artificial intelligence (AI) and consumer behavior using bibliometric analysis.
📊
Key Findings & Data:A search conducted in the Web of Science database using the keywords “AI” AND “Consumer,” covered publications from 1991 to 2025.
💡
Conclusion & Impact:The findings indicate a significant increase in publication volume beginning in 2020.
🔬Design:Review & Systematic Survey
📍Scope:2025 Verileri
⚙️Variables:Yapay Zekâ · Tüketici Davranışı · Bibliyometrik Analiz

Abstract

This study examines academic publications in the field of artificial intelligence (AI) and consumer behavior using bibliometric analysis. A search conducted in the Web of Science database using the keywords “AI” AND “Consumer,” covered publications from 1991 to 2025. The dataset includes academic articles, conference papers, reviews, editorials, book chapters, books, letters, and similar types of publications. A total of 1,980 records were analyzed using the VOSviewer software based criteria such as co-citation analysis, author collaboration networks, keyword co-occurrence mapping, and academic clustering. The findings indicate a significant increase in publication volume beginning in 2020. The most frequently addressed topics in the literature include customer satisfaction, human-robot interaction, data privacy, consumer trust, and technology acceptance. The analysis also reveals that countries such as the United States, China, the United Kingdom, and India are the most prominent in terms of academic output. Researchers such as Sean Sands, Stefano Puntoni, and Yuanyuan Cui are among the most prolific authors. This study aims to visualize the scientific interactions in the field of AI and consumer behavior domain, offering a deeper understanding of academic trends and providing a guiding framework for future research.

Özet

Bu çalışma, yapay zekâ (YZ) ve tüketici davranışı alanındaki akademik yayınları bibliyometrik analiz yöntemiyle incelemektedir. Web of Science veri tabanında “AI” AND “Consumer” anahtar kelimeleri kullanılarak gerçekleştirilen tarama, 1991–2025 yılları arasındaki yayınları kapsamış ve akademik makaleler, bildiriler, derlemeler, editoryal yazılar, kitap bölümleri, kitap, mektup vb. yayınlar analiz kapsamına dâhil edilmiştir. Toplam 1980 yayın, VOSviewer yazılımı aracılığıyla ortak atıf analizi, yazar iş birliği ağları, anahtar kelime eşleşmeleri ve akademik kümelenmeler gibi kriterler açısından değerlendirilmiştir. Bulgular, 2020 yılından itibaren yayın sayısında belirgin bir artış olduğunu göstermektedir. Çalışmalarda en çok ele alınan konuların müşteri memnuniyeti, insan-robot etkileşimi, veri gizliliği, tüketici güveni ve teknoloji kabulü olduğu görülmektedir. Analiz sonuçları, ABD, Çin, İngiltere ve Hindistan gibi ülkelerin akademik üretimde öne çıktığını göstermektedir. Sean Sands, Stefano Puntoni ve Yuanyuan Cui gibi araştırmacılar en fazla yayına sahip isimler arasında yer almaktadır. Bu çalışma, YZ ve tüketici davranışı alanındaki bilimsel etkileşimleri görselleştirerek akademik eğilimleri anlamayı amaçlamakta ve gelecek araştırmalar için yönlendirici bir çerçeve sunmaktadır.

🔖

Cite This Article

Sargın, S. (2026). BIBLIOMETRIC ANALYSIS OF PUBLICATIONS IN THE FIELD OF ARTIFICIAL INTELLIGENCE AND CONSUMER BEHAVIOR. Uluslararası Yönetim İktisat ve İşletme Dergisi, Cilt: 22 Sayı: 1, 1–42. https://doi.org/10.17130/ijmeb.1642518

📄 Full Text PDF Viewer

📄

PDF görüntüleyici hazırlanıyor...

References (50)

  1. Acquisti, A., Brandimarte, L., & Loewenstein, G. (2015). Privacy and human behavior in the age of information. Science, 347(6221), 509-514. https://doi.org/10.1126/science.aaa1465
  2. Adomavicius, G., & Tuzhilin, A. (2005). Toward the next generation of recommender systems: A survey of the state-of-the-art and possible extensions. IEEE Transactions on Knowledge and Data Engineering, 17(6), 734-749. https://doi.org/10.1109/tkde.2005.99
  3. Aguirre, E., Mahr, D., Grewal, D., Ruyter, K. D., & Wetzels, M. (2015). Unraveling the personalization–privacy paradox in the digital era: The effect of personalization on perceived privacy and consumer trust. Journal of Retailing, 91(1), 34-49. https://doi.org/10.1016/j.jretai.2014.09.005
  4. Ameen, N., Sharma, G., Tarba, S., Rao, A., & Chopra, R. (2022). Toward advancing theory on creativity in marketing and artificial intelligence. Psychology and Marketing, 39(9), 1802-1825. https://doi.org/10.1002/mar.21699
  5. Araujo, T., Helberger, N., Kruikemeier, S., & de Vreese, C. H. (2020). In AI we trust? Perceptions about automated decision-making by artificial intelligence. AI & Society, 35(3), 611–623. https://doi.org/10.1007/s00146-019-00931-w
  6. Aria, M., & Cuccurullo, C. (2017). Bibliometrix: An R-tool for comprehensive science mapping analysis. Journal of Informetrics, 11(4), 959–975. https://doi.org/10.1016/j.joi.2017.08.007
  7. Awad, N. F., & Krishnan, M. S. (2006). The personalization privacy paradox: An empirical evaluation of information transparency and the willingness to be profiled online for personalization. MIS Quarterly, 30(1), 13-28. https://doi.org/10.2307/25148715
  8. Binns, R. (2018). Fairness in machine learning: Lessons from political philosophy. İçinde Proceedings of the 2018 Conference on Fairness, Accountability, and Transparency (FAT) (pp. 149-159).
  9. Brynjolfsson, E., & McAfee, A. (2017). Machine, platform, crowd: Harnessing our digital future. W.W. Norton & Company.
  10. Chen, H., Chiang, R. H. L., & Storey, V. C. (2021). Business intelligence and analytics: From big data to big impact. MIS Quarterly, 36(4), 1165-1188. https://doi.org/10.2307/41703503
  11. Daugherty, P. R., Wilson, H. J., & Chowdhury, R. (2019). Human + Machine: Reimagining Work in the Age of AI. Harvard Business Review Press.
  12. Davenport, T. H., & Ronanki, R. (2018). Artificial intelligence for the real world. Harvard Business Review, 96(1), 108-116.
  13. Davenport, T. H., Guha, A., Grewal, D., & Bressgott, T. (2020). How artificial intelligence will change the future of marketing. Journal of the Academy of Marketing Science, 48(1), 24-42. https://doi.org/10.1007/s11747-019-00696-0
  14. Donthu, N., Kumar, S., Mukherjee, D., Pandey, N., & Lim, W. M. (2021). How to conduct a bibliometric analysis: An overview and guidelines. Journal of Business Research, 133, 285–296. https://doi.org/10.1016/j.jbusres.2021.04.070
  15. Doshi-Velez, F., & Kim, B. (2017). Towards a rigorous science of interpretable machine learning. arXiv preprint arXiv:1702.08608. https://arxiv.org/pdf/1702.08608
  16. Dwivedi, Y. K., Hughes, L., Ismagilova, E., Aarts, G., Coombs, C., Crick, T.,. & Williams, M. D. (2021). Artificial intelligence (AI): Multidisciplinary perspectives on emerging challenges, opportunities, and agenda for research, practice, and policy. International Journal of Information Management, 57, 101994. https://doi.org/10.1016/j.ijinfomgt.2019.08.002
  17. Falagas, M. E., Pitsouni, E. I., Malietzis, G. A., & Pappas, G. (2008). Comparison of PubMed, Scopus, Web of Science, and Google Scholar: Strengths and weaknesses. The FASEB Journal, 22(2), 338–342. https://doi.org/10.1096/fj.07-9492LSF
  18. Gefen, D., Karahanna, E., & Straub, D. W. (2003). Trust and TAM in online shopping: An integrated model. MIS Quarterly, 27(1), 51-90. https://doi.org/10.2307/30036519
  19. Gnewuch, U., Morana, S., & Maedche, A. (2017). Towards designing cooperative and social conversational agents for customer service. İçinde Proceedings of the Thirty Eighth International Conference on Information Systems, South Korea.
  20. Gretzel, U. (2011). Intelligent systems in tourism: A social science perspective. Annals of Tourism Research, 38(3), 757-779. https://doi.org/10.1016/j.annals.2011.04.014
  21. Grewal, D., Hulland, J., Kopalle, P. K., & Karahanna, E. (2020). The future of technology and marketing: A multidisciplinary perspective. Journal of the Academy of Marketing Science, 48, 1-8. https://doi.org/10.1007/s11747-019-00711-4
  22. Guerra-Tamez, C., Flores, K., Serna-Mendiburu, G., Robles, D., & Cortés, J. (2024). Decoding gen Z: AI's influence on brand trust and purchasing behavior. Frontiers in Artificial Intelligence, 7, 1-14. https://doi.org/10.3389/frai.2024.1323512
  23. He, A. Z. & Zhang, Y. (2023). AI-powered touch points in the customer journey: A systematic literature review and research agenda. Journal of Research in Interactive Marketing, 17(4), 620-639. https://doi.org/10.1108/jrim-03-2022-0082
  24. Hinz, O., Hann, I. H., & Spann, M. (2011). Price discrimination in e-commerce? An examination of dynamic pricing in name-your-own-price markets. MIS Quarterly, 35(1), 81-98. https://doi.org/10.2307/23043490
  25. Huang, M.-H., & Rust, R. T. (2021). A strategic framework for artificial intelligence in marketing. Journal of the Academy of Marketing Science, 49(1), 30-50. https://doi.org/10.1007/s11747-020-00749-9
  26. Kietzmann, J., Paschen, J., & Treen, E. (2018). Artificial intelligence in advertising: How marketers can leverage artificial intelligence along the consumer journey. Journal of Advertising Research, 58(3), 263-267. https://doi.org/10.2501/jar-2018-035
  27. Lemon, K. N., & Verhoef, P. C. (2016). Understanding customer experience throughout the customer journey. Journal of Marketing, 80(6), 69-96. https://doi.org/10.1509/jm.15.0420
  28. Loureiro, S. M. C., Guerreiro, J., & Tussyadiah, I. (2021). Artificial intelligence in business: State of the art and future research agenda. Journal of Business Research, 129, 911-926. https://doi.org/10.1016/j.jbusres.2020.11.001
  29. Loureiro, S., Jiménez‐Barreto, J., Bilro, R., & Romero, J. (2024). Me and my AI: Exploring the effects of consumer self‐construal and AI‐based experience on avoiding similarity and willingness to pay. Psychology and Marketing, 41(1), 151-167. https://doi.org/10.1002/mar.21913
  30. Luo, X., Tong, S., Fang, Z., & Qu, Z. (2019). Frontiers: Machines versus humans: The impact of AI chatbot disclosure on customer purchases. Marketing Science, 38(6), 937-947. https://doi.org/10.1287/mksc.2019.1192
  31. Maheswari, S. (2023). The transformative power of AI in marketing FMCG. International Journal for Multidisciplinary Research, 5(3), 1-8. https://doi.org/10.36948/ijfmr.2023.v05i03.3760
  32. Mittelstadt, B. D., Allo, P., Taddeo, M., Wachter, S., & Floridi, L. (2016). The ethics of algorithms: Mapping the debate. Big Data & Society, 3(2), 1-21. https://doi.org/10.1177/2053951716679679
  33. Mongeon, P., & Paul-Hus, A. (2016). The journal coverage of Web of Science and Scopus: A comparative analysis. Scientometrics, 106(1), 213–228. https://doi.org/10.1007/s11192-015-1765-5
  34. Noble, S. U. (2018). Algorithms of oppression: How search engines reinforce racism. İçinde Algorithms of oppression. NYU Press.
  35. Paesano, A. (2023). Artificial intelligence and creative activities inside organizational behavior. International Journal of Organizational Analysis, 31(5), 1694-1723. https://doi.org/10.1108/ijoa-09-2020-2421
  36. 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
  37. Pentina, I., & Tarafdar, M. (2014). From “information” to “knowing”: Exploring the role of social media in contemporary news consumption. Computers in Human Behavior, 35, 211-223. https://doi.org/10.1016/j.chb.2014.02.045
  38. Puntoni, S., Reczek, R. W., Giesler, M., & Botti, S. (2021). Consumers and artificial intelligence: An experiential perspective. Journal of Marketing, 85(1), 131-151. https://doi.org/10.1177/0022242920953847
  39. Russell, S., & Norvig, P. (2020). Artificial intelligence: A modern approach (4th ed.). Pearson.
  40. Rust, R. T., & Huang, M. H. (2014). The service revolution and the transformation of marketing science. Marketing Science, 33(2), 206-221. https://doi.org/10.1287/mksc.2013.0836
  41. Shankar, V. (2018). How artificial intelligence (AI) is reshaping retailing. Journal of Retailing, 94(4), vi–xi. https://doi.org/10.1016/s0022-4359(18)30076-9
  42. 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
  43. Smith, A. N., Fischer, E., & Yongjian, C. (2012). How does brand-related user-generated content differ across YouTube, Facebook, and Twitter? Journal of Interactive Marketing, 26(2), 102-113. https://doi.org/10.1016/j.intmar.2012.01.002
  44. Van Eck, N. J., & Waltman, L. (2010). Software survey: VOSviewer, a computer program for bibliometric mapping. Scientometrics, 84(2), 523–538. https://doi.org/10.1007/s11192-009-0146-3
  45. Wang, W., Chen, Z., & Kuang, J. (2025). Artificial intelligence-driven recommendations and functional food purchases: Understanding consumer decision-making. Foods, 14(6), 976. https://doi.org/10.3390/foods14060976
  46. Wirth, N. (2018). Hello marketing, what can artificial intelligence help you with?. International Journal of Market Research, 60(5), 435-438. https://doi.org/10.1177/1470785318776841
  47. Wirtz, J., Patterson, P. G., Kunz, W. H., Gruber, T., Lu, V. N., Paluch, S., & Martins, A. (2018). Brave new world: Service robots in the frontline. Journal of Service Management, 29(5), 907-931. https://doi.org/10.1108/josm-04-2018-0119
  48. Ye, Q., Law, R., Gu, B., & Chen, W. (2011). The influence of user-generated content on traveler behavior: An empirical investigation on the effects of e-word-of-mouth to hotel online bookings. Computers in Human Behavior, 27(2), 634-639. https://doi.org/10.1016/j.chb.2010.04.014
  49. Zaman, K. (2022). Transformation of marketing decisions through artificial intelligence and digital marketing. Journal of Marketing Strategies, 4(2), 353-364.
  50. Dwivedi, Y. K., Hughes, L., Ismagilova, E., Aarts, G., Coombs, C., Crick, T.,. & Williams, M. D. (2021). Artificial intelligence (AI): Multidisciplinary perspectives on emerging challenges, opportunities, and agenda for research, practice, and policy. International Journal of Information Management, 57, 101994. https://doi.org/10.1016/j.ijinfomgt.2019.08.002