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

作     者：Hong Thi Thu Nguyen Hong Thi Thu Nguyen

作者机构：University of Science and EducationThe University of DanangLe Duan RoadDanang 550000Vietnam 

出 版 物：《Chinese Journal of Population,Resources and Environment》 (中国人口·资源与环境（英文版）)

年 卷 期：2023年第21卷第1期

页      面：26-36页

学科分类：02[经济学] 0201[经济学-理论经济学] 020106[经济学-人口、资源与环境经济学] 

基　　金：SPSS International Business Machines Corporation, IBM 

主　　题：CHAID Discriminant analysis E-waste recycling intention Neural network Norm activation theory QUEST Sustainable development goals Valence theory 

摘      要：Electrical and electronic waste(e-waste)is a growing challenge,matching the widespread boom in the use of information and communication *** 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 *** deal with this thorny problem,it is of great interest to grasp consumers’disposal and re‐cycling behavioral ***,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 *** 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 *** re‐sults revealed that the social support value is by far the most critical predictor,followed by the utilitarian value,task difficulty,and monetary *** is also noteworthy that the awareness of consequences,education background,the ascription of responsibility,and age were also ranked as critical affecting *** lowest influential predictors found in this study were income and *** addition,a comparison was made in terms of the classification performance of the four utilized data mining *** 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 *** 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 ***,the adoption of data min‐ing algorithms for prediction is another insight of this study,which may shed the light o