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      Disruptions in sourcing and distribution practices of supply chains due to COVID-19 pandemic: a sustainability paradigm

      , ,
      Journal of Global Operations and Strategic Sourcing
      Emerald

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          Abstract

          Purpose

          The present research paper is an attempt to study how COVID-19 can affect the global sourcing practices of various supply chain intermediaries across the demand chain. This study aims to explore and is an attempt to understand the overall impact of COVID-19 on the sustainable operations of the firm such as sourcing, procurement, economic performance, social responsibility, consumption and distributions.

          Design/methodology/approach

          This study uses a quantitative technique using data collected from 708 respondents. Structural equation modeling (SEM) has been applied to test the proposed model and hypothesis.

          Findings

          The findings of the study suggest that sourcing practices, distribution and sustainability considerations of manufacturers, suppliers, distributors and retailers are affected by COVID-19 to a great extent but the pandemic has also led to making supply chain intermediaries understand the changing dynamics of the business scenario which can help them in their own strategic and business evolution.

          Research limitations/implications

          The current disruptions throughout global delivery chains caused by COVID-19 affect badly, the already poor-performing supply chains. Hence, the present study provides fresh insight on how organizations can limit the ill effects of COVID-19 by safeguarding some of their key sustainable operations in a post-pandemic business scenario.

          Originality/value

          The present study takes into consideration how core supply functions such as sourcing, distribution and manufacturing and various sustainable operations are disrupted by pandemic and its after-effects. This knowledge base can help business organizations to mitigate such problems/disruptions in the future.

          Related collections

          Most cited references149

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          • Article: not found

          Common method biases in behavioral research: A critical review of the literature and recommended remedies.

          Interest in the problem of method biases has a long history in the behavioral sciences. Despite this, a comprehensive summary of the potential sources of method biases and how to control for them does not exist. Therefore, the purpose of this article is to examine the extent to which method biases influence behavioral research results, identify potential sources of method biases, discuss the cognitive processes through which method biases influence responses to measures, evaluate the many different procedural and statistical techniques that can be used to control method biases, and provide recommendations for how to select appropriate procedural and statistical remedies for different types of research settings.
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            • Abstract: not found
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            Evaluating Structural Equation Models with Unobservable Variables and Measurement Error

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              • Article: not found

              An interactive web-based dashboard to track COVID-19 in real time

              In December, 2019, a local outbreak of pneumonia of initially unknown cause was detected in Wuhan (Hubei, China), and was quickly determined to be caused by a novel coronavirus, 1 namely severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). The outbreak has since spread to every province of mainland China as well as 27 other countries and regions, with more than 70 000 confirmed cases as of Feb 17, 2020. 2 In response to this ongoing public health emergency, we developed an online interactive dashboard, hosted by the Center for Systems Science and Engineering (CSSE) at Johns Hopkins University, Baltimore, MD, USA, to visualise and track reported cases of coronavirus disease 2019 (COVID-19) in real time. The dashboard, first shared publicly on Jan 22, illustrates the location and number of confirmed COVID-19 cases, deaths, and recoveries for all affected countries. It was developed to provide researchers, public health authorities, and the general public with a user-friendly tool to track the outbreak as it unfolds. All data collected and displayed are made freely available, initially through Google Sheets and now through a GitHub repository, along with the feature layers of the dashboard, which are now included in the Esri Living Atlas. The dashboard reports cases at the province level in China; at the city level in the USA, Australia, and Canada; and at the country level otherwise. During Jan 22–31, all data collection and processing were done manually, and updates were typically done twice a day, morning and night (US Eastern Time). As the outbreak evolved, the manual reporting process became unsustainable; therefore, on Feb 1, we adopted a semi-automated living data stream strategy. Our primary data source is DXY, an online platform run by members of the Chinese medical community, which aggregates local media and government reports to provide cumulative totals of COVID-19 cases in near real time at the province level in China and at the country level otherwise. Every 15 min, the cumulative case counts are updated from DXY for all provinces in China and for other affected countries and regions. For countries and regions outside mainland China (including Hong Kong, Macau, and Taiwan), we found DXY cumulative case counts to frequently lag behind other sources; we therefore manually update these case numbers throughout the day when new cases are identified. To identify new cases, we monitor various Twitter feeds, online news services, and direct communication sent through the dashboard. Before manually updating the dashboard, we confirm the case numbers with regional and local health departments, including the respective centres for disease control and prevention (CDC) of China, Taiwan, and Europe, the Hong Kong Department of Health, the Macau Government, and WHO, as well as city-level and state-level health authorities. For city-level case reports in the USA, Australia, and Canada, which we began reporting on Feb 1, we rely on the US CDC, the government of Canada, the Australian Government Department of Health, and various state or territory health authorities. All manual updates (for countries and regions outside mainland China) are coordinated by a team at Johns Hopkins University. The case data reported on the dashboard aligns with the daily Chinese CDC 3 and WHO situation reports 2 for within and outside of mainland China, respectively (figure ). Furthermore, the dashboard is particularly effective at capturing the timing of the first reported case of COVID-19 in new countries or regions (appendix). With the exception of Australia, Hong Kong, and Italy, the CSSE at Johns Hopkins University has reported newly infected countries ahead of WHO, with Hong Kong and Italy reported within hours of the corresponding WHO situation report. Figure Comparison of COVID-19 case reporting from different sources Daily cumulative case numbers (starting Jan 22, 2020) reported by the Johns Hopkins University Center for Systems Science and Engineering (CSSE), WHO situation reports, and the Chinese Center for Disease Control and Prevention (Chinese CDC) for within (A) and outside (B) mainland China. Given the popularity and impact of the dashboard to date, we plan to continue hosting and managing the tool throughout the entirety of the COVID-19 outbreak and to build out its capabilities to establish a standing tool to monitor and report on future outbreaks. We believe our efforts are crucial to help inform modelling efforts and control measures during the earliest stages of the outbreak.

                Author and article information

                Journal
                Journal of Global Operations and Strategic Sourcing
                JGOSS
                Emerald
                2398-5364
                2398-5364
                November 19 2021
                May 19 2022
                November 19 2021
                May 19 2022
                : 15
                : 2
                : 235-261
                Article
                10.1108/JGOSS-02-2021-0020
                3fd1a703-7055-4e15-bd60-695fb151869b
                © 2022

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