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Research articles

Revealing the reality behind consumers’ participation in WEEE treatment schemes: a mixed method approach

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Pages 2436-2467 | Received 19 Dec 2020, Accepted 29 Jul 2021, Published online: 28 Sep 2021
 

Abstract

With the enormous population growth and the ever-increasing use of various electronic devices in modern life, the proper disposal of the “Waste Electrical and Electronic Equipment” (WEEE) has been of paramount importance. The pervasive use of social media by customers has made governments and businesses use these platforms as a rich source of data to extract intelligence on consumer opinions. However, some scholars doubt the sufficiency of social media data concerning the design of a comprehensive list containing all influential factors on consumer behavior toward the proper treatment of WEEE. Thus, a mixed method of quantitative (by analyzing about 2,500,000 tweets from Twitter) and qualitative approaches (i.e. a literature thematic analysis followed by a three-phased Delphi method) has been adopted. Due to consumers’ different behavior based on their local status, the experts have been split into two different panels from developed and developing countries. They have also been provided with the findings from the literature along with the results from Twitter data analysis. The findings have revealed that economic incentives play a pivotal role in both categories. People in developing countries usually have concerns regarding socio-economic and socio-political issues, while in developed nations higher levels of influential factors exist, including proximity, suitability and ease of access, and so forth. The truth is that in order to have a green and pollution-free world, the whole world, whether developed or developing, must take joint steps to create public welfare, peace of mind and world peace. Otherwise, unilateral actions of countries will not have their desired effectiveness.

Supplemental data

Supplemental data for this article can be accessed here.

Notes

1 Part of Speech Tagging

2 Natural Language Toolkit

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