School of Business, National University of Ireland Maynooth, Maynooth, Ireland.
Department of Applied Informatics in Management, Faculty of Management and Economics, Gdansk University of Technology, Gdansk, Poland.
PLoS One. 2022 Sep 12;17(9):e0274539. doi: 10.1371/journal.pone.0274539. eCollection 2022.
In this paper, we explore the role of perceived emotions and crisis communication strategies via organizational computer-mediated communication in predicting public anxiety, the default crisis emotion. We use a machine-learning approach to detect and predict anxiety scores in organizational crisis announcements on social media and the public's responses to these posts. We also control for emotional and language tones in organizational crisis responses using a separate machine learning algorithm. Perceived organizational anxiety positively influences public anxiety, confirming the occurrence of emotional contagion from the organization to the public. Crisis response strategies moderated this relationship, so that responsibility acknowledgment lowered public anxiety the most. We argue that by accounting for emotions expressed in organizational crisis responses, organizations may be able to better predict and manage public emotions.
在本文中，我们探讨了通过组织计算机中介交流感知到的情绪和危机沟通策略在预测公众焦虑（默认危机情绪）中的作用。我们使用机器学习方法来检测和预测社交媒体上组织危机公告中的焦虑分数以及公众对这些帖子的反应。我们还使用另一个机器学习算法来控制组织危机反应中的情绪和语言语气。感知到的组织焦虑对公众焦虑有正向影响，证实了情绪从组织向公众传播的现象。危机应对策略调节了这种关系，因此承认责任的做法对降低公众焦虑最有效。我们认为，通过考虑组织危机应对中表达的情绪，组织可能能够更好地预测和管理公众的情绪。
