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Dual-Method PLS-SEM and Artificial Neural Networks (ANN) for Womens Entrepreneurial Empowerment Role in Economic System |
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PP: 1235-1252 |
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doi:10.18576/jsap/150505
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Author(s) |
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Muhammad Farman,
Zubaria Parvez,
Aseel Smerat,
Abdul Sattar Ghaffari,
Zöhre Serttaş,
Mohamad Hafez,
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Abstract |
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| In this paper, we developed a model for the women entrepreneurs are advanced by social and infrastructural factors
to determine their economic autonomy. The study employed a two-faceted method of analysis through the application of Partial Least Squares Structural Equation Modeling (PLS-SEM) to test hypotheses and Artificial Neural Networks (ANN) to validate the predictions. The mediation analysis proved that social capital intermediates the links between both market linkage and sociocultural interventions and access to finance which reveal that it plays a vital mediating role. Cronbach alpha values of 0.793 − 0.814 and the AVE values of more than 0.70 were used to ensure reliability and validity. In the case of ANN analysis, predictive accuracy, and model generalization were useful in validating the analysis, with R values of 0.842 to 0.862 in training, 0.789 to 0.794 in validation, and 0.831 to 0.840 in testing, and Mean Squared Error values of between 0.43and0.70 were used to confirm stabilized learning. The paper concludes that establishing trust-based community networks, market networks, and sociocultural support systems could be very important in augmenting financial autonomy and sustainable empowerment in rural women. It is based on the Social Capital Theory and it examines the role played by sociocultural intervention and market linkage infrastructure in the accessibility of finance by women mediated by social capital. Collectively, these results indicate that although PLS-SEM provides important linear causal associations, ANN
supplements the former, because it can represent nonlinear interdependencies and enhance predictive strength. |
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