Journal of Informetrics最新文献

Two decades after the inception of open access publishing (OA), its impact has remained a focal point in academic discourse. This study adopted a disruptive innovation framework to examine OA's influence on the traditional subscription market. It assesses the market power of gold journals (OA full adopters) in comparison with hybrid journals and closed-access journals (partial adopters and non-adopters). Additionally, it contrasts the market power between hybrid journals (partial adopters) and closed-access journals (non-adopters). Using the Lerner index to measure market power through price elasticity of demand, this study employs difference tests and multiple regressions. These findings indicate that OA full adopters disrupt the market power of non-adopting incumbents. However, by integrating the OA option into their business models, partial adopters can effectively mitigate this disruption and expand their influence from the traditional subscription market to the emerging OA paradigm.

Choosing the right partner is a key factor in the success of enterprise R&D cooperation, directly affecting innovation outcomes and market competitiveness. Technical similarity provides a common language and foundational understanding between enterprises, while technical complementarity offers opportunities for knowledge exchange and innovation. However, no previous research has effectively integrated these two features within a collaborator recommendation framework. This study aims to explore a method that combines technological similarity and complementarity for collaborator recommendations. We introduced the Technological Similarity and Complementarity Enhanced Collaborator Recommendation (TSCE-CR) model, which constructs a heterogeneous corporate collaboration network and designs a tailored loss function. This model effectively integrates features of technological similarity and complementarity, enabling the neural network to capture and elucidate the nonlinear and multidimensional relationships in corporate collaborations. Experimental validation on patent data in the field of artificial intelligence demonstrated that our TSCE-CR model excels in identifying potential collaborators, effectively confirming the critical role of technological complementarity in R&D collaboration. This research provides a flexible framework for future studies on collaborator recommendations and offers reliable decision-making support for enterprises in selecting R&D partners.

While providers try to control the quality of the data, the applicability of Altmetrics indicators to the assessment of scientific papers remains an open question. One important reason is that the citation counts used to explain and evaluate the applicability of Altmetrics in this regard do not directly and completely reflect the impact and quality of papers. In view of the fact that the introduction of citation trajectory helps to enrich our understanding of the impact and quality of papers, this study first discusses the correlation between citation counts and Altmetrics indicators of papers under different citation trajectory types on the basis of dividing five citation trajectory types and considering possible influences such as field and publication year. Then, after controlling the relevant variables, we construct a multinomial logistic regression with the citation trajectory type as the dependent variable to analyze the possible relationship between Altmetrics and the citation trajectory type of papers. Finally, we construct a decision tree model and a regression model after mixed sampling to verify the robustness of the regression results. The findings reveal that there were significant differences in the performance of Altmetrics indicators among papers with different citation trajectory types. The applicability of Altmetrics for evaluating papers with different citation trajectory types should be judged carefully. At the same time, it is suggested that robust Altmetrics (such as save) can be applied to assess the quality of papers and characterize the citation life cycle.

Over the last few years, ranking lists of academic journals have become one of the key indicators for evaluating individual researchers, departments and universities. How to optimally design such rankings? What can we learn from commonly used journal ranking lists? To address these questions, we propose a simple, theoretical model of optimal rewards for publication in academic journals. Based on a principal-agent model with researchers' hidden abilities, we characterize the optimal journal reward system, where all available journals are assigned to one of several categories or ranks. We provide a tractable example that has a closed-form solution and allows numerical applications. Finally, we show how to calibrate the distribution of researchers' ability levels implied by the observed journal ranking schemes.

The rise of social media has significantly influenced scholarly communication, knowledge dissemination, and research evaluation, leading to the enrichment of alternative metrics (altmetrics) for evaluating academic papers’ social impact, which assesses the social impact of academic papers through online activities, including reading, bookmarking, downloading, and commenting. However, these altmetrics often focus on the number of mentions on social media rather than thoroughly evaluating the source, content, and dissemination of these mentions. To address this gap, this study introduces the social media impact altmetric (SMIAltmetric), which is based on 44,087 publications and 860,680 tweets (now “posts”), a comprehensive scoring system for evaluating scientific papers on Twitter (now “X”), using diverse features, including literature-related, social media engagement-related, user-related, and content-related features. Employing Altmetric Attention Acores (AAS) as labels, we tested eight machine learning algorithms, with XGBoost demonstrating the highest accuracy at 0.8672. Crucial factors influencing SMIAltmetric, as identified by the SHAP value, were followers, retweets, mentions, and citation. Furthermore, consistency analysis and convergent validation between the proposed SMIAltmetric and AAS confirm the reliability and finer differentiation of SMIAltmetric. The proposed SMIAltmetric provides a more comprehensive understanding of a paper’s social media impact, enhancing the evaluation of scientific discourse and its engagement with society.

The integration of prior field knowledge in analytical or modeling processes is generally considered favorable across various disciplines, yet its utilization in Main Path Analysis (MPA) has been limited to gathering documents or validating the obtained main paths (MPs). This study envisions that prior knowledge about a field can be embodied in certain key documents that are considered seminal or crucial to the field's development. A so-called key-node path search is then employed to produce MPs that capture a distinct knowledge flow centering around these key documents. This study further proposes a unified approach that automatically and simultaneously produces the key-document MPs alongside the traditional MPs. Through this unified approach, the focused knowledge flow through the key documents and the field's overall knowledge flow, as revealed by the traditional MPs, can be concurrently observed to see how they interact, thereby providing additional insights into the field's development. Not only may the key-document MPs capture a meaningful development trajectory, but their complement to the traditional MPs can also hint at their respective representativeness. To establish this unified approach, this study formally demonstrates how the traditional MPs can be produced with key-node path searches, enabling their simultaneous creation alongside the key-document MPs. A case study is conducted based on patents in the field of Evolutionary Computation from an official artificial intelligence patent dataset to demonstrate the application of this unified approach.

Measuring the knowledge linkage between science and technology (S&T) is crucial for understanding the interactions between S&T and assisting decision-makers in strategizing research and development investments. Conventional analyses of S&T knowledge linkage have frequently overlooked the semantic structure of knowledge elements thereby introducing biases in the measurements. To address this issue, this study introduces a novel method predicated on the tree semantic structure, which quantifies the S&T linkage by considering the hierarchy and category of knowledge elements within an ontological framework. In this method, knowledge trees are constructed to represent the core knowledge of S&T literature, incorporating hierarchically organized MeSH descriptors. These knowledge trees are subsequently utilized to measure the knowledge linkage between S&T by integrating intra-branch knowledge similarity and inter-branch knowledge distribution. An empirical analysis was conducted on a substantial corpus of scientific publications and patents within the biomedicine sector. The findings predominantly revealed a stronger knowledge linkage between S&T in recent years, relative to the early 2000 s. It was also observed that patents are more inclined to include broader concepts in their titles and abstracts, in contract to the more specific concepts found in scientific publications. S&T literatures have increasingly focused on knowledge related to diseases, equipment, and health care. To verify the reliability of the proposed method, validation was performed with alternative measurements of knowledge linkage. In comparison to single-feature-based linkage measurements and network-based approaches, our proposed method demonstrates superior adaptability in capturing S&T linkage, especially when there is a marked disparity in the sample sizes of S&T literature. This study not only enriches the measurements of S&T knowledge linkage, but also furnishes empirical insights into the evolving patterns of S&T linkage within the biomedical domain.

Inequality is an inherent part of our lives: we see it in the distribution of incomes, talents, citations, to name a few. However, its intensity varies across environments: there are systems where the available resources are relatively evenly distributed but also where a small group of items or agents controls the majority of assets. Numerous indices for quantifying the degree of inequality have been proposed but in general, they work quite differently.

We recently observed (Siudem et al., 2020) that many rank-size distributions might be approximated by a time-dependent agent-based model involving a mixture of preferential (rich-get-richer) and accidental (sheer chance) attachment. In this paper, we point out its relationship to an iterative process that generates rank distributions of any length and a predefined level of inequality, as measured by the Gini index.

We prove that, under our model, the Gini, Bonferroni, De Vergottini, and Hoover indices are equivalent for samples of similar sizes. Given one of them, we can recreate the value of another measure. Thanks to the obtained formulae, we can also understand how they depend on the sample size. An empirical analysis of a large database of citation records in economics (RePEc) yields a good match with our theoretical derivations.