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. The application of reverse logistics by the manufacturers though serve the purpose but its success is highly dependent on the participation of the consumers. Hence, the present study is an attempt to gauge the level of participation of the consumers in the reverse exchange programs. Because of the predictability limitations of the typical Structural-Equation-Modelling models, the present study employs the deep learning of the dual-staged partial least squares-structural equation modelling artificial neural network approach. The findings of the study confirms the individual's attitude as the most significant determinant of the intention to exchange, followed by level of awareness and norms, whereas perceived behavior control was found to be least important though significant. Based on these findings, the manufacturers have been recommended to improve the consumers' involvement in reverse exchange programs, whereas government institutions are also recommended to encourage public-private partnerships in channelizing the product returns.