An ARDL-Inspired Conceptual Model: Analyzing the Short- and Long-Term Dynamics of AI, Big Data, and Trust in Marketing (2025-2030)
Article Sidebar
Main Article Content
Abstract
This study employs a qualitative adaptation of the Autoregressive Distributed Lag (ARDL) model to examine the interrelationships between artificial intelligence (AI) adoption, big data utilization, customer trust, and global marketing performance with projections toward 2030. Using annual data from 2020-2024, the analysis reveals strong positive long-term relationships between AI/big data adoption and marketing performance, while highlighting the complex mediating role of customer trust in this ecosystem. The findings indicate that AI demonstrates the largest impact coefficient (2.71), followed by big data (1.22) and customer trust (0.89). The bounds test confirms cointegration (F-statistic = 5.90 > critical value = 4.10), establishing a long-run equilibrium relationship. Projections suggest marketing performance will reach 11.3% by 2030, with AI contributing 4.1%, big data 2.8%, and customer trust 1.5% to this growth. However, these results are constrained by the limited sample size (5 years) and require cautious interpretation.
Metrics
Article Details
This work is licensed under a Creative Commons Attribution 4.0 International License.
References
Aguirre, E., Mahr, D., Grewal, D., de Ruyter, K., & Wetzels, M. (2015). Unraveling the personalization paradox: The effect of information collection and trust-building strategies on online advertisement effectiveness. Journal of Retailing, 91(1), 34-49. https://doi.org/10.1016/j.jretai.2014.09.005
Akaike, H. (1974). A new look at the statistical model identification. IEEE Transactions on Automatic Control, 19(6), 716-723. https://doi.org/10.1109/TAC.1974.1100705
Aridor, G., Che, Y. K., & Salz, T. (2020). The economic consequences of data privacy regulation: Empirical evidence from GDPR. NBER Working Paper, w26900. https://doi.org/10.3386/w26900
Berman, S. J. (2018). How digital transformation is transforming marketing. Strategy & Leadership, 46(5), 11-16. https://doi.org/10.1108/SL-08-2018-0080
Bleier, A., Goldfarb, A., & Tucker, C. (2020). Consumer privacy and the future of data-based innovation and marketing. International Journal of Research in Marketing, 37(3), 466-480. https://doi.org/10.1016/j.ijresmar.2020.03.006
Bughin, J., Hazan, E., Ramaswamy, S., Chui, M., Allas, T., Dahlström, P., Henke, N., & Trench, M. (2018). Artificial intelligence: The next digital frontier? McKinsey Global Institute.
Chowdhury, S., Dey, P., Joel-Edgar, S., Bhattacharya, S., Rodriguez-Espindola, O., Abadie, A., & Truong, L. (2023). Unlocking the value of artificial intelligence in human resource management through AI capability framework. Human Resource Management Review, 33(1), 100899. https://doi.org/10.1016/j.hrmr.2022.100899
Chui, M., Manyika, J., & Miremadi, M. (2018). What AI can and can't do (yet) for your business. McKinsey Quarterly, 1, 97-108.
Davenport, T. H. (2018). From analytics to artificial intelligence. Journal of Business Analytics, 1(2), 73-80. https://doi.org/10.1080/2573234X.2018.1543535
Davenport, T. H., Guha, A., Grewal, D., & Bressgott, T. (2020). How artificial intelligence will change the future of marketing. Journal of the Academy of Marketing Science, 48(1), 24-42. https://doi.org/10.1007/s11747-019-00696-0
Davenport, T. H., & Ronanki, R. (2018). Artificial intelligence for the real world. Harvard Business Review, 96(1), 108-116.
Dickey, D. A., & Fuller, W. A. (1979). Distribution of the estimators for autoregressive time series with a unit root. Journal of the American Statistical Association, 74(366a), 427-431. https://doi.org/10.1080/01621459.1979.10482531
Edelman. (2020). 2020 Edelman Trust Barometer. Edelman. https://www.edelman.com/trust/2020-trust-barometer
Edelman. (2024). 2024 Edelman Trust Barometer. Edelman. https://www.edelman.com/trust/2024-trust-barometer
Engle, R. F., & Granger, C. W. (1987). Co-integration and error correction: Representation, estimation, and testing. Econometrica, 55(2), 251-276. https://doi.org/10.2307/1913236
Erevelles, S., Fukawa, N., & Swayne, L. (2016). Big Data consumer analytics and the transformation of marketing. Journal of Business Research, 69(2), 897-904. https://doi.org/10.1016/j.jbusres.2015.07.001
Gartner. (2023). Gartner forecasts worldwide artificial intelligence software market to reach $62 billion in 2022. Gartner. https://www.gartner.com/en/newsroom/press-releases
Goldfarb, A., & Tucker, C. (2011). Privacy regulation and online advertising. Management Science, 57(1), 57-71. https://doi.org/10.1287/mnsc.1100.1246
Greene, W. H. (2018). Econometric analysis (8th ed.). Pearson Education.
Gupta, M., & George, J. F. (2016). Toward the development of a big data analytics capability. Information & Management, 53(8), 1049-1064. https://doi.org/10.1016/j.im.2016.07.004
Hamilton, J. D. (1994). Time series analysis. Princeton University Press.
Hoofnagle, C. J., van der Sloot, B., & Zuiderveen Borgesius, F. (2019). The European Union general data protection regulation: What it is and what it means. Information & Communications Technology Law, 28(1), 65-98. https://doi.org/10.1080/13600834.2019.1573501
Hyndman, R. J., & Athanasopoulos, G. (2018). Forecasting: Principles and practice (2nd ed.). OTexts.
IDC. (2023). Worldwide global datasphere forecast, 2023-2027. International Data Corporation.
Kannan, P. K., & Li, H. A. (2017). Digital marketing: A framework, review and research agenda. International Journal of Research in Marketing, 34(1), 22-45. https://doi.org/10.1016/j.ijresmar.2016.11.006
Keller, K. L. (2013). Strategic brand management: Building, measuring, and managing brand equity (4th ed.). Pearson.
Kumar, V., & Gupta, S. (2016). Conceptualizing the evolution and future of advertising. Journal of Advertising, 45(3), 302-317. https://doi.org/10.1080/00913367.2016.1199335
Kumar, V., Venkatesan, R., & Reinartz, W. (2016). Creating enduring customer value. Journal of Marketing, 80(6), 36-68. https://doi.org/10.1509/jm.15.0414
Lemon, K. N., & Verhoef, P. C. (2016). Understanding customer experience throughout the customer journey. Journal of Marketing, 80(6), 69-96. https://doi.org/10.1509/jm.15.0420
Makridakis, S., Spiliotis, E., & Assimakopoulos, V. (2018). Statistical and machine learning forecasting methods: Concerns and ways forward. PLoS ONE, 13(3), e0194889. https://doi.org/10.1371/journal.pone.0194889
Manyika, J., Silberg, J., & Presten, B. (2018). Notes from the AI frontier: Tackling bias in AI (and in humans). McKinsey Global Institute.
Martin, K. D., Kim, J. J., Palmatier, R. W., Steinhoff, L., Stewart, D. W., Walker, B. A., Wang, Y., & Weaven, S. K. (2019). Data privacy in retail: Navigating tensions and directing future research. Journal of Retailing, 96(4), 449-457. https://doi.org/10.1016/j.jretai.2020.10.002
McKinsey Global Institute. (2017). Artificial intelligence: The next digital frontier? McKinsey & Company.
McKinsey Global Institute. (2021). The state of AI in 2021. McKinsey & Company.
Pesaran, M. H., Shin, Y., & Smith, R. J. (2001). Bounds testing approaches to the analysis of level relationships. Journal of Applied Econometrics, 16(3), 289-326. https://doi.org/10.1002/jae.616
Politou, E., Alepis, E., & Patsakis, C. (2018). Forgetting personal data and revoking consent under the GDPR: Challenges and proposed solutions. Journal of Cybersecurity, 4(1), tyy001. https://doi.org/10.1093/cybsec/tyy001
Ransbotham, S., Kiron, D., Gerbert, P., & Reeves, M. (2020). Expanding AI's impact with organizational learning. MIT Sloan Management Review, 61(4), 1-17.
Reichheld, F. F. (2003). The one number you need to grow. Harvard Business Review, 81(12), 46-55.
Sharma, R., Mithas, S., & Kankanhalli, A. (2020). Transforming decision-making processes: A research agenda for understanding the impact of business analytics on organisations. European Journal of Information Systems, 29(1), 1-9. https://doi.org/10.1080/0960085X.2020.1733832
Shin, Y., Yu, B., & Greenwood-Nimmo, M. (2014). Modelling asymmetric cointegration and dynamic multipliers in a nonlinear ARDL framework. In W. C. Horrace & R. C. Sickles (Eds.), Festschrift in honor of Peter Schmidt: Econometric methods and applications (pp. 281-314). Springer. https://doi.org/10.1007/978-1-4899-8008-3_9
Statista. (2024). Digital marketing worldwide. Statista. https://www.statista.com/markets/424/topic/481/digital-marketing/
Wamba, S. F., Akter, S., Edwards, A., Chopin, G., & Gnanzou, D. (2015). How 'big data' can make big impact: Findings from a systematic review and a longitudinal case study. International Journal of Production Economics, 165, 234-246. https://doi.org/10.1016/j.ijpe.2014.12.031
Wedel, M., & Kannan, P. K. (2016). Marketing analytics for data-rich environments. Journal of Marketing, 80(6), 97-121. https://doi.org/10.1509/jm.15.0413
Zeithaml, V. A., Jaworski, B. J., Kohli, A. K., Tuli, K. R., Ulaga, W., & Zaltman, G. (2020). A theories-in-use approach to building marketing theory. Journal of Marketing, 84(1), 32-51. https://doi.org/10.1177/0022242919888477