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      Supply chain analytics and post-pandemic performance: mediating role of triple-A supply chain strategies

      , ,
      International Journal of Emerging Markets
      Emerald

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          Abstract

          Purpose

          The abrupt outbreak of coronavirus disease (COVID-19) hit every nation in 2020–2021, causing a worldwide pandemic. The worldwide COVID-19 epidemic, described as a “black swan”, has severely disrupted manufacturing firms' supply chain. The purpose of this study is to investigate how supply chain data analytics enable the effective deployment of agility, adaptability and alignment (3As) strategies, resulting in improving post-COVID disruption performance. It also analyses the indirect effect of supply chain data analytics on disruption performance through the 3As supply chain strategies.

          Design/methodology/approach

          The hypothesis and theoretical framework were tested using a questionnaire survey. The authors employed structural equation modelling through the SMART PLS version 3.2.7 to analyse data from 163 textile firms located in Pakistan.

          Findings

          The results revealed that the supply chain data analytics contributed positively and significantly to the agility and adaptability, while all 3As supply chain strategies impacted the PPERF substantially. Further, the connection between supply chain data analytics (SCDA) and disruption performance has substantially been influenced through 3As supply chain strategies.

          Practical implications

          The results imply that in the event of low likelihood, high effect disruptions, managers and decision-makers should focus their efforts on integrating data analytics capabilities with 3As supply chain policies to ensure long-term company success.

          Originality/value

          This research sheds fresh light on the importance of data analytics in effectively implementing 3As strategies for sustaining company performance amid COVID-19 disruptions.

          Related collections

          Most cited references95

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          Sources of method bias in social science research and recommendations on how to control it.

          Despite the concern that has been expressed about potential method biases, and the pervasiveness of research settings with the potential to produce them, there is disagreement about whether they really are a problem for researchers in the behavioral sciences. Therefore, the purpose of this review is to explore the current state of knowledge about method biases. First, we explore the meaning of the terms "method" and "method bias" and then we examine whether method biases influence all measures equally. Next, we review the evidence of the effects that method biases have on individual measures and on the covariation between different constructs. Following this, we evaluate the procedural and statistical remedies that have been used to control method biases and provide recommendations for minimizing method bias.
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            When to use and how to report the results of PLS-SEM

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              Explicating dynamic capabilities: the nature and microfoundations of (sustainable) enterprise performance

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                Author and article information

                Contributors
                (View ORCID Profile)
                (View ORCID Profile)
                Journal
                International Journal of Emerging Markets
                IJOEM
                Emerald
                1746-8809
                June 29 2022
                June 29 2022
                Article
                10.1108/IJOEM-11-2021-1744
                2c4c84e6-451a-490a-a9c4-ea2b56d1cb79
                © 2022

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