Validity is the extent to which a researcher can demonstrate the precision of their study data and their proficiency. Valid data aligns with the researcher's data and real occurrences related to the study subject. Product Moment Correlation is used to juxtapose the computed correlation coefficient of each item against the tabulated value of r at a 5% significance level during validity assessments. The validity of a questionnaire is affirmed when the calculated r value surpasses the tabulated r value. In comparison to the table, a questionnaire that has a lower r value is considered invalid.
| Variable | Item | r count | r table | Sig | Description |
|---|---|---|---|---|---|
| Implementation of PSAK 112 (X) | X.1 | 0,776 | 0,296 | 0,00 | Valid |
| X.2 | 0,849 | 0,296 | 0,00 | Valid | |
| X.3 | 0,875 | 0,296 | 0,00 | Valid | |
| X.4 | 0,774 | 0,296 | 0,00 | Valid | |
| X.5 | 0,700 | 0,296 | 0,00 | Valid | |
| X.6 | 0,737 | 0,296 | 0,00 | Valid | |
| X.7 | 0,356 | 0,296 | 0,00 | Valid | |
| X.8 | 0,933 | 0,296 | 0,00 | Valid | |
| X.9 | 0,706 | 0,296 | 0,00 | Valid | |
| X.10 | 0,933 | 0,296 | 0,00 | Valid | |
| X.11 | 0,563 | 0,296 | 0,00 | Valid | |
| Productive Waqf (Y) | Y.1 | 0,583 | 0,296 | 0,00 | Valid |
| Y.2 | 0,774 | 0,296 | 0,00 | Valid | |
| Y.3 | 0,780 | 0,296 | 0,00 | Valid | |
| Y.4 | 0,737 | 0,296 | 0,00 | Valid | |
| Y.5 | 0,456 | 0,296 | 0,00 | Valid | |
| Y.6 | 0,933 | 0,296 | 0,00 | Valid |
Sumber: Data di olah menggunakan SPSS 25, 2025
Table 4 indicates that the r value of the statement indicator is below 0.05. The anticipated r value surpasses 0.296 in the r table, hence corroborating each signal item. Consequently, the index is capable of assessing the study parameters.
Reliability refers to the consistency of test results when the same instrument is used to assess identical symptoms. The Cronbach's Alpha method deems a research instrument dependable if its reliability coefficient (r11) exceeds 0.6.
| Variable | Cronbach's Alpha | Description |
|---|---|---|
| Implementation of PSAK 112 | 0,720 | Reliable |
| Productive Waqf | 0,810 | Reliable |
All study variables had Cronbach's alpha values over 0.6, indicating reliance, as seen in Table 4.
Various statistical tests, ranging from fundamental to advanced, were chosen based on the data and study aims.
A frequency distribution graph of the present score is the most expensive method to confirm normalcy. Data visualizations unequivocally assist in confirming normalcy. In the presence of a non-normal distribution and substantial data volumes, conclusions may be inaccurate. The researcher assessed each variable for normalcy using two methods. The Kolmogorov-Smirnov test was first used. Probability tests may be used to verify regularity and prevent plotting errors, as shown below:
The significance value of 0.153 for the use of PSAK 112 was obtained by the Kolmogorov-Smirnov normality test, as shown in Table 5. If the significance value exceeds 0.1, the results of a normality test are considered to be normally distributed.
The coefficient (r) is utilized in a multicollinearity test to determine the precise association between two or more independent variables (X) in multiple regression analysis. Employ the Tolerance and Variance Inflation Factor (VIF) parameters to detect multicollinearity. When the tolerance value exceeds 0.10, multicollinearity is deemed to be nonexistent. Multicollinearity occurs when the threshold value is less than 10. Multicollinearity is indicated by a VIF value greater than 10. Multicollinearity is absent when the Variance Inflation Factor (VIF) is less than 10.
| Variable | VIF | Tolerance | Multicollinearity |
|---|---|---|---|
| Implementation of PSAK 112 | 1.217 | 0,821 | Not found |
The results of the test are presented in Table 7.
VIF requirement: VIF is less than 10. Conclusion: Multicollinearity does not occur.
X: Conclusion: Multicollinearity does not occur, as 1.217 is less than 10.
Conclusion: Multicollinearity does not occur, as the tolerance requirement is greater than 0.10.
X: Conclusion: Multicollinearity does not occur, as 0.821 is greater than 0.10.
This heteroscedasticity test evaluates the extent to which all data exhibit uniform absolute residual variance. Transgressing the homoscedasticity assumption makes the results inconsistent for both small and large groups, resulting in erroneous coefficient values. This research examines heteroscedasticity using a scatterplot analysis.
The following criteria indicate the presence or absence of heteroscedasticity in the scatterplot analysis:
1)The data points are scattered throughout the area where the number 0 should be.
2)Only the points above or below the line indicate clustering.
3)The data distribution should not exhibit a visible undulating pattern that widens, narrows, and then widens again.
4)The data distribution lacks a discernible pattern. See Figure 1:
Source: Data processed using SPSS 25
The graph displays data points that are positioned above or below zero. Both generic and particular datasets are valuable for analysis.
The Glejser test is used to assess heteroscedasticity. Heteroscedasticity is absent in a regression model if all explanatory variables have a significance value over 0.1, as determined by the Glejser test.
Source: Data processed using SPSS 25
The implementation of PSAK 112 resulted in a t-value of 4.822, a variable value of 0.594, and a significance level that exceeded 0.1, as indicated by the Glejser test. The findings reveal that the regression equation model exhibits homoscedasticity. The t-value exceeds 0.1, making it statistically significant.
Assessing this hypothesis test entails formulating null and alternative hypotheses, doing statistical analyses, computing the hypothesis, establishing the significance level, and deriving conclusions.
1)Forming a hypothesis in a descriptive sentence
H0: There is no partial significant effect of PSAK 112 Implementation on Productive Waqf.
Ha: There is a partial significant effect of PSAK 112 Implementation on Productive Waqf.
Determining the significance level (α)
2)Test Rules
If the calculated t-value < t-value, then Ha is rejected, while H0 is accepted.
If the calculated t-value > t-value, then Ha is accepted, while H0 is rejected.
3)Comparing the calculated t-value and t-value
By juxtaposing the t-table with the t-count, we may ascertain if the test criterion permits the acceptance of H0.
4)Making a Decision
Select either the null hypothesis (H0) or the alternative hypothesis (Ha) by making a decision.
Source: Data processed using SPSS 25
The regression equation may be derived from the table above:
Each instance of the independent variable is assessed in relation to the dependent variable in the partial test. The t-test results are presented in the table above, which indicates a significant value greater than 0.1. Consequently, the impact of variable X on the dependent variable Y is evaluated. The t-table value is 0.68276 when N = 30 samples and α = 0.05.
The t-value is 9.792 at a significance level of 5%, as indicated by PSAK 112 (X). The t-test result of 9.792 surpasses the t-table value of 0.68276, and the significance threshold of 0.00 is less than 0.05. As a result, the alternative hypothesis (Ha) is accepted, suggesting that PSAK 112 has a substantial impact on Productive Waqf (Y). The efficacy of Waqf in Medan City is affected by the use of several PSAK 112 standards.
The regression model's ability to account for the variance in the dependent variable is indicated by the coefficient of determination. A low R2 value suggests that the independent variable is ineffectual in clarifying the dependent variable. A dependent measurement has an R2 value approximately equal to 1. This culminates in Table 9:
Source: Data processed using SPSS 25
The use of PSAK 112 was indicated by the coefficient of determination analysis, which resulted in an adjusted R-square of 0.904. The independent variable was responsible for 90% of the effect. The remaining variables were explained by additional variables.
The relationship between the execution of PSAK 112 and Productive Waqf was determined through the use of multiple linear regression. This research used loglinear regression analysis. Table 10 presents the data:
Source: Data processed using SPSS 25
A fundamental linear regression equation is generated by the table above:
Y = a + bX + e
Y = 6.412+ 0.329X + e
This equation may be further clarified by the subsequent information:
1.The productive Waqf will rise by 6.412 if the PSAK 112 implementation variable remains unchanged.
2.The regression coefficient of PSAK 112 is 0.329, which suggests that a 1% increase in implementation leads to a 0.329% increase in the growth of Productive Waqf.
The Impact of PSAK 112 Implementation on the Development of Productive Waqf in Medan City
The findings of this study indicate that the implementation of PSAK 112 has a significant and positive impact on the development of productive waqf in Medan City. This is evidenced by a t-value of 9.792, significantly exceeding the t-table value of 0.68276, and a significance value of 0.000 (p < 0.05), indicating strong statistical support for the alternative hypothesis (Ha). Furthermore, the high Adjusted R² value of 0.904 implies that 90.4% of the variance in productive waqf growth can be explained by the implementation of PSAK 112.
Although the R² value indicates strong explanatory power, this high value deserves critical examination. It is possible that this value partially captures latent or unobserved variables not formally included in the model. For example, organizational culture, leadership style, type of waqf assets, and the internal governance mechanisms of nazhir institutions may serve as mediating or moderating variables. The absence of these variables in the current model may lead to an overestimation of the direct impact of PSAK 112. Future research could incorporate structural equation modeling (SEM) to test the mediating effect and better isolate the specific pathways through which PSAK 112 influences productive waqf.
This research enhances both Accountability and Stakeholder Theory. Stakeholder Theory posits that organizations, especially nazhir institutions, are obligated to maintain transparency and honesty with their stakeholders. PSAK 112 implements a waqf accounting framework to streamline waqf fund reporting. This enhances stakeholder trust in waqf asset management [23].
Teori Akuntabilitas mendukung hasil ini karena PSAK 112 menguraikan tugas-tugas pengelolaan aset wakaf, The accountability theory corroborates these results, since PSAK 112 delineates the Nazir's responsibilities for waqf asset management, assessment, presentation, and systematic reporting. According to Gray (1996), this enhances the accountability of waqf organizations in reporting their financial and asset operations. Gray defines accountability as a moral and professional obligation to appropriately use community resources [24].
This study further substantiates other studies, like those by [16] and [7], which demonstrated that PSAK 112 enhances waqf financial reporting and the legitimacy of the Nazir. [9] in Aceh also discovered that PSAK 112 would enhance the transparency and professionalism of waqf organizations. This research is distinctive since it investigates the concrete advantages of PSAK 112 on significant waqf development, especially in urban regions such as Medan, which face more socioeconomic constraints.
Although the R² value indicates strong explanatory power, this high value deserves critical examination. It is possible that this figure partially captures latent or unobserved variables not formally included in the model. For example, organizational culture, leadership style, type of waqf assets, and the internal governance mechanisms of nazhir institutions could serve as mediating or moderating variables. The absence of these variables in the current model may lead to an overestimation of the direct impact of PSAK 112. Future research could incorporate structural equation modeling (SEM) to test the mediating effect and better isolate the specific pathways through which PSAK 112 influences productive waqf.
While these findings align with previous research in Aceh and Batam, Medan presents a different context that may explain the greater impact. Medan is characterized by a relatively formal waqf management culture, with several nazhir institutions operating under government structures or large Islamic organizations. The bureaucratic discipline and compliance orientation of these organizations can enhance the fidelity of PSAK 112 implementation. Furthermore, Medan's socio-religious networks are closely linked to local economic development initiatives, which can enhance community responsiveness to waqf reform. This distinguishes Medan from, for example, Aceh, where waqf is deeply integrated into the traditional kinship-based system, or Batam, where corporate waqf is more prominent.
This study offers several practical recommendations:
1.For the Indonesian Waqf Board (BWI): Intensify capacity-building programs and ensure that smaller nazhir entities have access to technical support in implementing PSAK 112, particularly through regional waqf training centers.
2.For the Sharia Financial Services Authority (OJK Syariah): Consider issuing complementary compliance guidelines that align PSAK 112 with broader Islamic financial regulations, ensuring coherence between waqf and the Islamic banking ecosystem.
3.For Independent Auditors and Sharia Audit Institutions: Introducing a certified waqf audit mechanism to evaluate not only financial compliance but also sharia and social impact indicators. This can strengthen public trust and encourage greater waqf participation.
This study is limited by its relatively small and localized sample of 30 nazhirs in Medan, which may not fully capture the diversity of waqf management practices across Indonesia. Furthermore, the cross-sectional study design limits causal inference and ignores temporal variation in the implementation of PSAK 112. For future research, we recommend:
1.Using a longitudinal approach to observe changes in productive waqf over time post-implementation of PSAK 112.
2.Using a mixed-methods design, combining surveys with qualitative interviews to explore internal decision-making processes, constraints, and best practices among nazhirs.
3.Expanding the scope of the research by including comparative studies across provinces to better understand regional differences and refine the national policy framework.