Treasured trash? A consumer perspective on small Waste Electrical and Electronic Equipment (WEEE) divestment in Ireland

Abstract

Small Waste Electrical and Electronic Equipment (sWEEE) is a particularly problematic category of electronic waste. A growing body of research indicates that sWEEE tends to be either stockpiled or disposed of improperly (references). However, despite this, little attention has been given to the meanings people ascribe to their electronic and electrical possessions; meanings which continue to apply even when they are disused or broken. The purpose of this study was to generate insight into this area and to identify opportunities for intervention to increase sWEEE recycling. A quasi-ethnographic approach was used to investigate sWEEE disposal behaviour from the perspective of Irish consumers. The rationale for this approach was the need to reconcile the policy perspective on sWEEE with the subjective experiences and interpretations that drive people's behaviour. The findings reveal that from the time electronic and electrical devices enter consumers' lives until their disposal, they exist in fluid in-between states of meaning and have perceived value. Before divestment, sWEEE typically undergoes a four-stage journey: a) once electrical and electronic equipment (EEE) is no longer used, it tends to be either consciously stored or abandoned in the home (inactive EEE); b) a trigger prompts consumers to divest of the inactive EEE (critical moment); c) provoked to take action, consumers must decide precisely what to discard and how (transition from EEE to WEEE); d) consumers decide to recycle or not (divestment).The paper concludes by discussing the implications of these findings in terms of encouraging increased sWEEE recycling.

Design for Divestment in a Circular Economy: Stimulating Voluntary Return of Smartphones through Design

“…The many different factors influencing the divestment process make it impossible to establish direct causal relations between any one factor and the successful return of products. The factors went from user characteristics (e.g., gender [31] and competences [29]), to that of the product service system (e.g., physical condition of the product [32], or perceived distance to the collection point [33]), the consumption context (e.g., moving [34] or a replacement opportunity [35]) and option-related factors (e.g., lack of awareness [36]). These factors can therefore not be used as predictive triggers for the design of interventions that will actually make users return their products.…”

Decision Support Model for Evaluating Alternative Waste Electrical and Electronic Equipment Management Schemes—A Case Study

“…Indicatively, Kumar and Dixit [37] identified 7 primary barriers towards WEEE management in India and applied the decision-making trial and evaluation laboratory (commonly known as DEMATEL) method to prioritize these barriers and further unveil any underlining interdependencies. In this vein, Casey et al [38] applied a quasi-ethnographic approach to examine the behavior of Irish consumers towards the disposal of small WEEE. Furthermore, Ismail and Hanafiah [39] reviewed a significant number of studies considering WEEE management from a life cycle assessment viewpoint.…”

Unified Fuzzy Divergence Measures with Multi-Criteria Decision Making Problems for Sustainable Planning of an E-Waste Recycling Job Selection

“…There has been an extraordinary rise in consuming electronic equipment, especially computers and mobile phones. This tremendous increase in consumption has led to the accumulation of waste electrical and electrical equipment (WEEE) [49][50][51], which is normally discussed under the title of E-waste [52]. Across the globe, electronic equipment usage has become an indispensable part of daily life.…”

“…The decision experts furnish all the feasible evaluations regarding the alternative Y i with respect to criterion Z j , and construct aggregated decision matrix, which is given in Table 5 associated with Table 1 and Equations (43) and (47). According to their knowledge and experience regarding the criterion set, partial information of the weights is given by Step 1: Fuzzy IS and A-IS are calculated by using (49) and (50) are as follows:…”