Purpose: The study examines the synergy and hysteresis in the evolution of funding and its supported literature, depicts their temporal correlation mechanism, which aids in improving trend predictions.
Design/methodology/approach: The study uses the LDA model to identify topics in funding texts and supported papers. A cosine similarity algorithm was employed to estimate the nexus between topics and construct the topic evolution time series. Similarly, the hysteresis effect in topic evolution is analyzed based on topic popularity and content, leading to insights into their temporal correlation mechanism.
Findings: The study finds that fund and sponsored paper topics exhibit strong collaboration with a noticeable lag in evolution. The fund topics significantly influence sponsored paper topics after a two-year lag. Moreover, the lag effect is inversely proportional to the topic’s similarity.
Research limitations: We use the LDA model to determine the hysteresis effect in topic evolution despite its limitations in handling long-tail words and domain-specific vocabulary. Furthermore, the timing of the emergence of the focal topic in funds is undermined, affecting the findings.
Practical implications: These findings enhance the accuracy and scientific validity of trend prediction. Estimating and identifying patterns can help technology managers anticipate future research hotspots, supporting informed decision-making and technology management.
Originality/value: This study introduces a research framework to quantitatively and visually analyze the hysteresis effect, revealing the correlation and evolutionary patterns between fund research topics and their funded papers.

The author regrets that the paper titled “Gauging scholars’ acceptance of Open Access journals by examining the relationship between perceived quality and citation impact” (DOI: 10.2478/jdis-2025-0002), as published, contains errors in four of the table captions. For Tables 12–15, “CABS business journals” should read “CABS economics journals.” The tables do have the correct values for the economics journals, and the findings reported in the text do not need revision. The author apologizes for any inconvenience.

Purpose: Scholars face an unprecedented ever increasing demand for acting as reviewers for journals, recruitment and promotion committees, granting agencies, and research assessment agencies. Consequently, journal editors face an ever increasing scarcity of experts willing to act as reviewers. It is not infrequent that reviews diverge, which forces editors to recur to additional reviewers or make a final decision on their own. The purpose of the proposed bibliometric system is to support of editors' accept/reject decisions in such situations.
Design/methodology/approach: We analyse nearly two million 2017 publications and their scholarly impact, measured by normalized citations. Based on theory and previous literature, we extrapolated the publication traits of text, byline, and bibliographic references expected to be associated with future citations. We then fitted a regression model with the outcome variable as the scholarly impact of the publication and the independent variables as the above non-scientific traits, controlling for fixed effects at the journal level.
Findings: Non-scientific factors explained more than 26% of the paper's impact, with slight variation across disciplines. On average, OA articles have a 7% greater impact than non-OA articles. A 1% increase in the number of references was associated with an average increase of 0.27% in impact. Higher-impact articles in the reference list, the number of authors and of countries in the byline, the article length, and the average impact of co-authors' past publications all show a positive association with the article's impact. Female authors, authors from English-speaking countries, and the average age of the article's references show instead a negative association.
Research limitations: The selected non-scientific factors are the only observable and measurable ones to us, but we cannot rule out the presence of significant omitted variables. Using citations as a measure of impact has well-known limitations and overlooks other forms of scholarly influence. Additionally, the large dataset constrained us to one year's global publications, preventing us from capturing and accounting for time effects.
Practical implications: This study provides journal editors with a quantitative model that complements peer reviews, particularly when reviewer evaluations diverge. By incorporating non-scientific factors that significantly predict a paper's future impact, editors can make more informed decisions, reduce reliance on additional reviewers, and improve the efficiency and fairness of the manuscript selection process.
Originality/value: To the best of our knowledge, this study is the first one to specifically address the problem of supporting editors in any field in their decisions on submitted manuscripts with a quantitative model. Previous works have generally investigated the relationship between a few of the above publication traits and their impact or the agreement between peer-review and bibliometric evaluations of publications.

Purpose: This study explores the combined effects of structural and relational embeddedness within alliance networks on firm innovation. By focusing on the interplay between network structures and relationships, this study provides a nonlinear framework to unravel the complex dynamics between alliance networks and firm innovation performance within the manufacturing industry.
Design/methodology/approach: Using social network analysis, this study examines the topological structure of firms’ alliance networks. An exploratory approach involving K-Means clustering and decision tree methods is employed to identify heterogeneous network types within the alliance networks. The analysis further explores the nonlinear relationships between network characteristics, including closeness centrality, betweenness centrality, clustering coefficient, and relational attributes, including collaboration intensity and breadth, and their combined influence on firm innovation.
Findings: The study identified four distinct heterogeneous network types: dyadic, star, ringlike, and complex networks. Each type reveals unique network characteristics and their impact on innovation performance. Key decision rules were extracted, showing that strong relational embeddedness can hinder innovation in dyadic networks, while a greater distance from the central firm correlates with higher innovation performance in star alliance networks. For ringlike alliance networks, moderate cooperation intensity is beneficial for innovation when the clustering coefficient is not high. In complex alliance networks, the combined effects of cooperation intensity, breadth, and clustering coefficient significantly influence innovation.
Research limitations: The research presented in this study, while offering valuable insights into the relationship between alliance networks and firm innovation within the manufacturing sector, is subject to several limitations. A focus on the manufacturing industry may restrict the generalizability of our findings to other sectors, where the dynamics of innovation and collaboration might differ significantly. Additionally, our reliance on patent data, while providing a quantifiable measure of innovation, may overlook other forms of innovation that are equally critical in different contexts, such as service innovations or business model transformations.
Practical implications: This research offers significant insights into how firms can leverage both network structure and relational aspects to enhance innovation outcomes. By revealing the nonlinear and complex interactions between network embeddedness dimensions, this study makes a valuable contribution to both theory and practice. This highlights that strategic management of both structural and relational embeddedness can foster superior innovation performance, offering firms a competitive advantage by optimizing their alliance network configurations.
Originality/value: This study’s originality lies in its examination of the combined effects of structural and relational network embeddings on innovation performance. By identifying distinct network types and their impact on innovation, this study advances the theoretical understanding of how network characteristics interact to shape firm innovation. It contributes to the literature by offering a novel, multidimensional framework that integrates social network theory and resource-based view, providing new insights for firms to leverage their network positions and relationships for competitive advantage.

Purpose: Generally, the scientific comparison has been done with the help of the overall impact of scholars. Although it is very easy to compare scholars, but how can we assess the scientific impact of scholars who have different research careers? It is very obvious, the scholars may gain a high impact if they have more research experience or have spent more time (in terms of research career in a year). Then we cannot compare two scholars who have different research careers. Many bibliometrics indicators address the time-span of scholars. In this series, the h-index sequence and EM/EM’-index sequence have been introduced for assessment and comparison of the scientific impact of scholars. The h-index sequence, EM-index sequence, and EM’-index sequence consider the yearly impact of scholars, and comparison is done by the index value along with their component value. The time-series indicators fail to give a comparative analysis between senior and junior scholars if there is a huge difference in both scholars’ research careers.

Design/methodology/approach: We have proposed the cumulative index calculation method to appraise the scientific impact of scholars till that age and tested it with 89 scholars data.

Findings: The proposed mechanism is implemented and tested on 89 scholars’ publication data, providing a clear difference between the scientific impact of two scholars. This also helps in predicting future prominent scholars based on their research impact.

Research limitations: This study adopts a simplistic approach by assigning equal credit to all authors, regardless of their individual contributions. Further, the potential impact of career breaks on research productivity is not taken into account. These assumptions may limit the generalizability of our findings

Practical implications: The proposed method can be used by respected institutions to compare their scholars impact. Funding agencies can also use it for similar purposes.

Originality/value: This research adds to the existing literature by introducing a novel methodology for comparing the scientific impact of scholars. The outcomes of this research have notable implications for the development of more precise and unbiased research assessment frameworks, enabling a more equitable evaluation of scholarly contributions.

Purpose: Journal Impact Factors and other citation-based indicators are widely used and abused to help select journals to publish in or to estimate the value of a published article. Nevertheless, citation rates primarily reflect scholarly impact rather than other quality dimensions, including societal impact, originality, and rigour. In response to this deficit, Journal Quality Factors (JQFs) are defined and evaluated. These are average quality score estimates given to a journal’s articles by ChatGPT.
Design/methodology/approach: JQFs were compared with Polish, Norwegian and Finnish journal ranks and with journal citation rates for 1,300 journals with 130,000 articles from 2021 in large monodisciplinary journals in the 25 out of 27 Scopus broad fields of research for which it was possible. Outliers were also examined.
Findings: JQFs correlated positively and mostly strongly (median correlation: 0.641) with journal ranks in 24 out of the 25 broad fields examined, indicating a nearly science-wide ability for ChatGPT to estimate journal quality. Journal citation rates had similarly high correlations with national journal ranks, however, so JQFs are not a universally better indicator. An examination of journals with JQFs not matching their journal ranks suggested that abstract styles may affect the result, such as whether the societal contexts of research are mentioned.
Research limitations: Different journal rankings may have given different findings because there is no agreed meaning for journal quality.
Practical implications: The results suggest that JQFs are plausible as journal quality indicators in all fields and may be useful for the (few) research and evaluation contexts where journal quality is an acceptable proxy for article quality, and especially for fields like mathematics for which citations are not strong indicators of quality.
Originality/value: This is the first attempt to estimate academic journal value with a Large Language Model.

Google Gemini 1.5 Flash scores were compared with ChatGPT 4o-mini on evaluations of (a) 51 of the author’s journal articles and (b) up to 200 articles in each of 34 field-based Units of Assessment (UoAs) from the UK Research Excellence Framework (REF) 2021. From (a), the results suggest that Gemini 1.5 Flash, unlike ChatGPT 4o-mini, may work better when fed with a PDF or article full text, rather than just the title and abstract. From (b), Gemini 1.5 Flash seems to be marginally less able to predict an article’s research quality (using a departmental quality proxy indicator) than ChatGPT 4o-mini, although the differences are small, and both have similar disciplinary variations in this ability. Averaging multiple runs of Gemini 1.5 Flash improves the scores.