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Since the development of the COVID-19 vaccinations, questions surrounding race have been prominent in the literature on vaccine uptake. Early in the vaccine rollout, public health officials were concerned with the relatively lower rates of uptake among certain racial/ethnic minority groups. We suggest that this may also be patterned by racial/ethnic residential segregation, which previous work has demonstrated to be an important factor for both health and access to health care.

In this study, we examine county-level vaccination rates, racial/ethnic composition, and residential segregation across the U.S. We compile data from several sources, including the American Community Survey (ACS) and Centers for Disease Control (CDC) measured at the county level.

We find that just looking at the associations between racial/ethnic composition and vaccination rates, both percent Black and percent White are significant and negative, meaning that higher percentages of these groups in a county are associated with lower vaccination rates, whereas the opposite is the case for percent Latino. When we factor in segregation, as measured by the index of dissimilarity, the patterns change somewhat. Dissimilarity itself was not significant in the models across all groups, but when interacted with race/ethnic composition, it moderates the association. For both percent Black and percent White, the interaction with the Black-White dissimilarity index is significant and negative, meaning that it deepens the negative association between composition and the vaccination rate.

The analysis is only limited to county-level measures of racial/ethnic composition and vaccination rates, so we are unable to see at the individual-level who is getting vaccinated.

We find that segregation moderates the association between racial/ethnic composition and vaccination rates, suggesting that local race relations in a county helps contextualize the compositional effects of race/ethnicity.

This paper aims to analyse through a bibliometric study the academic literature that relates entrepreneurship to foods.

A database of 1,300 papers published in the ISI Web of Science was generated. The bibliometric techniques allowed us to describe scientific literature evolution, most productive authors, institutions and countries, most relevant sources and documents, trend topics and social structure.

The results illustrate an upward trend, more accentuated in the last four years, in publishing papers relating entrepreneurship to the food industry.

This research is novel because although numerous articles relate the food industry to entrepreneurship, no bibliometric articles that analyse the scientific production that relates both terms have been found in the literature.

This study aims to offer a bibliometric analysis of the already substantial and growing literature on global virtual teams (GVTs).

Using a systematic literature review approach, it identifies all articles in the Web of Science from 1999 to 2021 that include the term GVTs (in the title, the abstract or keywords) and finds 175 articles. The VOSviewer software was applied to analyze the bibliometric data.

The analysis revealed three dialogizing research clusters in the GVTs literature: a pioneering management information systems and organizational cluster, a general management cluster and a growing international management and behavioural studies cluster. Furthermore, it highlights the most cited articles, authors, journals and nations, and the network of strong and weak links regarding co-authorships and co-citations. Additionally, this study shows a change in research patterns regarding topics, journals and disciplinary approaches from 1999 to 2021. Finally, the analysis illustrates the position and centrality in the network of the most relevant actors.

The findings can guide management practitioners, educators and researchers to the most meaningful clusters of publications on GVTs, and help navigate and make sense of the vast body of the available literature. The importance of GVTs has been growing in the past two decades, and Covid-19 has accelerated the trend.

This study provides an updated and comprehensive systematic literature review on GVTs. To the best of the authors’ knowledge, it is also the first systematic literature review and bibliometry on GVTs. It concludes by suggesting future research paths.

This study, a theoretical article, aims to introduce new institutionalism as a framework through which business and management researchers can explore the significance of artificial intelligence (AI) in organizations. Although the new institutional theory is a fully established research program, the neo-institutional literature on AI is almost non-existent. There is, therefore, a need to develop a deeper understanding of AI as both the product of institutional forces and as an institutional force in its own right.

The authors follow the top-down approach. Accordingly, the authors first briefly describe the new institutionalism, trace its historical development and introduce its fundamental concepts: institutional legitimacy, environment and isomorphism. Then, the authors use those as the basis for the queries to perform a scoping review on the institutional role of AI in organizations.

The findings reveal that a comprehensive theory on AI is largely absent from business and management literature. The new institutionalism is only one of many possible theoretical perspectives (both contextually novel and insightful) from which researchers can study AI in organizational settings.

The authors use the insights from new institutionalism to illustrate how a particular social theory can fit into the larger theoretical framework for AI in organizations. The authors also formulate four broad research questions to guide researchers interested in studying the institutional significance of AI. Finally, the authors include a section providing concrete examples of how to study AI-related institutional dynamics in business and management.