A pathway to involve consumers for exchanging electronic waste: a deep learning integration of structural equation modelling and artificial neural network.

The pandemic of COVID-19 has disrupted every human life by putting the global activities at halt. In such a situation, people while staying at home tend to have an increased consumption which also leads to an increased level of waste generation. The case of electronic waste is also not different; however, it has severe repercussions while comparing it with other general household wastes.

Understanding consumer participation in managing ICT waste: Findings from two-staged Structural Equation Modeling-Artificial Neural Network approach.

For environmental management, the role of consumers is extremely important in the settings of reverse logistics. Though it is a manufacturer's extended responsibility to handle the waste however by becoming the supplier of the end of life products, consumers' participation needed to be encouraged and hence require proper attention. For the said purpose, the present study is conducted whereby crucial determinants of consumer reversing behavior were identified and analyzed by the help of a unique two-staged methodology of partial least square-structural equation modeling and artificial neural network.