Integrating the valence theory and the norm activation theory to understand consumers’ e-waste recycling intention

Abstract

Electrical and electronic waste (e-waste) is a growing challenge, matching the widespread boom in the use of information and communication technology. Opposite to an alarming increasing amount of e-waste, a low rate of consumer engagement in ensuring the proper disposal of such materials intensifies the pressure on the existing e-waste crisis. To deal with this thorny problem, it is of great interest to grasp consumers’ disposal and recycling behavioral intentions. Therefore, this study attempts to understand complementary perspectives around consumers’ e-waste recycling intention based on the integration of the valence theory and the norm activation theory. Four data mining models using classification and prediction-based algorithms, namely Chi-squared automatic interaction detector (CHAID), Neural network, Discriminant analysis, and Quick, unbiased, efficient statistical tree (QUEST), were employed to analyze a set of the 398 data collected in Vietnam. The results revealed that the social support value is by far the most critical predictor, followed by the utilitarian value, task difficulty, and monetary risk. It is also noteworthy that the awareness of consequences, education background, the ascription of responsibility, and age were also ranked as critical affecting factors. The lowest influential predictors found in this study were income and gender. In addition, a comparison was made in terms of the classification performance of the four utilized data mining techniques. Based on several evaluation measurements (confusion matrix, accuracy, precision, recall, specificity, F-measure, ROC curve, and AUC), the aggregated results suggested that CHAID and Neural network performed the best. The findings of this research are expected to assist policymakers and future researchers in updating all information surrounding consumer behavioral intention-related topics focusing on e-waste. Furthermore, the adoption of data mining algorithms for prediction is another insight of this study, which may shed the light on data mining applications in such environmental studies in the future.