Authors : Mathias Ewan Aigbogun; Esther Ojoma Ali; Chukwunweike C. Nwobi; Amina Catherine Ijiga; Idoko Peter Idoko

Volume/Issue : Volume 10 - 2025, Issue 1 - January

Google Scholar : https://tinyurl.com/5br6azx3

Scribd : https://tinyurl.com/wc77zca2

DOI : https://doi.org/ 10.5281/zenodo.14730645

Abstract : Omni-channel retailing has revolutionized the way businesses engage with customers, blending physical and digital channels to create seamless shopping experiences. This study explores the pivotal role of demographics in shaping omni-channel retailing strategies, focusing on how customer behavior and preferences vary across different demographic groups. By examining age, gender, income, education, and geographic location, the research highlights the influence of these factors on channel selection, purchasing behavior, and loyalty to omni-channel platforms. Leveraging data-driven insights and behavioral analysis, this study underscores the necessity for retailers to customize their strategies to cater to the unique needs of diverse customer segments. Furthermore, it investigates the interplay between technological adoption and demographic attributes, revealing key trends and preferences that drive engagement. The findings provide actionable recommendations for businesses to optimize their omni-channel strategies, enhance customer satisfaction, and achieve competitive advantage in a rapidly evolving retail landscape. This research contributes to the growing body of literature on personalized retail experiences and underscores the critical role of demographic-driven approaches in shaping the future of omni-channel retailing.

Keywords : Demographics, Omni-Channel Retailing Strategies, Customer Behavior, Customer Preferences.

References :

1. Abrudan, I., Dabija, D. C., & Grant, D. B. (2020). Omni-channel retailing: Challenges, opportunities, and trends. International Journal of Retail & Distribution Management, 48(5), 451-465. https://doi.org/10.1108/IJRDM-12-2019-0403
2. Bashiru, O., Ochem, C., Enyejo, L. A., Manuel, H. N. N., & Adeoye, T. O. (2024). The crucial role of renewable energy in achieving the sustainable development goals for cleaner energy. Global Journal of Engineering and Technology Advances, 19(03), 011-036. https://doi.org/10.30574/gjeta.2024.19.3.0099
3. Blackwell, R. D., Miniard, P. W., & Engel, J. F. (2006). Consumer behavior. Thomson/South-Western.
4. Creswell, J. W. (2014). Research design: Qualitative, quantitative, and mixed methods approaches (4th ed.). Sage Publications.
5. Fatima, T., & Siddiqui, D. A. (2023). The impact of omni-channel retailing on consumer behavior: A demographic analysis. Journal of Retailing and Consumer Services, 67, 102982. https://doi.org/10.1016/j.jretconser.2022.102982
6. Forood, A. M. K. (2024). Mechanisms of telomere dysfunction in cancer from genomic instability to therapy: A.
7. Galipoglu, E., Kotzab, H., Teller, C., Hüseyinoglu, I. O. Y., & Pöppelbuß, J. (2018). Omni-channel retailing research–State of the art and future research directions. International Journal of Physical Distribution & Logistics Management, 48(4), 363-391. https://doi.org/10.1108/IJPDLM-02-2017-0101
8. Godwins, O. P., Ochagwuba, E., Idoko, I. P., Akpa, F. A., Olajide, F. I., & Olatunde, T. I. (2024). Comparative analysis of disaster management strategies and their impact on nutrition outcomes in the USA and Nigeria. Business and Economics in Developing Countries (BEDC), 2(2), 34-42. http://doi.org/10.26480/bedc.02.2024.34.42
9. Hayes, L., & Kelliher, F. (2022). Gender differences in omni-channel shopping preferences and behaviors. Journal of Retailing and Consumer Services, 65, 102862.
10. https://doi.org/10.1016/j.jretconser.2021.102862
11. Ibokette, A. I., Aboi, E. J., Ijiga, A. C., Ugbane, S. I., Odeyemi, M. O., & Umama, E. E. (2024). The impacts of curbside feedback mechanisms on recycling performance of households in the United States. World Journal of Biology Pharmacy and Health Sciences, 17(2), 366-386.
12. Idoko, I. P., Igbede, M. A., Manuel, H. N. N., Adeoye, T. O., Akpa, F. A., & Ukaegbu, C. (2024). Big data and AI in employment: The dual challenge of workforce replacement and protecting customer privacy in biometric data usage. Global Journal of Engineering and Technology Advances, 19(02), 089-106. https://doi.org/10.30574/gjeta.2024.19.2.0080
13. Idoko P. I., Igbede, M. A., Manuel, H. N. N., Ijiga, A. C., Akpa, F. A., & Ukaegbu, C. (2024). Assessing the impact of wheat varieties and processing methods on diabetes risk: A systematic review. World Journal of Biology Pharmacy and Health Sciences, 18(02), 260–277. https://wjbphs.com/sites/default/files/WJBPHS-2024-0286.pdf
14. Idoko, I. P., Ijiga, O. M., Agbo, D. O., Abutu, E. P., Ezebuka, C. I., & Umama, E. E. (2024). Comparative analysis of Internet of Things (IOT) implementation: A case study of Ghana and the USA-vision, architectural elements, and future directions. World Journal of Advanced Engineering Technology and Sciences, 11(1), 180-199.
15. Ijiga, A. C., Abutu, E. P., Idoko, P. I., Agbo, D. O., Harry, K. D., Ezebuka, C. I., & Umama, E. E. (2024). Ethical considerations in implementing generative AI for healthcare supply chain optimization: A cross-country analysis across India, the United Kingdom, and the United States of America. International Journal of Biological and Pharmaceutical Sciences Archive, 07(01), 048–063. https://ijbpsa.com/sites /default/files/IJBPSA-2024-0015.pdf
16. Ijiga, A. C., Enyejo, L. A., Odeyemi, M. O., Olatunde, T. I., Olajide, F. I., & Daniel, D. O. (2024). Integrating community-based partnerships for enhanced health outcomes: A collaborative model with healthcare providers, clinics, and pharmacies across the USA. Open Access Research Journal of Biology and Pharmacy, 10(02), 081–104. https://oarjbp.com/c ontent/integrating-community-based-partnerships-enhanced-health-outcomes-collaborative-model
17. Ijiga, A. C., Olola, T. M., Enyejo, L. A., Akpa, F. A., Olatunde, T. I., & Olajide, F. I. (2024). Advanced surveillance and detection systems using deep learning to combat human trafficking. Magna Scientia Advanced Research and Reviews, 11(01), 267–286. https://magnascientiapub.com/journals/msarr/sites/default/files/MSARR-2024-0091.pdf
18. Jenča, A., Mills, D. K., Ghasemi, H., Saberian, E., Jenča, A., Karimi Forood, A. M., ... & Ebrahimifar, M. (2024). Herbal Therapies for Cancer Treatment: A Review of Phytotherapeutic Efficacy. Biologics: Targets and Therapy, 229-255.
19. Li, J., Zhang, X., & Lee, S. H. (2022). The role of technology in enhancing omni-channel retailing: A demographic perspective. Journal of Business Research, 145, 263-272. https://doi.org/10.1016/ j.jbusres.2022.02.032
20. Singh, A. S., & Masuku, M. B. (2014). Sampling techniques & determination of sample size in applied statistics research: An overview. International Journal of Economics, Commerce and Management, 2(11), 1-22.
21. Tabachnick, B. G., & Fidell, L. S. (2019). Using multivariate statistics (7th ed.). Pearson.
22. Venkatesh, V., Morris, M. G., Davis, G. B., & Davis, F. D. (2003). User acceptance of information technology: Toward a unified view. MIS Quarterly, 27(3), 425-478. https://doi.org/10.2307/30036540
23. Yang, Z., & Wang, X. (2021). Consumer behavior in omni-channel retailing: The role of technology and demographic differences. Journal of Business Research, 135, 215-227. https://doi.org/10.1016/j .jbusres.2021.06.052
24. Yasamineh, S., Mehrabani, F. J., Derafsh, E., Danihiel Cosimi, R., Forood, A. M. K., Soltani, S., ... & Gholizadeh, O. (2024). Potential use of the cholesterol transfer inhibitor U18666a as a potent research tool for the study of cholesterol mechanisms in neurodegenerative disorders. Molecular Neuro biology, 61(6), 3503-3527.