Search results

This paper aims to investigate whether the assumption of bias-free journalism is violated. If there is systematic news coverage bias inherent in business journalism, certain kinds of companies will have a systematically higher or lower visibility in business news. Such differential corporate visibility may undermine the validity of research that is based solely on business news as a data source.

A set of hypotheses is developed and statistically tested, concerning the corporate characteristics associated with business media coverage. Coverage of the 100 largest Finnish companies is examined within the three foremost Finnish business publications. Methodologically, uncorrelated principal components in regression analyses are used.

The main finding is that that financially low-performing companies and growing companies receive less coverage than well-performing or shrinking companies, indicating a possible bias in journalistic sourcing, attention or selection. Consequently, such companies may be relatively under-represented in data sets derived solely from business news sources.

Significantly greater in-depth understanding of the phenomenon could be obtained through studying the biases at play in day-to-day journalistic practices within editorial offices and news desks, which is beyond the present study. The study cautions against single sourcing strategies reliant on business news alone, and it strongly recommends that future studies complement business news data with other, non-news sources.

Organizational metrics such as financial performance appear to influence corporate visibility in business news, which may therefore skew individuals’ and investors’ attitudes to corporations. The existence of coverage bias is methodologically consequential because management research often sources data from business news, especially in event-based studies.

This study provides evidence that media visibility is influenced by company performance and change in company size, which could contribute to bias in business news coverage. This should be taken into account in future studies that use business news data.

Arguing that the print media act as a claims-making forum for the social construction and contestation of crises, the aim of this paper is to explore how the print media mediated two audits commissioned following a high-profile salary cap breach in the National Rugby League (NRL) in Australia.

This paper draws upon critical discourse analysis to examine the media coverage of the two audits by the two major Australian media organisations, News Limited and Fairfax Media Limited. The analysis is based on a qualitative study complemented by quantitative techniques that explore critical incidents and representations in the daily press.

The paper illustrates the way in which News Limited, the owner of the infringing club, mobilised its media platform to promote favourable viewpoints and interpretations and how these were challenged in the Fairfax press. Evidence of both coverage bias and statement bias in the treatment of the two audits is produced.

This paper provides evidence that commercial interests of owner/publishers coloured media coverage of the two audits, which were central pillars of the crisis management strategy of News Limited and the NRL. Implications for the media's contribution to public accountability, accounting outputs and impression management, and the growing commercial diversification and reach of media outlets are considered.

This study aims to examine the impact of controversial governance practices on media coverage under a specific context. Based on the attribution theory, this study develops a theoretical framework to explore how antecedent factors can influence attribution process under a particular cultural context.

This paper presents a behavioral view of the media and corporate governance to demonstrate how media attributes different reasons for the same controversial governance practice in Chinese-specific context. Using 1,198 non-state-owned listed company observations in China as the study sample, cross-section data are used to build a multiple linear regression mode to test hypotheses.

The analysis indicates that the media imposes fewer penalties on founder-CEO firms than on non-founder-CEO firms for engaging in controversial governance practices, such as CEO compensation. CEO tenure negatively moderates the effect of CEO compensation on negative media coverage in non-founder-CEO firms. The positive media bias evidence for founder-CEO firms exists only when the firm is better performed.

This study’s contribution to the governance literature starts with its logical reasoning of basic assumptions in the agency theory, and that media penalty will arise when managers impose actions that against interests of shareholders or other stakeholders. This study shows that the rule is not always true. The findings also bridge the connection of governance literature and reputation literature to better explain how media can act as a social arbitration role.

This study provides insights into how belief and information of reputational evaluators affect attribution consequences on controversial governance practices. Moreover, this study looks beyond the internal elements and focuses on China’s traditional cultural context as well. Specifically, the authors concentrate on the attribution process by showing the importance of evaluators’ framing tendency with regard to controversial practices. The results extend the knowledge about how conformity makes media coverage shows a bias effect on interactions during the evaluation process.

What is the relationship between a social movement and the media coverage it receives? Using data on the Tea Party and supplementing it with a broad dataset of coverage in nearly 200 state and local newspapers over an 18-month period, I address key questions on the recursive relationship between media coverage and mobilization. Results provide support for the mobilizing influence of the media. Instead of following protest activity as post-facto news, coverage tended to precede mobilization and was its most important predictor. Second, the conservative media occupied a distinct and indirect position in impacting mobilization. Though not direct predictors of mobilization, conservative media coverage was a strong predictor of subsequent coverage in the broader media. Further, this influence was asymmetrical, with the general media having no impact on conservative media. Finally, results suggest that the conservative frame of “liberal media bias” enabled a unique mobilizing effect where negative coverage in the broader media increased mobilization. These findings shed light on the dynamic relationship between movements, protests, and the media, and that of conservative movements in particular.

The purpose of this paper is to contribute to the empirical evidence about crop yield distributions that are often used in practical models evaluating crop yield risk and insurance. Additionally, a simulation approach is used to compare the performance of alternative specifications when the underlying form is not known, to identify implications for the choice of parameterization of yield distributions in modeling contexts.

Using a unique high-quality farm-level corn yield data set, commonly used parametric, semi-parametric, and non-parametric distributions are examined against widely used in-sample goodness-of-fit (GOF) measures. Then, a simulation framework is used to assess the out-of-sample characteristics by using known distributions to generate samples that are assessed in an insurance valuation context under alternative specifications of the yield distribution.

Bias and efficiency trade-offs are identified for both in- and out-of-sample contexts, including a simple insurance rating application. Use of GOF measures in small samples can lead to inappropriate selection of candidate distributions that perform poorly in straightforward economic applications. The β distribution consistently overstates rates even when fitted to data generated from a β distribution, while the Weibull consistently understates rates; though small sample features slightly favor Weibull. The TCMN and kernel density estimators are least biased in-sample, but can perform very badly out-of-sample due to overfitting issues. The TCMN performs reasonably well across sample sizes and initial conditions.

Economic applications should consider the consequence of bias vs efficiency in the selection of characterizations of yield risk. Parsimonious specifications often outperform more complex characterizations of yield distributions in small sample settings, and in cases where more demanding uses of extreme-event probabilities are required.

The study helps provide guidance on the selection of distributions used to characterize yield risk and provides an extensive empirical demonstration of yield risk measures across a high-quality set of actual farm experiences. The out-of-sample examination provides evidence of the impact of sample size, underlying variability, and region of the probability measure used on the performance of candidate distributions.

The purpose of this paper is to examine how upstream social marketing can benefit from using social media commentary to identify cognitive biases. Using reactions to leading media/news publications/articles related to climate and energy policy in Australia, this paper aims to understand underlying community cognitive biases and their reasonings.

Social listening was used to gather community commentary about climate and energy policy in Australia. This allowed the coding of natural language data to determine underlying cognitive biases inherent in the community. In all, 2,700 Facebook comments were collected from 27 news articles dated between January 2018 and March 2020 using exportcomments.com. Team coding was used to ensure consistency in interpretation.

Nine key cognitive bias were noted, including, pessimism, just-world, confirmation, optimum, curse of knowledge, Dunning–Kruger, self-serving, concision and converge biases. Additionally, the authors report on the interactive nature of these biases. Right-leaning audiences are perceived to be willfully uninformed and motivated by self-interest; centric audiences want solutions based on common-sense for the common good; and left-leaning supporters of progressive climate change policy are typically pessimistic about the future of climate and energy policy in Australia. Impacts of powerful media organization shaping biases are also explored.

Through a greater understanding of the types of cognitive biases, policy-makers are able to better design and execute influential upstream social marketing campaigns.

The study demonstrates that observing cognitive biases through social listening can assist upstream social marketing understand community biases and underlying reasonings towards climate and energy policy.

A regression kink design (RKD or RK design) can be used to identify casual effects in settings where the regressor of interest is a kinked function of an assignment variable. In this chapter, we apply an RKD approach to study the effect of unemployment benefits on the duration of joblessness in Austria, and discuss implementation issues that may arise in similar settings, including the use of bandwidth selection algorithms and bias-correction procedures. Although recent developments in nonparametric estimation (Calonico, Cattaneo, & Farrell, 2014; Imbens & Kalyanaraman, 2012) are sometimes interpreted by practitioners as pointing to a default estimation procedure, we show that in any given application different procedures may perform better or worse. In particular, Monte Carlo simulations based on data-generating processes that closely resemble the data from our application show that some asymptotically dominant procedures may actually perform worse than “sub-optimal” alternatives in a given empirical application.

This investigation analyzes Asian, European and North American coverage of esports' justice, equity, diversity, and inclusion (JEDI) issues as a case study of media organizations' communications on these topics.

This quantitative content analysis describes coverage of esports' race, gender, age and social class issues to draw inferences about media organizations' abilities to meet the organizations' social responsibilities when reporting on organizational JEDI issues.

There were significant differences across continents; however, most stories only mentioned gender and age, seldom noting esports' race or social class issues.

Although all stories analyzed were published in English, the findings extend research suggesting culture may shape the tones, frames and salience of social justice issues in the media.

JEDI issues were not the most prominent topic in at least 80% of the coverage, indicating the normative framework guiding professional journalism since the Cold War fails to guide responsible engagement with contemporary social justice issues.

As one of the first studies analyzing media coverage of organizational JEDI issues, the results of this content analysis (N = 763) provide a quantitative basis for a critique of media organizations' social responsibility when reporting on these issues.

We know a great deal about the ways in which routines of news coverage may bias newspaper content, but little about how different article retrieval practices influence newspaper data assembled by scholars. Using the New York Times as a source of data on social movement activity, we compare depictions of protest by the African-American Civil Rights movement over time produced using the two most common article retrieval methods: index versus full-story coding. Full-story coding clearly offers more depth and greater breadth in terms of the events identified. Moreover, many of the same event characteristics associated with selection bias in newspaper reporting (e.g., size and confrontational nature of a protest event, presence of counter-demonstrators or police, and event sponsorship by a recognized social movement organization) are selected upon again when stories are indexed by New York Times staff.

We develop a Bayesian approach for modeling brain activation and connectivity from functional magnetic resonance image (fMRI) data. Our approach simultaneously estimates local hemodynamic response functions (HRFs) and activation parameters, as well as global effective and functional connectivity parameters. Existing methods assume identical HRFs across brain regions, which may lead to erroneous conclusions in inferring activation and connectivity patterns. Our approach addresses this limitation by estimating region-specific HRFs. Additionally, it enables neuroscientists to compare effective connectivity networks for different experimental conditions. Furthermore, the use of spike and slab priors on the connectivity parameters allows us to directly select significant effective connectivities in a given network.

We include a simulation study that demonstrates that, compared to the standard generalized linear model (GLM) approach, our model generally has higher power and lower type I error and bias than the GLM approach, and it also has the ability to capture condition-specific connectivities. We applied our approach to a dataset from a stroke study and found different effective connectivity patterns for task and rest conditions in certain brain regions of interest (ROIs).