Robust Predictors of Mobile Phone Reliance for Information Seeking: A Multi-Stage Empirical Analysis and Validation

This study examines factors driving reliance on mobile phones as a primary information source. Using Information-Seeking Complementarity Theory (ISCT), which posits that frequent use of diverse media channels builds digital habits that reinforce mobile reliance, we analyze World Values Survey (WVS) Time Series 1981–2022 (v4.0), validated with WVS v5.0 and Integrated Values Survey (IVS). A multi-stage pipeline integrates AdaBoost (R 4.3.1), LASSO/BMA (Stata v17), Histogram Gradient Boosting (Python 3.12.7), and mixed-effects logistic regression. Missing data (DK/NA) were excluded or median-imputed. The final model (AUC-ROC > 0.85) identifies five robust predictors: age (negative), and positive associations with digital mail, online social networks, peer interaction, and radio listening—all stable across methods, datasets, and reverse causality checks. Subgroup analysis reveals stronger effects among males, unmarried individuals, urban residents, and higher education/employment groups. Nomograms enable probabilistic forecasting and policy simulation. By identifying technology-agnostic behavioral drivers validated across three decades of global survey data (1981–2022), with mobile reliance measured from 2010 onward, this work provides a transparent, replicable predictive framework with implications for emerging AI and wearable contexts.