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REKLAMCILIKTA YAPAY ZEKA: HEDEFLEME VE KİŞİSELLEŞTİRMEYE YÖNELİK BİBLİYOMETRİK ANALİZ*

Yıl 2024, Cilt: 12 Sayı: 2, 366 - 394, 26.12.2024
https://doi.org/10.14514/beykozad.1551121

Öz

Son yıllarda yapay zeka (Artifical Intellegence-AI) ve uygulamalarının kullanımı yaygınlaşmakta ve gelişimini her geçen gün artırarak sürdürmektedir. Yapay zeka ve uygulamalarının gelişimine bağlı olarak reklamcılık alanında kullanılması tüketici içgörülerini yakalama, medya planlama ve satın alma, reklamın etkinliğini ölçme, yeni reklamların tasarlanması, hedef kitleye ulaşma ve kişiselleştirme açısından kolaylıklar sağlamaktadır. Hedef kitleye ulaşmada yapay zeka, elde ettiği verileri derinlemesine analiz ederek tüketici davranışlarını, demografik bilgileri, çevresel faktörleri analiz eder ve reklamcıların hedef kitleyi daha iyi anlamalarına yardımcı olmaktadır. Tüketicinin özelliklerini, ilgi alanlarını ve davranışlarını bilmek, reklamverenin en uygun ürün veya hizmet ile tüketicinin karşısına çıkmasına olanak tanır. Müşteri yolculuğunun kişiselleştirilmesinde ise yapay zeka algoritmaları reklam ögelerini analiz ederek tüketicinin ilgisine göre sunduğu ürün veya hizmet ile etkileşimi en üst seviyeye çıkarmayı hedeflemektedir. Yapay zekanın hedefleme ve kişiselleştirme ile reklamcılığı yeniden şekillendirdiği, doğru hedef kitleye doğru mesajı doğru zamanda iletmede önemli katkılar sağladığı düşünülmektedir. Bu çalışmada, literatürde son 10 yılda (2014-2024) yapay zekanın reklamcılık alanında kullanılmasına yönelik hedefleme ve kişiselleştirmeyi içeren makalelerin bibliyometrik çerçevede analiz edilmesi ve reklamcılık alanındaki gelişiminin haritalandırılması amaçlanmaktadır. Bu amaç doğrultusunda belirlenen anahtar kelimeler ve çeşitli eleme kriterleri uygulanarak Web of Science ve Scopus veri tabanlarında toplam 790 makaleye ulaşılmıştır. Yapılan analizler sonucu 2014 yılında konu ile ilgili makale sayısının 4 olduğu, 2019 yılında bu sayının 24’e ulaştığı ve 2024 yılına gelindiğinde ise 201 makalenin yayınlandığı tespit edilmiştir. Makalelerin yıllık büyüme oranının ise %52,27 olduğu görülmektedir. Bu çalışma, alandaki yayın eğilimlerimin tespit ederek gelecek çalışmalar için araştırmacılara ipuçları sunmaktadır.

Etik Beyan

“Reklamcılıkta Yapay Zeka: Hedefleme ve Kişiselleştirmeye Yönelik Bibliyometrik Analiz” başlıklı makalede herhangi bir insan faktörünün araştırma nesnesi olarak kullanılmadığı için etik kurul onayı gerektirmemektedir.

Kaynakça

  • Aria, M., ve Cuccurullo, C. (2017). bibliometrix: An R-tool for comprehensive science mapping analysis. Journal of Informetrics, 11(4), 959-975. https://doi.org/https://doi.org/10.1016/j.joi.2017.08.007
  • Adalı, G., Yardibi, F., Aydın, Ş., Güdekli, A., Aksoy, E., & Hoştut, S. (2024). Gender and Advertising: A 50-Year Bibliometric Analysis. Journal of Advertising, 1–21. https://doi.org/10.1080/00913367.2024.2343291
  • Baek, T. H. (2023). Digital Advertising in the Age of Generative AI. Journal of Current Issues & Research in Advertising, 44(3), 249–251. https://doi.org/10.1080/10641734.2023.2243496
  • Baek, T. H., and M. Kim. 2023. “Ai Robo-Advisor Anthropomorphism: The Impact of Anthropomorphic Appeals and Regulatory Focus on Investment Behaviors.” Journal of Business Research 164(open in a new window): 114039. https://doi.org/10.1016/j.jbusres.2023.114039
  • Baek, T. H., M. Bakpayev, S. Yoon, and S. Kim. 2022. “Smiling AI Agents: How Anthropomorphism and Broad Smiles Increase Charitable Giving.” International Journal of Advertising 41(open in a new window) (5(open in a new window)): 850–867. https://doi.org/10.1080/02650487.2021.2011654
  • Bakpayev, M., T. H. Baek, P. van Esch, and S. Yoon. 2022. “Programmatic Creative: AI Can Think but It Cannot Feel.” Australasian Marketing Journal 30(open in a new window) (1(open in a new window)): 90–95. https://doi.org/10.1016/j.ausmj.2020.04.002
  • Boerman, S. C., Kruikemeier, S., & Zuiderveen Borgesius, F. J. (2017). Online Behavioral Advertising: A Literature Review and Research Agenda. Journal of Advertising, 46(3), 363-376. https://doi.org/10.1080/00913367.2017.1339368
  • Boyko, N., ve Kholodetska, Y. (2022). Using Artificial Intelligence Algorithms in Advertising. 2022 IEEE 17th International Conference on Computer Sciences and Information Technologies (CSIT)
  • Ciuchita, R., Gummerus, J.K., Holmlund, M. and Linhart, E.L. (2023), "Programmatic advertising in online retailing: consumer perceptions and future avenues", Journal of Service Management, Vol. 34 No. 2, pp. 231-255. https://doi.org/10.1108/JOSM-06-2021-0238
  • Chen, G., Xie, P., Dong, J., & Wang, T. (2019). Understanding Programmatic Creative: The Role of AI. Journal of Advertising, 48(4), 347–355. https://doi.org/10.1080/00913367.2019.1654421
  • Choi, J.-A., ve Lim, K. (2020). Identifying machine learning techniques for classification of target advertising. ICT Express, 6(3), 175-180. https://doi.org/https://doi.org/10.1016/j.icte.2020.04.012
  • Davenport, T., Guha, A., Grewal, D., ve 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
  • Donthu, N., S. Kumar, D. Mukherjee, N. Pandey, and W. M. Lim. (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.
  • Dwivedi, Y. K., Ismagilova, E., Hughes, D. L., Carlson, J., Filieri, R., Jacobson, J., Jain V., Karjaluoto H., Kefi H., Krishen, A.S., Kumar V., Rahman, M.M., Raman R., Rauschnabel P.A., Rowley J., Salo J., Tran G.A., & Wang, Y. (2021). Setting the future of digital and social media marketing research: Perspectives and research propositions. International journal of information management, 59, 102168. https://doi.org/10.1016/j.ijinfomgt.2020.102168 Egghe, L. 2006. “Theory and Practise of the G-Index.” Scientometrics 69 (1): 131–152. https://doi.org/ 10.1007/s11192-006-0144-7 .
  • Ford, J., Jain, V., Wadhwani, K., & Gupta, D. G. (2023). AI advertising: An overview and guidelines. Journal of Business Research, 166, 114124. https://doi.org/https://doi.org/10.1016/j.jbusres.2023.114124
  • Gao, B., Wang, Y., Xie, H., Hu, Y., & Hu, Y. (2023). Artificial Intelligence in Advertising: Advancements, Challenges, and Ethical Considerations in Targeting, Personalization, Content Creation, and Ad Optimization. Sage Open, 13(4). https://doi.org/10.1177/21582440231210759
  • Gupta, S., Paul, J., Stoner, J. L., & Aggarwal, A. (2024). Digital transformation, online advertising, and consumer behaviour. International Journal of Advertising, 1-24. https://doi.org/10.1080/02650487.2024.2317632
  • Häglund, E., ve Björklund, J. (2024). AI-Driven Contextual Advertising: Toward Relevant Messaging Without Personal Data. Journal of Current Issues & Research in Advertising, 1-19. https://doi.org/10.1080/10641734.2024.2334939
  • Hirsch, J. E. 2005. “An Index to Quantify an individual’s Scientific Research Output.” Proceedings of the National Academy of Sciences 102 (46): 16569–16572. https://doi.org/10.1073/pnas.0507655102.
  • Hocutt, D. L. (2024). Composing with generative AI on digital advertising platforms. Computers and Composition, 71, 102829. https://doi.org/10.1016/j.compcom.2024.102829 Iyer, G., Soberman, D., & Villas-Boas, J. M. (2005). The Targeting of Advertising. Marketing Science, 24(3), 461-476. https://doi.org/10.1287/mksc.1050.0117
  • Jukić, D. (2023). Time To Say Goodbye: A Neuromarketing Perspective. International Scientific Conference „Marketing and Media Identity: AI–The Future of Today “,
  • Khandelwal, A. R., Yadav, R., Chaturvedi, A., & Kumar, A. S. (2024). Examining the Impact of AI and Digital Marketing on Consumer Purchase Intention. In Emerging Developments and Technologies in Digital Government (pp. 220-242). IGI Global.
  • Kietzmann, J., Paschen, J., & Treen, E. (2018). Artificial Intelligence in Advertising. Journal of Advertising Research, 58(3), 263. https://doi.org/10.2501/JAR-2018-035
  • Kozyreva, A., Lorenz-Spreen, P., Hertwig, R., Lewandowsky, S., & Herzog, S. M. (2021). Public attitudes towards algorithmic personalization and use of personal data online: Evidence from Germany, Great Britain, and the United States. Humanities and Social Sciences Communications, 8(1), 1-11.
  • Kraus, S., Breier, M., Lim, W. M., Dabić, M., Kumar, S., Kanbach, D., Mukherjee, D., Corvello, V., Piñeiro-Chousa, J., Liguori, E., Palacios-Marqués, D., Schiavone, F., Ferraris, A., Fernandes, C., & Ferreira, J. J. (2022). Literature reviews as independent studies: guidelines for academic practice. Review of Managerial Science, 16(8), 2577-2595. https://doi.org/10.1007/s11846-022-00588-8
  • Laux, J., Stephany, F., Russell, C., Wachter, S., & Mittelstadt, B. (2022). The Concentration-after-Personalisation Index (CAPI): Governing effects of personalisation using the example of targeted online advertising. Big Data & Society, 9(2), 20539517221132535.
  • Leszczynska, M., ve Baltag, D. (2024). “Can I have it non-personalised?” An Empirical Investigation of Consumer Willingness to Share Data for Personalized Services and Ads. Journal of Consumer Policy, 47(3), 345-372. https://doi.org/10.1007/s10603-024-09568-9
  • Liu, J., Li, X., & Wang, S. (2020). What have we learnt from 10 years of fintech research? Ascientometric analysis. Technological Forecasting and Social Change, 155, 120022. https://doi.org/10.1016/j.techfore.2020.120022
  • Ljepava, N. (2022). AI-enabled marketing solutions in Marketing Decision making: AI application in different stages of marketing process. TEM Journal, 11(3), 1308-1315.
  • Lo, S. L., Cornforth, D., & Chiong, R. (2015). Effects of training datasets on both the extreme learning machine and support vector machine for target audience identification on twitter. Proceedings of ELM-2014 Volume 1: Algorithms and Theories.
  • Longoni, C., Bonezzi, A., & Morewedge, C. K. (2019). Resistance to medical artificial intelligence. Journal of Consumer Research, 46(4), 629-650. https://doi.org/10.1093/jcr/ucz013
  • Malthouse, E., ve Copulsky, J. (2023). Artificial intelligence ecosystems for marketing communications. International Journal of Advertising, 42(1), 128-140. https://doi.org/10.1080/02650487.2022.2122249
  • Martín-García, N., & Alvarado-López, M. C. (2022). The relationship between advertising effectiveness and creativity: a critical approach to the campaigns winning Efi and the El Sol festival (2011-2020). Revista Mediterránea de Comunicación/Mediterranean Journal of Communication, 13(2), 279-300. https://www.doi.org/10.14198/MEDCOM.21745
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ARTIFICIAL INTELLIGENCE IN ADVERTISING: BIBLIOMETRIC ANALYSIS FOR TARGETING AND PERSONALIZATION

Yıl 2024, Cilt: 12 Sayı: 2, 366 - 394, 26.12.2024
https://doi.org/10.14514/beykozad.1551121

Öz

In recent years, the use of artificial intelligence (AI) and its applications has become widespread and continues to develop day by day. Depending on the development of artificial intelligence and its applications, its use in the field of advertising provides convenience in terms of capturing consumer insights, media planning and purchasing, measuring the effectiveness of advertising, designing new advertisements, reaching the target audience and personalization. In reaching the target audience, artificial intelligence analyzes the data it obtains in depth, analyzes consumer behavior, demographic information, environmental factors and helps advertisers better understand the target audience. Knowing the characteristics, interests and behaviors of the consumer allows the advertiser to come to the consumer with the most suitable product or service. In personalizing the customer journey, artificial intelligence algorithms analyze advertising elements and aim to maximize interaction with the product or service it offers according to the consumer's interest. It is thought that artificial intelligence reshapes advertising with targeting and personalization, and makes significant contributions in delivering the right message to the right target audience at the right time. This study aims to analyze the articles in the literature that include targeting and personalization for the use of artificial intelligence in advertising in the last 10 years (2014-2024) within a bibliometric framework and to map its development in the field of advertising. For this purpose, a total of 790 articles were reached in the Web of Science and Scopus databases by applying the determined keywords and various elimination criteria. As a result of the analyses, it was determined that the number of articles on the subject was 4 in 2014, this number reached 24 in 2019, and 201 articles were published in 2024. It is seen that the annual growth rate of the articles is 52.27%. This study identifies the publication trends in the field and provides clues to researchers for future studies.

Kaynakça

  • Aria, M., ve Cuccurullo, C. (2017). bibliometrix: An R-tool for comprehensive science mapping analysis. Journal of Informetrics, 11(4), 959-975. https://doi.org/https://doi.org/10.1016/j.joi.2017.08.007
  • Adalı, G., Yardibi, F., Aydın, Ş., Güdekli, A., Aksoy, E., & Hoştut, S. (2024). Gender and Advertising: A 50-Year Bibliometric Analysis. Journal of Advertising, 1–21. https://doi.org/10.1080/00913367.2024.2343291
  • Baek, T. H. (2023). Digital Advertising in the Age of Generative AI. Journal of Current Issues & Research in Advertising, 44(3), 249–251. https://doi.org/10.1080/10641734.2023.2243496
  • Baek, T. H., and M. Kim. 2023. “Ai Robo-Advisor Anthropomorphism: The Impact of Anthropomorphic Appeals and Regulatory Focus on Investment Behaviors.” Journal of Business Research 164(open in a new window): 114039. https://doi.org/10.1016/j.jbusres.2023.114039
  • Baek, T. H., M. Bakpayev, S. Yoon, and S. Kim. 2022. “Smiling AI Agents: How Anthropomorphism and Broad Smiles Increase Charitable Giving.” International Journal of Advertising 41(open in a new window) (5(open in a new window)): 850–867. https://doi.org/10.1080/02650487.2021.2011654
  • Bakpayev, M., T. H. Baek, P. van Esch, and S. Yoon. 2022. “Programmatic Creative: AI Can Think but It Cannot Feel.” Australasian Marketing Journal 30(open in a new window) (1(open in a new window)): 90–95. https://doi.org/10.1016/j.ausmj.2020.04.002
  • Boerman, S. C., Kruikemeier, S., & Zuiderveen Borgesius, F. J. (2017). Online Behavioral Advertising: A Literature Review and Research Agenda. Journal of Advertising, 46(3), 363-376. https://doi.org/10.1080/00913367.2017.1339368
  • Boyko, N., ve Kholodetska, Y. (2022). Using Artificial Intelligence Algorithms in Advertising. 2022 IEEE 17th International Conference on Computer Sciences and Information Technologies (CSIT)
  • Ciuchita, R., Gummerus, J.K., Holmlund, M. and Linhart, E.L. (2023), "Programmatic advertising in online retailing: consumer perceptions and future avenues", Journal of Service Management, Vol. 34 No. 2, pp. 231-255. https://doi.org/10.1108/JOSM-06-2021-0238
  • Chen, G., Xie, P., Dong, J., & Wang, T. (2019). Understanding Programmatic Creative: The Role of AI. Journal of Advertising, 48(4), 347–355. https://doi.org/10.1080/00913367.2019.1654421
  • Choi, J.-A., ve Lim, K. (2020). Identifying machine learning techniques for classification of target advertising. ICT Express, 6(3), 175-180. https://doi.org/https://doi.org/10.1016/j.icte.2020.04.012
  • Davenport, T., Guha, A., Grewal, D., ve 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
  • Donthu, N., S. Kumar, D. Mukherjee, N. Pandey, and W. M. Lim. (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.
  • Dwivedi, Y. K., Ismagilova, E., Hughes, D. L., Carlson, J., Filieri, R., Jacobson, J., Jain V., Karjaluoto H., Kefi H., Krishen, A.S., Kumar V., Rahman, M.M., Raman R., Rauschnabel P.A., Rowley J., Salo J., Tran G.A., & Wang, Y. (2021). Setting the future of digital and social media marketing research: Perspectives and research propositions. International journal of information management, 59, 102168. https://doi.org/10.1016/j.ijinfomgt.2020.102168 Egghe, L. 2006. “Theory and Practise of the G-Index.” Scientometrics 69 (1): 131–152. https://doi.org/ 10.1007/s11192-006-0144-7 .
  • Ford, J., Jain, V., Wadhwani, K., & Gupta, D. G. (2023). AI advertising: An overview and guidelines. Journal of Business Research, 166, 114124. https://doi.org/https://doi.org/10.1016/j.jbusres.2023.114124
  • Gao, B., Wang, Y., Xie, H., Hu, Y., & Hu, Y. (2023). Artificial Intelligence in Advertising: Advancements, Challenges, and Ethical Considerations in Targeting, Personalization, Content Creation, and Ad Optimization. Sage Open, 13(4). https://doi.org/10.1177/21582440231210759
  • Gupta, S., Paul, J., Stoner, J. L., & Aggarwal, A. (2024). Digital transformation, online advertising, and consumer behaviour. International Journal of Advertising, 1-24. https://doi.org/10.1080/02650487.2024.2317632
  • Häglund, E., ve Björklund, J. (2024). AI-Driven Contextual Advertising: Toward Relevant Messaging Without Personal Data. Journal of Current Issues & Research in Advertising, 1-19. https://doi.org/10.1080/10641734.2024.2334939
  • Hirsch, J. E. 2005. “An Index to Quantify an individual’s Scientific Research Output.” Proceedings of the National Academy of Sciences 102 (46): 16569–16572. https://doi.org/10.1073/pnas.0507655102.
  • Hocutt, D. L. (2024). Composing with generative AI on digital advertising platforms. Computers and Composition, 71, 102829. https://doi.org/10.1016/j.compcom.2024.102829 Iyer, G., Soberman, D., & Villas-Boas, J. M. (2005). The Targeting of Advertising. Marketing Science, 24(3), 461-476. https://doi.org/10.1287/mksc.1050.0117
  • Jukić, D. (2023). Time To Say Goodbye: A Neuromarketing Perspective. International Scientific Conference „Marketing and Media Identity: AI–The Future of Today “,
  • Khandelwal, A. R., Yadav, R., Chaturvedi, A., & Kumar, A. S. (2024). Examining the Impact of AI and Digital Marketing on Consumer Purchase Intention. In Emerging Developments and Technologies in Digital Government (pp. 220-242). IGI Global.
  • Kietzmann, J., Paschen, J., & Treen, E. (2018). Artificial Intelligence in Advertising. Journal of Advertising Research, 58(3), 263. https://doi.org/10.2501/JAR-2018-035
  • Kozyreva, A., Lorenz-Spreen, P., Hertwig, R., Lewandowsky, S., & Herzog, S. M. (2021). Public attitudes towards algorithmic personalization and use of personal data online: Evidence from Germany, Great Britain, and the United States. Humanities and Social Sciences Communications, 8(1), 1-11.
  • Kraus, S., Breier, M., Lim, W. M., Dabić, M., Kumar, S., Kanbach, D., Mukherjee, D., Corvello, V., Piñeiro-Chousa, J., Liguori, E., Palacios-Marqués, D., Schiavone, F., Ferraris, A., Fernandes, C., & Ferreira, J. J. (2022). Literature reviews as independent studies: guidelines for academic practice. Review of Managerial Science, 16(8), 2577-2595. https://doi.org/10.1007/s11846-022-00588-8
  • Laux, J., Stephany, F., Russell, C., Wachter, S., & Mittelstadt, B. (2022). The Concentration-after-Personalisation Index (CAPI): Governing effects of personalisation using the example of targeted online advertising. Big Data & Society, 9(2), 20539517221132535.
  • Leszczynska, M., ve Baltag, D. (2024). “Can I have it non-personalised?” An Empirical Investigation of Consumer Willingness to Share Data for Personalized Services and Ads. Journal of Consumer Policy, 47(3), 345-372. https://doi.org/10.1007/s10603-024-09568-9
  • Liu, J., Li, X., & Wang, S. (2020). What have we learnt from 10 years of fintech research? Ascientometric analysis. Technological Forecasting and Social Change, 155, 120022. https://doi.org/10.1016/j.techfore.2020.120022
  • Ljepava, N. (2022). AI-enabled marketing solutions in Marketing Decision making: AI application in different stages of marketing process. TEM Journal, 11(3), 1308-1315.
  • Lo, S. L., Cornforth, D., & Chiong, R. (2015). Effects of training datasets on both the extreme learning machine and support vector machine for target audience identification on twitter. Proceedings of ELM-2014 Volume 1: Algorithms and Theories.
  • Longoni, C., Bonezzi, A., & Morewedge, C. K. (2019). Resistance to medical artificial intelligence. Journal of Consumer Research, 46(4), 629-650. https://doi.org/10.1093/jcr/ucz013
  • Malthouse, E., ve Copulsky, J. (2023). Artificial intelligence ecosystems for marketing communications. International Journal of Advertising, 42(1), 128-140. https://doi.org/10.1080/02650487.2022.2122249
  • Martín-García, N., & Alvarado-López, M. C. (2022). The relationship between advertising effectiveness and creativity: a critical approach to the campaigns winning Efi and the El Sol festival (2011-2020). Revista Mediterránea de Comunicación/Mediterranean Journal of Communication, 13(2), 279-300. https://www.doi.org/10.14198/MEDCOM.21745
  • Moher, D., Liberati, A., Tetzlaff, J., & Altman, D. G. (2009). Preferred reporting items for systematic reviews and meta-analyses: the PRISMA statement. BMJ, 339, b2535. https://doi.org/10.1136/bmj.b2535
  • Nicolaou, C. (2022). Generations and branded content from and through the internet and social media: Modern communication strategic techniques and practices for brand sustainability—The Greek case study of LACTAchoc¬olate. Sustainability, 15(1), 584. https://doi.org/10.3390/su15010584
  • Niziaieva, V., Liganenko, M., Muntyan, I., Ohiienko, M., Goncharenko, M., & Nazarenko, O. (2022). Balancing interests in the field of tourism based on digital marketing tools. Journal of Information Technology Management, 14(open in a new window), 59–77. https://doi.org/10.22059/jitm.2022.88875
  • Pahari, S., Bandyopadhyay, A., V. M, V. K., & Pingle, S. (2024). A bibliometric analysis of digital advertising in social media: the state of the art and future research agenda. Cogent Business & Management, 11(1), 2383794. https://doi.org/10.1080/23311975.2024.2383794
  • Paul, J., Lim, W. M., O’Cass, A., Hao, A. W., & Bresciani, S. (2021). Scientific procedures and rationales for systematic literature reviews (SPAR-4-SLR). International Journal of Consumer Studies, 45(4), O1-O16. https://doi.org/https://doi.org/10.1111/ijcs.12695
  • Singh, P. K., & Singh, B. K. (2019). Analysis of Social Structures in Scientometrics. In: B. K. Singh, R. J. Maurya, Krishna Kumar Kesharwani, & Sarvesh Kumar (Eds.), Academic libraries: Collection to connectivity (Acollection of essays in honour of Dr. T. N. Dubey) (pp. 245–254). Shree Publishers & Distributors.
  • Singh, A. P., Behera, R. K., & Bala, P. K. (2024). Evolution of sustainable retailing and how it influences consumer behavior: a bibliometric review. The International Review of Retail, Distribution and Consumer Research, 1–31. https://doi.org/10.1080/09593969.2024.2381066
  • Shu, Sheng, and Yi Liu. 2021. "Looking Back to Move Forward: A Bibliometric Analysis of Consumer Privacy Research" Journal of Theoretical and Applied Electronic Commerce Research 16, no. 4: 727-747. https://doi.org/10.3390/jtaer16040042
  • Statista, (2024, Kasım 02). “Statista Market Insights” https://www.statista.com/outlook/dmo/digital-advertising/worldwide#ad-spending
  • Taylor, C. R., & Carlson, L. (2021). The future of advertising research: new directions and research needs. Journal of Marketing Theory and Practice, 29(1), 51–62. https://doi.org/10.1080/10696679.2020.1860681
  • Van Esch, P., & Stewart Black, J. (2021). Artificial Intelligence (AI): Revolutionizing Digital Marketing. Australasian Marketing Journal, 29(3), 199-203. https://doi.org/10.1177/18393349211037684
  • Vasconcelos, L.F., Sigahi, T.F.A.C., Pinto, J.d.S., Rampasso, I.S. and Anholon, R. (2023), "Supply chain management maturity and business models: scientific mapping using SciMAT", Benchmarking: An International Journal, Vol. ahead-of-print No. ahead-of-print. https://doi.org/10.1108/BIJ-04-2023-0255
  • Yun, J. T., and J. Strycharz. 2023. “Building the Future of Digital Advertising One Block at a Time: How Blockchain Technology Can Change Advertising Practice and Research.” Journal of Current Issues & Research in Advertising 44(open in a new window) (1(open in a new window)): 24–37. https://doi.org/10.1080/10641734.2022.2090464
Toplam 46 adet kaynakça vardır.

Ayrıntılar

Birincil Dil Türkçe
Konular Yapay Zeka (Diğer), İletişim Çalışmaları, Reklam
Bölüm Research Article
Yazarlar

Bekir Bulut 0000-0001-7749-748X

Ali Erkam Yarar 0000-0002-0919-314X

Yayımlanma Tarihi 26 Aralık 2024
Gönderilme Tarihi 16 Eylül 2024
Kabul Tarihi 10 Aralık 2024
Yayımlandığı Sayı Yıl 2024 Cilt: 12 Sayı: 2

Kaynak Göster

APA Bulut, B., & Yarar, A. E. (2024). REKLAMCILIKTA YAPAY ZEKA: HEDEFLEME VE KİŞİSELLEŞTİRMEYE YÖNELİK BİBLİYOMETRİK ANALİZ*. Beykoz Akademi Dergisi, 12(2), 366-394. https://doi.org/10.14514/beykozad.1551121
AMA Bulut B, Yarar AE. REKLAMCILIKTA YAPAY ZEKA: HEDEFLEME VE KİŞİSELLEŞTİRMEYE YÖNELİK BİBLİYOMETRİK ANALİZ*. Beykoz Akademi Dergisi. Aralık 2024;12(2):366-394. doi:10.14514/beykozad.1551121
Chicago Bulut, Bekir, ve Ali Erkam Yarar. “REKLAMCILIKTA YAPAY ZEKA: HEDEFLEME VE KİŞİSELLEŞTİRMEYE YÖNELİK BİBLİYOMETRİK ANALİZ*”. Beykoz Akademi Dergisi 12, sy. 2 (Aralık 2024): 366-94. https://doi.org/10.14514/beykozad.1551121.
EndNote Bulut B, Yarar AE (01 Aralık 2024) REKLAMCILIKTA YAPAY ZEKA: HEDEFLEME VE KİŞİSELLEŞTİRMEYE YÖNELİK BİBLİYOMETRİK ANALİZ*. Beykoz Akademi Dergisi 12 2 366–394.
IEEE B. Bulut ve A. E. Yarar, “REKLAMCILIKTA YAPAY ZEKA: HEDEFLEME VE KİŞİSELLEŞTİRMEYE YÖNELİK BİBLİYOMETRİK ANALİZ*”, Beykoz Akademi Dergisi, c. 12, sy. 2, ss. 366–394, 2024, doi: 10.14514/beykozad.1551121.
ISNAD Bulut, Bekir - Yarar, Ali Erkam. “REKLAMCILIKTA YAPAY ZEKA: HEDEFLEME VE KİŞİSELLEŞTİRMEYE YÖNELİK BİBLİYOMETRİK ANALİZ*”. Beykoz Akademi Dergisi 12/2 (Aralık 2024), 366-394. https://doi.org/10.14514/beykozad.1551121.
JAMA Bulut B, Yarar AE. REKLAMCILIKTA YAPAY ZEKA: HEDEFLEME VE KİŞİSELLEŞTİRMEYE YÖNELİK BİBLİYOMETRİK ANALİZ*. Beykoz Akademi Dergisi. 2024;12:366–394.
MLA Bulut, Bekir ve Ali Erkam Yarar. “REKLAMCILIKTA YAPAY ZEKA: HEDEFLEME VE KİŞİSELLEŞTİRMEYE YÖNELİK BİBLİYOMETRİK ANALİZ*”. Beykoz Akademi Dergisi, c. 12, sy. 2, 2024, ss. 366-94, doi:10.14514/beykozad.1551121.
Vancouver Bulut B, Yarar AE. REKLAMCILIKTA YAPAY ZEKA: HEDEFLEME VE KİŞİSELLEŞTİRMEYE YÖNELİK BİBLİYOMETRİK ANALİZ*. Beykoz Akademi Dergisi. 2024;12(2):366-94.