Trust-Layer Marketing (TLM) Dynamics in High-Fraud Markets: A Multi-Sector Empirical Assessment in Emerging Economies

This study investigates the dynamics of Trust-Layer Marketing (TLM) in high-fraud emerging economies by examining how fraud intensity, regulatory quality, digital trust infrastructure, economic development, and financial inclusion shape Trust-Layer Market Performance (TLMP). Using a balanced panel of fifteen emerging economies from 2015 to 2024, the study employs two-way fixed effects, system GMM, panel threshold regression, and random forest machine-learning techniques to provide robust, multi-method evidence. The results show that higher fraud and corruption significantly reduce TLMP, while stronger regulatory quality and digital trust infrastructure consistently enhance market performance across all models. A significant corruption threshold is identified, revealing that the positive effects of regulatory quality and digital trust are amplified in high-corruption environments. Economic development and financial inclusion also exert strong positive influences, although the latter weakens under extreme corruption. Diagnostic tests confirm the model's validity across both econometric and predictive analyses. The study contributes novel empirical insights by integrating linear, dynamic, and non-linear modelling with machine learning to advance the TLM framework. It concludes that strengthening regulatory trust, expanding digital trust systems, and reducing corruption are central to improving market performance in emerging economies.