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The paper aims to investigate the importance of leadership – distributed leadership (DL) – for the relationship between organizational learning (OL) and business sustainability (BS).

Extensive literature research was carried out to investigate the relationship among leadership, OL and BS. Two theoretical frameworks of the relationship among DL, OL and BS were formulated and tested on the basis of the empirical studies conducted in 694 Polish and Danish companies. The moderated multiple regression and mediation analysis were used.

In-depth, critical literature analysis has shown that the theoretical foundation of the relationship between leadership and BS is limited and not empirically verified. However, the empirical study has revealed a positive, statistically significant effect of DL on both OL and BS and the mediating role of OL on the relationship between DL and BS (a partial and complimentary mediation).

It would be valuable to simultaneously consider other leadership types (beyond DL) in terms of their impact on OL and BS. Additionally, due to the nature of BS challenges and the specificity of DL, other factors influencing BS should be included for a more profound understanding of the relationships under investigation. Finally, additional contextual factors need to be taken into account.

To the best of the authors’ knowledge, the paper is one of the first studies that present the relationship between OL and BS with reference to factors influencing BS, i.e. leadership. The value of the paper is the development of two alternative models of the relationship among DL, OL and BS and their verification through large-scale empirical cross-country research. Furthermore, the results obtained in the course of the research open up new research directions with respect to the development of the concept of sustainable leadership and deepen the knowledge of the relationship between leadership types and OL.

No comprehensive statistical assessment of publication bias has been conducted in remdesivir-based intervention research for COVID-19 patients. This study aims to examine all meta-analyses of the efficacy of remdesivir interventions in COVID-19 patients and perform a statistical assessment of publication bias.

This is an analytic study conducted to assess the impact of publication bias on the results of meta-analyses of remdesivir-based interventions in patients infected with COVID-19. All English full-text meta-analyses published in peer-reviewed journals in 2019–2021 were included. A computerized search of PubMed and Web of Science electronic databases was performed on December 24, 2021. The trim-and-fill method calculated the number of missing studies and the adjusted cumulative effect sizes.

The final analysis comprised 21 studies with 88 outcomes. The investigation revealed missing studies in 46 outcomes (52%). Seventy-six missing studies were replaced in the outcomes using the trim-and-fill procedure. The adjusted recalculated effect sizes of the 27 outcomes increased by an average of 0.04. In comparison, the adjusted effect size of 18 outcomes fell by an average of 0.036. Only 14 out of 46 outcomes with publication bias were subjected to a gray literature search (30%). To discover related research, no gray literature search was conducted in most outcomes with publication bias (n = 32; 70%). In conclusion, the reported effect estimates regarding the effect of remdesivir in COVID-19 patients are only slightly affected by publication bias and can be considered authentic. Health-care decision-makers in COVID-19 should consider current research results when making clinical decisions.

Most health decisions are based on the effect sizes revealed in meta-analyses. When deciding on remdesivir-based treatment for COVID-19 patients, therefore, the outcomes of this investigation may be of paramount importance to health policymakers, leading to better treatment strategies.

According to the results, no major publication bias and missing studies were detected on average. Therefore, the calculated effect sizes of remdesivir-based interventions on meta-analyses can be used as authentic and unbiased benchmarks by health-care decision-makers in treating patients with COVID-19.

This is the first study to examine the effect of publication bias and gray literature searches on the results of meta-analyses of treatment with COVID-19 (remdesivir).

The growing interest on ecosystems on both managerial and academic side reflects a vibrant field of study, nevertheless, the subject is very heterogeneous and in particular concerning a particular form of ecosystems: collaborative ecosystems, the purpose of this study is to shed light on these.

The definition gap existent and the multiplicity of fields and composite studies on collaborative ecosystems have made it necessary to conduct a systematic literature review of the phenomenon, a bibliometric analysis and content analysis.

The research offers an overview of the phenomenon, three main cluster categories emerged: ecosystem, competition and open innovation; this cluster offers a comprehensive schematization of the research topics.

Limitations of this research are that the investigation has been confined to specific keywords related to collaborative ecosystems that led to a strict selection of papers to be analyzed and in having confined the investigation to publications in the managerial area. Further research will be able to expand the selection of keywords or use more strings and proceed to a comparative analysis of results. Another limitation is the small number of databases assessed (only Scopus and Web of Science) and have confined the analysis to specific clusters that emerged from the quantitative analysis of papers.

By proposing a taxonomy of collaborative ecosystems, the paper highlights what solutions and architectures an ecosystem manager must consider when developing collaborative ecosystems, what factors he or she must pay attention to in order to ensure their development and survival, and what elements determine the development of an ecosystem, in particular, the management of innovation and knowledge and the scarcity of resources in a hyper-competitive environment.

A combination of research methods was used to identify the intellectual structure of collaborative ecosystems. In addition, we propose a taxonomy for collaborative ecosystems, opening the study to new lines of investigation and providing a new research agenda.