| Title | Unlocking sustainable supply chain performance through dynamic data analytics: a multiple mediation model of sustainable innovation and supply chain resilience |
|---|---|
| ID_Doc | 73555 |
| Authors | Piprani, AZ; Khan, SAR; Salim, R; Rahman, MK |
| Title | Unlocking sustainable supply chain performance through dynamic data analytics: a multiple mediation model of sustainable innovation and supply chain resilience |
| Year | 2023 |
| Published | |
| Abstract | This article provides a theoretical framework for comprehending the connections between dynamic data analytics capability (DDAC), innovation capabilities (IC), supply chain resilience (RES), and sustainable supply chain performance (SSCP). Since this is the first empirical investigation of the sequential mediation effect between DDAC and SSCP through IC and RES, it fills a critical need in the supply chain literature. A quantitative methodology was used, involving a survey questionnaire distributed to 259 large Pakistani manufacturing firms. We used PLS-SEM to test for the expected associations. Findings show that using DDAC has a beneficial effect on both innovative and resilient capabilities, which in turn leads to better SSCP. The research illuminates the sequential mediating roles of product, process, and resilience, underlining the need of combining data-driven innovation with resilience in order to achieve sustainable supply chain performance. These results provide useful guidance for businesses that want to boost their sustainability results by taking a more all-encompassing approach to data-driven innovation and resilience. |
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