This article develops a theoretical model to explain how public opinion can lead to the deinstitutionalization of a practice. Our model draws upon the 'spiral of silence' theory, that originated in the mass communication literature, and which suggests that social actors tend to support majority views. At the micro level, this behavior triggers a spiral of silence that leads to homogenous public opinion. We use analogical reasoning to posit the existence of a spiral of silence at the institutional field level.When public opinion becomes hostile to a particular practice, institutional fields tend to resist this external opposition. Insiders face the dilemma of whether to align with the majority view expressed by public opinion, or to comply with the one expressed at the field level. After discussing the mechanisms by which insider voices mediate and diffuse the hostility of public opinion at the field level, we discuss the boundary conditions applicable to our analogy. Our paper advances the understanding of nested and connected climates of opinion and bridges the gap between insider-and outsiderdriven deinstitutionalization.

Despite the importance that the media has in regard to influencing people’s perceptions of wrongdoing, organizational scholars have paid little attention to how the media reports wrongdoing. This article starts to address this gap by considering how the media frames corporate scandals. We empirically examine how four different German newspapers reported on the Volkswagen diesel scandal. We inductively identify the constitutive elements of a general corporate scandal frame. Then, we analyze how each newspaper framed the scandal through combinations of different elements. We identify from our dataset four frames of corporate scandals that newspapers applied: legalistic, contextual, reputational, and scapegoating. Our article testifies to the importance of cross-fertilization between research on mass communication and political science on one side, and organizational research on the other side and, more generally, it calls for more attention to be given to the media in the study of scandals and organizational wrongdoing.

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This commentary aims to complement and enrich Zavyalova and her colleagues’ model by offering to consider the media as a set of heterogeneous actors, with their own motives and strategic decisions. The following development will make three key points: (i) Media reporting can be heterogeneous, often due to partisanship: A variance exists in events’ attention and presentation across media outlets. This variance is often not randomly distributed; different media outlets choose the cues that matter to them, and report them in a way that aligns with their motives and strategic goals. This is of crucial importance because: (ii) Different media outlets have different reaches: Media outlets reach broader or smaller audiences depending on whether these audiences have self-selected to follow these outlets. Thus, variances in perceptions among audiences can be due to the specific narrative an audience is exposed to. The previous two points are currently augmented by the recent surge of online and social media, which leads to: (iii) The media field has become more fragmented: Anyone can use social media and online platforms—such as Facebook or Twitter—to act as media themselves. This makes capturing media discourse more challenging.

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Biodiesel is capable of replacing diesel because it has similar physicochemical properties, but this biofuel is susceptible to oxidation, which makes the application of antioxidant substances necessary. For this study, alcoholic extracts of senna leaves, hibiscus flowers, and blackberry were used. Biodiesel samples were submitted to physicochemical analysis to evaluate interference in the volume of these alcoholic extracts with antioxidant properties. The data obtained were processed using the neural network of the multilayer perceptron type (MLP). For the network’s training, 200 epochs were used. The samples were randomly divided into three groups, with 70% used for training, 15% for testing, and 15% for validation. The type of extract was considered as a categorical variable, the extract volume as a target variable, and the other ones as input variables. Among the 200 networks trained, with 5 to 20 hidden layers, the 5 with the best performance were highlighted. The Tukey test applied to the means showed no significant difference at the 5% level, between the value of the added volume and the means value predicted by the networks. The sensitive analysis showed that the most important input variable for the construction of the model was the type of extract.