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

Konstantinos Petridis*, Nikolaos Petridis, Emmanouil Stiakakis, Prasanta Dey

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review


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.
Original languageEnglish
Pages (from-to)15-26
Number of pages12
JournalComputers and Industrial Engineering
Early online date14 Mar 2017
Publication statusPublished - 1 Jun 2017

Bibliographical note

© 2017, Elsevier. Licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International


  • WEEE
  • e-waste
  • e-waste rejection rate
  • survival analysis
  • Weibull Parametric Accelerated Failure Time model


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