Faith, technology, and gambling: How blockchain awareness shapes anti-gambling behavior in Indonesian Muslim society

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Type of the article: Research Article

Abstract
The paper deals with the intersection of technological awareness and moral behavior in the context of online gambling. The study analyzes the influence of blockchain understanding on anti-online gambling attitudes, with religiosity and perceived social impact serving as mediators. The study’s object comprises 532 millennial Muslims in Indonesia, selected through simple random sampling, and surveyed from July to October 2024. Respondents met inclusion criteria such as age 17-50 years, Indonesian citizenship, Muslim identity, and knowledge of blockchain. Structural Equation Modeling (SEM) based on Partial Least Squares (PLS) was employed to assess proposed relationships.
The results demonstrate that blockchain understanding has a significant positive direct effect on online gambling attitudes (T = 3.974; p < 0.001), suggesting that higher blockchain literacy may actually increase openness toward gambling, possibly due to perceived anonymity and security. In contrast, blockchain understanding significantly enhances religiosity (T = 58.653; p < 0.001) and perceived social impact (T = 4.929; p < 0.001), both of which positively influence anti-online gambling attitudes (T = 11.370 and T = 11.574; p < 0.001). Furthermore, indirect effects confirm that both religiosity (T = 9.822; p < 0.001) and perceived social impact (T = 20.224; p < 0.001) significantly mediate the relationship between blockchain understanding and anti-gambling attitudes. The study concludes that while blockchain knowledge alone may increase gambling engagement, its influence can be redirected toward anti-gambling behavior through enhanced moral and social awareness. The findings offer practical value in designing educational interventions that integrate technological literacy with religious and social ethics.

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    • Table 1. Profile of the respondents
    • Table 2. Variables and indicators
    • Table 3. Outer loading
    • Table 4. Fornell-Larcker criterion
    • Table 5. Average Variance Extracted (AVE)
    • Table 6. Reliability and validity
    • Table 7. Fit indices
    • Table 8. Coefficient of determination
    • Table 9. Direct effect
    • Table 10. Indirect effect
    • Conceptualization
      Sumar’in Sumar’in
    • Data curation
      Sumar’in Sumar’in, Sumin Sumin
    • Formal Analysis
      Sumar’in Sumar’in, Andiyono Andiyono, Sumin Sumin, Mokmin Basri
    • Methodology
      Sumar’in Sumar’in, Andiyono Andiyono, Sumin Sumin, Mokmin Basri
    • Project administration
      Sumar’in Sumar’in
    • Supervision
      Sumar’in Sumar’in, Andiyono Andiyono, Sumin Sumin, Mokmin Basri
    • Validation
      Sumar’in Sumar’in
    • Writing – original draft
      Sumar’in Sumar’in, Andiyono Andiyono, Sumin Sumin, Mokmin Basri
    • Writing – review & editing
      Sumar’in Sumar’in, Andiyono Andiyono, Sumin Sumin, Mokmin Basri
    • Investigation
      Andiyono Andiyono
    • Resources
      Sumin Sumin, Mokmin Basri
    • Software
      Sumin Sumin
    • Visualization
      Sumin Sumin