The dynamic interdependencies among the negativity and the positivity in news and user-generated content about safety in a firm's products and the firm's product recalls.


Journal

PloS one
ISSN: 1932-6203
Titre abrégé: PLoS One
Pays: United States
ID NLM: 101285081

Informations de publication

Date de publication:
2024
Historique:
received: 17 01 2024
accepted: 27 05 2024
medline: 8 8 2024
pubmed: 8 8 2024
entrez: 8 8 2024
Statut: epublish

Résumé

This article examines the dynamic interdependencies among the negativity and the positivity in news and user-generated content about safety in a firm's products (or the lack thereof) and the firm's product recalls. The authors use a panel vector autoregression (PVAR) to unearth theoretically novel and managerially relevant asymmetric associations. Specifically, they find that the negativity in the news negatively correlates with recalls, whereas the negativity in UGC positively correlates with recalls. Whereas the positivity in the news positively correlates with recalls, the positivity in UGC does not matter. Further, the negativity in the news and the negativity in UGC substitute for each other, whereas their positive counterparts complement each other's associations with recalls. Lastly, the negativity and positivity in the news have significant, though differently patterned, long-term associations with recalls. The findings contribute to research on the associations between earned media and managerial decisions in the product market.

Identifiants

pubmed: 39116143
doi: 10.1371/journal.pone.0305287
pii: PONE-D-24-02306
doi:

Types de publication

Journal Article

Langues

eng

Sous-ensembles de citation

IM

Pagination

e0305287

Informations de copyright

Copyright: © 2024 Astvansh, Wang. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Déclaration de conflit d'intérêts

The authors have declared that no competing interests exist.

Auteurs

Vivek Astvansh (V)

Associate Professor of Quantitative Marketing and Analytics, Desautels Faculty of Management, McGill University, Montreal, Canada.
Bensadoun School of Retail Management, McGill University, Montreal, Canada.
Adjunct Associate Professor of Data Science, Luddy School of Informatics, Computing and Engineering, Indiana University Bloomington, Bloomington, IN, United States of America.
Affiliate, Environmental Resilience Institute, Indiana University, Montréal, QC, Canada.

Yen-Yao Wang (YY)

Department of Business Analytics and Information Systems, Harbert College of Business, Auburn University, Auburn, AL, United States of America.

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Classifications MeSH