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An explanatory approach to assess resilience: An evaluation of competitive priorities for logistics organizations

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  • Baştuğ, Sedat
  • Yercan, Funda

Abstract

The aim of this study is to analyze social media messages, specifically tweets from logistics organizations and retweets of their customers regarding competitive priorities that create a sustainable competitive advantage during the current Covid-19 pandemic. Sentiment analysis with Support Vector Machine (SVM) methodology w used to assess a sample of more than 941 Covid-19 related tweets and re-tweets by top logistics organizations (logistics service providers) selected from Europe, the North America, Far East Asia, and Middle East. The dataset was collected from December 2019 to November 2020. Our findings suggest that shippers have a negative perceptions on delivery and efficiency. Shippers typically consider multiple factors in making their decisions, but often focusing on delivery and efficiency as the primary criteria. As a result, this study focuses on the analysis of sustainable competitive priorities influencing the development of transport and supply chain policies by logistics service to achieve their goals within the current operational conditions due to the pandemic.

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  • Baştuğ, Sedat & Yercan, Funda, 2021. "An explanatory approach to assess resilience: An evaluation of competitive priorities for logistics organizations," Transport Policy, Elsevier, vol. 103(C), pages 156-166.
    • Handle: RePEc:eee:trapol:v:103:y:2021:i:c:p:156-166
    DOI: 10.1016/j.tranpol.2021.01.016
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    Cited by:

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    2. Li, Siping & Zhou, Yaoming, 2024. "Integrating equity and efficiency into urban logistics resilience under emergency lockdowns," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 183(C).
    3. Wang, Xueqin & Wong, Yiik Diew & Li, Kevin X. & Yuen, Kum Fai, 2021. "Shipping industry's sustainability communications to public in social media: A longitudinal analysis," Transport Policy, Elsevier, vol. 110(C), pages 123-134.
    4. Suriyan Jomthanachai & Wai Peng Wong & Khai Wah Khaw, 2024. "An Application of Machine Learning to Logistics Performance Prediction: An Economics Attribute-Based of Collective Instance," Computational Economics, Springer;Society for Computational Economics, vol. 63(2), pages 741-792, February.
    5. Li, Siping & Zhou, Yaoming & Kundu, Tanmoy & Sheu, Jiuh-Biing, 2021. "Spatiotemporal variation of the worldwide air transportation network induced by COVID-19 pandemic in 2020," Transport Policy, Elsevier, vol. 111(C), pages 168-184.

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