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This study examines the tax avoidance behavior of firms prior to the issuance, and following the resolution, of SEC tax comment letters. We find that firms that appear to engage in greater tax avoidance are more likely to receive a tax-related SEC comment letter. We also find that firms receiving a tax-related SEC comment letter, relative to firms receiving a non-tax comment letter, subsequently decrease their tax avoidance behavior consistent with an increase in expected tax costs. Additionally, we document evidence consistent with other firms that do not receive a comment letter reacting to multiple publicly disclosed tax-related comment letters within their industry by lowering their reported GAAP ETR, consistent with an indirect effect of regulatory scrutiny on certain types of tax avoidance.

A growing body of work makes use of in order to investigate the working of neural models, often considered black boxes. Recently, an ongoing debate emerged surrounding the limitations of the probing paradigm. In this work, we point out the inability to infer behavioral conclusions from probing results, and offer an alternative method that focuses on how the information is being used, rather than on what information is encoded. Our method, , follows the intuition that the utility of a property for a given task can be assessed by measuring the influence of a causal intervention that removes it from the representation. Equipped with this new analysis tool, we can ask questions that were not possible before, for example, is part-of-speech information important for word prediction? We perform a series of analyses on BERT to answer these types of questions. Our findings demonstrate that conventional probing performance is not correlated to task importance, and we call for increased scrutiny of claims that draw behavioral or causal conclusions from probing results.

While the disruptive potential of artificial intelligence (AI) and big data has been receiving growing attention and concern in a variety of research and application fields over the last few years, it has not received much scrutiny in contemporary entrepreneurship research so far. Here we present some reflections and a collection of papers on the role of AI and big data for this emerging area in the study and application of entrepreneurship research. While being mindful of the potentially overwhelming nature of the rapid progress in machine intelligence and other big data technologies for contemporary structures in entrepreneurship research, we put an emphasis on the reciprocity of the coevolving fields of entrepreneurship research and practice. How can AI and big data contribute to a productive transformation of the research field and the real-world phenomena (e. g., “smart entrepreneurship”)? We also discuss, however, ethical issues as well as challenges around a potential contradiction between entrepreneurial uncertainty and rule-driven AI rationality. The editorial gives researchers and practitioners orientation and showcases avenues and examples for concrete research in this field. At the same time, however, it is not unlikely that we will encounter unforeseeable and currently inexplicable developments in the field soon. We call on entrepreneurship scholars, educators, and practitioners to proactively prepare for future scenarios.

Under increased pressure to report environmental impacts, some firms selectively disclose relatively benign impacts, creating an impression of transparency while masking their true performance. We theorize circumstances under which firms are less likely to engage in such selective disclosure , focusing on organizational and institutional factors that intensify scrutiny and expectations of transparency and that foster civil society mobilization. We test our hypotheses using a novel panel data set of 4,750 public companies across many industries that are headquartered in 45 countries during 2004–2007. Results show that firms that are more environmentally damaging, particularly those in countries where they are more exposed to scrutiny and global norms, are less likely to engage in selective disclosure. We discuss contributions to research on institutional theory, strategic management, and information disclosure.

Do firms gain environmental legitimacy when they conform to external expectations regarding the natural environment? Drawing on institutional logic and signaling theory, we investigate sources of heterogeneity in the impacts of environmental actions on environmental legitimacy. Longitudinal data (1997-2001) about 325 publicly traded U.S. firms in polluting industries support the notion that environmental actions help firms gain environmental legitimacy. However, some actions instead can harm this legitimacy if environmental performance deteriorates and the firm is subject to intense scrutiny from nongovernmental organizations. Thus, an important contribution of this research is to identify conditions under which greenwashing can backfire.

There are four areas in which soft power is open to critical scrutiny. First, research has centred on large and/or developed nations, notably in North America, Europe, east Asia and the BRICs. Second, scholars have called for greater clarity of the concept, noting that it lacks clear explanation of how instances of attraction equate to various power outcomes. Third, others suggest Joseph Nye developed an Americanized-centric understanding of soft power, and hence a narrow account of what constitutes ‘attraction’. Finally, research has failed to examine how states’ soft power attempts can backfire, leading to what we call ‘soft disempowerment’. Drawing on the case of Qatar—with a particular focus on the state’s acquisition of the 2022 FIFA World Cup finals—we seek to offer responses to these criticisms. We do this by refining the concept of soft power to take account of variegated power outcomes, and by focusing on a small state and a non-American context, in order to explore the intersections of soft power and soft disempowerment. In doing so, we introduce the ‘soft power–soft disempowerment nexus’ which, we go on to argue, affords an analytical framework for examining how soft power works and how it may be hampered through negative international scrutiny.

Immigration scholars have increasingly questioned the idea that “illegality” is a fixed, inherent condition. Instead, the new consensus is that immigration laws produce “illegality.” But can “illegality” be socially constructed? When initially judging who is an “illegal immigrant,” common observers and even authorities typically do not rely on an individual’s documentation. Instead, people rely on shared stereotypes to assign “illegality” to certain bodies, a condition we refer to as “social illegality.” Ethnographers have documented that individual traits like occupation or national-origin may trigger illegality suspicions, but it is not clear how widespread these stereotypes are, or whether all stereotypes are equally consequential. To address this question, we examine the personal attributes shaping perceived “illegality.” We apply a paired conjoint survey experiment on a nationally representative sample of 1,515 non-Hispanic white U.S. adults to assess the independent effect of each dimension. We find that national origin, social class, and criminal background powerfully shape perceptions of illegality. These findings reveal a new source of ethnic-based inequalities—“social illegality”—that may potentially increase law enforcement scrutiny and influence the decisions of hiring managers, landlords, teachers, and other members of the public.

Corporate greenwashing has accelerated in recent years, bringing in its wake growing skepticism about corporate green claims. Although a theory of the drivers and deterrents of greenwashing has begun to emerge, it is static in nature and does not incorporate the full range of ways in which firms can misrepresent their environmental performance. Our contribution is threefold. First, we extend the theory of organizational information disclosure to incorporate the possibility of undue modesty about a firm’s environmental, social, and governance practices. Second, we hypothesize about the drivers of exaggeration and undue modesty based on which of a firm’s stakeholders are salient at a given point in time; to do so, we place the firm within a dynamic context that has largely been missing in the prior literature. Third, we test our hypotheses using a data set that allows us to directly compare corporate green claims against actual performance. Results reveal that corporate output growth, deregulation, and low profits under deregulation significantly affect the choice between greenwashing and brownwashing. The effects of growth and profits are mitigated by external scrutiny.

In response to growing concerns of bias, discrimination, and unfairness perpetuated by algorithmic systems, the datasets used to train and evaluate machine learning models have come under increased scrutiny. Many of these examinations have focused on the contents of machine learning datasets, finding glaring underrepresentation of minoritized groups. In contrast, relatively little work has been done to examine the norms, values, and assumptions embedded in these datasets. In this work, we conceptualize machine learning datasets as a type of informational infrastructure, and motivate a genealogy as method in examining the histories and modes of constitution at play in their creation. We present a critical history of ImageNet as an exemplar, utilizing critical discourse analysis of major texts around ImageNet’s creation and impact. We find that assumptions around ImageNet and other large computer vision datasets more generally rely on three themes: the aggregation and accumulation of more data, the computational construction of meaning, and making certain types of data labor invisible. By tracing the discourses that surround this influential benchmark, we contribute to the ongoing development of the standards and norms around data development in machine learning and artificial intelligence research.

Are populists really more emotional than mainstream parties? The alleged link between populism and emotional communication has barely been subject to systematic empirical scrutiny. I use literature on populist communication and appraisal theory to generate expectations about which political parties use which emotiona appeals. I test these claims by applying a novel emotional dictionary to a large set of text data including more than 700,000 press releases and tweets from three European countries. As expected, I find that populist parties use significantly more negative emotional appeals (anger, fear, disgust, sadness) and less positive emotional appeals (joy, enthusiasm, pride, hope) than mainstream parties. Furthermore, I find that political actors adapt the usage of emotional appeals to different purposes depending on the communication medium and the politicians' status level. This study entails important implications for the research on emotional appeals in politics and populist communication.