From Data to Market Impact: A Conceptual Process Model Linking Big Data Analytics to Sustainable Marketing Performance Through Intelligence Transformation
DOI:
https://doi.org/10.51137/wrp.ijsbe.637Keywords:
Big Data Analytics, Marketing Performance, Intelligence Transformation, Dynamic Capabilities, Sustainable Competitive AdvantageAbstract
The competitive marketing environment has changed as businesses increasingly use big data analytics (BDA), but there is limited theoretical understanding of how raw data translates into measurable marketing results. The paper introduces a five-stage process model showing how BDA data streams transform into sustainable marketing results through intelligence transformation as the mediating component. The model developed from Resource-Based View (RBV), Dynamic Capabilities Theory, and Data-Information-Knowledge-Wisdom (DIKW) hierarchy principles was used to conduct systematic literature synthesis through peer-reviewed sources between 2018 and 2024. It was created through structured literature synthesis, identifying 76 sources via PRISMA-based screening from Web of Science, Scopus, and Google Scholar. The model identifies intelligence transformation as the pivotal mediating stage at which the BDA-performance relationship is most decisively shaped. The study presents five research-based hypotheses that identify organizational absorptive capacity and data governance maturity as crucial factors that define the study's limits. The study results provide a step-by-step implementation guide and KPI framework to help marketing managers and chief data officers achieve their strategic and sustainable marketing goals through BDA investments, while the research presents a unified theoretical framework that shows how organized data sensing develops into sustained competitive advantage.
References
Amado, A., Cortez, P., Rita, P., & Moro, S. (2018). Research trends on big data in marketing: A text mining and topic modelling-based literature analysis. European Research on Management and Business Economics, 24(1), 1–7. https://doi.org/10.1016/j.iedeen.2017.06.002
Arunachalam, D., Kumar, N., & Kawalek, J. P. (2018). Understanding big data analytics capabilities in supply chain management: Unravelling the issues, challenges and implications for practice. Transportation Research Part E: Logistics and Transportation Review, 114, 416–436. https://doi.org/10.1016/j.tre.2017.04.001
Bag, S., Gupta, S., Kumar, A., & Sivarajah, U. (2021). An integrated artificial intelligence framework for knowledge creation and B2B marketing rational decision making for improving firm performance. Industrial Marketing Management, 92, 178–189. https://doi.org/10.1016/j.indmarman.2020.12.001
Barney, J. B. (1991). Firm resources and sustained competitive advantage. Journal of Management, 17(1), 99–120. https://doi.org/10.1177/014920639101700108
Brewis, C., Dibb, S., & Meadows, M. (2023). Leveraging big data for strategic marketing: A dynamic capabilities model for incumbent firms. Technological Forecasting and Social Change, 190, 122402. https://doi.org/10.1016/j.techfore.2023.122402
Cohen, W. M., & Levinthal, D. A. (1990). Absorptive capacity: A new perspective on learning and innovation. Administrative Science Quarterly, 35(1), 128–152. https://doi.org/10.2307/2393553
Day, G. S. (1994). The capabilities of market-driven organizations. Journal of Marketing, 58(4), 37–52. https://doi.org/10.1177/002224299405800404
Eisenhardt, K. M. (1989). Building theories from case study research. Academy of Management Review, 14(4), 532–550. https://doi.org/10.5465/amr.1989.4308385
Fosso Wamba, S., Gunasekaran, A., Papadopoulos, T., & Ngai, E. (2018). Big data analytics in logistics and supply chain management. The International Journal of Logistics Management, 29(2), 478-484. https://doi.org/10.1108/IJLM-02-2018-0026
Frizzo-Barker, J., Chow-White, P. A., Adams, P. R., Mentanko, J., Ha, D., & Green, S. (2020). Blockchain as a disruptive technology for business: A systematic review. International Journal of Information Management, 51, 102029. https://doi.org/10.1016/j.ijinfomgt.2019.10.014
Galbraith, J. R. (1974). Organization design: An information processing view. Interfaces, 4(3), 28–36. https://doi.org/10.1287/inte.4.3.28
Goodell, J. W., Kumar, S., Lim, W. M., & Pattnaik, D. (2021). Artificial intelligence and machine learning in finance: Identifying foundations, themes and research clusters from bibliometric analysis. Journal of Behavioral and Experimental Finance, 32, 100577. https://doi.org/10.1016/j.jbef.2021.100577
Gupta, S., Leszkiewicz, A., Kumar, V., Bijmolt, T., & Potapov, D. (2020). Digital analytics: Modelling for insights and new methods. Journal of Interactive Marketing, 51, 26–43. https://doi.org/10.1016/j.intmar.2020.04.003
Kohli, A. K., & Jaworski, B. J. (1990). Market orientation: The construct, research propositions and managerial implications. Journal of Marketing, 54(2), 1–18. https://doi.org/10.1177/002224299005400201
Landis, J. R., & Koch, G. G. (1977). The measurement of observer agreement for categorical data. Biometrics, 33(1), 159–174. https://doi.org/10.2307/2529310
Lim, W. M., Yap, S. F., & Makkar, M. (2020). Home sharing in marketing and tourism at a tipping point: What do we know, how do we know, and where should we be heading? Journal of Business Research, 122, 534–566. https://doi.org/10.1016/j.jbusres.2020.08.051
Mikalef, P., Boura, M., Lekakos, G., & Krogstie, J. (2021). Big data analytics and firm performance: Findings from a mixed-method approach. Journal of Business Research, 98, 261–276. https://doi.org/10.1016/j.jbusres.2019.01.044
Moher, D., Liberati, A., Tetzlaff, J., Altman, D. G., & The PRISMA Group. (2009). Preferred reporting items for systematic reviews and meta-analyses: The PRISMA statement. PLOS Medicine, 6(7), e1000097. https://doi.org/10.1371/journal.pmed.1000097
Ozbayoglu, A. M., Gudelek, M. U., & Sezer, O. B. (2020). Deep learning for financial applications: A survey. Applied Soft Computing, 93, 106384. https://doi.org/10.1016/j.asoc.2020.106384
Peng, Y., Albuquerque, P. H. M., Kimura, H., & Saavedra, C. A. P. B. (2021). Feature selection and deep neural networks for stock price direction forecasting using technical analysis indicators. Machine Learning with Applications, 5, 100060. https://doi.org/10.1016/j.mlwa.2021.100060
Peng, Y., & Nagata, M. H. (2020). An empirical overview of nonlinearity and overfitting in machine learning using Covid-19 data. Chaos, Solitons and Fractals, 139, 110055. https://doi.org/10.1016/j.chaos.2020.110055
Rowley, J. (2007). The wisdom hierarchy: Representations of the DIKW hierarchy. Journal of Information Science, 33(2), 163–180. https://doi.org/10.1177/0165551506070706
Harris, S.G. (1990), The fifth discipline: The art and practice of the learning organization, by Peter Senge, New York: Doubleday/Currency, 1990. Hum. Resour. Manage., 29: 343-348. https://doi.org/10.1002/hrm.3930290308
Sheng, J., Amankwah‐Amoah, J., Khan, Z., & Wang, X. (2021). COVID‐19 pandemic in the new era of big data analytics: Methodological innovations and future research directions. British Journal of Management, 32(4), 1164-1183. https://doi.org/10.1111/1467-8551.12441
Teece, D. J. (2007). Explicating dynamic capabilities: The nature and microfoundations of (sustainable) enterprise performance. Strategic Management Journal, 28(13), 1319–1350. https://doi.org/10.1002/smj.640
Teece, D. J., Pisano, G., & Shuen, A. (1997). Dynamic capabilities and strategic management. Strategic management journal, 18(7), 509-533. https://doi.org/10.1002/(SICI)1097-0266(199708) 18:7<509::AID-SMJ882>3.0.CO;2-ZDigital Object Identifier (DOI)
Van de Ven, A. H. (1992). Suggestions for studying strategy process: A research note. Strategic Management Journal, 13(S1), 169–188. https://doi.org/10.1002/smj.4250131013
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Copyright (c) 2026 Dorcas Awino Onyango, Prof. Edward Otieno Owino, Dr. Juniter Kwamboka Mokua, Dr. Charles Githira Wanyoike (Author)
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