THE EVALUATION OF AI INTEGRATION IN INNOVATIVE DIGITAL MARKETING STRATEGIES

Abstract

Purpose- This study aims to provide a bibliometric review of publications where the terms 'digital marketing' and 'artificial intelligence' are used together. Leading publications, authors, countries, and institutions in the Web of Science (WoS) database have been examined to achieve this goal. Additionally, this article investigates the combined use of digital marketing and artificial intelligence. Furthermore, it aims to offer insights into artificial intelligence strategies for marketing that businesses can employ. Methodology- The research employs the technique of bibliometric analysis. The Bibliometrix package within R Studio and its web-based component, Biblioshiny, were utilized for analysis. Searches were conducted in the Web of Science database using the keywords 'Digital Marketing' and 'Artificial Intelligence' in the title, abstract, and keywords sections. Findings- As a result of the analysis, a total of 60 publications authored by 140 researchers and distributed across 46 journals between 2017 and 2023 were identified. Examination of the included publications reveals frequent usage of terms such as 'artificial intelligence,' 'creativity,' 'analytics,' 'impact,' 'expertise,' 'social networks,' 'big data,' 'governance,' 'success,' and 'AI.' Upon scrutinizing the authors' countries, India emerged as the leading contributor, followed by Spain and the USA. Moreover, Finland (370), Spain (92), and France (58) had the highest citation counts. Conclusion- This research aims to contribute to researchers interested in working in digital marketing and artificial intelligence by examining its past and present. For this purpose, 60 relevant studies from the literature were systematically reviewed and analyzed across various categories. Additionally, the examined publications' conceptual, intellectual, and social structures were illuminated.

Keywords

• Digital marketing
• artificial intelligence
• AI systems
• bibliometric analysis
• bibliometrix

References

1. Aria, M., & Cuccurullo, C. (2017). Bibliometrix: An R-tool for comprehensive science mapping analysis. Journal of Informetrics, 11(4), 959-975.
2. Bala, M., & Verma, D. (2018). A critical review of digital marketing. M. Bala, D. Verma (2018). A Critical Review of Digital Marketing. International Journal of Management, IT & Engineering, 8(10), 321-339.
3. Batra, S., Saini, M., Yadav, M., & Aggarwal, V. (2023). Mapping the intellectual structure and demystifying the research trend of cross listing: a bibliometric analysis. Managerial Finance, 49(6), 992-1016.
4. Bawack, R. E., Wamba, S. F., Carillo, K. D. A., & Akter, S. (2022). Artificial intelligence in e-commerce: a bibliometric study and literature review. Electronic Markets, 32(1), 297-338.
5. Bradford, S.C. (1985), Sources of information on specific subjects, Journal of Information Science, 10(4), 173-180.
6. Broadus, R. N. (1987). Toward a definition of “bibliometrics”. Scientometrics, 12, 373-379.
7. Büyükkidik, S. (2022). A Bibliometric Analysis: A tutorial for the bibliometrix package in R using IRT literature. Journal of Measurement and Evaluation in Education and Psychology, 13(3), 164-193.
8. Chae, B. (2022). Mapping the evolution of digital business research: A bibliometric review. Sustainability, 14(12), 6990.

9. Chaffey, D., & Smith, P. R. (2017). Digital marketing excellence: Planning, Optimizing, and Integrating Online Marketing. Taylor & Francis.
10. Chaffey, D., & Ellis-Chadwick, F. (2019). Digital Marketing. Pearson UK.
11. Dimitrieska, S., Stankovska, A., & Efremova, T. (2018). Artificial intelligence and marketing. Entrepreneurship, 6(2), 298-304.
12. 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.
13. Elhajjar, S., Karam, S., & Borna, S. (2021). Artificial intelligence in marketing education programs. Marketing Education Review, 31(1), 2-13.
14. Eriksson, T., Bigi, A., & Bonera, M. (2020). Think with me, or think for me? On the future role of artificial intelligence in marketing strategy formulation. The TQM Journal, 32(4), 795-814.
15. Fathali, Z., Kodia, Z., & Ben Said, L. (2022). Stock market prediction of Nifty 50 index applying machine learning techniques. Applied Artificial Intelligence, 36(1), 2111134.
16. Faruk, M., Rahman, M., & Hasan, S. (2021). How digital marketing evolved over time: A bibliometric analysis on scopus database. Heliyon, 7(12). https://doi.org/10.1016/j.heliyon.2021.e08603
17. Frost, R., & Strauss, J. (2016). E-marketing. Routledge.
18. Haleem, A., Javaid, M., Qadri, M. A., Singh, R. P., & Suman, R. (2022). Artificial intelligence (AI) applications for marketing: A literature-based study. International Journal of Intelligent Networks, 3,119-132.
19. Han, R., Lam, H. K., Zhan, Y., Wang, Y., Dwivedi, Y. K., & Tan, K. H. (2021). Artificial intelligence in business-to-business marketing: a bibliometric analysis of current research status, development and future directions. Industrial Management & Data Systems, 121(12), 2467-2497.
20. Hollensen, S. (2019). Marketing management: A relationship approach. Pearson.
21. Huang, M. H., & Rust, R. T. (2018). Artificial intelligence in service. Journal of service research, 21(2), 155-172.
22. Huang, M. H., & Rust, R. T. (2021). A strategic framework for artificial intelligence in marketing. Journal of the Academy of Marketing Science, 49, 30–50.
23. Jain, A., Yadav, A. K., & Shrivastava, Y. (2020). Modeling and optimization of different quality characteristics in electric discharge drilling of titanium alloy sheet. Materials Today: Proceedings, 21, 1680-1684.
24. Jarek, K., & Mazurek, G. (2019). Marketing and Artificial Intelligence. Central European Business Review, 8(2), 87-99.
25. Kannan, P. K. (2017). Digital marketing: A framework, review and research agenda. International Journal of Research in Marketing, 34(1), 22-45.
26. Kumar, P., Dwivedi, Y. K., & Anand, A. (2021). Responsible artificial intelligence (AI) for value formation and market performance in healthcare: The mediating role of patient’s cognitive engagement. Information Systems Frontiers, 5, 1-24.
27. Li, J. H. (2018). Cyber security meets artificial intelligence: a survey. Frontiers of Information Technology & Electronic Engineering, 19(12), 1462-1474.
28. Li, H., An, H., Wang, Y., Huang, J., & Gao, X. (2016). Evolutionary features of academic articles co-keyword network and keywords co-occurrence network: Based on two mode affiliation network. Physica A: Statistical Mechanics and its Applications, 450, 657-669.
29. Luger, G. F. (2009). Artificial Intelligence: Structures and Strategies for Complex Problem Solving. Pearson Education.
30. Marinchak, C. M., Forrest, E., & Hoanca, B. (2018). Artificial intelligence: Redefining marketing management and the customer experience. International Journal of E-Entrepreneurship and Innovation (IJEEI), 8(2), 14-24.
31. Mangold, W. G., & Faulds, D. J. (2009). Social media: The new hybrid element of the promotion mix. Business Horizons, 52(4), 357–365.
32. Murgai, A. (2018). Transforming digital marketing with artificial intelligence. International Journal of Latest Technology in Engineering, Management & Applied Science, 7(4), 259-262.
33. Nilsson, N. J. (1998). Artificial intelligence: A new synthesis. Morgan Kaufmann.
34. Norton, M. J. (2001). Bibliometrics. Introductory concepts in information science. Medford: ASIS.
35. Osareh, F. (1996). Bibliometrics, citation analysis and co-citation analysis: A review of literature I. Libri, 46(3), 149-158.
36. Pritchard, A. (1969) Statistical bibliography or bibliometries? Journal of Documentation, 25, 348-359.
37. Rathore, B. (2023). Digital Transformation 4.0: Integration of Artificial Intelligence & Metaverse in Marketing. Eduzone: International Peer Reviewed/Refereed Multidisciplinary Journal, 12(1), 42-48.
38. Russell, S. J., & Norvig, P. (2016). Artificial Intelligence: A Modern Approach. Pearson.
39. Ryan, D., & Jones, C. (2009). Understanding digital marketing: Marketing strategies for engaging the digital generation. Kogan Page Publishers.
40. Shankar, V., & Parsana, S. (2022). An overview and empirical comparison of natural language processing (NLP) models and an introduction to and empirical application of autoencoder models in marketing. Journal of the Academy of Marketing Science, 50(6), 1324-1350.
41. Smith, A. N., & Zook, Z. (2011). Marketing communications: integrating offline and online with social media. Journal of Marketing Communications, 17(2), 123-138.
42. Tiwari, R., Srivastava, S., & Gera, R. (2020). Investigation of artificial intelligence techniques in finance and marketing. Procedia Computer Science, 173, 149-157.
43. Tripathi, M., Kumar, S., Sonker, S. K., & Babbar, P. (2018). Occurrence of author keywords and keywords plus in social sciences and humanities research: A preliminary study. COLLNET Journal of Scientometrics and Information Management, 12(2), 215-232.
44. Tubaro, P., & Casilli, A. A. (2019). Micro-work, artificial intelligence and the automotive industry. Journal of Industrial and Business Economics, 46, 333-345.
45. Viju, V. G. W., & Ganesh, V. (2013). Application of Bradford’s Law of scattering to the literature of library & information science: a study of doctoral theses citations submitted to the Universities of Maharashtra, India. Library. Philosophy and Practice, 5, 1-45.
46. Zupic, I., & Cater, T. (2015). Bibliometric methods in management and organization. Organizational Research Methods, 18(3), 429-472