The rural consumer adoption of sustainable energy: a PLS-SEM-ANN approach of conceptual model development and cross-country validation of Pakistan and Malaysia

Abstract

Sustainable and alternative energy sources of biofuel and solar power panel have been revolutionizing the lives and economy of many countries. However, these changes mainly occur in the urban areas and the rural population section has long been ignored by policy makers and government in the provision of energy. It is only recently that solar and biofuel are nally making in road to provide cheap and clean energy sources to rural population. As a result, literature on consumer behavior of rural population towards sustainable energy sources are still very scarce. The present research aims to ful l this gap by developing a conceptual model to investigate the adoption of solar power and biofuel energy resources in the cross-cultural setting of Malaysia and Pakistan. The data was collected from the rural areas of Pakistan and Malaysia. The two-stage data analysis method of partial least square structural equation modeling (PLS-SEM) and arti cial neural network (ANN) have been applied to satisfy both linear and nonlinear regression assumption respectively. The results show that consumer in rural areas of Pakistan are willing and possess intention to adopt both biofuel and solar power for commercial and domestic use. Additionally, the results con rm that Branding, Economic and Altruistic factors are important in yielding intention to use towards biofuel and solar power panel in Pakistan which are validated by the results obtained in Malaysia. Other factors such as climate change awareness, retailer services quality and ease of use are also important. The results offer wide ranging theoretical and managerial implications.

Determinants of Students’ Satisfaction with AI Tools in Education: A PLS-SEM-ANN Approach

“…Further, the present research has employed the two-stage methodology of partial least squares structural equation modeling (PLS-SEM) and artificial neural network (ANN) for analyzing the data [73]. The two-stage PLS-SEM-ANN is a complementary methodology of data analysis, in which PLS-SEM is used to test the hypothesis [74], and ANN is used to determine the relative importance of each factor in determining students' satisfaction with AI tools [75].…”

“…The present research has employed the two-stage methodology of PLS-SEM and ANN. The two-stage methodology of PLS-SEM-ANN analysis is being widely used by researchers from across the discipline [74]. It is argued that for predicting behavior, linear approaches, such as PLS-SEM, may have certain limitations as behavior generally is non-linear, and it is required that the prediction should be made using any non-linear analytical method.…”