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YAPAY ZEKÂ VE TÜKETİCİ DAVRANIŞI ALANINDAKİ YAYINLARIN BİBLİYOMETRİK ANALİZİ

Yıl 2026, Cilt: 22 Sayı: 1, 1 - 42, 26.03.2026
https://doi.org/10.17130/ijmeb.1642518
https://izlik.org/JA86ZL63XL

Öz

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.

Kaynakça

  • 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
  • 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
  • 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
  • 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
  • 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
  • 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
  • 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
  • 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).
  • Brynjolfsson, E., & McAfee, A. (2017). Machine, platform, crowd: Harnessing our digital future. W.W. Norton & Company.
  • 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
  • Daugherty, P. R., Wilson, H. J., & Chowdhury, R. (2019). Human + Machine: Reimagining Work in the Age of AI. Harvard Business Review Press.
  • Davenport, T. H., & Ronanki, R. (2018). Artificial intelligence for the real world. Harvard Business Review, 96(1), 108-116.
  • 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
  • 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
  • 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
  • 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
  • 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
  • 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
  • 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.
  • 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
  • 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
  • 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
  • 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
  • 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
  • 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
  • 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
  • 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
  • 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
  • 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
  • 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
  • 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
  • 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
  • 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
  • Noble, S. U. (2018). Algorithms of oppression: How search engines reinforce racism. İçinde Algorithms of oppression. NYU Press.
  • 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
  • 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
  • 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
  • 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
  • Russell, S., & Norvig, P. (2020). Artificial intelligence: A modern approach (4th ed.). Pearson.
  • 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
  • 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
  • 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
  • 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
  • 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
  • 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
  • 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
  • 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
  • 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
  • Zaman, K. (2022). Transformation of marketing decisions through artificial intelligence and digital marketing. Journal of Marketing Strategies, 4(2), 353-364.

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

Yıl 2026, Cilt: 22 Sayı: 1, 1 - 42, 26.03.2026
https://doi.org/10.17130/ijmeb.1642518
https://izlik.org/JA86ZL63XL

Öz

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.

Kaynakça

  • 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
  • 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
  • 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
  • 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
  • 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
  • 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
  • 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
  • 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).
  • Brynjolfsson, E., & McAfee, A. (2017). Machine, platform, crowd: Harnessing our digital future. W.W. Norton & Company.
  • 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
  • Daugherty, P. R., Wilson, H. J., & Chowdhury, R. (2019). Human + Machine: Reimagining Work in the Age of AI. Harvard Business Review Press.
  • Davenport, T. H., & Ronanki, R. (2018). Artificial intelligence for the real world. Harvard Business Review, 96(1), 108-116.
  • 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
  • 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
  • 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
  • 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
  • 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
  • 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
  • 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.
  • 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
  • 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
  • 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
  • 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
  • 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
  • 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
  • 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
  • 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
  • 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
  • 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
  • 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
  • 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
  • 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
  • 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
  • Noble, S. U. (2018). Algorithms of oppression: How search engines reinforce racism. İçinde Algorithms of oppression. NYU Press.
  • 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
  • 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
  • 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
  • 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
  • Russell, S., & Norvig, P. (2020). Artificial intelligence: A modern approach (4th ed.). Pearson.
  • 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
  • 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
  • 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
  • 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
  • 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
  • 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
  • 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
  • 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
  • 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
  • Zaman, K. (2022). Transformation of marketing decisions through artificial intelligence and digital marketing. Journal of Marketing Strategies, 4(2), 353-364.
Toplam 49 adet kaynakça vardır.

Ayrıntılar

Birincil Dil Türkçe
Konular İşletme
Bölüm Araştırma Makalesi
Yazarlar

Sinem Sargın 0000-0002-7504-154X

Gönderilme Tarihi 18 Şubat 2025
Kabul Tarihi 24 Haziran 2025
Yayımlanma Tarihi 26 Mart 2026
DOI https://doi.org/10.17130/ijmeb.1642518
IZ https://izlik.org/JA86ZL63XL
Yayımlandığı Sayı Yıl 2026 Cilt: 22 Sayı: 1

Kaynak Göster

APA Sargın, S. (2026). YAPAY ZEKÂ VE TÜKETİCİ DAVRANIŞI ALANINDAKİ YAYINLARIN BİBLİYOMETRİK ANALİZİ. Uluslararası Yönetim İktisat ve İşletme Dergisi, 22(1), 1-42. https://doi.org/10.17130/ijmeb.1642518


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