Abstract
The mobile game “Immortal Conquest,” created by NetEase Games, caused a dramatic user dissatisfaction event after an introduction of a sudden and uninvited “pay-to-win” update. As a result, many players filed grievances against NetEase in a court. The official game website issued three apologies, with mix results, to mitigate the crisis. The goal of the present study is to understand user feedback content from the perspective of Situational Crisis Communication Theory through semantic network analysis and sentiment analysis to explore how an enterprise’s crisis communication strategy affects users’ attitudes. First, our results demonstrate that the diminishing crisis communication strategies (excuse and justification) do not change players’ negative attitudes. It was not a failure because it successfully alleviated the players’ legal complaints and refocused their attention on the game itself. Second, the rebuild (apology & compensation) strategy was effective because it significantly increased the percentage of positive emotions and regenerated expectations for the game. The litigation crisis was identified within gamer communications with respect to Chinese gaming companies for the first time. Nevertheless, this does not indicate an increase in overall legal awareness among the larger Chinese population. It may only reflect greater legal awareness among Chinese online gamers. Fourth, gamers emphasized that they and enterprises should be equally involved when communicating with each other. Finally, in-game paid items should be reasonably priced, otherwise, they will drive users to competitors.
Highlights
With the outbreak of COVID-19, China has paid unprecedented attention to crisis communication via social media (Chen et al, 2020), prioritizing it as a tool for crisis communication in the face of major public health emergencies (Su et al, 2021)
Semantic Network Analysis (SNA) was used in RQ1, and the specific process can be divided into following steps: (4a) used Term Frequency–Inverse Document Frequency (TF-IDF) to extract feature words; (5a) obtained a co-occurrence matrix based on feature words; (6a) used Gephi to conduct clustering analysis
Sentiment analysis is divided into machine learning and sentiment lexicon, and the latter is adopted in this study
Summary
With the outbreak of COVID-19, China has paid unprecedented attention to crisis communication via social media (Chen et al, 2020), prioritizing it as a tool for crisis communication in the face of major public health emergencies (Su et al, 2021). Enterprises use crisis communication on social media to maintain their reputation and market position (Utz et al, 2013; Bratu, 2019). Enterprises must evaluate whether crisis communication strategies have the expected impact on users. In 2021, academic studies on social media crisis communication mainly focused on communication strategies (Triantafillidou and Yannas, 2020; Wang et al, 2021) and information transmission modes (Mirbabaie et al, 2021; Puyod and Charoensukmongkol, 2021). There were few studies on whether crisis communication strategies of enterprises can affect users’ attitudes (Stieglitz et al, 2015; Mirbabaie and Zapatka, 2017)
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