Journals / Black Sea Journal of Public and Social Science / 2026 / Sayı: 9

Bibliometric Analysis of Studies on Artificial Intelligence and Econometric Methods in the Field of Marketing

Pazarlama Alanında Yapay Zekâ ve Ekonometrik Yöntemlerle Yapılan Çalışmaların Bibliyometrik Analizi

Pages
132–148
Publication Date
2026-09-10
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AI Summary & Key Takeaways

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

The findings indicate that AI-based approaches have become significantly prominent in the marketing literature, especially in recent years, while their integrated use with econometric methods has remained limited.

📌 3 Key Takeaways

🎯
Objective & Hypothesis:With technological advancements, the understanding of marketing has evolved from being merely a promotional activity for businesses to becoming a data-driven and strategic decision-making area.
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Key Findings & Data:The rapid increase in product and service diversity has complicated consumer decision-making processes, while also increasing the need for businesses to gain a competitive advantage and build customer loyalty.
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Conclusion & Impact:The findings indicate that AI-based approaches have become significantly prominent in the marketing literature, especially in recent years, while their integrated use with econometric methods has remained limited.
🔬Design:Araştırma Makalesi
📍Scope:Comprehensive Sample
⚙️Variables:Yapay zekâ · Ekonometrik yöntemler · Bibliyometrik analiz

Abstract

With technological advancements, the understanding of marketing has evolved from being merely a promotional activity for businesses to becoming a data-driven and strategic decision-making area. The rapid increase in product and service diversity has complicated consumer decision-making processes, while also increasing the need for businesses to gain a competitive advantage and build customer loyalty. In this transformation process, quantitative analysis methods and advanced data processing approaches have become increasingly important in improving the effectiveness of marketing activities. This study aims to systematically examine academic research in the field of marketing using econometric analyzes and artificial intelligence-based methods within the framework of the bibliometric analysis method. Within the scope of this study, publications obtained from the Web of Science database were analyzed to reveal publication trends in the literature, countries, authors, keyword clusters, and collaboration networks. The capacity of econometric analyzes to explain relationships between variables and generate causal inferences in marketing research, and the potential of artificial intelligence methods to identify patterns from large-scale data, develop predictions, and contribute to decision-making processes, were evaluated together. The findings indicate that AI-based approaches have become significantly prominent in the marketing literature, especially in recent years, while their integrated use with econometric methods has remained limited. In this context, the study reveals the methodological evolution of data-driven approaches in the field of marketing and highlights research gaps in the combined use of econometric and artificial intelligence-based methods. The findings of the study contribute to the literature by providing a methodological framework for future interdisciplinary marketing research.

Özet

Teknolojik gelişmelerle birlikte pazarlama anlayışı, işletmeler açısından yalnızca bir tanıtım faaliyeti olmaktan çıkarak veri odaklı ve stratejik bir karar alanına dönüşmüştür. Ürün ve hizmet çeşitliliğinin hızla artması, tüketicilerin karar verme süreçlerini karmaşıklaştırırken, işletmelerin rekabet avantajı elde etme ve müşteri sadakati oluşturma gerekliliğini de artırmıştır. Bu dönüşüm sürecinde, pazarlama faaliyetlerinin etkinliğinin artırılmasında nicel analiz yöntemleri ve ileri veri işleme yaklaşımları giderek daha önemli hale gelmiştir. Bu çalışma, pazarlama alanında ekonometrik analizler ve yapay zekâ temelli yöntemler kullanılarak gerçekleştirilen akademik araştırmaları, bibliyometrik analiz yöntemi çerçevesinde sistematik biçimde incelemeyi amaçlamaktadır. Çalışma kapsamında Web of Science veri tabanından elde edilen yayınlar analiz edilerek, literatürdeki yayın eğilimleri, ülkeler, yazarlar, anahtar kelime kümelenmeleri ve iş birliği ağları ortaya konulmuştur. Ekonometrik analizlerin pazarlama araştırmalarında değişkenler arasındaki ilişkileri açıklama ve nedensel çıkarımlar üretme kapasitesi, yapay zekâ yöntemlerinin ise büyük ölçekli verilerden örüntüleri belirleme, tahminler geliştirme ve karar destek süreçlerine katkı sağlama potansiyeli birlikte değerlendirilmiştir. Elde edilen bulgular, pazarlama literatüründe özellikle son yıllarda yapay zekâ temelli yaklaşımların belirgin biçimde ön plana çıktığını, buna karşın ekonometrik yöntemlerle bütünleşik kullanımın sınırlı kaldığını göstermektedir. Bu bağlamda çalışma, pazarlama alanında veri merkezli yaklaşımların metodolojik evrimini ortaya koymakta ve ekonometrik ve yapay zekâ temelli yöntemlerin birlikte ele alınmasına yönelik araştırma boşluklarını görünür kılmaktadır. Çalışmanın bulguları, gelecekte yapılacak disiplinler arası pazarlama araştırmaları için yöntemsel bir çerçeve sunarak literatüre katkı sağlamaktadır.

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Cite This Article

Fendoğlu, E., Işık, E. N., Yilmaz, H. (2026). Bibliometric Analysis of Studies on Artificial Intelligence and Econometric Methods in the Field of Marketing. Black Sea Journal of Public and Social Science, Sayı: 9, 132–148. https://doi.org/10.52704/bssocialscience.1970731

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References (78)

  1. Aguinis, H., Beltran, J. R., & Cope, A. (2024). How to use generative AI as a human resource management assistant. Organizational Dynamics, 53(1), 101029.
  2. Aksu, Y. Z. (1973). İşletmelerde yönetim aracı olarak pazarlama araştırması. https://acikerisim.uludag.edu.tr/server/api/core/bitstreams/7d3c9b2d-c929-48b7-8ebd-0a2b63d5ff2e/content (erişim tarihi: 05 Haziran 2026).
  3. Aktaş, M. & Çavuşoğlu, S. (2023). Pazarlamada yapay zekâ. In: Yılmaz, E. S. (ed.), Dijitalleşme ve Pazarlama Araştırmaları. Özgür Yayınları. https://doi.org/10.58830/ozgur.pub398.c1662
  4. Aktaş, N. (2023). Brıcs-T Ülkelerinde İnovasyonun Rekabete Etkisi: Ekonometrik Bir Analiz. Eurasian Econometrics Statistics & Emprical Economics Journal, 23, 37–47. http://dx.doi.org/10.17740/eas.stat.2023-V23-04
  5. Alkaddour, M. (2022). Pazarlamada yapay zeka kullanımı. İşletme ve Girişimcilik Araştırmaları Dergisi, 1, 48-66.
  6. Alkaraan, F., Elmarzouky, M., de Sousa Jabbour, A. B. L., Jabbour, C. J. C., & Gulko, N. (2025). Maximising sustainable performance: Integrating servitisation innovation into green sustainable supply chain management under the influence of governance and Industry 4.0. Journal of Business Research, 186, 115029. https://doi.org/10.1016/j.jbusres.2024.115029
  7. Angrist, J. D., & Pischke, J. S. (2008). Mostly harmless econometrics: An empiricist's companion. Princeton university press. (http://diglib.globalcollege.edu.et:8080/xmlui/bitstream/handle/123456789/141/Angrist%20J.D.%2C%20Pischke%20J.-S.%20Mostly%20Harmless%20Econometrics%20%28PUP%2C%202008%29%28ISBN%20069112034X%29%28O%29%28290s%29_GL_.pdf?sequence=1&isAllowed=y, erişim tarihi:21.08.2026)
  8. Athey, S., & Imbens, G. W. (2019). Machine learning methods that economists should know about. Annual review of economics, 11(1), 685-725.
  9. Atlı, H. F. (2023). Pazarlama araştırmalarında yapay zeka (AI) ile müşteri deneyimi: Tüketici davranışlarının analizinde yapay zeka çözümleri. In: Yılmaz, E. S. (ed.), Dijitalleşme ve pazarlama araştırmaları. Özgür Yayınları. https://doi.org/10.58830/ozgur.pub398.c1663
  10. Bassen, A., Buchholz, D., Lopatta, K., & Rudolf, A. R. (2024). Biodiversity management and stock price crash risk. Business Strategy and the Environment, 33(5), 4788-4805.
  11. Bayuk, M. N., & Demir, B. N. (2019). Endüstri 4.0 kapsaminda yapay zekâ ve pazarlamanin geleceği. International Journal of Social, Humanities and Administrative Sciences, 5(19), 781-799.
  12. Beichert, M., Bayerl, A., Goldenberg, J., & Lanz, A. (2024). Revenue generation through influencer marketing. Journal of Marketing, 88(4), 40-63. https://doi.org/10.1177/00222429231217471
  13. Borrero, J. D., & Borrero-Domínguez, J. D. (2023). Enhancing Short-Term Berry Yield Prediction For Small Growers Using A Novel Hybrid Machine Learning Model. Horticulturae, 9(5), 549.
  14. Bulut, H., & Doğan, A. R. (2020). Rekabet Gücü ve Büyüme İlişkisi: Brıcs-T Ülkeleri üzerine ekonometrik bir analiz. International Journal of Entrepreneurship and Management Inquiries, 4(Özel Sayı 1), 126–144.
  15. Cameron, L. D. (2024). The making of the “good bad” job: How algorithmic management manufactures consent through constant and confined choices. Administrative Science Quarterly, 69(2), 458-514. https://doi.org/10.1177/00018392241236163
  16. Ceran, Y., Ortakarpuz, M., & Erkoçak, H. E. (2022). Uluslararası Stratejik Pazarlama Kararları ve Lojistik 4.0 Bağlamında Lojistik Maliyetler ile Kârlılık İlişkisi. Avrupa Bilim ve Teknoloji Dergisi, 35, 102–110. https://doi.org/10.31590/ejosat.1058377
  17. Cheng, J. (2021). Application of big data analysis to agricultural production, agricultural product marketing, and influencing factors in intelligent agriculture. Journal of Computing and Information Technology, 29(3), 151–165. https://doi.org/10.20532/cit.2021.1005404
  18. Chintalapati, S., & Pandey, S. K. (2022). Artificial intelligence in marketing: A systematic literature review. International Journal of Market Research, 64(1), 38-68. DOI: https://doi.org/10.1177/14707853211018428
  19. Chowdhury, A. R., Paul, R., & Rozony, F. Z. (2025). A systematic review of demand forecasting models for retail e-commerce enhancing accuracy in inventory and delivery planning. International Journal of Scientific Interdisciplinary Research, 6(1), 01-27. https://doi.org/10.63125/mbbfw637
  20. Chowdhury, S., Budhwar, P., & Wood, G. (2024). Generative artificial intelligence in business: towards a strategic human resource management framework. British Journal of Management, 35(4), 1680-1691. https://doi.org/10.1111/1467-8551.12824
  21. Cillo, P., & Rubera, G. (2025). Generative AI in innovation and marketing processes: A roadmap of research opportunities. Journal of the Academy of Marketing Science, 53(3), 684-701. https://doi.org/10.1007/s11747-024-01044-7
  22. Davenport, T., 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
  23. Dekimpe, M. G., & Hanssens, D. M. (2024). Persistence modeling in marketing: descriptive, predictive, and normative uses. Australasian Marketing Journal, 32(2), 91-97. https://doi.org/10.1177/14413582231222311
  24. Ding, M. A., & Goldfarb, A. (2023). The Economics of Artificial Intelligence: A Marketing Perspective. Artificial Intelligence in Marketing, 20, 13–76. https://doi.org/10.1108/S1548-643520230000020002
  25. 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
  26. Ekinci, G., & Özsaatcı, F. G. B. (2023). Yapay zekâ ve pazarlama alanındaki yayınların bibliyometrik analizi. Sosyoekonomi, 31(56), 369-388. https://doi.org/10.17233/sosyoekonomi.2023.02.17
  27. Ercan, F. (2020). Turizm pazarlamasında yapay zekâ teknolojilerinin kullanımı ve uygulama örnekleri. Ankara Hacı Bayram Veli Üniversitesi Turizm Fakültesi Dergisi, 23(2), 394-410. https://doi.org/10.34189/tfd.23.02.009
  28. Estevez, M., Ballestar, M. T., & Sainz, J. (2024). A primer on out-of-the-box AI marketing mix models. IEEE Transactions on Engineering Management, 72, 282-294.
  29. Ezike, N. C., Boykin, A. A., Dobbs, P. D., Mai, H., & Primack, B. A. (2022). Exploring factors that predict marketing of e-cigarette products on twitter: infodemiology approach using time series. JMIR Infodemiology, 2(2), e37412. https://doi.org/10.2196/37412
  30. Fashogbon, B. A., Adeleke, R. O., & Olowe, O. A. (2025). The Application of Artificial Intelligence in economics: A review of current trends and future directions. International Journal of Education, Leadership, Artificial Intelligence, Computing, Business, Life Sciences, and Society, 2(02), 67-89. https://doi.org/10.65222/VIRAL.2025.9.10
  31. Gedik, Y. (2020). Pazarlamada yeni bir pencere: Dijital pazarlama. Journal of Business in the Digital Age, 3(1), 63-75. https://doi.org/10.46238/jobda.726408
  32. Grigoraș, A., & Leon, F. (2023). Transformer-based model for predicting customers’ next purchase day in e-commerce. Computation, 11(11), 210. https://doi.org/10.3390/computation11110210
  33. Gujarati, D. N., & Porter, D. C. (2009). Basic econometrics (5th ed.). McGraw-Hill/Irwin.
  34. Gür, Y. E. (2022). Yapay zekâ ve pazarlama ilişkisi. Fırat Üniversitesi Uluslararası İktisadi Ve İdari Bilimler Dergisi, 6(2), 131–148.
  35. Güven, M. (2021). Kırmızı ette duyusal pazarlama ve tüketici davranışı analizi: Antalya ili örneği (Yüksek Lisans Tezi, Akdeniz Üniversitesi).
  36. 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
  37. Jarek, K., & Mazurek, G. (2019). Marketing and artificial intelligence. Central European Business Review, 8(2). https://doi.org/10.18267/j.cebr.213
  38. Kalinina, E. (2026). Measuring Marketing Impact Through Predictive Analytics. Journal of Consumer Behavior, Marketing, and Management, 2(1), 16-21. https://doi.org/10.54536/jcbmm.v2i1.7269 (https://journals.e-palli.com/home/index.php/jcbmm/article/view/7269, erişim tarihi:21.08.2026).
  39. Kopuk, E., & Bayraç, H. N. (2023). OICA Üyesi AB (14) ülkelerinde inovasyon belirleyicilerinin otomotiv sektörü rekabet gücü üzerindeki etkisi. Çankırı Karatekin Üniversitesi İİBF Dergisi, 13(2), 633–663. https://doi.org/10.18074/ckuiibfd.1199305
  40. Köroğlu, Y. (2017). Yapay zeka’nın teorik ve pratik sınırları. Bogaziçi Üniversitesi Yayınevi. 1(10).
  41. Kreicbergs, T., Ščeulovs, D., & Bernovskis, A. (2024). Brand and masculinity archetypes as an innovative research approach for analyzing consumer preferences on masculinity in advertising. Journal of Open Innovation: Technology, Market, and Complexity, 10(1), 100184. https://doi.org/10.1016/j.joitmc.2023.100184
  42. Kuruca, Y., Üstüner, M., & Şimşek, I. (2022). Dijital pazarlamada yapay zekâ kullanımı: Sohbet robotu (Chatbot). Medya ve Kültür, 2(1), 88-113.
  43. Li, B. (2025). Research on the Application of Machine Learning Algorithms in Marketing Data Analysis under the Background of Artificial Intelligence. In Proceedings of the 2025 International Conference on Artificial Intelligence and Smart Manufacturing. https://doi.org/10.1145/3756423.3756451
  44. Li, R., Cao, Z., Ye, H., & Yue, X. (2021). Application and development trend of artificial intelligence in enterprise marketing. In Journal of Physics: Conference Series https://doi.org/10.1088/1742-6596/1881/2/022032
  45. Ma, L., & Sun, B. (2020). Machine learning and AI in marketing–Connecting computing power to human insights. International Journal of Research in Marketing, 37(3), 481-504. https://doi.org/10.1016/j.ijresmar.2020.04.005
  46. McCarthy, J., Minsky, M. L., Rochester, N., & Shannon, C. E. (2006). A proposal for the Dartmouth summer research project on artificial intelligence: August 31, 1955. AI magazine, 27(4), 12-14. (http://jmc.stanford.edu/articles/dartmouth/dartmouth.pdf, erişim tarihi: 21.08.2026).
  47. Mariani, M., & Dwivedi, Y. K. (2024). Generative artificial intelligence in innovation management: A preview of future research developments. Journal of Business Research, 175, 114542. https://doi.org/10.1016/j.jbusres.2024.114542
  48. Mongeon, P., & Paul-Hus, A. (2016). The journal coverage of Web of Science and Scopus: a comparative analysis. Scientometrics, 106(1), 213-228.
  49. Mullainathan, S., & Spiess, J. (2017). Machine learning: An applied econometric approach. Journal of economic perspectives, 31(2), 87-106. https://doi.org/10.1257/jep.31.2.87
  50. Nabiyev, V. V. (2012). Yapay Zekâ. Seçkin Yayıncılık. 776s, Ankara
  51. Obolenska, T., Shatarska, I., & Shevtsov, Y. (2019). The use of the "Rational" system of global marketing communications in management of international enterprises. Problems and Perspectives in Management, 17(3), 14. https://doi.org/10.21511/ppm.17(3).2019.02
  52. Okay, S. (2023). Dijital pazarlamada yapay zekâ ve makine öğrenimi kullanımı. Elektronik Sosyal Bilimler Dergisi, 22(85), 135-142. https://doi.org/10.17755/esosder.115771
  53. Olstad, D. L., & Boyland, E. (2023). Towards effective restriction of unhealthy food marketing to children: unlocking the potential of artificial intelligence. International Journal of Behavioral Nutrition and Physical Activity, 20(1), 61. https://doi.org/10.1186/s12966-023-01458-6
  54. Ouedraogo, N. (2021). Burkina Faso'da susam üretiminin karlılığı analizi ve satış sisteminin ekonometrik analizi: Boucle Du Mouhoun bölgesi örneği (Yüksek Lisans Tezi, Akdeniz Üniversitesi).
  55. Padilla, W. R., García, J., & Molina, J. M. (2019). Knowledge extraction and improved data fusion for sales prediction in local agricultural markets. Sensors, 19(2), 286. https://doi.org/10.3390/s19020286
  56. Paetz, F., Steiner, W. J., & Hruschka, H. (2022). Advanced data analysis techniques with marketing applications. Journal of Business Economics, 92(4), 557-561. https://doi.org/10.1007/s11573-022-01101-z
  57. Paschen, J., Kietzmann, J., & Kietzmann, T. C. (2019). Artificial intelligence (AI) and its implications for market knowledge in B2B marketing. Journal of Business & Industrial Marketing, 34(7), 1410-1419. https://doi.org/10.1108/JBIM-10-2018-0295
  58. Poyraz, E., & Mirgen, Ç. (2020). Reklam Giderlerinin Marka Değerine Etkisinin Panel Regresyon Yöntemiyle İncelenmesi: Türkiye’deki Firmalara Yönelik Bir Araştırma. International Review of Economics and Management, 8(1), 107-123. https://doi.org/10.18825/iremjournal.743292
  59. Rauschnabel, P. A., Hüttl‐Maack, V., Ahuvia, A. C., & Schein, K. E. (2024). Augmented reality marketing and consumer‒brand relationships: How closeness drives brand love. Psychology & Marketing, 41(4), 819-837. https://doi.org/10.1002/mar.21953
  60. Sarıoğlu, B., & Develi, E. İ. (2022). Pazarlamada kampanya yönetimi ve yapay zekâ kullanımı. Uluslararası Halkla İlişkiler ve Reklam Çalışmaları Dergisi, 5(2), 91-124.
  61. Shaily, S. A., & Emma, N. N. (2021). Integration of artificial intelligence marketing to get brand recognition for social business. International Review of Management and Marketing, 11(4), 29-37. https://doi.org/10.32479/irmm.11542
  62. Şalvarlı, M., & Kayışkan, D. (2022). An overview of the emerging role of artificial intelligence in marketing. İzmir Yönetim Dergisi, 2(2), 106-115. https://doi.org/10.56203/iyd.1052548
  63. Şenyapar, H. N. D. (2024). Üretken yapay zekâ ve pazarlama stratejileri: SWOT analizi perspektifi. R&S-Research Studies Anatolia Journal, 7(1), 72-96.
  64. Tase, M., & Lulaj, E. (2022). The effect of perceptions on tourism: an econometric analysis of the ımpacts and opportunities for economic and financial development in Albania and Kosovo. Sustainability, 14(13), 7659. https://doi.org/10.3390/su14137659
  65. Teka, Y., Malin, H., & Wolde, A. O. (2019). Analysis of factors affecting mango market supply in Gomma district, Southwest Ethiopia. Agricultural Socio-Economics Journal, 19(3), 181-190. http://dx.doi.org/10.21776/ub.agrise.2019.019.3.7
  66. Thomas, A. (2024). Digitally transforming the organization through knowledge management: a socio-technical system (STS) perspective. European Journal of Innovation Management, 27(9), 437-460. https://doi.org/10.1108/EJIM-02-2024-0114
  67. Turing, A. (1950). Computing Machinery and Intelligence. (https://ebiquity.umbc.edu/_file_directory_/papers/1389.pdf, erişim tarihi: 21.08.2026).
  68. Ünal, A., & Türk, M. (2020). Üniversite öğrencisi seçmenlerin oy verme kararları üzerinde siyasi pazarlama unsurlarının etkisini belirlemeye yönelik bir alan çalışması. Hitit Üniversitesi Sosyal Bilimler Enstitüsü Dergisi, 13(1), 144-168. https://doi.org/10.17218.hititsosbil.675777
  69. Verma, S., Sharma, R., Deb, S., & Maitra, D. (2021). Artificial intelligence in marketing: Systematic review and future research direction. International Journal of Information Management Data Insights, 1(1), 100002. https://doi.org/10.1016/j.jjimei.2020.100002
  70. Vlačić, B., Corbo, L., e Silva, S. C., & Dabić, M. (2021). The evolving role of artificial intelligence in marketing: A review and research agenda. Journal of Business Research, 128, 187-203. https://doi.org/10.1016/j.jbusres.2021.01.055
  71. Waltenrath, A. (2024). Consumers’ ambiguous perceptions of advertising disclosures in influencer marketing: Disentangling the effects on current and future social media engagement. Electronic Markets, 34(1), 8. https://doi.org/10.1007/s12525-023-00679-8
  72. Wang, J., & Li, A. (2022). The impact of green advertising information quality perception on consumers’ response: an empirical analysis. Sustainability, 14(20), 13248. https://doi.org/10.3390/su142013248
  73. Wooldridge, J. M. (2020). Introductory econometrics: A modern approach (7th ed.). Cengage Learning.
  74. Würfel, M., Han, Q., & Kaiser, M. (2021). Online advertising revenue forecasting: An interpretable deep learning approach. In 2021 IEEE International Conference on Big Data (Big Data) (pp. 1980-1989). https://doi.org/10.1109/BigData52589.2021.9672010
  75. Yurdakul, E. M. (2020). Türkiye’de lojistik sektörü ve ekonomik büyüme arasındaki ilişkinin VAR analizi ile incelenmesi. Sosyal Ekonomik Araştırmalar Dergisi, 20(40), 174-185.
  76. Yusupova, D. (2026). Iste’molchi xulq-atvoriga ta’sir etuvchi omillarni ekonometrik modellashtirish. Iqtisodiy Taraqqiyot Va Tahlil, 4(1), 265-271. https://doi.org/10.60078/2992-877X-2026-vol4-iss1-pp265-271
  77. Ziakis, C., & Vlachopoulou, M. (2023). Artificial intelligence in digital marketing: Insights from a comprehensive review. Information, 14(12), 664. https://doi.org/10.3390/info14120664
  78. Zupic, I., & Čater, T. (2015). Bibliometric methods in management and organization. Organizational research methods, 18(3), 429-472. https://research.gold.ac.uk/id/eprint/26859/1/Zupic%20Cater%202015%20-%20Bibliometric%20methods%20in%20management%20and%20organization.pdf (erişim tarihi: 06 Haziran 2026).