Despite recurrent observations that media reputations of agencies matter to understand their reform experiences, no studies have theorized and tested the role of sentiment. This study uses novel and advanced BERT language models to detect attributions of responsibility for positive/negative outcomes in media coverage towards 14 Flemish (Belgian) agencies between 2000-2015 through supervised machine learning, and connects these data to the Belgian State Administration Database on the structural reforms these agencies experienced. Our results reflect an inverted U-shaped relationship: more negative reputations increase the reform likelihood of agencies, yet up to a certain point at which the reform likelihood drops again. Variations in positive and neutral reputational signals do not impact the reform likelihood of agencies. Our study contributes to understanding the role of reputation as an antecedent of structural reforms. Complementing and enriching existing perspectives, the paper shows how the sentiment in reputational signals accumulates and informs political-administrative decision-makers to engage in structural reforms.

中文翻译：

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结构性改革的声誉视角：媒体声誉与公共机构结构性改革可能性的关系


尽管人们反复观察到，机构的媒体声誉对于理解他们的改革经历很重要，但没有研究对情绪的作用进行理论化和测试。本研究使用新颖和先进的 BERT 语言模型，通过监督机器学习检测 2000 年至 2015 年间媒体报道中对 14 个佛兰德（比利时）机构的积极/消极结果的责任归因，并将这些数据与比利时国家管理局数据库联系起来这些机构经历的结构改革。我们的结果反映了一个倒 U 型关系：更多的负面声誉增加了机构改革的可能性，但到了一定程度，改革的可能性再次下降。积极和中立声誉信号的变化不会影响机构改革的可能性。我们的研究有助于理解声誉作为结构性改革前因的作用。本文补充和丰富了现有观点，展示了声誉信号中的情绪如何积累，并为政治行政决策者参与结构性改革提供信息。