Data-driven analysis of competitive marketing performance under uncertainty: The Effect of multiple interactions between big data, marketing analytics capabilities, artificial intelligence, and holistic marketing decision-making

Document Type : Original Article

Authors

1 Associate Professor, Department of Business Management, Faculty of Management, Kharazmi University, Tehran, Iran.

2 MSc., Department of Business Management, Faculty of Management, Kharazmi University, Tehran, Iran.

Abstract
With the widespread digitalization of businesses, big data and advanced analytics have become increasingly critical in shaping strategic marketing decisions. This study examines the impact of big data utilization (BDU) on competitive marketing performance (CMP), taking into account the interactions between artificial intelligence (AI) adoption, marketing analytics capabilities (MAC), holistic marketing decision-making (HMD), and market uncertainty (MU). Adopting a positivist paradigm and a deductive approach, the research is applied in purpose and descriptive-survey in nature. The statistical population comprised marketing managers, and the sample size was determined to be 263 using G*Power3. Data were collected via a standardized online questionnaire through non-probability convenience sampling and analyzed using structural equation modeling with SmartPLS3 software. The findings indicate that, under conditions of MU, BDU enhances companies’ CMP through the mediating effects of AI adoption, MAC, and HMD processes. The novelty of this research lies in the development of an integrated framework that, for the first time, combines AI technologies, analytical capabilities, and strategic decision-making within volatile market environments. This model offers a practical roadmap for strengthening data-driven readiness among marketing managers and optimizing marketing performance through the convergence of technology and analytics.

Keywords


[1] Lee, B. K. (2025). Digital product management: strategic planning and market opportunity. CRC Press. https://doi.org/10.1201/9781003484295 
[2] Johnson, D. S., Sihi, D., & Muzellec, L. (2021, September). Implementing big data analytics in marketing departments: Mixing organic and administered approaches to increase data-driven decision making. In Informatics (Vol. 8, No. 4, p. 66). MDPI. https://doi.org/10.3390/informatics8040066 
[3] Bella, K. M. J. (2024). A study on marketing analytics and artificial intelligence. SELP Journal of Social Science-A Blind Review & Refereed Quarterly Journal, XV (55), 53-57.
[4] Baqai, S. R., & Qureshi, J. (2024). Adopting artificial intelligence and marketing analytics for data-driven decisions and cutting edge solutions. In IBA SBS 4th International Conference 2025. Retrieved from https://ir.iba.edu.pk/sbsic/2024/program/38 
[5] Rahman, M. S., Hossain, M. A., & Fattah, F. A. M. A. (2021). Does marketing analytics capability boost firms' competitive marketing performance in data-rich business environment?. Journal of Enterprise Information Management, 35(2), 455-480. https://doi.org/10.1108/JEIM-05-2020-0185 
[6] Cao, G., Tian, N., & Blankson, C. (2022). Big data, marketing analytics, and firm marketing capabilities. Journal of Computer Information Systems, 62(3), 442-451. https://doi.org/10.1080/08874417.2020.1842270 
[7] Zaman, K. (2022). Transformation of marketing decisions through artificial intelligence and digital marketing. Journal of Marketing Strategies, 4(2), 353-364. https://doi.org/10.52633/jms.v4i2.210 
[8] Sharfaei, S., Wei Ong, J., & Ojo, A. O. (2023). The impact of market uncertainty on international SME performance. Cogent Business & Management, 10(1), 2198160. https://doi.org/10.1080/23311975.2023.2198160 
[9] Hossain, M. A., Agnihotri, R., Rushan, M. R. I., Rahman, M. S., & Sumi, S. F. (2022). Marketing analytics capability, artificial intelligence adoption, and firms' competitive advantage: Evidence from the manufacturing industry. Industrial Marketing Management, 106, 240-255. https://doi.org/10.1016/j.indmarman.2022.08.017 
[10] Cao, G., Duan, Y., & El Banna, A. (2019). A dynamic capability view of marketing analytics: Evidence from UK firms. Industrial Marketing Management, 76, 72-83. https://doi.org/10.1016/j.indmarman.2018.08.002 
[11] Hossain, M. A., Akter, S., Yanamandram, V., & Wamba, S. F. (2023). Data-driven market effectiveness: The role of a sustained customer analytics capability in business operations. Technological Forecasting and Social Change, 194, 122745. https://doi.org/10.1016/j.techfore.2023.122745 
 [12] Gupta, S., Justy, T., Kamboj, S., Kumar, A., & Kristoffersen, E. (2021). Big data and firm marketing performance: Findings from knowledge-based view. Technological Forecasting and Social Change, 171, 120986. https://doi.org/10.1016/j.techfore.2021.120986 
[13] Rahimi celver, H. and akbariarbatan, G. (2024). Providing an optimal business model based on creating data marketplaces in industrial units. Management Research in Iran, 28(1), 154-174. https://mri.modares.ac.ir/article_644.html?lang=en [In Persian]
[14] Yazdani, H. R., Sohrabi, B., & jalilian, M. (2021). Identifying Qualitative Indicators For Evaluating IoT Business Models Based On Big Data Analysis In The Smart City. Modern Research in Decision Making, 6(2), 125-154. https://dor.isc.ac/dor/20.1001.1.24766291.1400.6.2.6.6 [In Persian]
[15] Zarei, G., Mohammad khani, R., & fathi, H. (2024). Investigating and identifying the consequences of using artificial intelligence in marketing. Management Research in Iran, 28(2), 1-31. https://mri.modares.ac.ir/article_645.html?lang=en [In Persian]
[16] Wibisono, O., Ari, H. D., Widjanarti, A., Zulen, A. A., & Tissot, B. (2019). The use of big data analytics and artificial intelligence in central banking. IFC Bulletins, Bank for International Settlements, 50, 1-20. https://www.bis.org/ifc/publ/ifcb50.pdf
[17] Ifekanandu, C. C., Ezirim, A. C., & Kingsley, U. A. (2023). Artificial intelligence adoption and marketing performance of quoted manufacturing firms in Nigeria. Int. J. Innov. Sci. Res. Technol, 8, 1194-1207. https://doi.org/10.5281/zenodo.8305001 
[18] 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
[19] Nnaji, U. O., Benjamin, L. B., Eyo-Udo, N. L., & Etukudoh, E. A. (2024). A review of strategic decision-making in marketing through big data and analytics. Magna Scientia Advanced Research and Reviews, 11(1), 084-091. https://doi.org/10.30574/msarr.2024.11.1.0077 
[20] Jabbar, A., Akhtar, P., & Dani, S. (2020). Real-time big data processing for instantaneous marketing decisions: A problematization approach. Industrial Marketing Management, 90, 558-569. https://doi.org/10.1016/j.indmarman.2019.09.001 
[21] 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. https://doi.org/10.1108/TQM-12-2019-0303 
[22] Hicham, N., Nassera, H., & Karim, S. (2023). Strategic framework for leveraging artificial intelligence in future marketing decision-making. Journal of Intelligent Management Decision, 2(3), 139-150. https://doi.org/10.56578/jimd020304 
[23] Conejo, A. J., Carrión, M., & Morales, J. M. (2010). Decision making under uncertainty in electricity markets (Vol. 1, pp. 376-384). New York: Springer. https://doi.org/10.1007/978-1-4419-7421-1 
[24] Chen, J., Zhou, W., & Frankwick, G. L. (2025). Firm AI adoption intensity and marketing performance. Journal of Computer Information Systems, 65(2), 172-189. https://doi.org/10.1080/08874417.2023.2277751
[25] Shirmohammadzadeh, S. (2024). The impact of general analytical capabilities on marketing agility and effectiveness under potential market turbulence (Unpublished master’s thesis). Nima Institute of Higher Education. https://ganj.irandoc.ac.ir/5/articles/2d872371a293bec2d2883f73081087bc [In Persian]
[26]  Demirağ, F. (2025). The impact of AI-supported marketing capabilities and analytics on SMEs' customer agility and marketing performance. International Journal of Social Sciences and Education Research, 11(1), 1-14. https://doi.org/10.24289/ijsser.1601570
[27] Kotler, P., & Keller, K. L. (2016). Marketing management (15th ed.). Pearson Education
[28] Alsheibani, S., Cheung, Y., & Messom, C. (2018). Artificial intelligence adoption: AI-readiness at firm-level. In Pacific Asia Conference on Information Systems 2018 (p. 37). Association for Information Systems. https://aisel.aisnet.org/pacis2018/37 
[29] Avadi, M., Kordnaeij, A., Khodad Hosseini, S. H., & Ganjali, A. (2022). Examining The Role of Historical Dimensions of The Organization in The Managers' Decision to Implement Strategic Changes; A Systematic Review. Modern Research in Decision Making, 7(4), 157-188. https://dor.isc.ac/dor/20.1001.1.24766291.1401.7.4.7.4 [In Persian]
[30] Ashill, N. J., & Jobber, D. (2014). The effects of the external environment on marketing decision-maker uncertainty. Journal of Marketing Management, 30(3-4), 268-294. http://dx.doi.org/10.1080/0267257X.2013.811281
[31] Wolf, E. J., Harrington, K. M., Clark, S. L., & Miller, M. W. (2013). Sample size requirements for structural equation models: An evaluation of power, bias, and solution propriety. Educational and psychological measurement, 73(6), 913-934. https://doi.org/10.1177/0013164413495237
[32] Faul, F., Erdfelder, E., Buchner, A., & Lang, A. G. (2009). Statistical power analyses using G* Power 3.1: Tests for correlation and regression analyses. Behavior research methods, 41(4), 1149-1160. doi: https://link.springer.com/article/10.3758/BRM.41.4.1149 
[33] Hair, J. F., Hult, G. T. M., Ringle, C. M., & Sarstedt, M. (2022). A primer on partial least squares structural equation modeling (PLS-SEM) (3 ed.). Thousand Oaks, CA: Sage. https://doi.org/10.1007/978-3-319-57413-4_15
[34] Shrestha, N. (2021). Factor analysis as a tool for survey analysis. American journal of Applied Mathematics and statistics, 9(1), 4-11. http://dx.doi.org/10.12691/ajams-9-1-2 
[35] George, D., & Mallery, P. (2019). IBM SPSS statistics 25 step by step: A simple guide and reference (15th ed.). New York: Routledge. https://doi.org/10.4324/9780429056765
[36] Sarstedt, M., & Cheah, J. H. (2019). Partial least squares structural equation modeling using SmartPLS: a software review. Journal of Marketing Analytics, 7(3), 196-202. https://doi.org/10.1057/s41270-019-00058-3 
[37] Hair, J. F., Sarstedt, M., Ringle, C. M., & Gudergan, S. P. (2024). Advanced Issues in Partial Least Squares Structural Equation Modeling (PLS-SEM) (2e). Thousand Oaks, CA: Sage. https://tore.tuhh.de/handle/11420/52983