The Underlying Drivers of Underprivileged Households' Intention and Behavior towards Community Forestry Management: A Study Using Structural Equation Modelling and Artificial Neural Network Approach

Abstract

In acknowledging the significant role of forestry on the environmental, social, and economic sustainability of local communities, this study focused on examining how different factors affect the intentional behavior towards community forestry among the poor households in Malaysia. Employing theory of planned behavior (TPB) in an expanded model, this study collected data from 420 underprivileged households from 10 states in Malaysia using a survey questionnaire. Final analysis is performed using two methods, one being the well-established, conventional way of partial least square-structural equation modelling (PLS-SEM); the other being a frontier technology of computing using artificial neural network (ANN), which is generated through a deep learning algorithm to achieve the maximum possible accuracy for each of the five scenarios aforementioned. The study found that perceived benefits (PB) and eco-literacy (EL) have a significant positive effect on the attitude towards environment (ATE) while normative belief (NB) and motivation (MO) have a significant positive effect on subjective norms (SUN). Perceived control (PC) has a significant positive effect on perceived behavioral control (PBC). ATE, SUN, and PBC have a significant positive effect on the intention towards community forestry (ITCF), whereas the ITCF has a significant positive effect on community forestry adoption behavior (CFAB). When formulating and enforcing carbon reduction and poverty elevating programs through community forestry, the Malaysian government should consider the perceptions of poor families and the prerogative from their special reference groups to enhance the perceived ability of the vulnerable groups for positive and effective pro-environmental behavior that can lead to sustainable forestry management.