Evaluating the Effectiveness of Interventions to Reduce Islamophobia in Post-9/11 Citizen Diplomacy Settings: A Bayesian Meta-Analysis - Munawar Fuad; Muhammad Salman Abidin (UIN Sunan Gunung Djati Bandung, 2024)
Purpose: This study aims to evaluate the effectiveness of interventions designed to reduce Islamophobia, particularly in the context of citizen diplomacy, following the rise in Islamophobia after the 9/11 attacks. Specifically, it investigates the impact of both face-to-face and virtual intergroup contact in mitigating Islamophobia, addressing the growing need for effective strategies to counter negative perceptions of Muslims in the post-9/11 era. Methodology: A Bayesian meta-analysis was conducted using data from 760 studies indexed in Scopus, focusing on interventions targeting Islamophobia. After screening for eligibility, 4 studies were included for analysis. Both fixed-effects and random-effects models were employed within the Bayesian framework to assess the intervention's impact and account for variability across studies. Findings: The results show strong support for the effectiveness of interventions in reducing Islamophobia, with a posterior probability of 77.7% for the fixed-effects model, suggesting consistent reductions across studies. The random-effects model revealed some variability in the effectiveness of interventions, though the overall impact was still significant. These findings emphasize the potential of citizen diplomacy, including both in-person and virtual engagement, to reduce prejudice and foster greater intergroup understanding. Research Implications: This study underscores the critical role of citizen diplomacy in countering Islamophobia. The findings provide valuable insights for policymakers, non-governmental organizations, and educational institutions seeking to implement or enhance initiatives aimed at promoting intercultural dialogue. The study also demonstrates the utility of Bayesian meta-analysis in synthesizing research on intervention efficacy, offering a rigorous framework for future investigations into prejudice reduction strategies across various cultural contexts. Originality/Value: This research is one of the first to apply Bayesian meta-analysis to evaluate interventions aimed at reducing Islamophobia within citizen diplomacy. It offers a novel perspective by examining both face-to-face and virtual contact as methods of combating Islamophobia, areas that have been underexplored in existing literature. The integration of Bayesian techniques allows for more accurate, dynamic insights into the effectiveness of these interventions, providing a flexible model that can be adapted to other areas of social prejudice reduction.