Designing a Model for Public Monitoring of Public Policies with a Crowdsourcing Approach in the Domain of Public Services

Abstract

The primary aim of this article is to design a model for public monitoring of public policies using a crowdsourcing approach in the domain of public services. The research method employed is mixed (qualitative-quantitative). To conduct the study, in addition to document analysis, thematic analysis using MAXQDA12 software was applied to identify factors influencing the socialization of secondary school students. The statistical population includes all experts in the field of public policymaking, as well as managers and specialists in the domain of public services. A total of 16 interviews were conducted until theoretical saturation was achieved. Each semi-structured interview lasted between 45 and 75 minutes and was conducted with experts in 2023. The interviews were analyzed in terms of basic, organizing, and overarching themes. To assess data normality and sample size adequacy, the Kolmogorov-Smirnov test was utilized, and structural equation modeling was employed to design the model. Based on the semi-structured interviews, three dimensions (macro-level monitoring [external], internal monitoring [organizational], and international monitoring), 11 components (organizing themes), and 53 indicators (overarching themes) were extracted. The results of the Kolmogorov-Smirnov test revealed that the sample size was adequate, and the data were not normally distributed. Furthermore, the final model's findings indicated the effect of macro-level (external) monitoring with a coefficient of 0.758, compared to internal (organizational) monitoring with a coefficient of 0.703 and international monitoring with a coefficient of 0.701. Finally, to validate the model, five factors—philosophy and objectives, theoretical foundations, operational principles, and implementation mechanisms—were used, demonstrating that the model is sufficiently valid.