Investigating the factors that affect the time of maximum rejection rate of e-waste using survival analysis


This study aims at investigating the factors which influence positively or negatively electronic waste (e-waste) rejection rates. E-waste quantities have been calculated based on historical sales data worldwide and lifespan distribution. The methodology, which is adopted in this paper in order to estimate the effect that economic, cultural, and demographic factors have upon the time at which maximum e-waste rejection is achieved, is a Weibull parametric accelerated failure time model. Considering the event at which the maximum rejection of e-waste takes place as the dependent variable, it is assumed that it is a function of economic (GDP, GINI index, Internet users, exports/imports and prices), demographic (dependency ratio), and cultural covariates (literacy, masculinity, uncertainty avoidance). The variables are fed to the model after transformation into two major constructs derived from Factor Analysis: the first construct is Wealth (exports, imports, and GDP) and the second is Economic Disparity (size of households, literacy, Internet users, and GINI). The results demonstrate that the time of maximum e-waste rejection rate is prolonged by economic disparity and cultural variables (uncertainty avoidance), while wealth causes a shorter time of rejection rate. The proposed methodology is of great value, as its application could provide useful information in order to develop policies for optimal management of e-waste quantities.

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Divisions: College of Business and Social Sciences > Aston Business School
College of Business and Social Sciences > Aston Business School > Aston India Foundation for Applied Research
College of Business and Social Sciences > Aston Business School > Operations & Information Management
Additional Information: © 2017, Elsevier. Licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International
Uncontrolled Keywords: WEEE,e-waste,e-waste rejection rate,survival analysis,Weibull Parametric Accelerated Failure Time model,Computer Science(all),Engineering(all)
Publication ISSN: 1879-0550
Last Modified: 17 Apr 2024 07:14
Date Deposited: 15 Mar 2017 17:00
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Related URLs: http://www.scop ... tnerID=8YFLogxK (Scopus URL)
PURE Output Type: Article
Published Date: 2017-06-01
Published Online Date: 2017-03-14
Accepted Date: 2017-03-11
Submitted Date: 2016-07-21
Authors: Petridis, Konstantinos
Petridis, Nikolaos
Stiakakis, Emmanouil
Dey, Prasanta (ORCID Profile 0000-0002-9984-5374)

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