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Pazarlamada Yapay Zekanın Bibliyometrik Analiz Yöntemi ile İncelenmesi

Year 2024, Volume: 5 Issue: 1, 97 - 109, 30.03.2024
https://doi.org/10.52835/19maysbd.1445578

Abstract

Bu çalışma pazarlama alanında yapay zekâ (YZ) araştırmalarının bibliyometrik incelemesini gerçekleştirmektedir. VOSviewer programı kullanılarak Web of Science veri tabanındaki YZ konulu makaleler üzerinde yazarlık, alıntılar, yayın sıklığı ve kurumların coğrafi kökenleri gibi çeşitli boyutları incelemiştir. Araştırma bulguları, pazarlama alanındaki YZ araştırmalarının dağınık bir yapıda olduğunu ve henüz belirgin bir akademik grubun öne çıkmadığını göstermektedir. Aynı zamanda 2018 sonrasında pazarlama dergilerinde YZ makalelerinde belirgin bir artış olduğu ve en etkili çalışmaların genellikle Amerika, İngiltere ve Avustralya'daki araştırmacılardan geldiği belirlenmiştir. Çalışma makine öğrenimi, büyük veri ve derin öğrenme gibi anahtar kelimeler etrafında şekillenen ve gelecekte yapay zekâ destekli teknolojilere odaklanılması beklenen mevcut araştırma eğilimlerini vurgulamaktadır. Ayrıca VOSviewer'ın kapsamlı veri analizi yeteneklerinin, YZ'nin pazarlamadaki rolü üzerine yapılan araştırmaları zenginleştirme potansiyeline sahip olduğu sonucuna varılmıştır. Bu araştırma pazarlamada YZ araştırmalarının mevcut durumunu değerlendirerek bu alandaki gelecek araştırmalar için bir temel oluşturmaktadır.

Ethical Statement

Çalışma etik kurulu kararı gerektirmemektedir.

Supporting Institution

Bu çalışma için herhangi bir destek söz konusu değildir.

References

  • Ameen, N., Sharma, G. D., Tarba, S., Rao, A. ve Chopra, R. (2022). Toward advancing theory on creativity in marketing and artificial intelligence. Psychology & Marketing, 39(9), 1802–1825. https://doi.org/10.1002/mar.21699
  • Anayat, S. ve Rasool, G. (2024). Artificial intelligence marketing (AIM): connecting-the-dots using bibliometrics. Journal of Marketing Theory and Practice, 32(1), 114-135.
  • Bhardwaj, A. K., Garg, A., Ram, S., Gajpal, Y. ve Zheng, C. (2020). Research Trends in Green Product for Environment: A Bibliometric Perspective. International Journal of Environmental Research and Public Health, 17(22), 8469. https://doi.org/10.3390/ijerph17228469
  • Cukier, K. (2019). Ready for robots: how to think about the future of AI. Foreign Aff.
  • Currim, I. S. ve Schneider, L. G. (1991). A Taxonomy of Consumer Purchase Strategies in a Promotion Intensive Environment. Marketing Science, 10(2), 91–110. https://doi.org/10.1287/mksc.10.2.91
  • 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(1), 24–42. https://doi.org/10.1007/s11747-019-00696-0
  • Dhir, A., Kaur, P. ve Rajala, R. (2018). Why do young people tag photos on social networking sites? Explaining user intentions. International Journal of Information Management, 38(1), 117–127. https://doi.org/10.1016/j.ijinfomgt.2017.07.004
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  • Eyre-Walker, A. ve Stoletzki, N. (2013). The Assessment of Science: The Relative Merits of Post-Publication Review, the Impact Factor, and the Number of Citations. PLoS Biology, 11(10), e1001675. https://doi.org/10.1371/journal.pbio.1001675
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  • Gaviria-Marin, M., Merigó, J. M. ve Baier-Fuentes, H. (2019). Knowledge management: A global examination based on bibliometric analysis. Technological Forecasting and Social Change, 140, 194–220. https://doi.org/10.1016/j.techfore.2018.07.006
  • Huang, M.-H. ve Rust, R. T. (2018). Artificial Intelligence in Service. Journal of Service Research, 21(2), 155–172. https://doi.org/10.1177/1094670517752459
  • Hussein, Z. (2017). Leading to Intention: The Role of Attitude in Relation to Technology Acceptance Model in E-Learning. Procedia Computer Science, 105, 159–164. https://doi.org/10.1016/j.procs.2017.01.196
  • Hussain, W. M. H. W. ve Ayob, A. H. (2023). Trends in Digital Marketing Research: A Bibliometric Analysis. International Journal of Marketing, Communication and New Media, 11(20).
  • Longoni, C., Bonezzi, A. ve Morewedge, C. K. (2019). Resistance to Medical Artificial Intelligence. Journal of Consumer Research, 46(4), 629–650. https://doi.org/10.1093/jcr/ucz013
  • Mariani, M. M., Perez‐Vega, R. ve Wirtz, J. (2022). AI in marketing, consumer research and psychology: A systematic literature review and research agenda. Psychology & Marketing, 39(4), 755–776. https://doi.org/10.1002/mar.21619
  • Martin, B. A. S., Jin, H. S., Wang, D., Nguyen, H., Zhan, K. ve Wang, Y. X. (2020). The influence of consumer anthropomorphism on attitudes towards artificial intelligence trip advisors. Journal of Hospitality and Tourism Management, 44, 108–111. https://doi.org/10.1016/j.jhtm.2020.06.004
  • Montano, D. E. ve Kasprzyk, D. (2015). Theory of reasoned action, theory of planned behavior, and the integrated behavioral model. Health Behavior: Theory, Research and Practice, 70(4), 231.
  • Nisson, C. ve Earl, A. (2020). The Theories of Reasoned Action and Planned Behavior. In The Wiley Encyclopedia of Health Psychology (pp. 755–761). Wiley. https://doi.org/10.1002/9781119057840.ch129
  • Pitt, C., Mulvey, M. ve Kietzmann, J. (2018). Quantitative insights from online qualitative data: An example from the health care sector. Psychology & Marketing, 35(12), 1010–1017. https://doi.org/10.1002/mar.21152
  • Poushneh, A. (2021). Humanizing voice assistant: The impact of voice assistant personality on consumers’ attitudes and behaviors. Journal of Retailing and Consumer Services, 58, 102283. https://doi.org/10.1016/j.jretconser.2020.102283
  • Rai, A. (2020). Explainable AI: from black box to glass box. Journal of the Academy of Marketing Science, 48(1), 137–141. https://doi.org/10.1007/s11747-019-00710-5
  • Roberts, J. (2016). Thinking Machines: The Search for Artificial Intelligence Distillations. Chemical Heritage Foundation, 17, 2017.
  • Russell, S. J. ve Norvig, P. (2010). Artificial intelligence a modern approach.
  • Sohn, K., Sung, C. E., Koo, G. ve Kwon, O. (2020). Artificial intelligence in the fashion industry: consumer responses to generative adversarial network (GAN) technology. International Journal of Retail & Distribution Management, 49(1), 61–80. https://doi.org/10.1108/IJRDM-03-2020-0091
  • Suraña‐Sánchez, C. ve Aramendia‐Muneta, M. E. (2024). Impact of artificial intelligence on customer engagement and advertising engagement: A review and future research agenda. International Journal of Consumer Studies, 48(2), e13027.
  • Technopedia. (2020). Artificial Intelligence definitions, retrieved on January 28th 2020 from. Van Eck, N. J. ve Waltman, L. (2019). Manual for VOSviwer version 1.6. 10. CWTS Meaningful Metrics.
  • Venkatesh, Morris, Davis ve Davis. (2003). User Acceptance of Information Technology: Toward a Unified View. MIS Quarterly, 27(3), 425. https://doi.org/10.2307/30036540
  • Wang, M. H., Wang, W. C., Lin, C. H. ve Chen, Y. T. (2020). Implementation of AI e-commerce model for medical beauty industry: A case study in Penghu. Journal of Accounting, Finance & Management Strategy, 15(1). Wong, D. ve Romano, L. (2018). VOSviewer. 7131, 219–220.
  • Ziakis, C. ve Vlachopoulou, M. (2023). Artificial intelligence in digital marketing: Insights from a comprehensive review. Information, 14(12), 664.

Investigation of Artificial Intelligence in Marketing Using Bibliometric Analysis Method

Year 2024, Volume: 5 Issue: 1, 97 - 109, 30.03.2024
https://doi.org/10.52835/19maysbd.1445578

Abstract

This study presents a bibliometric review of artificial intelligence (AI) research in the marketing field. The VOSviewer program was used to analyze various dimensions, including authorship, citations, publication frequency, and geographical origins of institutions, in articles on AI from the Web of Science database. The research findings indicate that AI research in marketing has a dispersed structure, and no clear academic group has emerged yet. It has been determined that there has been a significant increase in AI articles in marketing journals after 2018. The most influential studies generally come from researchers in America, England, and Australia. The study highlights current research trends centered around keywords such as machine learning, big data, and deep learning. There is an expected focus on AI-enabled technologies in the future. It is concluded that VOSviewer's data analysis capabilities can enhance research on the role of AI in marketing. This study evaluates the current state of AI research in marketing, laying the groundwork for future research in this field.

References

  • Ameen, N., Sharma, G. D., Tarba, S., Rao, A. ve Chopra, R. (2022). Toward advancing theory on creativity in marketing and artificial intelligence. Psychology & Marketing, 39(9), 1802–1825. https://doi.org/10.1002/mar.21699
  • Anayat, S. ve Rasool, G. (2024). Artificial intelligence marketing (AIM): connecting-the-dots using bibliometrics. Journal of Marketing Theory and Practice, 32(1), 114-135.
  • Bhardwaj, A. K., Garg, A., Ram, S., Gajpal, Y. ve Zheng, C. (2020). Research Trends in Green Product for Environment: A Bibliometric Perspective. International Journal of Environmental Research and Public Health, 17(22), 8469. https://doi.org/10.3390/ijerph17228469
  • Cukier, K. (2019). Ready for robots: how to think about the future of AI. Foreign Aff.
  • Currim, I. S. ve Schneider, L. G. (1991). A Taxonomy of Consumer Purchase Strategies in a Promotion Intensive Environment. Marketing Science, 10(2), 91–110. https://doi.org/10.1287/mksc.10.2.91
  • 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(1), 24–42. https://doi.org/10.1007/s11747-019-00696-0
  • Dhir, A., Kaur, P. ve Rajala, R. (2018). Why do young people tag photos on social networking sites? Explaining user intentions. International Journal of Information Management, 38(1), 117–127. https://doi.org/10.1016/j.ijinfomgt.2017.07.004
  • Ekinci, G. ve Bilginer-Ozsaatci, F. G. (2023). Bibliometric analysis of publications in artificial intelligence and marketing.
  • Eyre-Walker, A. ve Stoletzki, N. (2013). The Assessment of Science: The Relative Merits of Post-Publication Review, the Impact Factor, and the Number of Citations. PLoS Biology, 11(10), e1001675. https://doi.org/10.1371/journal.pbio.1001675
  • Gao, S., Krogstie, J. ve Gransæther, P. A. (2008). Mobile Services Acceptance Model. 2008 International Conference on Convergence and Hybrid Information Technology, 446–453. https://doi.org/10.1109/ICHIT.2008.252
  • Gaviria-Marin, M., Merigó, J. M. ve Baier-Fuentes, H. (2019). Knowledge management: A global examination based on bibliometric analysis. Technological Forecasting and Social Change, 140, 194–220. https://doi.org/10.1016/j.techfore.2018.07.006
  • Huang, M.-H. ve Rust, R. T. (2018). Artificial Intelligence in Service. Journal of Service Research, 21(2), 155–172. https://doi.org/10.1177/1094670517752459
  • Hussein, Z. (2017). Leading to Intention: The Role of Attitude in Relation to Technology Acceptance Model in E-Learning. Procedia Computer Science, 105, 159–164. https://doi.org/10.1016/j.procs.2017.01.196
  • Hussain, W. M. H. W. ve Ayob, A. H. (2023). Trends in Digital Marketing Research: A Bibliometric Analysis. International Journal of Marketing, Communication and New Media, 11(20).
  • Longoni, C., Bonezzi, A. ve Morewedge, C. K. (2019). Resistance to Medical Artificial Intelligence. Journal of Consumer Research, 46(4), 629–650. https://doi.org/10.1093/jcr/ucz013
  • Mariani, M. M., Perez‐Vega, R. ve Wirtz, J. (2022). AI in marketing, consumer research and psychology: A systematic literature review and research agenda. Psychology & Marketing, 39(4), 755–776. https://doi.org/10.1002/mar.21619
  • Martin, B. A. S., Jin, H. S., Wang, D., Nguyen, H., Zhan, K. ve Wang, Y. X. (2020). The influence of consumer anthropomorphism on attitudes towards artificial intelligence trip advisors. Journal of Hospitality and Tourism Management, 44, 108–111. https://doi.org/10.1016/j.jhtm.2020.06.004
  • Montano, D. E. ve Kasprzyk, D. (2015). Theory of reasoned action, theory of planned behavior, and the integrated behavioral model. Health Behavior: Theory, Research and Practice, 70(4), 231.
  • Nisson, C. ve Earl, A. (2020). The Theories of Reasoned Action and Planned Behavior. In The Wiley Encyclopedia of Health Psychology (pp. 755–761). Wiley. https://doi.org/10.1002/9781119057840.ch129
  • Pitt, C., Mulvey, M. ve Kietzmann, J. (2018). Quantitative insights from online qualitative data: An example from the health care sector. Psychology & Marketing, 35(12), 1010–1017. https://doi.org/10.1002/mar.21152
  • Poushneh, A. (2021). Humanizing voice assistant: The impact of voice assistant personality on consumers’ attitudes and behaviors. Journal of Retailing and Consumer Services, 58, 102283. https://doi.org/10.1016/j.jretconser.2020.102283
  • Rai, A. (2020). Explainable AI: from black box to glass box. Journal of the Academy of Marketing Science, 48(1), 137–141. https://doi.org/10.1007/s11747-019-00710-5
  • Roberts, J. (2016). Thinking Machines: The Search for Artificial Intelligence Distillations. Chemical Heritage Foundation, 17, 2017.
  • Russell, S. J. ve Norvig, P. (2010). Artificial intelligence a modern approach.
  • Sohn, K., Sung, C. E., Koo, G. ve Kwon, O. (2020). Artificial intelligence in the fashion industry: consumer responses to generative adversarial network (GAN) technology. International Journal of Retail & Distribution Management, 49(1), 61–80. https://doi.org/10.1108/IJRDM-03-2020-0091
  • Suraña‐Sánchez, C. ve Aramendia‐Muneta, M. E. (2024). Impact of artificial intelligence on customer engagement and advertising engagement: A review and future research agenda. International Journal of Consumer Studies, 48(2), e13027.
  • Technopedia. (2020). Artificial Intelligence definitions, retrieved on January 28th 2020 from. Van Eck, N. J. ve Waltman, L. (2019). Manual for VOSviwer version 1.6. 10. CWTS Meaningful Metrics.
  • Venkatesh, Morris, Davis ve Davis. (2003). User Acceptance of Information Technology: Toward a Unified View. MIS Quarterly, 27(3), 425. https://doi.org/10.2307/30036540
  • Wang, M. H., Wang, W. C., Lin, C. H. ve Chen, Y. T. (2020). Implementation of AI e-commerce model for medical beauty industry: A case study in Penghu. Journal of Accounting, Finance & Management Strategy, 15(1). Wong, D. ve Romano, L. (2018). VOSviewer. 7131, 219–220.
  • Ziakis, C. ve Vlachopoulou, M. (2023). Artificial intelligence in digital marketing: Insights from a comprehensive review. Information, 14(12), 664.
There are 30 citations in total.

Details

Primary Language Turkish
Subjects Business Administration
Journal Section Research Articles
Authors

Seyda Fatih Harmandaroğlu 0000-0001-5111-2940

Publication Date March 30, 2024
Submission Date March 1, 2024
Acceptance Date March 26, 2024
Published in Issue Year 2024 Volume: 5 Issue: 1

Cite

APA Harmandaroğlu, S. F. (2024). Pazarlamada Yapay Zekanın Bibliyometrik Analiz Yöntemi ile İncelenmesi. 19 Mayıs Sosyal Bilimler Dergisi, 5(1), 97-109. https://doi.org/10.52835/19maysbd.1445578