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Stakeholder sentiment in service supply chains: big data meets agenda-setting theory

With growing reluctance to store and disseminate sensitive data throughout a supply chain network, there is a need to understand sentiment of big data and ways of control to achieve greater economic viability in the service-oriented supply chain which reflect a greater focus on knowledge sharing fro...

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Detalles Bibliográficos
Autores principales: Cao, Ray Qing, Schniederjans, Dara G., Gu, Vicky Ching
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Springer Berlin Heidelberg 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7864136/
http://dx.doi.org/10.1007/s11628-021-00437-w
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author Cao, Ray Qing
Schniederjans, Dara G.
Gu, Vicky Ching
author_facet Cao, Ray Qing
Schniederjans, Dara G.
Gu, Vicky Ching
author_sort Cao, Ray Qing
collection PubMed
description With growing reluctance to store and disseminate sensitive data throughout a supply chain network, there is a need to understand sentiment of big data and ways of control to achieve greater economic viability in the service-oriented supply chain which reflect a greater focus on knowledge sharing from traditional supply chains. Social network data were collected after referencing a focal corporate media (CM) document. This study provides causal inference by first conducting a CM document search and then a social network post web scrape of postings that reference the CM document while controlling for time and other demographic variables. This study finds salience of the big data topic positively impacts stakeholder sentiment but not when future applications are discussed.
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spelling pubmed-78641362021-02-09 Stakeholder sentiment in service supply chains: big data meets agenda-setting theory Cao, Ray Qing Schniederjans, Dara G. Gu, Vicky Ching Serv Bus Empirical Article With growing reluctance to store and disseminate sensitive data throughout a supply chain network, there is a need to understand sentiment of big data and ways of control to achieve greater economic viability in the service-oriented supply chain which reflect a greater focus on knowledge sharing from traditional supply chains. Social network data were collected after referencing a focal corporate media (CM) document. This study provides causal inference by first conducting a CM document search and then a social network post web scrape of postings that reference the CM document while controlling for time and other demographic variables. This study finds salience of the big data topic positively impacts stakeholder sentiment but not when future applications are discussed. Springer Berlin Heidelberg 2021-02-05 2021 /pmc/articles/PMC7864136/ http://dx.doi.org/10.1007/s11628-021-00437-w Text en © The Author(s), under exclusive licence to Springer-Verlag GmbH, DE part of Springer Nature 2021 This article is made available via the PMC Open Access Subset for unrestricted research re-use and secondary analysis in any form or by any means with acknowledgement of the original source. These permissions are granted for the duration of the World Health Organization (WHO) declaration of COVID-19 as a global pandemic.
spellingShingle Empirical Article
Cao, Ray Qing
Schniederjans, Dara G.
Gu, Vicky Ching
Stakeholder sentiment in service supply chains: big data meets agenda-setting theory
title Stakeholder sentiment in service supply chains: big data meets agenda-setting theory
title_full Stakeholder sentiment in service supply chains: big data meets agenda-setting theory
title_fullStr Stakeholder sentiment in service supply chains: big data meets agenda-setting theory
title_full_unstemmed Stakeholder sentiment in service supply chains: big data meets agenda-setting theory
title_short Stakeholder sentiment in service supply chains: big data meets agenda-setting theory
title_sort stakeholder sentiment in service supply chains: big data meets agenda-setting theory
topic Empirical Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7864136/
http://dx.doi.org/10.1007/s11628-021-00437-w
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AT guvickyching stakeholdersentimentinservicesupplychainsbigdatameetsagendasettingtheory