Information processing model for quality assurance in reverse logistics supply chains of demolition waste

Purpose: Quality assurance (QA) plays an important role in the reverse logistics supply chains (RLSCs) of demolition waste (DW); however, it is adversely impacted by information deficiencies, which create epistemic uncertainties that are not isolated but integrated. No previous study has modelled the effect of interrelated epistemic uncertainties on an operational performance criterion, such as QA. This study aimed to develop and empirically validate an information processing model for QA in RLSCs of DW. Design/methodology/approach: An explanatory sequential mixed-method research approach was followed. First, 20 structured interviews were conducted with experts in the DW management sector. Then, the Bayesian belief network (BBN) modelling approach was used to develop the conceptual information processing for QA. Finally, a focus group discussion was conducted to validate the developed model empirically. Findings: The study developed a conceptual information processing model, where the impact on QA was reflected through the combined effect of macro-, meso- and micro-level uncertainties in the RLSCs. The demolisher’s epistemic uncertainties are propagated through macro-level uncertainties to meso- and micro-level uncertainties and then connect with the waste processor’s uncertainties through “mixed waste received from demolishers”. Originality/value: The model can be used to enhance the practitioners' understanding of the impact of epistemic uncertainties on QA within the supply chain. This study also contributes to the knowledge by identifying cause-and-effect relationships between epistemic uncertainties for QA, which is an overlooked area in the discipline.