972
Views
11
CrossRef citations to date
0
Altmetric
Original Articles

Is Positive Always Positive? The Effects of Precrisis Media Coverage on Rumor Refutation Effectiveness in Social Media

&
Pages 98-116 | Published online: 01 Dec 2014
 

Abstract

While providing an unparalleled platform to enable consumers to share information easily with others, social media also threatens organizational control of information and increases risk when organizations become the focus of harmful rumors. Denials, or statements refuting rumors, are able to reduce consumers’ belief in and intention to share rumors. However, the evidence of their effectiveness is not uniformly supportive. Although scholars have highlighted the importance of identifying the moderators for rumor refutation effectiveness, media, as one of the essential infomediaries to shape consumers’ perceptions about firms, was largely overlooked in literatures. This study, therefore, explores the extent to which effective rumor refutation is a function of precrisis media coverage. Drawing on the literature in expectancy violation and media influences on social perceptions, we assessed the influence of precrisis media coverage on rumor refutation effectiveness in a scenario-based experiment. The findings suggest that the tenor of media coverage moderates the relationship between the quality of refutation arguments and refutation effectiveness. When the media coverage is positive, high-quality refutation arguments will not result in significant lower levels of belief in, and intentions to share, the rumor than low-quality arguments. This study contributes a clearer understanding of rumor refutation effectiveness, as well as furnishing important insights regarding the value of media coverage and reputation.

Notes

1 Rumors might be positive or negative. Compared with positive ones, negative rumors are more harmful to a company’s performance; in the rest of this article, we focus on such negative rumors.

Additional information

Funding

This research is supported by the National Natural Science Foundation of China (project nos. 71072045, 71002029, and 71372035).

Notes on contributors

Quansheng Wang

Quansheng Wang is professor at and Head of the Department of Marketing and Electronic Business, School of Management, Nanjing University. He received his Doctoral Degree in Business Administration from Nanjing University. Professor Wang’s research interests include online consumer behavior and e-business strategy. His research about online consumer channel choice and online rumor refutation has been supported by the National Natural Science Foundation of China. His work appears in such forums as Electronic Markets, the Journal of Organizational and End User Computing, and Proceedings of the Annual International Conference on Electronic Commerce.

Peijian Song

Peijian Song is an associate professor in the Department of Marketing and Electronic Business, School of Management, Nanjing University. He received his Doctoral Degree in Management Science from Fudan University. His research interests include technology innovation adoption and diffusion, and platform ecosystem. His work has been published in such journals as Decision Support Systems, IEEE Transactions on Engineering Management, Journal of Electronic Commerce Research, Electronic Commerce Research and Applications, Electronic Markets, and Journal of Global Information Management, and in conference proceedings for the Academy of Management and the International Conference on Information Systems.

Log in via your institution

Log in to Taylor & Francis Online

PDF download + Online access

  • 48 hours access to article PDF & online version
  • Article PDF can be downloaded
  • Article PDF can be printed
USD 61.00 Add to cart

Issue Purchase

  • 30 days online access to complete issue
  • Article PDFs can be downloaded
  • Article PDFs can be printed
USD 480.00 Add to cart

* Local tax will be added as applicable

Related Research

People also read lists articles that other readers of this article have read.

Recommended articles lists articles that we recommend and is powered by our AI driven recommendation engine.

Cited by lists all citing articles based on Crossref citations.
Articles with the Crossref icon will open in a new tab.