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Table 5. Path Coefficients of Structural Model The Contribution of Cooperatives towards Women Empowerment finds statistically significant for social, economic, political, and environmental dimensions. All four paths explaining the Contribution of Cooperative societies towards Women Empowerment possess significant path coefficients, where p-value < 0.05 and t value > 1.96 (Hair et al., 2017). However, the three paths testing the moderating effects of age, education, and experience of women are statistically insignificant as they hold a p-value less than 0.05 (Age*Contribution of Cooperatives > Women Empowerment path: p-value = 0.495, t value = 0.682, Education*Contribution of Cooperatives > Women Empowerment path: p-value = 0.057, t value = 1.907, Experience *Contribution of Cooperatives > Women Empowerment path: p-value = 0.620, t value = 0.497) The significant paths should second-order model results that some paths are statistically substantial while some don’t (Table 5). Once the properties of the measurement model are assessed for reliability and validity, the structural mode’ is estimated to evaluate the effect size of t of structural model usually involves assess coefficient of determination, deciding on ef al., 2014). The significance of the path coe As such, path coefficients of those the p-va he exogenous variab ect size, and testing ue is less than 0.05 ( e on the endogenous variable. Assessment ing the path coefficients, assessing the collinearity, estimating the or predictive relevance of the model (Hair et ficient can be assessed using the P-value and t-value of the path. or 95% confidence level) and t-value greater than 1.96 (for a 2-tailed test) are considered significant (Hair et al., 2017). The bootstrapping process of the second-order model results that some paths are statistically substantial while some don’t (Table 5). than 1.96 (for a 2-tailed test) are considered significant (Hair et al., 2017). The bootstrapping process of the

Table 5 Path Coefficients of Structural Model The Contribution of Cooperatives towards Women Empowerment finds statistically significant for social, economic, political, and environmental dimensions. All four paths explaining the Contribution of Cooperative societies towards Women Empowerment possess significant path coefficients, where p-value < 0.05 and t value > 1.96 (Hair et al., 2017). However, the three paths testing the moderating effects of age, education, and experience of women are statistically insignificant as they hold a p-value less than 0.05 (Age*Contribution of Cooperatives > Women Empowerment path: p-value = 0.495, t value = 0.682, Education*Contribution of Cooperatives > Women Empowerment path: p-value = 0.057, t value = 1.907, Experience *Contribution of Cooperatives > Women Empowerment path: p-value = 0.620, t value = 0.497) The significant paths should second-order model results that some paths are statistically substantial while some don’t (Table 5). Once the properties of the measurement model are assessed for reliability and validity, the structural mode’ is estimated to evaluate the effect size of t of structural model usually involves assess coefficient of determination, deciding on ef al., 2014). The significance of the path coe As such, path coefficients of those the p-va he exogenous variab ect size, and testing ue is less than 0.05 ( e on the endogenous variable. Assessment ing the path coefficients, assessing the collinearity, estimating the or predictive relevance of the model (Hair et ficient can be assessed using the P-value and t-value of the path. or 95% confidence level) and t-value greater than 1.96 (for a 2-tailed test) are considered significant (Hair et al., 2017). The bootstrapping process of the second-order model results that some paths are statistically substantial while some don’t (Table 5). than 1.96 (for a 2-tailed test) are considered significant (Hair et al., 2017). The bootstrapping process of the

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The study followed the quantitative research approach. It aimed to assess the contribution of cooperatives ward rural women empowerment. Notably, it tested the social, economic, political, and environmental
The study followed the quantitative research approach. It aimed to assess the contribution of cooperatives ward rural women empowerment. Notably, it tested the social, economic, political, and environmental
Source: n = 2600, Survey (2021). Table 1. Demographic Profiles of Respondents (30%), and 52% of them have completed secondary education (Table 1). he respondents of the study comprised women of different ages and educational backgrounds.
Source: n = 2600, Survey (2021). Table 1. Demographic Profiles of Respondents (30%), and 52% of them have completed secondary education (Table 1). he respondents of the study comprised women of different ages and educational backgrounds.
Table 2. Cooperative Society Membership Attributes Assessment of Measurement Model credit facilities. However, most members' membership period is less than ten years (Table 2).
Table 2. Cooperative Society Membership Attributes Assessment of Measurement Model credit facilities. However, most members' membership period is less than ten years (Table 2).
Source: n = 2600, Survey (2021).
Source: n = 2600, Survey (2021).
Source: n = 2600, Survey (2021). Table 4. Properties of First-Order Measurement Model Next, the first-order constructs are examined for their discriminant validity. For an acceptable level « Jiscriminant validity, Fornell and Larcker (1981) recommended that the AVE of a latent variable should k jigher than the squared correlations between the latent variables and all other variables (Chin, 2010; Forne and Larcker, 1981). Table 4 demonstrates the correlation matrix with the square roots of AVEs on the diagon ine (in Bold), which indicates an acceptable level of discriminant validity according to the Fornell and Larck criterion (i.e., AVE criterion). Additionally, cross-loadings are also used as a discriminant validity measul where it is expected to load highest on the construct it is associated with (Henseler et al., 2015; Voorhees « al., 2016). Examination of loading of each indicator on its respective latent variable ensured that all are loade lighest on the latent variable for which they are assigned. Thus, all the constructs of the first-order mod vere confirmed to hold an acceptable level of discriminant validity. were confirmed to hold an acceptable level of discriminant validity.
Source: n = 2600, Survey (2021). Table 4. Properties of First-Order Measurement Model Next, the first-order constructs are examined for their discriminant validity. For an acceptable level « Jiscriminant validity, Fornell and Larcker (1981) recommended that the AVE of a latent variable should k jigher than the squared correlations between the latent variables and all other variables (Chin, 2010; Forne and Larcker, 1981). Table 4 demonstrates the correlation matrix with the square roots of AVEs on the diagon ine (in Bold), which indicates an acceptable level of discriminant validity according to the Fornell and Larck criterion (i.e., AVE criterion). Additionally, cross-loadings are also used as a discriminant validity measul where it is expected to load highest on the construct it is associated with (Henseler et al., 2015; Voorhees « al., 2016). Examination of loading of each indicator on its respective latent variable ensured that all are loade lighest on the latent variable for which they are assigned. Thus, all the constructs of the first-order mod vere confirmed to hold an acceptable level of discriminant validity. were confirmed to hold an acceptable level of discriminant validity.
Figure 2. Path Diagram-Structural Equation Modelling depicted in the path diagram of figure 2. The coefficient of determination (R?) is examined to weigh the explained variance. PLS algorithm of the structural model resulted in 0.579 of R* value. Based on the independent variables’ ability to account for 57.9% variance of the dependent variable, it is concluded that there is a substantial level of influence by the cooperative societies on rural women empowerment (Hair et al., 2017; Cohen, 1988). The above results are depicted in the path diagram of figure 2.
Figure 2. Path Diagram-Structural Equation Modelling depicted in the path diagram of figure 2. The coefficient of determination (R?) is examined to weigh the explained variance. PLS algorithm of the structural model resulted in 0.579 of R* value. Based on the independent variables’ ability to account for 57.9% variance of the dependent variable, it is concluded that there is a substantial level of influence by the cooperative societies on rural women empowerment (Hair et al., 2017; Cohen, 1988). The above results are depicted in the path diagram of figure 2.
Table 6. VIF Values of Structural Model
Table 6. VIF Values of Structural Model
Table 7. Effect Sizes (f?) of Structural Model Based on the decision criterion, all the independent variables possess a of the dependent variable (Cohen, 1988; Henseler et al., 2009). Finally, t its predictive relevance via blindfolding (q?). Stone-Geisser Predictive re larger than 0 (0 <) indicates that exogenous constructs have predictive re smaller effect size on evance suggests that (Stone, 1974; Geisser, 1974; Hair et al., 2017). The blindfolding of Construct Cross validated results in a 0.149 Q2 value well above the threshold value of 0. It implies has significant predictive relevance over women empowerment. he variance he structural model is assessed fot he Q2 value evance over endogenous construc’ Redundancy hat the contribution of cooperatives Based on the decision criterion, all the independent variables possess a smaller effect size on the variance The effect size (f#) of the PLS algorithm is the next measure of the structural model. The effect size is definec as "the increase in R? relative to the proportion of variance of the endogenous latent variable that remains unexplained" (Cohen, 1988; Henseler et al., 2009). Hair et al. (2017) and Cohen (1988) noted > 0.35 — f value is regarded as larger effect size, > 0.15 - f? value: medium effect size and > 0.02 f2 value equal to smaller effect size. Table 7 contains the effect size of corresponding latent constructs. smaller effect size. Table 7 contains the effect size of corresponding latent constructs.
Table 7. Effect Sizes (f?) of Structural Model Based on the decision criterion, all the independent variables possess a of the dependent variable (Cohen, 1988; Henseler et al., 2009). Finally, t its predictive relevance via blindfolding (q?). Stone-Geisser Predictive re larger than 0 (0 <) indicates that exogenous constructs have predictive re smaller effect size on evance suggests that (Stone, 1974; Geisser, 1974; Hair et al., 2017). The blindfolding of Construct Cross validated results in a 0.149 Q2 value well above the threshold value of 0. It implies has significant predictive relevance over women empowerment. he variance he structural model is assessed fot he Q2 value evance over endogenous construc’ Redundancy hat the contribution of cooperatives Based on the decision criterion, all the independent variables possess a smaller effect size on the variance The effect size (f#) of the PLS algorithm is the next measure of the structural model. The effect size is definec as "the increase in R? relative to the proportion of variance of the endogenous latent variable that remains unexplained" (Cohen, 1988; Henseler et al., 2009). Hair et al. (2017) and Cohen (1988) noted > 0.35 — f value is regarded as larger effect size, > 0.15 - f? value: medium effect size and > 0.02 f2 value equal to smaller effect size. Table 7 contains the effect size of corresponding latent constructs. smaller effect size. Table 7 contains the effect size of corresponding latent constructs.
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Women's EmpowermentRural sector
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