Article ID: | iaor2017173 |
Volume: | 26 |
Issue: | 1 |
Start Page Number: | 100 |
End Page Number: | 115 |
Publication Date: | Jan 2017 |
Journal: | Production and Operations Management |
Authors: | Kleber Rainer, Voigt Guido, Souza Gilvan C, Abbey James D |
Keywords: | management, behaviour, statistics: empirical |
Recent research indicates that consumers hold significant concerns about the quality of remanufactured products. To better understand this phenomenon, this manuscript combines surveys and experimental studies to identify the antecedents of perceived quality–in the form of perceived risk of functionality and cosmetic defects–and their significant impact on consumers' willingness to pay (wtp) for remanufactured electronics products. The study also controls for alternative explanations for wtp suggested in the literature, such as consumers' wtp for new products, environmental beliefs, disgust aversion toward used products, brand perceptions, risk aversion, and various demographic traits. Importantly, the study empirically estimates the magnitude and distribution of discount factors for remanufactured electronics products–the ratio between wtp for a remanufactured product and wtp for a corresponding new product–among consumers. Finally, the manuscript analytically compares a monopolist's decision to include remanufactured products in its portfolio under both the empirically derived discount factor distributions and the classical linear demand model, which assumes constant discount factors. Interestingly, the classical linear demand model remains reasonably robust for high‐level insights, such as the presence of cannibalization and market expansion effects. However, the analytical model that uses the empirically‐derived distributions of discount factors demonstrates significantly higher profitability than predicted by the classical linear model. This fundamental link between risk perceptions, wtp for remanufactured products, and profitability provides new insights on how to manage demand and product pricing in closed‐loop supply chains.