Financial Statement Accountability, Transparency, and Managerial Competence in Zakat Institutions
-
Budi Al Amin
*
Universitas Bina Sarana Informatika, Surakarta,
Indonesia
- Nani Irma Susanti Universitas Dharma AUB, Surakarta, Indonesia
- Sundari Sundari Universitas Duta Bangsa, Surakarta, Indonesia (*) Corresponding Author
Abstract
Ensuring accountability within zakat institutions is vital for managing public funds with professionalism and transparency, thereby safeguarding public trust and preventing misappropriation. This research investigates manager competence effects and transparency on financial reports accountability at Lazismu in Central Java. Managerial competence is defined by knowledge, technical skills, and professional ethics, while transparency is characterized by the institution's commitment to information disclosure. To achieve the research objectives, a quantitative approach was applied through primary data collection using a questionnaire instrument. A total of 100 respondents who serve as managers at Lazismu in the Central Java region participated in this survey. The valid data were then processed using Structural Equation Modeling (SEM) based on Partial Least Squares (PLS) data analysis techniques. In-depth analysis of the measurement and structural models was carried out with the assistance of SmartPLS 4.0 software. The findings show that both zakat manager competence and transparency significantly impact financial statement accountability. These results underscore that enhanced amil knowledge and greater transparency lead to improved financial reporting accountability. These research findings serve to enhance internal regulatory frameworks, thereby facilitating the transparent and professional management of zakat funds by these institutions. In conclusion, the structural model generates an R-square value of 0.963, which implies that managerial competence and transparency jointly explain 96.3% of the variance in financial statement accountability. Moreover, the positive Q-square value substantiates the model's strong predictive relevance.
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References
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