**3. Results and Discussion**

#### *3.1. Respondent Characteristics*

This study uses respondents who are owners of agricultural land in Pandaan District and still maintain their land. Respondents in this study joined farmer groups in each village in Pandaan District. This study involved 500 respondents with different characteristics. Concerning age, most people are of productive age; there were 81 respondents (16.2%) aged 25–40 years, 126 respondents (25.2%) aged 41–45 years, and 117 respondents (48.6%) aged 46–50 years. Furthermore, concerning the livelihoods of the respondents, 341 respondents (68.2%) indicated their main livelihood as food agriculture land farmers. Another 159 respondents (31.8%) were food agricultural landowners with side jobs such as village officials, factory employees, private employees, civil servants, entrepreneurs, laborers, traders, breeders, and Linmas. As for the landowner respondents, most of them had an

area of 0.5 ha of agricultural land, specifically 182 respondents (36.4%). Next, based on the income of the respondents in this study, most of them had a relatively high income according to the Pasuruan Regency Minimum Wage (UMK) in 2020 of IDR 4,190,133, because, apart from getting a basic salary, the respondents also had additional wages from side jobs. Then, based on education, it can be seen that 289 respondents had their most recent education in high school.

#### *3.2. Intention to Change SFAL Based on Social Capital*

The intention to change land is a variable that aims to find out how the landowner wants to maintain or change their land. SFAL land change decisions in Pandaan District will be linked to social capital. The results of the intention to change SFAL of the landowners in Pandaan District can be seen in Figure 2.

**Figure 2.** SFAL owners' decisions in Pandaan District to change their land.

Figure 2 illustrates that from the total number of SFAL landowners in Pandaan District, 76% answered that they did not intend to change the land, while 24% said they intended to change the land (Figure 2). These results are linked to social capital in Pandaan District. The distribution of the percentage of respondents in land change decisions in each village in Pandaan District is shown in Figure 3.

**Figure 3.** SFAL owners' intention in each village in Pandaan District to change their land.

Figure 3 illustrates that 100% of landowners in Wedoro Village, Sebani Village, Karangjati Village, and Durensewu Village have no intention to change their land, while in Banjarkejen Village, Kutorejo Village, Plintahan Village, Sumbergedang Village, Tawangrejo Village, and Pertungasri Village, more than 50% of respondents answered that they wanted to change their land (Figure 3). After knowing the land change decisions in Pandaan District, the next step was to find their relationship to social capital using structural equation modeling (SEM) analysis applied in PLS-SEM software.

#### *3.3. Social Capital of SFAL Landowners' Intentions*

SEM analysis requires several assumptions to be met, including sample size, normality, outliers, and multicollinearity. According to the assessment of the normality output table, most univariate and multivariate normality tests usually are distributed because they fall within the 2.58 range. With the condition that *p* is less than 0.05, the evaluation of outliers also meets the requirements. Furthermore, the determinant value of the covariance matrix is 0.000 for the value of multicollinearity.

The assumptions of SEM analysis for this study were met based on some of the explanations. Furthermore, the model test and structural model measurements were performed. The measurement of the model test with the resulting model has not yet met the good fit criteria, so changes must be made so that the model produces good fit results (Table 2). The model of SFAL owners who want to change land is presented in Figure 4. There are some indicators that do not meet the requirements or are invalid, so these indicators were discarded. The discarded indicators on the trust variable include K2. Meanwhile, on the social network variable, the discarded indicators include J1, J8, J9, J10, and J11. The discarded indicators for social norms are N3 and N4.

**Figure 4.** The first model of CFA (**a**) and the second-phase CFA model (**b**) of SFAL landowners intentions.

We made modifications in Figure 4, showing that the modification results bring the model to a good fit. Then, regression weights results must be analyzed to examine the influence relationship between variables. Testing the relationship between latent variables is based on the critical ratio (CR) value and the significance probability value. The critical ratio (CR) criteria are 1.96 and a *p*-value of 0.05. The regression weights produced the following results (Table 4):

• The influence of norms on trust: Testing of the norm variable's relationships to trust showed a critical ratio (CR) of 1.210 (≥1.96), with an estimate value of 0.135 and a probability value of 0.222 (*p* < 0.05). This proves that there is an insignificant positive relationship between norms and trust. Trust is the basis for creating social relationships and networks. In a society that has a high level of trust, there tends to be positive social rules and interpersonal relationships that support cooperation.



**Table 4.** Comparison of cut-off values between first and second CFA model.

The relationships between latent variables of social capital in the forms of trust, network, and norms have positive and negative effects on each other, and all are not significantly related to each other. From the relationships of the three variables, the significant effect is only between the trust variable and networks. It is therefore interpreted that the trust of SFAL owners who wish to change their land has a strong influence on forming a network, while the prevailing norms have a negative and insignificant influence on the network. So, it is obvious that the relationship between the three social capital variables is not strong, which makes it easy for residents who own SFAL to want to change their land. For this reason, it can be concluded that the weaker/lower the relationship between variables forming social capital, the higher the intention of SFAL landowners to make land-use changes. The standardized regression weights value can see the value of the influence of the relationship between trust and networks of 0.484, while the value of the influence of norms on trust is 0.146.

#### Social Capital of SFAL Owners Who Are Not Willing to Sell Their Land

As mentioned, according to the assessment of the normality output table, most univariate and multivariate normality tests usually are distributed because they fall within the 2.58 range. With the condition that *p* is less than 0.05, the evaluation of outliers also meets the requirements. Furthermore, the determinant value of the covariance matrix is 0.000 for the value of multicollinearity. Based on what was explained, the assumptions of the SEM analysis for this study were met (Figure 5).

Furthermore, the model test and structural model measurements were performed. However, the measurement of the model test with the resulting model has not yet met the good fit criteria, so changes must be made so that the model produces good fit results. For example, the population model of SFAL owners who want to change their land is presented in Figure 3. Based on the results of SEM, the discarded indicators on the trust variable include K4, K5, and K8. Meanwhile, the discarded indicators on the social network variable include J1, J4, J6, J7, J8, and J9. The discarded indicators for social norms are N3 and N4. It can be said that five indicators in the "trust" and "social network" variables and two indicators in the "norm" variable are indicators that can measure the social capital variables of SFAL owners who are not willing to sell their land.

**Figure 5.** SEM result for social capital of SFAL owners who do not want to change their land use.

The regression weights results must be examined to examine the influence of the relationships between variables. Then, the critical ratio (CR) and significance probability values test the relationship between latent variables (Table 5). Following the modifications in Table 6, it is obvious that the modification results bring the model to a good fit.

**Table 5.** Value of R square Model.


**Table 6.** Value of Path Coefficients on Relationship Latent Variables.



• The influence of trust on networks: The influence of the trust variable on networks can be seen with a CR value of 6.654 with a *p*-value of 0.000 (very small and below 0.05). This proves that there is a significant positive relationship between trust and networks. This way, the stronger the trust, the stronger the community network of SFAL owners in Pandaan District will be.

The relationships between latent social capital variables in the form of trust, network, and norms significantly influence each other. The norm and network variables significantly affect the trust variable (Figure 6) (Table 6).

**Figure 6.** Result of Bootstrapping Social capital of SFAL owners.
