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This study investigates how media brand knowledge, defined as a structural feature of the message, influences emotional and attentional responses to, and memory of, news messages.

Self-reports, facial electromyography (EMG) and electroencephalography were used as indices of emotional valence, arousal and attention in response to 42 news messages, which varied along the valence and involvement dimensions and were framed with different media brands varying along the familiarity and credibility dimensions.

Compared to the no-brand condition, news framed with brands elicited more attention. The memory tests indicated that strong media brands override the effect of involvement in information encoding, whereas details of news presented with Facebook were not well encoded. However, the headlines of news framed with Facebook were well retrieved. In addition, negative and high-involvement news elicited higher arousal ratings and corrugator EMG activity. News framed with familiar and high-credibility brands elicited higher arousal ratings.

Relevant for both brand managers and audiences, the findings show that building credibility and familiarity both work as brand attributes to differentiate media brands and influence information processing.

The results highlight the importance of media brands in news reading: as a structural feature, the brand is used as a proxy to process the message content. The study contributes by investigating how the type of source influences the reception and encoding of the mediated information; by investigating the emotional effects of brands; and by confirming previous findings in media psychology literature.

The use of social networking websites by companies to disclose corporate news and by investors to collect information for investment purposes is increasing rapidly. However, the role of investors’ affective reactions to corporate disclosures on social networking websites is under-researched. This paper aims to examine how the disclosure platform (disclosing news on a company’s Facebook Web page or the corporate investor relations Web page) and news valence (positive or negative) jointly influence investors’ affective reactions to corporate news and stock price change judgments.

The authors conduct an experimental study using 364 participants from Amazon’s Mechanical Turk website as a proxy for reasonably informed investors.

Results show that the disclosure platform influences investors’ affective reactions and stock price change judgments when the corporate news is negative, but not when the corporate news is positive. In addition, investors’ affective reactions mediate the influence of the disclosure platform on investors’ stock price change judgments when the corporate news is negative rather than positive.

This paper extends the theory on affective reactions to a social networking context by showing that differences in disclosure platforms and news valence influence investors’ affective reactions to corporate news. In addition, the study’s theory and findings have significant implications for researchers, company managers and public relations specialists, capital market participants, regulators and investor education organizations and users of social networking websites.

Negative media coverage has important impacts on firm financial performance, but existing studies have inconsistent views of this relationship and lack a unified theoretical framework to explain how such impacts arise. This study aims to bridge this gap in the literature.

This study uses two sets of data encompassing publicly listed companies in Shanghai and Shenzhen stock exchanges from 2013 to 2019, which are covered by the China Stock Market and Accounting Research Database.

This study finds that the number of negative news coverages has an inverted U-shaped relationship with firm financial performance; this relationship is weakened by the proportion of shares held by institutional investors and strengthened by advertising intensity.

This study suggests that corporate executives should be aware of the potential value of a limited amount of negative news coverage and react with tolerance and caution when their companies encounter it.

This study uses two different routes provided in the elaboration likelihood model theory to fully explain the processes underlying changes in investors’ attitudes toward firms experiencing negative media coverage.

Social media (e.g., e-WOM) and traditional media (e.g., media coverage) serve different roles in a firm's marketing activities and also interact with each other, which in turn affect the market outcome. In addition, how market outcome affects the two types of media in turn has not been examined, which brings the need for a holistic framework. The rare study that examines this relation mostly relies on the volume of media rather than the valence. This study examines the interdependent relation between the volume and valence of social media, the volume of traditional media and TV ratings.

Forty-one South Korean TV drama shows from October 2014 to March 2016 were analyzed using the 3SLS estimation to examine the interdependent relation between the variables.

First, the volume of traditional media has a negative effect on the volume of social media. Second, ratings negatively affect the valence of social media. Third, the volume of traditional media is found to have a negative effect on ratings. This is explained by the displacement effect.

This study is one of the very few studies that examine the interdependent relation between various earned media and market outcomes in one framework. In addition, it has originality in that it considers the valence of social media, which is an important dimension in analyzing earned media. Our results show negative effects of news media on TV ratings and e-WOM, which diverge from common intuition.

This paper aims to examine how content relevancy influences consumers’ preference to receive and share fake news. Further, it investigates how these receivers perceive the social image of the people who share fake news. Finally, this study examines how brand strength and valence and credibility of fake content influence consumer’s word-of-mouth recommendations, purchase intentions and attitude toward the brand.

Three experiments were conducted to test the hypotheses. The data was analyzed using a two-way analysis of variance and PROCESS techniques.

Findings indicate that people prefer to receive and share relevant content, even if it is fake. Sharing fake news conveys the sender’s sociability but also creates a negative perception of narcissism. Individuals are more likely to recommend a brand if the fake news is perceived as credible and positive (vs negative). Finally, brand-strength can help brands to negate the harmful effects of fake news.

Future research can explore the role of group dynamics, tie-strength and media richness (text, image and videos) in the dispersion of fake news and its impact on brands.

Marketers should communicate and educate consumers that sharing fake content can harm their social image, which can reduce information dispersion. Marketers should also improve brand-strength that can protect the brand against the adverse impact of fake news.

This study contributes to the emerging literature on fake news by studying the impact of fake news on consumer intentions and attitudes toward the brand, which are critical for the success of any brand.

The creation and dissemination of fake news can have severe consequences for a company’s brand. Researchers, policymakers and practitioners are eagerly searching for solutions to get us out of the “fake news crisis”. Here, one approach is to use automated tools, such as artificial intelligence (AI) algorithms, to support managers in identifying fake news. The study in this paper demonstrates how AI with its ability to analyze vast amounts of unstructured data, can help us tell apart fake and real news content. Using an AI application, this study examines if and how the emotional appeal, i.e., sentiment valence and strength of specific emotions, in fake news content differs from that in real news content. This is important to understand, as messages with a strong emotional appeal can influence how content is consumed, processed and shared by consumers.

The study analyzes a data set of 150 real and fake news articles using an AI application, to test for differences in the emotional appeal in the titles and the text body between fake news and real news content.

The results suggest that titles are a strong differentiator on emotions between fake and real news and that fake news titles are substantially more negative than real news titles. In addition, the results reveal that the text body of fake news is substantially higher in displaying specific negative emotions, such as disgust and anger, and lower in displaying positive emotions, such as joy.

This is the first empirical study that examines the emotional appeal of fake and real news content with respect to the prevalence and strength of specific emotion dimensions, thus adding to the literature on fake news identification and marketing communications. In addition, this paper provides marketing communications professionals with a practical approach to identify fake news using AI.

The purpose of this paper is to examine the factors that significantly affect the prediction of fake news from the virality theory perspective. The paper looks at a mix of emotion-driven content, sentimental resonance, topic modeling and linguistic features of news articles to predict the probability of fake news.

A data set of over 12,000 articles was chosen to develop a model for fake news detection. Machine learning algorithms and natural language processing techniques were used to handle big data with efficiency. Lexicon-based emotion analysis provided eight kinds of emotions used in the article text. The cluster of topics was extracted using topic modeling (five topics), while sentiment analysis provided the resonance between the title and the text. Linguistic features were added to the coding outcomes to develop a logistic regression predictive model for testing the significant variables. Other machine learning algorithms were also executed and compared.

The results revealed that positive emotions in a text lower the probability of news being fake. It was also found that sensational content like illegal activities and crime-related content were associated with fake news. The news title and the text exhibiting similar sentiments were found to be having lower chances of being fake. News titles with more words and content with fewer words were found to impact fake news detection significantly.

Several systems and social media platforms today are trying to implement fake news detection methods to filter the content. This research provides exciting parameters from a viral theory perspective that could help develop automated fake news detectors.

While several studies have explored fake news detection, this study uses a new perspective on viral theory. It also introduces new parameters like sentimental resonance that could help predict fake news. This study deals with an extensive data set and uses advanced natural language processing to automate the coding techniques in developing the prediction model.

Researchers have long been interested in understanding why and how corporate managers issue earnings guidance and the effect of such guidance on stakeholders’ (investors’ and managers’) behavior. Several recent studies have employed the experimental approach to address these issues. The purpose of this paper is to analyze and synthesize the literature on experimental studies of management earnings guidance. Consistent with the literature, I organize the synthesis to reflect (a) whether, why and how management issues guidance; (b) investors’ reactions to guidance; (c) the effect of guidance on management behavior. In addition, I provide institutional information (e.g., nature and timing of guidance) about guidance as well as provide several directions for future research. The synthesis reveals that the experimental studies have made a unique contribution to this literature by (i) providing evidence on process variables that underlie some empirical associations, (ii) directly measuring managers’ personal attributes and, (iii) closing the causality gap in the guidance literature.

This study examines the effect of target-and-incentive-consistency of unexpected positive earnings news on investors’ use of corporate social responsibility (CSR) performance information in their pricing decisions.

A 2 × 2 full factorial between-participants experiment is conducted.

Target-and-incentive-consistency of unexpected positive earnings news moderates the effect of CSR performance on investors’ pricing decisions.

Its findings shed insights on investors’ use of a mix of CSR, financial and governance information, support the financial information elasticity effect and add to the effect of financial information on investors’ use of nonfinancial information.

The effect of inelastic financial information in mitigating the CSR information effect can benefit investors who do not plan to use a CSR investment strategy. Knowledge of investors’ conditional use of CSR information can benefit firm managers and policy makers.

Its findings support a heretofore unexamined theoretical underpinning for the effect of financial information on investors’ use of nonfinancial information.