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This study aims to examine the current status and different characteristic features of research data repositories established by BRICS nations in order to understand the research data infrastructure within BRICS countries.

The data were collected from the re3data repository (http://www.re3data.org/), focusing specifically on BRICS nations. The data were analyzed to grasp the current status of research data repositories in BRICS countries. The dataset was retrieved on March 2, 2024. A total of 195 Research Data Repositories (RDRs) originating from BRICS countries were identified and selected for comprehensive analysis.

As of March 2, 2024, re3data.org indexes a total of 3,192 Research Data Repositories (RDRs) worldwide, with BRICS nations contributing 195. China leads among BRICS nations, followed by India, Russia, and Brazil. Scientific and Statistical Formats are the most shared content categories, followed closely by Standard Office Documents. There is notable diversity in the subjects covered by RDRs across BRICS nations. English is the primary interface language, followed by Chinese and Portuguese. “House, tailor-made” software is widely used for creating RDRs, followed by Dataverse and DSpace. Fourteen metadata standards are found, with Dublin Core metadata being the most prevalent, followed by the DataCite Metadata Scheme. Most repositories are disciplinary, followed by institutional ones. Most repositories specify data upload types, with “restricted” being the most common, followed by closed types. Open access is predominant in data access, followed by restricted access and embargo periods, while a small number restrict access entirely.

The present study will help gauge the strengths and weaknesses of the RDRs of BRICS nations and also learn how open these RDRs are for data access and upload provisions. The study contributes to a broader comprehension of the accessibility and usability of research data within the BRICS community, ultimately fostering greater transparency, collaboration, and knowledge dissemination within the scientific community, thus fostering greater innovation and advancement in research endeavors. The study provides a nuanced understanding of the research data infrastructure within BRICS nations, highlighting key trends, strengths, and areas for potential improvement.

This paper aims to assess the relationship between energy poverty and the well-being of people using Amartya Sen’s capability approach to development as theoretical underpinning.

The study uses household-level energy access data collected by the Harvard Dataverse in 2015 and 2018. The authors use multidimensional indices to measure energy poverty and well-being. Further, the authors apply quantile regression approach to measure the relationship between energy poverty and well-being.

The study’s findings reveal that energy poverty and well-being are negatively related. India has made progress in reducing energy poverty and improving well-being during the study period. However, progress in reducing energy poverty is largely due to improved access to electricity and improvement in well-being due to income and financial inclusion. Using modern cooking fuel has a greater negative impact on well-being compared to lighting using electricity. Further, households spending a greater proportion of their income on modern energy fuels leads to a lower quality of life as it precludes them from using it for other purposes. The study records wide variations in the observed relationship between energy poverty and well-being across various socioeconomic groups.

This calls for improvement in the production and distribution of modern energy resources, which have substantial welfare implications.

This is the first study to measure the relationship between energy poverty and quality of life using multidimensional indices. The findings of this paper have policy implications for the pricing of energy resources and energy access measures.

The rapid spread of the COVID-19 pandemic upended societies across the world, with billions forced into lockdowns. As countries contemplated instating and rolling back lockdown measures, and considered the impact of pandemic fatigue on policy measures, and furthermore to prepare for the improved management of future pandemics, this study examines the effectiveness of policy measures in limiting the spread of infections and fatalities.

The methodological approach in the study centres on a fixed effects panel regression analysis and employs the COVID-19 Government Response Stringency Index, which comprises eight containment measures and three health campaigns, with progressive degrees of stringency, in order to investigate the efficacy of government policies.

Findings suggest that some government policies were effective at reducing implicit mortality rates, infection cases and fatalities during the first four months of the COVID-19 pandemic. Solid stringency measures to reduce mortality rates include public gathering restrictions on more than 100 attendees, and international travel limits for developed countries and islands. Fatalities can further be reduced through the closing of public transport, whereas infection cases also experience benefits from public information campaigns. Comparable results are observed in a robustness test across 12 months.

Some non-pharmaceutical policies are shown to be more effective than others at reducing the spread of infections, fatalities and mortality rates, and support policymakers to manage future pandemics more effectively.

In the process of making agricultural production decisions in rural households, severe weather conditions, either extreme cold or heat, may squeeze the labor input in the agricultural sector, leading to a reallocation of labor between the agricultural and non-agricultural sectors. By applying a dataset with a wide latitude range, this study empirically confirms the influence of extreme temperatures on the agricultural labor reallocation, reveal the mechanism of farmers’ adaptive behavioral decision and therefore enriches the research on the impact of climate change on rural labor markets and livelihood strategies.

This study utilizes data from Chinese meteorological stations and two waves of China Household Income Project to examine the impact and behavioral mechanism of extreme temperatures on rural labor reallocation.

(1) Extremely high and low temperatures had led to a reallocation of labor force from agricultural activities to non-farm employment, with a more pronounced effect from extreme high temperature events. (2) Extreme temperatures influence famers’ decision in abandoning farmland and reducing investment in agricultural machinery, thus creating an interconnected impact on labor mobility. (3) The reallocation effect of rural labor induced by extreme temperatures is particularly evident for males, persons that perceives economic hardship or labor in economically active areas.

By applying a dataset with a wide latitude range, this study empirically confirms the influence of extreme temperatures on the agricultural labor reallocation, and reveals the mechanism of farmers’ adaptive behavioral decision and therefore enriches the research on the impact of climate change on rural labor markets and livelihood strategies.

Drawing on the literature on technological acquisition and the knowledge-based view , this study examines how technological overlap between acquiring and target firms influences acquisition premiums. We further explore how the resulting synergies are contingent on the dynamic characteristics of the target firm, specifically its technology clockspeed and industry munificence. Technology clockspeed indicates the pace of technological evolution, reflecting internal dynamic resources, while industry munificence represents the abundance of external resources. These boundary conditions illustrate the dynamics of synergies, explaining their moderation effects on acquisition premiums.

We analyze a sample of 369 technological acquisitions by publicly traded U.S. firms between 1990 and 2011. To test our hypotheses, we used the ordinary least squares regression model with robust standard errors clustered by acquiring firms. In the robustness checks, we applied the generalized estimating equations to account for non-independent observations in our sample and verified that the results were robust to an alternative two-way clustering approach.

We suggest that a low level of technological overlap between an acquiring firm and its target firm leads the acquiring firm to offer a high acquisition premium because of the expected synergistic potential that evolves from combining two distant technological bases. We further find that this effect is contingent on the target firm's technology clockspeed and industry munificence. Specifically, the negative effect is amplified when target firms exhibit a rapid pace of technological evolution, whereas it is weakened when target firms operate in highly munificent industries characterized by robust growth and abundant resource flows.

This study has several limitations, but it offers opportunities for future research. First, our sample is limited to domestic acquisitions between U.S. publicly traded firms, which may restrict generalizability. Cross-border acquisitions could reveal different dynamics, as technology leakage and national security concerns might make technological overlap a more sensitive factor. Additionally, private firms were not included, and their distinct strategic considerations could provide further insights. Future research could explore post-acquisition data to validate these synergies and expand the scope to include international contexts and private firms for a comprehensive analysis.

Our findings highlight important implications for managers in technology sector acquisitions. This study underscores the need for a thorough evaluation of target firms to avoid misjudging synergies. Low technological overlap can heighten expectations for value creation, making it crucial for executives to accurately assess potential synergies to prevent overestimation. Managers should consider both internal resources and external industry conditions when evaluating synergies. Ultimately, these insights help managers offer informed prices that reflect true strategic synergies, adopting effective valuation practices to mitigate risks of financial overpayments and poor post-merger performance.

The social implications of our findings emphasize the broader impact of acquisition decisions on innovation and competition within the technology sector. By ensuring accurate valuation and avoiding overpayment, companies can allocate resources more efficiently, fostering sustainable growth and innovation. This diligent approach can reduce the risk of corporate failures.

This study makes two key theoretical contributions. First, it identifies technological overlap as a critical determinant of acquisition premiums in technological acquisitions, addressing gaps in the literature that focused on CEO characteristics and managerial attention. Second, it expands the theoretical framework by highlighting the dynamic nature of synergies, influenced by the target firm's technology clockspeed and industry munificence. By integrating both acquiring and target firm characteristics, this study provides a relational perspective on value creation, explaining why firms pay high premiums and offering a more comprehensive understanding of the strategic motivations in technological acquisitions.

In this paper, the impact of the 2004 European Union accession on income inequalities within New Member States is analyzed.

An empirical analysis is conducted with nine New Member States over the period 1991–2015, with 55 economies serving as a control group. The newly introduced (by de Chaisemartin and D’Haultfœuille, 2023) method belonging to the family of difference-in-differences (DID) estimators is applied to allow for multiple non-binary treatments.

While accession to the European Union had a positive and significant impact on the market and net Gini coefficients in the treated countries, no evidence of the impact of accession on redistribution was found. Single-unit estimates signal that income inequalities rose due to EU membership in some member countries; the most convincing evidence shows that income distribution in Latvia was especially affected.

The author applied the method which addresses the presence of multiple non-binary treatments. Full-fledged membership was preceded by association status, and accession to the EU was accompanied or followed by engagement in other layers of integration (European Monetary Union and Schengen Area). Controlling for these features, the author was able to assess whether the pure EU effect contributed to increases in income inequalities.

The study aims to identify the practical borders of AI legal personality and accountability in human-centric services.

Using a framework tailored for AI studies, this research analyses structured interview data collected from auditors based in Poland.

The study identified new constructs to complement the taxonomy of arguments for AI legal personality: cognitive strain, consciousness, cyborg paradox, reasoning replicability, relativism, AI misuse, excessive human effort and substitution.

The insights presented herein are primarily derived from the perspectives of Polish auditors. There is a need for further exploration into the viewpoints of other key stakeholders, such as lawyers, judges and policymakers, across various global contexts.

The findings of this study hold significant potential to guide the formulation of regulatory frameworks tailored to AI applications in human-centric services. The proposed sui generis AI personality institution offers a dynamic and adaptable alternative to conventional legal personality models.

The outcomes of this research contribute to the ongoing public discourse on AI’s societal impact. It encourages a balanced assessment of the potential advantages and challenges associated with granting legal personality to AI systems.

This paper advocates for establishing a sui generis AI personality institution alongside a joint accountability model. This dual framework addresses the current uncertainties surrounding human, general AI and super AI characteristics and facilitates the joint accountability of responsible AI entities and their ultimate beneficiaries.

The study aims to profile the scientific retractions in the top five global universities and provide descriptive statistics on specific subjects.

The data for reasons behind retractions is manually extracted from the Retraction Watch Database. The top five global universities according to the Times Higher Education global ranking of 2024 are selected for this study.

The study found that Stanford University emerged with the highest number of retractions in the assessment across institutions in the field of basic life sciences and health sciences. Notably, the predominant reasons for these retractions were identified, with “unreliable results” being the most prevalent, accounting for 53 retractions. Following closely was the category of “errors in results and/or conclusions”, contributing to 51 retractions. MIT has the longest time between publication and retraction of any subject group, with an average of 1,701 days.

This study has some limitations, as it only analysed the retractions of the top five global universities.

The study provides a comprehensive analysis of retractions in academic publishing, focusing on reasons, time gaps, article types and accessibility categories across prestigious universities. The paper underscores the critical role of retractions in maintaining the integrity of scientific literature, emphasizing the importance of transparent correction and responsible peer review to ensure the reliability and trustworthiness of published research. Results show that common reasons for retractions include duplication, fake peer review and plagiarism, underlining the need for ethical research standards.

This study aims to exploit Tobin’s Q model of investment to examine the relationship between corporate governance and green innovation.

The study is based on a sample of 3,896 firms from 2002 to 2021, covering 45 countries worldwide. The authors adopt Tobin’s Q model to conceptualize the relationship between corporate governance and investment in green research and development (R&D). The authors argue that agency costs and financial market frictions affect corporate investment and are fundamental factors in R&D activities. By limiting agency conflicts, effective governance favors efficiency, facilitates access to external financing and encourages green innovation. The authors analyzed the causal effect by using the system-generalized method of moments (system-GMM).

The results reveal that the better the corporate governance, the more the firm invests in green R&D. A 1%-point increase in the corporate governance ratings leads to an increase in green R&D expenses to the total asset ratio of about 0.77 percentage points. In addition, an increase in the score of each dimension (strategy, management and shareholder) of corporate governance results in an increase in the probability of green product innovation. Finally, green innovation is positively related to firm environmental performance, including emission reduction and resource use efficiency.

The findings provide implications to support managers and policymakers on how to improve sustainability through corporate governance. Governance mechanisms will help resolve agency problems and, in turn, encourage green innovation.

Understanding the impact of corporate governance on green innovation may help firms combat climate change, a crucial societal concern. The present study helps achieve one of the precious UN’s sustainable development goals: Goal 13 on climate action.

This study goes beyond previous research by adopting Tobin’s Q model to examine the relationship between corporate governance and green R&D investment. Overall, the results suggest that effective corporate governance is necessary for environmental efficiency.

This study addresses the need for a theory-guided, rich, descriptive account of research data repositories' (RDRs) understanding of data quality and the structures of their data quality assurance (DQA) activities. Its findings can help develop operational DQA models and best practice guides and identify opportunities for innovation in the DQA activities.

The study analyzed 122 data repositories' applications for the Core Trustworthy Data Repositories, interview transcripts of 32 curators and repository managers and data curation-related webpages of their repository websites. The combined dataset represented 146 unique RDRs. The study was guided by a theoretical framework comprising activity theory and an information quality evaluation framework.

The study provided a theory-based examination of the DQA practices of RDRs summarized as a conceptual model. The authors identified three DQA activities: evaluation, intervention and communication and their structures, including activity motivations, roles played and mediating tools and rules and standards. When defining data quality, study participants went beyond the traditional definition of data quality and referenced seven facets of ethical and effective information systems in addition to data quality. Furthermore, the participants and RDRs referenced 13 dimensions in their DQA models. The study revealed that DQA activities were prioritized by data value, level of quality, available expertise, cost and funding incentives.

The study's findings can inform the design and construction of digital research data curation infrastructure components on university campuses that aim to provide access not just to big data but trustworthy data. Communities of practice focused on repositories and archives could consider adding FAIR operationalizations, extensions and metrics focused on data quality. The availability of such metrics and associated measurements can help reusers determine whether they can trust and reuse a particular dataset. The findings of this study can help to develop such data quality assessment metrics and intervention strategies in a sound and systematic way.

To the best of the authors' knowledge, this paper is the first data quality theory guided examination of DQA practices in RDRs.