Factors driving the implementation of reverse logistics: A quantified model for the construction industry. (September 2018)

• Record Type: Journal Article
• Authors: Chileshe, Nicholas; Rameezdeen, Raufdeen; Hosseini, M. Reza; Martek, Igor; Li, Hong Xian; Panjehbashi-Aghdam, Parinaz
• Is Part Of: Waste management. Volume 79(2018)
• Page Start: 48
• Page End: 57
• Publication Date: 2018-09
• Subjects: Influencing drivers -- Reverse logistics -- Quantification -- Strength -- Structural equation modeling -- Construction projects; Hazardous wastes -- Periodicals; Refuse and refuse disposal -- Periodicals; 363.728
• DOI: 10.1016/j.wasman.2018.07.013
• Languages: English
• ISSNs: 0956-053X

Abstract

Highlights: Provides a model of RL driver implementation in the construction context. Identifies material salvage costs as a primal disincentive to RL uptake. Identifies current regulatory mechanisms as insufficiently effective. Social drivers of RL are shown to be not important on decisions regarding RL uptake. Sets out an agenda to enhance the identified drivers. Abstract: In the light of increased environmental concerns and the unsustainability of current construction practices, 'reverse logistics' (RL) has emerged as a remedial strategy, whereby decommissioned buildings are salvaged and returned back through the value chain for recovery, refurbishment and reuse. The drivers that impact the uptake of RL are known, but if sustainability outcomes are to be enhanced, the strength of those drivers must be quantified in order to ascertain where efforts should be focused. This study aims to quantify the effects of known drivers on RL, and in so doing identify action items with the greatest potential to positively improve RL outcomes. RL drivers are culled from extant research, and categorized as economic, environmental, or social forces. A conceptual model is developed and tested against questionnaire results drawn from 49 expert respondents active in the South Australian construction industry. The results are analyzed using structured equation modeling. Economic and environmental drivers, such as the continuing relative high cost of salvaged items, along with expediency of cost, time and quality objectives overshadowing regulatory demands for use of such salvaged items, are shown to predict 34% of the variations in implementing RL. Of particular interest is the finding contradicting previous studies, showing that social drivers, such as perceived benefits from 'going green' had no significant impact. Thus, the road-map to improving RL outcomes lies in reducing costs of salvaged materials, augmenting environmental policies that promoted their use, and to initiate a regulatory framework to generate compliance. This insight will be of interest to industry policymakers and environmental strategists alike.