| Title | Learning-based dynamic capabilities in closed-loop supply chains: an expert study |
|---|---|
| ID_Doc | 29309 |
| Authors | Ritola, I; Krikke, H; Caniëls, MCJ |
| Title | Learning-based dynamic capabilities in closed-loop supply chains: an expert study |
| Year | 2022 |
| Published | International Journal Of Logistics Management, 33.0, 5 |
| Abstract | Purpose Product returns information gives firms an opportunity for continuous strategic adaptation by allowing them to understand the reasons for product returns, learning from them and improving their products and processes accordingly. By applying the Dynamic Capabilities (DCs) view in the context of closed-loop supply chains (CLSC), this study explores how firms can continuously learn from product returns information. Design/methodology/approach This study adopts a qualitative Delphi study-inspired approach. Experts from industry and academia are interviewed in two interview rounds. First round of interviews are based on extant research, while the second round allows the experts to elaborate and correct the results. Findings This study culminates into a conceptual model for incremental learning from product returns information. The results indicate incremental learning from product returns can potentially lead to a competitive advantage. Additionally, the authors identify the sources of information, capabilities along with their microfoundations and the manifestations of product return information. Three propositions are formulated embedding the findings in DC theory. Research limitations/implications This study supports extant literature in confirming the value of product returns information and opens concrete avenues for research by providing several propositions. Practical implications This research elucidates the practices, processes and resources required for firms to utilize product returns information for continuous strategic adaptation. Practitioners can use these results while implementing continuous learning practices in their organizations. Originality/value This study presents the first systematic framework for incremental learning from product returns information. The authors apply the DC framework to a new functional domain, namely CLSC management and product returns management. Furthermore, the authors offer a concrete example of how organizational learning and DC intersect, thus advancing DC theoretical knowledge. |
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