Management and Economics
| Open Access | AI-DRIVEN DIGITAL MARKETING ANALYTICS AND LEADERSHIP STRATEGIES FOR SUSTAINABLE BUSINESS INNOVATION: A MIXED-METHODS RESEARCH STUDY
Kazi Rezwana Alam , College of Graduate Studies, Central Michigan University, Michigan 48859, USA Kazi Rakib Hasan Saurav , College of Graduate Studies, Central Michigan University, Michigan 48859, USA Jesmin Ul Zannat Kabir , College of Graduate Studies, Central Michigan University, Michigan 48859, USA Md Saidur Rahman , College of Graduate School, South Dakota State University, South Dakota 57007, USA Md Imrul Hasan , College of Graduate and Professional Studies, Trine University, Indiana 46703, USA Chapal Barua , College of Graduate Studies, Central Michigan University, Michigan 48859, USAAbstract
This is a mixed-method research paper that explores how digital marketing analytics through artificial intelligence (AI) can transform business innovation in a sustainable way in the economic, social, and environmental facets. Despite the fast advancement of AI in marketing where 73% of the companies are already adopting AI tools in at least one marketing function, only a small percentage (5%) of companies attain full, quantifiable value. The study is a mix of quantitative data on the industry world data and qualitative data on the literature on leadership and sustainability to look into the impact of AI features of predictive analytics, customer segmentation, sentiment analysis, dynamic personalization, and automated optimization on marketing performance and sustainability outcomes. The statistically gathered evidence has shown that AI-based marketing correlates with 32% increase in ROI, as well as 40-fold campaign success, decrease in marketing wastes and 18% decrease in digital carbon footprint. Qualitative analysis indicates that effective leadership marked by data literacy building, ethical AI governance, interfunctional cooperation, and goal orientation towards ESG are important mediators in the influence of AI because they create the opportunity to implement it responsibly and create value in the long term. The research brings to the picture a whole-rounded conceptual framework that depicts the interconnection between AI capabilities, strategic leadership, and sustainable innovation. On the whole, the results indicate that AI-based digital marketing, with the assistance of effective leadership practices, is a strong tool that help organizations to become highly competitive, improve their operational performance, and achieve sustainable growth.
Keywords
AI Marketing, Digital Analytics, Sustainable Innovation
References
Afeltra, G., Alerasoul, S. A., & Strozzi, F. (2023). The evolution of sustainable innovation: from the past to the future. European Journal of innovation management, 26(2), 386-421.
Alhitmi, H. K., Mardiah, A., Al-Sulaiti, K. I., & Abbas, J. (2024). Data security and privacy concerns of AI-driven marketing in the context of economics and business field: an exploration into possible solutions. Cogent Business & Management, 11(1), 2393743.
Chen, L., Jiang, M., Jia, F., & Liu, G. (2022). Artificial intelligence adoption in business-to-business marketing: toward a conceptual framework. Journal of Business & Industrial Marketing, 37(5), 1025-1044.
Corrêa, N. K., Galvão, C., Santos, J. W., Del Pino, C., Pinto, E. P., Barbosa, C., ... & de Oliveira, N. (2023). Worldwide AI ethics: A review of 200 guidelines and recommendations for AI governance. Patterns, 4(10).
Dawadi, S., Shrestha, S., & Giri, R. A. (2021). Mixed-methods research: A discussion on its types, challenges, and criticisms. Journal of Practical Studies in Education, 2(2), 25-36.
Fernandes, S., Sheeja, M. S., & Parivara, S. (2024). Potential of AI for a sustainable, inclusive, and ethically responsible future. Fostering Multidisciplinary Research for Sustainability, 196.
Fields, Z. (Ed.). (2023). Multidisciplinary Approaches in AI, Creativity, Innovation, and Green Collaboration. IGI Global.
Gündüzyeli, B. (2024). Artificial Intelligence in Digital Marketing Within the Framework of Sustainable Management. Sustainability, 16(23), 10511.
Hossain, E., Ashik, A. A. M., Rahman, M. M., Khan, S. I., Rahman, M. S., & Islam, S. (2023). Big data and migration forecasting: Predictive insights into displacement patterns triggered by climate change and armed conflict. Journal of Computer Science and Technology Studies, 5(4): 265–274. https://doi.org/10.32996/jcsts.2023.5.4.27/.
Hossain, E., Shital, K. P., Rahman, M. S., Islam, S., Khan, S. I., & Ashik, A. A. M. (2024). Machine learning-driven governance: Predicting the effectiveness of international trade policies through policy and governance analytics. Journal of Trends in Financial and Economics, 1(3), 50–62. https://doi.org/10.61784/jtfe3053
Islam, S., Hossain, E., Rahman, M. S., Rahman, M. M., Khan, S. I., & Ashik, A. A. M. (2023). Digital Transformation in SMEs: Unlocking Competitive Advantage through Business Intelligence and Data Analytics Adoption. 5 (6):177-186. https://doi.org/10.32996/jbms.2023.5.6.14
Islam, S., Khan, S. I., Ashik, A. A. M., Hossain, E., Rahman, M. M., & Rahman, M. S. (2024). Big data in economic recovery: A policy-oriented study on data analytics for crisis management and growth planning. Journal of Computational Analysis and Applications (JoCAAA), 33(7), 2349–2367. Retrieved from https://www.eudoxuspress.com/index.php/pub/article/view/3338
Jain, V., Wadhwani, K., & Eastman, J. K. (2024). Artificial intelligence consumer behavior: A hybrid review and research agenda. Journal of consumer behaviour, 23(2), 676-697.
Khan, S. I., Rahman, M. S., Ashik, A. A. M., Islam, S., Rahman, M. M., & Hossain, E. (2024). Big Data and Business Intelligence for Supply Chain Sustainability: Risk Mitigation and Green Optimization in the Digital Era. European Journal of Management, Economics and Business, 1(3): 262-276. https://doi.org/10.59324/ejmeb.2024.1(3).23
Kristian, A., Goh, T. S., Ramadan, A., Erica, A., & Sihotang, S. V. (2024). Application of ai in optimizing energy and resource management: Effectiveness of deep learning models. International Transactions on Artificial Intelligence, 2(2), 99-105.
Kumar, D., & Suthar, N. (2024). Ethical and legal challenges of AI in marketing: an exploration of solutions. Journal of Information, Communication and Ethics in Society, 22(1), 124-144.
Munir, S., Mahmood, G., Abdullah, F., & Noreen, A. (2023). Exploring the impact of digital leadership on sustainable performance with mediating role of artificial intelligence. Journal of Accounting and Finance in Emerging Economies, 9(3), 213-226.
Nozari, H., Szmelter-Jarosz, A., & Ghahremani-Nahr, J. (2022). Analysis of the challenges of artificial intelligence of things (AIoT) for the smart supply chain (case study: FMCG industries). Sensors, 22(8), 2931.
Pandey, S., Gupta, S., & Chhajed, S. (2021). ROI of AI: Effectiveness and measurement. INTERNATIONAL JOURNAL OF ENGINEERING RESEARCH & TECHNOLOGY (IJERT) Volume, 10.
Pratama, R. A., Khadija, M. A., Paradhita, A. N., & Nurharjadmo, W. (2024, July). AI-Driven Predictive Analytics to Enhance Digital Marketing Strategies in Domain and Hosting Business. In 2024 International Conference on Data Science and Its Applications (ICoDSA) (pp. 195-200). IEEE.
Rahman, M. M., Juie, B. J. A., Tisha, N. T., & Tanvir, A. (2022). Harnessing predictive analytics and machine learning in drug discovery, disease surveillance, and fungal research. Eurasia Journal of Science and Technology, 4(2), 28-35. https://doi.org/10.61784/ejst3099
Tanvir, A., Juie, B. J. A., Tisha, N. T., & Rahman, M. M. (2020). Synergizing big data and biotechnology for innovation in healthcare, pharmaceutical development, and fungal research. International Journal of Biological, Physical and Chemical Studies, 2(2), 23–32. https://doi.org/10.32996/ijbpcs.2020.2.2.4.
Tarisayi, K. S. (2024, March). Strategic leadership for responsible artificial intelligence adoption in higher education. In CTE workshop proceedings (Vol. 11, pp. 4-14).
Tarisayi, K. S. (2024, March). Strategic leadership for responsible artificial intelligence adoption in higher education. In CTE workshop proceedings (Vol. 11, pp. 4-14).
Teixeira, N., & Pacione, M. (2024). Implications of artificial intelligence on leadership in complex organizations: An exploration of the near future.
Thilagavathy, N., & Kumar, E. P. (2021). Artificial intelligence on digital marketing-an overview.
Thinnakkakath, G. (2024). Exploring the Adoption of AI Solutions in Marketing: A Qualitative Case Study (Doctoral dissertation, University of the Cumberlands).
Vummadi, J. R., & Hajarath, K. (2024). Integration of emerging technologies AI and ML into strategic supply chain planning processes to enhance decision-making and agility. International Journal of Supply Chain Management, 9(2), 77-87.
Wu, C. W., & Monfort, A. (2023). Role of artificial intelligence in marketing strategies and performance. Psychology & Marketing, 40(3), 484-496.
Xu, S., Kee, K. F., Li, W., Yamamoto, M., & Riggs, R. E. (2024). Examining the diffusion of innovations from a dynamic, differential-effects perspective: A longitudinal study on AI adoption among employees. Communication Research, 51(7), 843-866.
Zaghmout, B., Achi, F., & Padmini Ema, U. (2024). Navigating digital leadership: How UK-based marketing leaders are redefining brand trust in an AI-driven era. British Journal of Management and Marketing Studies, 7(4), 126-142.
Ziakis, C., & Vlachopoulou, M. (2023). Artificial intelligence in digital marketing: Insights from a comprehensive review. Information, 14(12), 664.
Download and View Statistics
Copyright License
Copyright (c) 2024 Kazi Rezwana Alam, Kazi Rakib Hasan Saurav, Jesmin Ul Zannat Kabir, Md Saidur Rahman, Md Imrul Hasan, Chapal Barua

This work is licensed under a Creative Commons Attribution 4.0 International License.
Authors retain the copyright of their manuscripts, and all Open Access articles are disseminated under the terms of the Creative Commons Attribution License 4.0 (CC-BY), which licenses unrestricted use, distribution, and reproduction in any medium, provided that the original work is appropriately cited. The use of general descriptive names, trade names, trademarks, and so forth in this publication, even if not specifically identified, does not imply that these names are not protected by the relevant laws and regulations.

