Hashtags and Crises

On 19 November 2018, the corporate world was treated to a world of circus courtesy of Nissan, a big motor company based in Japan and with huge stakes in French. The Chairman of the company, Carlos Ghosn, and other company officials were dramatically arrested for flouting corporate rules. It had been alleged that he has been engaged in financial misconduct, by taking personal use of the company’s funds. 

Reporters have taken up the matter and are using the hashtag #Ghosngate to report the issue. Since his arrest in Japan, Ghosn has stirred a lot of controversies since he was a superstar in the motor industry after he successfully rescued Nissan from an imminent collapse due to bankruptcy. Blogs and other news outlet channels, especially the relatively new ones such as microblogging sites and social media networks such as Facebook and Twitter, have been taking the issue quite phenomenally. The issue is more pronounced in motor sites where people are marveling at the ever-rising career star of Ghosn and the dramatic fall. 

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When an organization is developing and implementing a crisis media plan, they should be aware of the power of social media. On this, an organization needs to evaluate its issues at stake. An organization needing synergy from the public according to Lachlan, Spence, Lin, Najarian, and Del Greco (2016), needs to have a hashtag that will distract the attention of the public, otherwise, the organization that will be gravely affected by a hashtag. For instance, Nissan’s reputation is at stake and the effect is reflected even in its performance in the stock market. Investors and customers alike will tend to avoid the company due to the media attention it is attracting. Before releasing such news to the public, an organization should try to handle the crisis in-house without the glare of the media cameras and hashtags. 

ReferencesLachlan, K. A., Spence, P. R., Lin, X., Najarian, K., & Del Greco, M. (2016). Social media and crisis management: CERC, search strategies, and Twitter content. Computers in Human Behavior, 54, 647-652.

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