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

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 exist‐ing e-waste crisis.To deal with this thorny problem,it is of great interest to grasp consumers’disposal and re‐cycling 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 re‐sults 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 evalua‐tion 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 surround‐ing consumer behavioral intention-related topics focusing on e-waste.Furthermore,the adoption of data min‐ing algorithms for prediction is another insight of this study,which may shed the light on data mining applica‐tions in such environmental studies in the future.

Developing an Extended Theory of Planned Behavior Model for Small E-Waste Recycling:An Analysis of Consumer Behavior Determinants

WEEE(Waste Electrical and Electronic Equipment),also called e-waste,has become the world’s fastest-growing waste stream but only has a 17.4%recycling rate according to the Global E-waste Monitor 2020.The collection rate for sWEEE(Small WEEE),e.g.,used mobile phones,is even lower than 10%due to stockpiling at home and improper disposal via the general waste or private informal channels.The purpose of this paper was to(a)review how the TPB(Theory of Planned Behavior)was applied in the literature related to consumer behavioral intention for WEEE recycling and determine the most significant TPB predictors by comparing their standardized influence coefficients across various studies,and(b)identify additional behavior determinants specific to sWEEE recycling and thus develop a new TPB-sWEEE theoretical framework by integrating the TPB model with these new determinants in order to improve the predictability of consumers’sWEEE recycling behavioral intention.This paper reviewed over 30 peer-reviewed studies from Scopus and high impact factor journals and led to the following three key findings:(a)More variables of recycling behavioral intention determinants such as the size of WEEE need to be considered;(b)Eight sWEEE-specific behavioral determinants have been identified and included in the newly developed TPB-sWEEE model,including consumers’Sentimental Attachment,Data Security Concern,Economic Incentives,and How-to Knowledge;(c)For some sWEEE-specific determinants,their extent of influence differs across various regions and countries.This paper contributes to the sWEEE-specific consumer recycling behavior theoretical framework to fill the existing gap in the literature and provides recommendations through the new model for research-based behavioral intervention strategies and policy designs globally.