Volume 10, Issue 4

ABSTRACT Purpose Despite the global shutdown of universities and research laboratories in 2020 due to the COVID-19 pandemic, a significant and unexpected increase in scientific production was observed during 2020 and especially in 2021 (Rousseau et al., 2023). A plausible explanation is that researchers took advantage of the lockdown period to write and develop pre-existing ideas. But what happened once that stockpile of ideas was exhausted? Design/methodology/approach This study aims to address that question by analyzing the scientific output of fourteen highly productive countries using data from three databases: WoS, Scopus, and OpenAlex. Findings Our analysis shows that, following the production peak in 2021, there was a general decline over the next two years (2022 and 2023) across most Western countries, including Japan and Brazil. However, this trend was not observed in China or India, which have maintained sustained growth since 2021. Russia, by contrast, has shown a continuous decline since 2021, likely related to its involvement in armed conflicts. Notably, this pattern of decline persists even when excluding the broad category of Life Sciences and Biomedicine . Research limitation The observed phenomenon cannot be fully explained. A broader understanding would require the wide distribution of a questionnaire among researchers and institutions. Practical implications This study provides insight into how the scientific system responded through its publication output to the temporary suspension of research institutions’ activities during the COVID-19 lockdown. Originality/value Our analysis contributes to understanding the unusual trends in research publications due to the pandemic’s influence. It can be seen as a discussion of a natural experiment in the science of science.

ABSTRACT Purpose The study evaluates the relative roles of Domestic-Funded Enterprises (DFEs) and Foreign-Funded Enterprises (FFEs) across Chinese provinces. It further examines how industrial structures differ by ownership at both regional and national scales. Drawing on these findings, the analysis traces the geographic shift of FFEs and offers evidence-based guidance for shaping foreign investment policy. Design/methodology/approach This study uses Inter-Country Input-Output (ICIO) and Inter-Region Input-Output (IRIO) tables to build two extended datasets: Ownership-Extended Inter-Country Input-Output (OE-ICIO) and Ownership-Extended Multi-Region Input-Output (OE-IRIO) tables. These are then combined to construct the Regionally-Ownership Dually-Extended Multi-Regional Input-Output (RODE-MRIO) table and Global Domestic and Foreign Enterprises Network (GDFEN) model to explore the industrial relocation trends of FFEs in China. Findings The results indicate that: (1) From 2010 to 2019, FFEs in China showed an overall decline in influence, profitability, and network robustness. (2) Eastern and developed regions saw a clear retreat of FFEs, with a shift toward domestic control. Central regions and more developed regions played a transitional role. Moreover, western and less developed regions remained stable but still relied on low-end and resource-based activities. (3) FFEs’ influence has been gradually shifting towards less-developed central and western provinces due to rising labor costs and stricter environmental regulations. Research limitations First, only general policy recommendations are proposed, without exploring the design of specific policy instruments. Second, the significant impact of recent trade measures on the behavior of FFEs has not been considered. Third, further research is needed to deepen the analysis by comparing regional ownership dynamics in China with those in other major economies. Practical implications This research provides valuable insights for policymakers aiming to enhance regional economic development and improve China’s position within Global Value Chains (GVCs). Originality/value The originality of this paper lies in its development of a new model that combines geographic, sectoral, and ownership dimensions. This model allows for a clearer analysis of the roles of foreign and DFEs in China’s regional economy. It offers insights into industrial transfer trends within global value chains and provides a framework for analyzing and forecasting future shifts.

ABSTRACT Purpose To describe the characteristics of research outputs using persistent identifiers generated by ResearchGate to gain insight into what publications are shared and disseminated through this functionality, revealing their academic and non-academic impact. Design/methodology/approach A total of 1,092,934 RG-DOIs were collected, using the DataCite API, along with bibliographic metadata for the associated registered output (RG-DOI publications). The subsequent analysis evaluated the publication date, document type, and language. These values were crossreferenced against the full text of a random sample of 666 records to verify accuracy. Findings RG-DOIs have served primarily to identify and make accessible scholarly gray literature, including posters, presentations, conference papers, and theses, with notable emphasis on publications in Spanish and Portuguese. Around 41,000 citations from Web of Science indexed publications to RG publications are evidence of their infrequent but perceptible use in scholarly discourse. The declining number of registrations of RG-DOIs observed may indicate a shift in researcher preferences to alternative platforms for DOI generation. Research limitations The study uncovered substantial inconsistencies in DataCite metadata, which can be attributed to the automated DOI registration process and internal changes in the available document types on ResearchGate. Practical implications The study encountered challenges in conducting a quantitative analysis due to inconsistencies in the metadata. These have potential implications for researchers, practitioners, and librarians relying on RG-DOIs to conduct bibliometric or bibliographic analysis. Originality/value This study is the first comprehensive analysis of RG-DOIs and, as such, provides a unique perspective into academic gray literature. It also sheds light on the quality of ResearchGate data transmitted to DataCite when registering DOIs.

ABSTRACT Purpose With the deep integration of information technologies into urban governance, smart communities have emerged as pivotal platforms for advancing sustainable urban development. However, existing research has not offered a systematic analysis or clear presentation of the field’s academic evolution and thematic structure. This study examines the literature on smart communities published between 2000 and 2024. Employing data analysis and visualization tools, it aims to trace the evolution and development trends of smart community research, map its core themes and their interrelationships, and provide actionable insights for policymaking and practical implementation. Design/methodology/approach Based on 2,347 publications indexed in the Web of Science from 2000 to 2024, this study employed CiteSpace and VOSviewer to conduct co-citation analysis, keyword co-occurrence mapping, national collaboration network analysis, author and institutional contribution assessment, burst detection, and hotspot term analysis. The literature screening adhered to predefined publication-type criteria and citation-count thresholds to ensure that the results were representative and reliable. Findings This study, through literature analysis and data visualization in the field of smart communities, yields the following principal conclusions. First, the application of digital twin technology in optimizing smart community resources has attracted growing attention, demonstrating considerable potential in urban management, infrastructure maintenance, and resident services. Second, as technology advances, digitaltwin applications are evolving towards greater precision and efficiency, particularly by deepening their support for resource allocation and decision-making processes. Finally, the future development of smart communities will increasingly depend on the deep integration of digital twins with other cutting-edge technologies, thereby driving intelligent management and optimization of community resources. Research limitations This literature repository excludes grey literature and non-English publications, potentially underestimating the representativeness of grassroots innovation. Furthermore, the temporal analysis was constrained by the citation-lag effects of publications from 2000 to 2024. Practical implications This study proposes a decision-support toolkit tailored for municipal planners and policymakers. The toolkit comprises three core intervention strategies: multiscale environmental sensing, participatory governance protocols, and regenerative technology pathways. These measures are designed to advance the implementation of the United Nations Sustainable Development Goal 11: Sustainable Cities and Communities. Originality/value By explicitly defining and applying the “thematic knowledge framework,” this paper offers a concise roadmap for the evolution of smart community research. It also provides precise guidance for designing and implementing community development strategies that align with Sustainable Development Goals.

ABSTRACT Purpose This paper examines African Journals Online (AJOL) as a bibliometric resource, providing a structured dataset of journal and publication metadata. In addition, it integrates AJOL data with OpenAlex to enhance metadata coverage and improve interoperability with other bibliometric sources. Design/methodology/approach The journal list and publications indexed in AJOL were retrieved using web scraping techniques. This paper details the database construction process, highlighting its strengths and limitations, and presents a descriptive analysis of AJOL’s indexed journals and publications. Findings The publication analysis demonstrates a steady growth in the number of publications over time but reveals significant disparities in their distribution across African countries. This paper presents an example of the possibility of integrating both sources using author country data from OpenAlex. The analysis of author contributions reveals that African journals serve as both regional and international venues, confirming that African journals play a dual role in fostering both regional and global research engagement Research limitations While AJOL contains relevant information for identifying and providing insights about African publications and journals, its metadata are limited. Therefore, the kind of analysis that can be performed with the database presented here is also limited. The integration with OpenAlex aims to overcome some of the limitations. Finally, although some automatic citation procedures have been performed, the metadata has not been manually curated. Therefore, if errors or inaccuracies are present in the AJOL, they may be reproduced in this database. Practical implications The database introduced in this article contributes to the accessibility of African scholarly publications by providing structured, accessible metadata derived from the AJOL. It facilitates bibliometric analyses that are more representative of African research activities. This contribution complements ongoing efforts to develop alternative data sources and infrastructure that better reflect the diversity of global knowledge production. Originality/value This paper presents a novel database for bibliometric analysis and offers a detailed report of the retrieval and construction procedures. The inclusion of matched data with OpenAlex further enhances the database’s utility. By showcasing AJOL’s potential, this study contributes to the broader goal of fostering inclusivity and improving the representation of African research in global bibliometric analyses.

ABSTRACT Purpose To explore how different types of research funding affect research papers, with implications for optimizing funding policies and promoting sustainable research development. Design/methodology/approach We used social network analysis and citation analysis to compare the influence of funded and non-funded papers, as well as among different funding types. Multidimensional scaling and cohesive subgroup analysis revealed thematic differences. Findings Funded papers do not always show higher academic influence than non-funded ones, but multifunded papers perform better than single-funded ones. Papers funded by international institutions and HKMT have a greater impact on the international academic community. Funded papers emphasize innovation and interdisciplinarity; non-funded papers focus more on classical theory application. Research limitations This study used only the WoS Core Collection, potentially missing other funding sources. Practical implications The findings inform the refinement of funding policies and support strategies that encourage impactful and innovative research. Originality/value This study offers a multi-level empirical analysis of how funding shapes research influence and thematic trends.

ABSTRACT Purpose This study aims to analyze academic research on Artificial Intelligence (AI) applications and tools in academic libraries, focusing on publications from the Scopus database between 2014 and 2024. Design/methodology/approach The study adheres to the PRISMA protocol, using VOSviewer, Bibliometrix, and Rstudio’s Biblioshiny function for bibliographic analysis and visualization. Findings The study highlights how the potential of AI in academic libraries may be increased by changing user needs and technical advancements. It comprises four thematic clusters: foundational technologies (machine learning, natural language processing, and automation), emerging innovations (generative AI), user-centric applications (chatbots), and the importance of AI literacy. It also reveals research gaps in automation and strategic AI integration, providing recommendations for improving library services. Research limitations The study is limited to articles published between 2014 and 2024 in the Scopus database, potentially excluding previous foundational work and research from other sources. Practical implications The study offers policymakers and library practitioners insightful information on effectively utilizing AI tools. This may result in overlooking earlier foundational work and research from multiple sources. Originality/value The study discovers the role of artificial intelligence (AI) in modernizing academic libraries, identifying research gaps, and providing strategic insights to improve technology and user experience.

ABSTRACT Purpose This study aims to address the lack of a unified grading framework in Chinese reading by proposing a comprehensive model integrating linguistic complexity and reading experience, to evaluate the difficulty level of Chinese books. Design/methodology/approach We utilized the Probabilistic Fuzzy Linguistic Technique (PFLT) for subjective indicators related to reading experience. Combined with collected objective indicators data, the Entropy Weight Method (EWM) is used for objective weight allocation, and Tomada De Decisão Interativa Multicritério (TODIM) is applied for multi-criteria decision-making. Findings The results demonstrate that the proposed framework significantly enhances the alignment between text complexity and reader engagement, achieving a high consistency with authoritative benchmarks. Research limitations The study may be limited by the scope of subjective and objective indicators used; further validation with more diverse data sources could strengthen generalizability. Practical implications This approach shows promise in advancing the standardization and objectivity of Chinese reading grading systems, with potential applications in educational and publishing contexts. Originality/value This study offers a structured evaluation model that incorporates both linguistic complexity and experiential factors, providing a novel and comprehensive method for assessing Chinese text difficulty.

ABSTRACT Purpose This paper focuses on scientific journals’ policies on open access and open science. The subject has gained increasing relevance, driven by the need for more-democratic access to knowledge and improved research visibility, which require eliminating the financial, legal, and technical barriers that restrict access to scientific output. Design/methodology/approach This paper uses the findings of FECYT’s 2023 Assessment of the Editorial and Scientific Quality of Spanish Scientific Journals, with 254 participating journals, as its case study. Open science indicators assess the transparency of policies on content access, reuse, openness, and reproducibility. Nonparametric tests analyse the relationship between the indicators and the dimensions of publisher type and subject area. Findings High compliance rates are found for indicators related to publication licences and intellectual property rights. Only 37% of the journals examined post their editorial policy on Sherpa Romeo. Ninety-four percent publish open access. However, open peer review is rarely applied (0.38% of the journals). Journals in Communication, Information and Scientific Documentation, Fine Arts, Education Science, and Biomedical Sciences have high compliance percentages. Most journals (83%) are institutional, with universities and associations generally exhibiting better results. Research limitations This study is based on specific indicators that do not cover all the factors that influence the transition toward open science; for example, editorial culture and technological infrastructure are not envisaged. Furthermore, differences in open science implementation are identified between disciplinary areas and between publisher types, but the underlying causes of these differences are not thoroughly investigated. Future research could address these points for a fuller understanding. Practical implications This study highlights the need for journals to improve transparency by adopting open peer review and clear policies. These changes enhance accessibility and credibility, fostering inclusive knowledge dissemination. Institutions and policymakers should support these efforts to boost research impact. Originality/value This study offers insights into open science practices in Spanish journals, a growing academic topic. Its originality lies in examining open science indicators across disciplines and publishers. By identifying strengths and gaps, the study helps journals enhance transparency.

ABSTRACT Purpose This study investigates the impact of domestic mobility on Chinese scientists’ academic performance and explores the predictors influencing their chances of moving to more prestigious institutions. Design/methodology/approach Using publication and affiliation data from OpenAlex, we identified 2,896 scientists who relocated between cities in China from 2014 to 2017. We applied propensity score matching (PSM) to compare their academic outcomes post-mobility with a matched group of non-mobile peers. Multiple performance metrics were examined, including publication count, citation impact, number of collaborators, and university prestige. Ordered logistic regression was used to analyze factors influencing moves to higher-level institutions. Findings Mobility enhances collaboration by increasing the number of coauthors but is associated with a short-term decline in citation impact. Scientists were more likely to move to lower-prestige universities. However, prior collaboration breadth and citation count positively predicted transitions to more prestigious institutions, while the number of publications did not. Research limitations This study focuses on intra-national mobility within China from 2014 to 2017 and relies on quantitative data, lacking personal or qualitative variables such as gender, discipline-specific norms, or institutional culture. Data coverage for Chinese-language publications may also be limited. Practical implications This research provides insights into academic hiring patterns and the trade-offs involved in scientist mobility. It offers valuable guidance for institutions aiming to enhance faculty recruitment and retention, as well as for researchers considering career transitions. Originality/value This is a quantitative analysis of domestic scientist mobility in China using matched comparison and multi-dimensional academic indicators. The integration of university prestige metrics (Double First-Class and citation-based rankings) offers a nuanced view of career dynamics within the Chinese higher education system.

ABSTRACT Purpose This study explores how academic mobility influenced research productivity and citation impact among Ukrainian scholars from 2005 to 2023. It examines the effects of evolving research assessment policies on scholarly publishing, particularly in the context of institutional affiliations and national versus international mobility. Design/methodology/approach Using publication and citation data from the Dimensions database, this study analyses two cohorts of Ukrainian scholars who began publishing during 2005–2013 and 2014–2023. The analysis categorises scholars by affiliation type (universities vs. National Academy of Sciences of Ukraine), and mobility status (non-mobile, nationally mobile, and internationally mobile), enabling a comparative evaluation of research output and impact. Findings The findings reveal a structural shift in affiliations, with a sharp increase in university-affiliated scholars and a decline in the National Academy of Sciences of Ukraine scholars. Although research output grew, citation impact remained low, particularly among non-mobile scholars. International mobility consistently correlates with higher productivity and impact, while national mobility yields modest gains, likely due to uniform constraints across domestic institutions. The post-2013 policy changes increased publication volume but not citation impact, as they focused primarily on quantity, not quality. Furthermore, Russo-Ukrainian war has severely constrained academic work. Research limitations The analysis is limited by the bibliometric database coverage and the reliance on initial and final affiliations as a proxy for mobility, which may overlook interim mobility events. Practical implications The findings suggest that research policy in Ukraine should go beyond incentivising publication counts and instead foster quality through enhanced infrastructure, sustained funding, and expanded international collaboration opportunities. Originality/value This study offers new insights into how mobility patterns interact with institutional structures and national policy in a war-affected research system.

ABSTRACT Purpose This study aims to integrate large language models (LLMs) with interpretable machine learning methods to develop a multimodal data-driven framework for predicting corporate financial fraud, addressing the limitations of traditional approaches in long-text semantic parsing, model interpretability, and multisource data fusion, thereby providing regulatory agencies with intelligent auditing tools. Design/methodology/approach Analyzing 5,304 Chinese listed firms’ annual reports (2015-2020) from the CSMAD database, this study leverages the Doubao LLMs to generate chunked summaries and 256-dimensional semantic vectors, developing textual semantic features. It integrates 19 financial indicators, 11 governance metrics, and linguistic characteristics (tone, readability) with fraud prediction models optimized through a group of Gradient Boosted Decision Tree (GBDT) algorithms. SHAP value analysis in the final model reveals the risk transmission mechanism by quantifying the marginal impacts of financial, governance, and textual features on fraud likelihood. Findings The study found that LLMs effectively distill lengthy annual reports into semantic summaries, while GBDT algorithms (AUC > 0.850) outperform the traditional Logistic Regression model in fraud detection. Multimodal fusion improved performance by 7.4%, with financial, governance, and textual features providing complementary signals. SHAP analysis revealed financial distress, governance conflicts, and narrative patterns (e.g., tone anchoring, semantic thresholds) as key fraud indicators, highlighting managerial intent in report language. Research limitations This study identifies three key limitations: 1) lack of interpretability for semantic features, 2) absence of granular fraud-type differentiation, and 3) unexplored comparative validation with other deep learning methods. Future research will address these gaps to enhance fraud detection precision and model transparency. Practical implications The developed semantic-enhanced evaluation model provides a quantitative tool for assessing listed companies’ information disclosure quality and enables practical implementation through its derivative real-time monitoring system. This advancement significantly strengthens capital market risk early warning capabilities, offering actionable insights for securities regulation. Originality/value This study presents three key innovations: 1) A novel “chunking-summarizationembedding” framework for efficient semantic compression of lengthy annual reports (30,000 words); 2) Demonstration of LLMs’ superior performance in financial text analysis, outperforming traditional methods by 19.3%; 3) A novel “language-psychology-behavior” triad model for analyzing managerial fraud motives.

ABSTRACT Purpose This research endeavors to investigate the impact of open government data on corporate investment, emphasizing the exploration of underlying mechanisms, heterogeneous effects, and implications for investment efficiency. Utilizing the implementation of government data open platforms as a quasi-natural experiment, this study aims to elucidate how public data transparency affects firms’ investment decisions and resource allocation. Design/methodology/approach This study employs a staggered Difference-in-Differences (DID) model as its principal methodological framework. This approach facilitates causal inference by examining the differential changes in corporate investment between firms influenced by the data openness policy and those that remain unaffected over time. Findings The findings indicate that open government data substantially enhance corporate investment levels. A mechanistic analysis identifies three principal channels through which this effect is mediated: alleviation of overall financing constraints, reduction of financing costs, and expansion of the financing scale. A heterogeneity analysis suggests that the positive impact is more pronounced in state-owned enterprises, high-tech firms, and companies experiencing elevated levels of macroeconomic uncertainty. Moreover, the transparency of government data improves the responsiveness of corporate investment to emerging opportunities, thereby augmenting the overall efficiency of corporate investment. Research limitations This study primarily examined the influence of government data transparency on corporate investment, while not accounting for the effects of macroeconomic variability, internal corporate governance frameworks, and industry-specific regulatory policies. Practical implications Government open data platforms can effectively boost corporate investment and resource allocation. Policymakers should focus on improving the quality and accessibility of these data, especially in areas with high economic uncertainty, to support business investments. Firms, particularly high-tech and financially constrained firms, can use open data to ease capital limitations and find investment opportunities. Regulators should promote data transparency to enhance economic vitality through efficient corporate investments. Originality/value This study enhances the existing literature by offering causal evidence of the impact of open government data on corporate investment, a subject that has been relatively underexplored empirically. By employing a quasi-natural experiment centered on the implementation of government data platforms, this study adopts a robust methodological approach to address endogeneity issues, thereby advancing methodological rigor in investigations of public data governance and corporate behavior.

ABSTRACT Purpose Explore the factors affecting medical data sharing in clinical research scenarios from the user’s perspective, reveal the differences between different user groups, and deepen the understanding of medical data sharing mechanisms. Design/methodology/approach By integrating the UTAUT model, trust theory and self-efficacy theory, introducing the concepts of data transparency and individual innovation, and combining internal and external motivators, we constructed a conceptual model of medical data users’ sharing behavior in clinical research scenarios. We conducted empirical research by collecting 360 pieces of first-hand data from clinical researchers. Findings Among the internal motivators, effort expectation had a higher impact on sharing intention than performance expectation, individual innovation and self-efficacy had a higher impact on sharing behavior than trust. Trust does not show a significant impact on sharing intention, but it has a significant positive influence on sharing behavior. Among the external motivators, community influence and data transparency both positively affect sharing intention. In addition, users with different working years, professional status, data level needs, and different sharing experiences showed significant differences in healthcare data sharing. Research limitations Our sample of clinical researchers from China was used as empirical data. Further research is needed to examine the generality of the study findings. Practical implications The findings enhance healthcare data stakeholders’ understanding of healthcare data sharing in clinical research scenarios and provide theoretical and practical insights for relevant researchers. Originality/value In this study, the UTAUT model, trust theory and self-efficacy theory were integrated and applied to clinical research scenarios for the first time, and the concepts of data transparency and individual innovation were introduced, and the CRS-USB conceptual model was constructed and validated to extend the UTAUT model.

ABSTRACT Purpose This study explores the implications of low journal self-citation rate (LJSCR). While some interpret LJSCR as a sign that a journal’s authors do not cite each other’s work, others see it as a mark of prestige, reflecting greater recognition from outside the journal. We argue that these perspectives are not contradictory: low self-citation can be prestigious precisely because it reflects low self-readership. Design/methodology/approach We analyze the physics and mathematics of journal self-citation. Our findings show that the self-citation rate (i) increases with journal market share, (ii) approaches a well-defined upper bound, (iii) this upper bound remains significantly below unity, and (iv) without a minimum level of market share, self-citation is virtually absent. Here, market share refers to the proportion of a journal’s publications relative to its Web of Science (WOS) subject category. To test our analysis, we examine 61 journal-years of data from three major hybrid fluid dynamics journals: Journal of Fluid Mechanics, Physical Review Fluids , and Physics of Fluids . Findings We identify a consistent relationship between journal self-citation and market share. A striking result is the mathematical analogy we establish between journal self-citation behavior and the concentration of intermediates in consecutive irreversible unimolecular chemical reactions. We also observe that creating specialized subdisciplinary journals (“twigging”) can reduce self-citation rates by narrowing scope. Research limitations The study is limited to fluid dynamics journals. Broader validation across disciplines is needed. Practical implications Editors and publishers can apply these insights to interpret citation metrics and assess the visibility and readership of their journals. Originality/value This work redefines LJSCR as a counterintuitive metric—one that may reflect both low author engagement and high external impact. It introduces a novel physics-based model to understand citation behavior across journals.

ABSTRACT Purpose This study investigates factors associated with scientific recognition, examining how collaboration networks influence the path to ACM fellowship. Design/methodology/approach We analyzed 1,497 ACM fellows (1994-2023) using linear regression on 286,791 publication records, examining co-authorship patterns and institutional overlaps while controlling for productivity metrics. Findings Collaboration with ACM fellows among new electees increased from 43% to over 90%. Collaborating with ACM fellows is associated with achieving fellowship 3.8 years earlier, with frequent, recent collaborations and prestigious collaborators exhibiting even shorter time intervals to recognition. Gender and institutional factors also significantly impact timing. Research limitations The study is correlational, focuses on one society, and may not capture all forms of scientific contribution beyond traditional metrics. Practical implications Current processes may favor well-connected candidates. Reforms should increase transparency and expand recognition criteria to address biases and promote inclusivity. Originality/value This provides the first comprehensive three-decade analysis of ACM fellowship patterns, revealing the growing importance of strategic networking in scientific recognition and offering evidencebased recommendations for more inclusive evaluation processes.