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The unprecedented power of Big Tech corporations and the complexity of regulating them requires considering the role of news media as a corporate accountability mechanism. The aim of this article is to examine news media's watchdog role through a performative lens, extending it to the corporate context by looking at three manifestations of performance: media visibility, way of coverage, and sourcing patterns. Based on a longitudinal quantitative content analysis of 920 news stories on Big Tech from four media outlets (2000–2021) in the United States and Germany, we explain variances in the watchdog role taking an over-time, cross-country, and cross-political leaning perspective. Our findings suggest that Big Tech corporations receive little but increasing media attention over the years that is driven by events. Similarly, news coverage contains an increasingly strong presence of a detached watchdog role performance. A more interventionist watchdog role is also on the rise – even though marginally – and political actors are increasingly given a voice in news coverage. From a comparative perspective, our results show that Big Tech corporations are more visible in the US and across right leaning news outlets. US journalists exhibit a more detached approach, while German journalists perform a significantly higher interventionist performance. Corporate sources are overall prominent in the media arena yet dominate more in the US. In conclusion, US and German news media are critical but passive observers over Big Tech corporations and can therefore be labeled a tame yet increasingly growling watchdog when it comes to holding Big Tech accountable. [ABSTRACT FROM AUTHOR]

With the "visual turn" online, images have become increasingly important for companies to attract stakeholders to their online CSR communication. To investigate in how far businesses' visual sustainability language reflects a balanced triple bottom line, this study compared the most profitable European corporations' websites images (N = 21,841) and visual Twitter posts (N = 3,637) through automated content analysis using computer vision algorithms. The findings of this big data‐analysis reveal that European companies overemphasize the financial bottom line on both owned and shared media. The channel matters as firms are more likely to communicate people‐, planet‐, and profit‐related images via social media than their website. Corporations from environmentally sensitive industries tend to highlight the social dimension, though this is where they impact less, and do so more often through their website. Thus, this study confirms the criticism that the business case is dominant in CSR strategies also for visual communication. [ABSTRACT FROM AUTHOR]

The increasing volume of images published online in a wide variety of contexts requires communication researchers to address this reality by analyzing visual content at a large scale. Ongoing advances in computer vision to automatically detect objects, concepts, and features in images provide a promising opportunity for communication research. We propose a research protocol for Automated Visual Content Analysis (AVCA) to enable large-scale content analysis of images. It offers inductive and deductive ways to use commercial pre-trained models for theory building in communication science. Using the example of corporations' website images on sustainability, we show in a step-by-step fashion how to classify a large sample (N = 21,876) of images with unsupervised and supervised machine learning, as well as custom models. The possibilities and pitfalls of these approaches are discussed, ethical issues are addressed, and application examples for future communication research are detailed. [ABSTRACT FROM AUTHOR]

When it comes to corporate social responsibility (CSR) reports, skepticism and mistrust toward companies often prevail among both stakeholders and scholars. However, the overall content quality of CSR reports has only sparsely evoked researchers' attention. Therefore, this paper focuses on the credibility of CSR reports, based on both human and software-enhanced quantitative content analysis of 237 CSR reports from 11 European countries. Credibility is conceptualized as a multilayered construct along CSR and communication theories, filling the theory void in the field. The influence of contextual (e.g., industry), format, and firm-level (e.g., size) factors on reporting credibility is also investigated. The results show that European CSR reports do not score high on credibility, leaving much room for improvement. The study finds that it is standardization and content that matter most for reporting credibility; external influences are secondary, at best. To be considered credible, CSR reports must first be understandable to their readers; in addition, credibility involves truth, sincerity, and stakeholder specificity. Voluntary standardization affects CSR reporting credibility positively, whereas regulation does not yet have the same positive effect. Based on the empirical results of the content analysis, we present a new two-phase-model of reporting credibility . For practice, we suggest that companies focus on the contents of their reports to render them more credible. To raise credibility levels consistently, policy makers can create a level playing field in CSR reporting regulation, as already initiated by several countries. [ABSTRACT FROM AUTHOR]

The aim of this article is to discuss quantitative content analysis as established in communication sciences as a method for research in business ethics. We argue that communication sciences and business ethics are neighboring disciplines, which allow the transfer of quantitative content analysis from communication sciences to business ethics. Technically, quantitative content analysis can be applied through human as well as software coding. Examples for both applications are provided and discussed. We make reference to the software solutions ' Leximancer', ' Crawdad', and ' Wordle', and examine their suitability and limitations with regard to ethical questions for researching business ethics. We recommend a mixed-method approach, combining human and software coding. Furthermore, we propose a three-step process that discusses quantitative content analysis as a method for business ethics, including an ethical research objective, and most importantly, concluding with ethical reasoning and interpretation of the quantitative results. [ABSTRACT FROM AUTHOR]

• Studies on organization-stakeholder relationships online are business-centered. • New definition of relationships focuses on awareness and communication. • Emphasizes a PR-specific, communicative perspective on relationships. • Digital data points are underused in public relations research. • Meta-data indicate the beginning and end of a relationship. The digital age is a game-changer for the communication between organizations and stakeholders. Relationships are pivotal to public relations. However, their conceptualizations, measures, and normative assumptions have neither been analyzed systematically and across disciplines, nor have they been studied in light of the changing digital communication landscape. This article re-examines the relationship paradigm in public relations and marketing in an online environment. By means of a systematic review, it seeks to explicate communicative relationships between organizations and their diverse stakeholders, to review how they are operationalized and measured, and to illuminate their normative evaluations. This conceptual specification is guided by systematic sampling and content analysis of all primary research on organization-stakeholder relationships in the broader social sciences. Results of a comprehensive analysis of 74 articles suggest that studies overemphasize the business contexts, follow an instrumental orientation based on transactions rather than communication, and lack analyses of digital data. To explicate a PR understanding of digital communicative organization-stakeholder relationships, a definition is provided and a research agenda is offered on theory, measures, and blind spots. [ABSTRACT FROM AUTHOR]