AI-driven anchoring bias in mobile recharge platforms: a PLS-SEM analysis

The study examines the structural dynamics between AI dependence and anchoring bias on mobile recharge platforms in India. It integrates a layered behavioural economic theoretical framework. It examines how AI dependence amidst a complex choice environment, perceived cognitive load, easy digital access and higher search costs influence anchoring bias in the choice of mobile recharge plans. Data is collected from the four telecom categories in India among 301 consumers who purchase prepaid mobile recharge plans via digital platforms. The PLS-SEM analysis finds that the complexity of choice and resultant higher search costs are the primary drivers of AI dependence. It implies that AI recommendation systems, which are aimed to reduce search effort, rather limit consumers’ independent evaluation of plans by creating anchoring bias. Further, the study employs complementary robustness checks using Gaussian Copula for instrument-free endogeneity correction, a quadratic effect for testing non-linearity and FIMIX PLS to confirm the absence of unobserved heterogeneity. The results reveal a significant inverted U-shaped relationship between AI dependence and anchoring bias, indicating a cognitive saturation point at which the bias plateaus at extreme levels of AI dependence. The study calls for policy interventions like targeted nudges for consumers and appropriate AI inclusive competition policies.