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<p>The rapid development of industrial practices has made it necessary to identify factors affecting solid waste source separation behaviors among industrial staff. To this aim, this cross-sectional study was conducted on 301 participants from 4 industrial factories in Ahvaz, Iran in 2022. To collect the data, a valid and reliable questionnaire which involved 12 constructs based on Extended Parallel Process Model was used. Data were analyzed using Statistical package for social sciences version 26 and intelligent partial least squares-structural equation modeling to identify the relationship between variables. The extensive statistical model showed two types of paths related to solid waste separation at source, namely adaptive and maladaptive process paths. Results revealed significant relationships for adaptive response paths, including <i>susceptibility→perceived severity</i> (β=0.809, t=26.934), <i>susceptibility→response efficacy</i> (β=0.537, t=6.921), <i>response efficacy→self-efficacy</i> (β=0.164, t=3.364), and <i>perceived severity→self-efficacy</i> (β=0.447, t=6.591). There were relationships between <i>response efficacy→attitude</i> (β=0.449, t=6.289), and <i>self-efficacy→intention</i> (β=0.523, t=10.183). Results for maladaptive paths revealed positive and significant relationships between <i>defensive avoidance→message minimization</i> (β=0.415, t=6.883) and <i>minimization→manipulation</i> (β=0.781, t=24.023). The average variance extracted was 0.704, and the composite reliability average was above the threshold level of 0.7. The present study revealed that the model extracted in this study has new dimensions compared to the initial Extended Parallel Process Model and could predict factors related to solid waste management among the staff of industrial factories. Intention was the most influential path in predicting waste management behavior. The proposed models could serve as a framework for environmental-friendly programs.</p>

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Factors affecting solid waste separation behaviors at source among industrial staff: Structural equation modeling

  • 期刊论文

  • 发表日期：

    2025年1月18日

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  • A. Shafiei-Alavijeh,
  • N. Kaydi,
  • S. Jorfi,
  • N. Jaafarzadeh Haghighifard,
  • A. Derakhshannejad,
  • M. Araban

摘要

The rapid development of industrial practices has made it necessary to identify factors affecting solid waste source separation behaviors among industrial staff. To this aim, this cross-sectional study was conducted on 301 participants from 4 industrial factories in Ahvaz, Iran in 2022. To collect the data, a valid and reliable questionnaire which involved 12 constructs based on Extended Parallel Process Model was used. Data were analyzed using Statistical package for social sciences version 26 and intelligent partial least squares-structural equation modeling to identify the relationship between variables. The extensive statistical model showed two types of paths related to solid waste separation at source, namely adaptive and maladaptive process paths. Results revealed significant relationships for adaptive response paths, including susceptibility→perceived severity (β=0.809, t=26.934), susceptibility→response efficacy (β=0.537, t=6.921), response efficacy→self-efficacy (β=0.164, t=3.364), and perceived severity→self-efficacy (β=0.447, t=6.591). There were relationships between response efficacy→attitude (β=0.449, t=6.289), and self-efficacy→intention (β=0.523, t=10.183). Results for maladaptive paths revealed positive and significant relationships between defensive avoidance→message minimization (β=0.415, t=6.883) and minimization→manipulation (β=0.781, t=24.023). The average variance extracted was 0.704, and the composite reliability average was above the threshold level of 0.7. The present study revealed that the model extracted in this study has new dimensions compared to the initial Extended Parallel Process Model and could predict factors related to solid waste management among the staff of industrial factories. Intention was the most influential path in predicting waste management behavior. The proposed models could serve as a framework for environmental-friendly programs.