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An ARDL-Inspired Conceptual Model: Analyzing the Short- and Long-Term Dynamics of AI, Big Data, and Trust in Marketing (2025-2030)

Author

Listed:
  • Amina lahmari

    (University Of Oum El Bouaghi (Algeria))

  • Khaled REDJEM

    (University of setif 1 (Algeria))

  • Doaa Shohaieb

    (Aston university (United Kingdom ))

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.

Suggested Citation

  • Amina lahmari & Khaled REDJEM & Doaa Shohaieb, 2025. "An ARDL-Inspired Conceptual Model: Analyzing the Short- and Long-Term Dynamics of AI, Big Data, and Trust in Marketing (2025-2030)," International Journal of Economic Performance (IJEP), ALPEC The performance of Algerian economic institutions in light of international economic mobility , Boumerdes University, vol. 8(02), pages :251-268, December.
    • Handle: RePEc:bjm:ijep00:v:8:y:2025:i:02:id:386
    DOI: 10.54241/2065-008-002-014
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    References listed on IDEAS

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    1. M. Hashem Pesaran & Yongcheol Shin & Richard J. Smith, 2001. "Bounds testing approaches to the analysis of level relationships," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 16(3), pages 289-326.
    2. Thomas H. Davenport, 2018. "From analytics to artificial intelligence," Journal of Business Analytics, Taylor & Francis Journals, vol. 1(2), pages 73-80, July.
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