Journal of Informetrics最新文献

Understanding the antecedents of policy adoption is essential for facilitating policy diffusion and designing follow-up policies. Previous research on drivers of policy adoption primarily focused on local attributes and government interactions, often neglecting the influence of the policy information itself. This study systematically investigates how policy information (policy design, topics, and attributes) shapes its adoption. Drawing on the Elaboration Likelihood Model (ELM), we developed a framework to explain how such policy information embedded in policy documents influences policy adoption through central and peripheral routes. The adoption performance of each policy is quantified based on a novel policy citation approach. An empirical analysis of large-scale energy policies in China demonstrates that differentiated policy designs and topics exert heterogeneous effects on the intensity and speed of policy adoption. Moreover, their impact on subsequent policy adoptions is more pronounced than on first-time policy adoptions. Policy attributes such as institutional collaboration, reasonable timing agendas, and referencing high-impact policies positively influence policy adoption performance. Additionally, the validity level of a policy positively moderates the relationship between content information and adoption performance. Our research provides practical implications for policymakers to strategically craft appropriate policy-making and targeted promotion strategies for effective policy diffusion.

Scientific collaborations are widely acknowledged as a key factor in advancing contemporary science. Using the absence of prior collaboration relationships among all team members of a focal paper to assess team freshness, previous studies demonstrated strong associations between team freshness and the ability to generate original and multidisciplinary papers. However, the intricate interplay between team freshness and the experiences of new members remains less explored. For example, individuals can be classified based on their experiences or ages, distinguishing between newcomers with limited experience and incumbents with a track record of publications. Using the existing definition of team freshness, we focus on categorizing fresh teams into two distinct types: those consisting of fresh incumbents and those with fresh newcomers. Utilizing a comprehensive dataset comprising over 5 million articles, we systematically investigate the relationship between team freshness, the freshness of incumbents, and the freshness of newcomers on various creativity measurements. Our analysis yields several key findings. Firstly, both team freshness and the freshness of newcomers display a declining trend over time, whereas the freshness of incumbents remains stable. Secondly, we observe strong positive associations between team freshness, the freshness of incumbents, and the freshness of newcomers with regard to various creativity indicators. Strikingly, we emphasize substantial promotional powers of the freshness of incumbents on creativity, even after adjusting for overall team freshness. Our results have important policy implications related to the formation of creative teams.

To identify collaboration trends with coauthors, this paper elaborates a theoretical framework by introducing a measure to quantify exploration of the author in joining teams of coauthors with respect to the extreme exploration possibilities. Using the clustering coefficient, we gauge the team exploration from the author-centric vista evaluating configuration values of the ego networks. This value is normalized with respect to the maximum exploration possibilities for the author facilitating us to derive a measure, viz., the team exploration score for the team exploration strategy. We further derive a dynamical version of this measure. The average profiles of the exploration strategies are compared for the authors from the USA, England, and India publishing in a rapidly growing and collaboration-extensive field, viz. artificial intelligence in computer science, in the time window from 1990 to 2020. The bibliometric data are sourced from the Clarivate Web of Science. Configuration values are evaluated in the ascending year of publications in year-long time windows to compute the team exploration score for each author. Our analysis shows that the annually averaged profiles of authors corresponding to the three countries are almost constantly increasing toward high team exploration scores. Also, in the career-averaged profiles, authors publishing more than 20 papers have mostly adopted exploratory strategies.

In the rapidly evolving landscape of industrial and societal progress, technology convergence plays a pivotal role. This dynamic process is usually characterized by the emergence of new nodes and new links. With the long-term and recent interests in predicting technology convergence, link prediction has become the primary approach on the basis of large-scale patent data. Though, the problem of node dynamics is still not addressed in the literature. For this purpose, this paper presents a technology convergence prediction framework with three core modules as follows. (1) A candidate node set is introduced during the network construction phase, mimicking the generation of newly-emerging nodes. (2) An inductive graph representation learning approach is deployed to generate feature vectors for newly-emerging nodes as well as existing ones. (3) The evaluation criteria are revised to shift from the predictable range to the actual predicted range, which can provide a more realistic assessment of predictive performance. Finally, experimental results on the domain of cancer drug development validate the feasibility and effectiveness of our framework in capturing the dynamics of technology convergence, especially concerning the relationships of newly emerged nodes and links. This study provides valuable insights into technology convergence dynamics and points to future research and applications.

A trend in the academic field is agglomerations among scholars to generate knowledge with a disruptive influence on science and technology; however, the benefits have not been fully substantiated. This paper analyzes over 660,000 papers on artificial intelligence published from 1961 to 2023. We propose a method to calculate the innovative capacity of disruptive knowledge based on the similarity of historical, current, and future keywords, finding that scholars who commence their scientific endeavors earlier possess a heightened capability for disruptive knowledge innovation as Dkc index. The analysis reveals that multiagglomeration scholars have the highest average number of publications and citations, followed by agglomeration-flow scholars. Moreover, a larger agglomeration results in a lower ability to disrupt and consolidate knowledge innovation. Multiagglomeration and agglomeration-flow scholars harm disruptive/consolidative innovations. However, as the agglomeration effect intensifies, these two types of scholars from the disruptive perspective and multiagglomeration scholars from the consolidation perspective have a diminishing marginal effect on innovation capacity. The agglomeration size acts as a partial intermediary in the Multi→Size→Dkc index from the dual perspective and as a full mediator in the Flow→Size→Dkc index from the disruptive perspective, but only with a direct effect from the consolidative perspective.

There exists a growing discourse around the domination of Big Tech on the landscape of artificial intelligence (AI) research, yet our comprehension of this phenomenon remains cursory. This paper aims to broaden and deepen our understanding of Big Tech's reach and power within AI research. It highlights the dominance not merely in terms of sheer publication volume but rather in the propagation of new ideas or memes. Current studies often oversimplify the concept of influence to the share of affiliations in academic papers, typically sourced from limited databases such as arXiv or specific academic conferences.

The main goal of this paper is to unravel the specific nuances of such influence, determining which AI ideas are predominantly driven by Big Tech entities. By employing network and memetic analysis on AI-oriented paper abstracts and their citation network, we are able to grasp a deeper insight into this phenomenon. By utilizing two databases: OpenAlex and S2ORC, we are able to perform such analysis on a much bigger scale than previous attempts.

Our findings suggest that while Big Tech-affiliated papers are disproportionately more cited in some areas, the most cited papers are those affiliated with both Big Tech and Academia. Focusing on the most contagious memes, their attribution to specific affiliation groups (Big Tech, Academia, mixed affiliation) seems equally distributed between those three groups. This suggests that the notion of Big Tech domination over AI research is oversimplified in the discourse.

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.