Artificial Intelligence in Social Media Management in Israel: Enhancing Audience Engagement Through Predictive Analytics

This study investigates how artificial intelligence (AI) and predictive analytics can enhance audience engagement in social media management within the Israeli digital ecosystem. Building on digital transformation and marketing analytics literature, the research develops and tests a predictive engagement model that integrates three capability dimensions: behavioral activity (posting frequency), technological capability (AI usage intensity), and organizational infrastructure (data governance maturity). To demonstrate the empirical testing procedure, the study employs dataset of 250 observations and applies correlation analysis, multicollinearity diagnostics, and multiple regression models with interaction effects. Results show that posting frequency is a significant predictor of engagement; however, explanatory power increases substantially when AI usage and governance maturity are included. AI usage intensity emerges as a strong driver of engagement performance, while data governance maturity also contributes positively, indicating that predictive effectiveness depends on both technological adoption and organizational readiness. Importantly, the interaction analysis suggests that AI amplifies the marginal impact of posting frequency on engagement, positioning AI as a performance multiplier rather than a simple add-on. Although findings are based on synthetic data and therefore do not support population-level inference, the study offers a scalable, context-relevant framework that can be validated using real engagement data from Israeli firms and public institutions.