Journal of the Association for Information Science and Technology最新文献

Universities face increasing demands to improve their visibility, public outreach, and online presence. There is a broad consensus that scientific reputation significantly increases the attention universities receive. However, in most cases estimates of scientific reputation are based on composite or weighted indicators and absolute positions in university rankings. In this study, we adopt a more granular approach to assessment of universities' scientific performance using a multidimensional set of indicators from the Leiden Ranking and testing their individual effects on university Wikipedia page views. We distinguish between international and local attention and find a positive association between research performance and Wikipedia attention which holds for regions and linguistic areas. Additional analysis shows that productivity, scientific impact, and international collaboration have a curvilinear effect on universities' Wikipedia attention. This finding suggests that there may be other factors than scientific reputation driving the general public's interest in universities. Our study adds to a growing stream of work which views altmetrics as tools to deepen science–society interactions rather than direct measures of impact and recognition of scientific outputs.

Although research establishes a link between knowledge-intensive human resource (HR) practices (KIHRP) and knowledge-intensive team (KIT) performance, knowledge is limited about the underlying mechanisms and boundary conditions that determine this relationship. This study integrates the ability–motivation–opportunity (AMO) framework and theory of team adaptation into an information processing perspective to present a cohesive model that explains the mediating role of team knowledge sharing and reflexivity processes, and moderation of organization's technological knowledge management (KM) capability to explain the effect of KIHRP on KIT performance. Data were collected in three waves and from three sources consisting of 380 knowledge workers from 123 teams in 74 organizations in Pakistan. The findings indicate that KIHRP relates positively to KIT performance directly as well as via team knowledge sharing and reflexivity processes where the organization's technological KM capability further strengthens this relationship.

Empirical evidence demonstrates that citations received by scholarly publications follow a pattern of preferential attachment, resulting in a power-law distribution. Such asymmetry has sparked significant debate regarding the use of citations for research evaluation. However, a consensus has yet to be established concerning the historical trends in citation concentration. Are citations becoming more concentrated in a small number of articles? Or have recent geopolitical and technical changes in science led to more decentralized distributions? This ongoing debate stems from a lack of technical clarity in measuring inequality. Given the variations in citation practices across disciplines and over time, it is crucial to account for multiple factors that can influence the findings. This article explores how reference-based and citation-based approaches, uncited articles, citation inflation, the expansion of bibliometric databases, disciplinary differences, and self-citations affect the evolution of citation concentration. Our results indicate a decreasing trend in citation concentration, primarily driven by a decline in uncited articles, which, in turn, can be attributed to the growing significance of Asia and Europe. On the whole, our findings clarify current debates on citation concentration and show that, contrary to a widely-held belief, citations are increasingly scattered.

Public health surveillance based on data analytics plays a crucial role in detecting and responding to public health crises, such as infectious disease outbreaks. Previous information science research on the topic has focused on developing analytical algorithms and visualization tools. This study seeks to extend the research by investigating information practices in data analytics for public health surveillance. Through a case study of how data analytics was conducted for surveilling Influenza A and COVID-19 outbreaks, both exploration information practices (i.e., probing, synthesizing, exchanging) and exploitation information practices (i.e., scavenging, adapting, outreaching) were identified and detailed. These findings enrich our empirical understanding of how data analytics can be implemented to support public health surveillance.

Online opinion formation has received much scholarly attention since the mass proliferation of social networks. Inter alia, online opinions have been viewed as a new part of public deliberation. However, the pre-Internet era's vision on deliberation imposes extremely high demands on users as deliberators. We argue that opinion formation online neither pursues the goals nor follows the rules of institutionalized consensus-oriented round-table deliberative processes. Moreover, the growing academic evidence shows that opinion formation online is predominantly cumulative, not deliberative in nature. Thus, we introduce the concept of cumulative deliberation as an alternative and addition to classic institutional deliberation and argue that it describes opinion formation online more precisely. Importantly, it allows for two crucial additions to the deliberation theory, which are the use of systemic approaches to measuring and predicting public opinion and new normativity that sees a user as an initially neutral discussion unit. It also allows for healthier distinction between “natural” user communication and intentional counter-deliberative distortions in online communication, like computational propaganda or cyberbullying. We end up with suggesting a research agenda on cumulative deliberation.

Reviews have long been recognized as among the most important forms of scientific communication. The rapid growth of the primary literature has further increased the need for reviews to distill and interpret the literature. This review on Reviews and Reviewing: Approaches to Research Synthesis encompasses the evolution of the review literature, taxonomy of review literature, uses and users of reviews, the process of preparing reviews, assessment of review quality and impact, the impact of information technology on the preparation of reviews, and research opportunities for information science related to reviews and reviewing. In addition to providing a synthesis of prior research, this review seeks to identify gaps in the published research and to suggest possible future research directions.

While technology affords creation of digital collections, and promises access to all, the reality is that many cultural data collections exist in a precarious ecosystem, where erratic funding, fragmented support, and disconnected expertise threaten their continued existence. As a significant branch of the broader information ecosystem, cultural data collections range in size and scope, from national institutions to bespoke local collections supported by individuals. This exploratory, qualitative study engaged cultural data experts in Australia, Canada, and the United Kingdom to map the broad cultural data ecosystem and to identify opportunities for healthier growth. The development and maintenance of cultural data collections requires integration across the spheres of expertise of creators, curators, subject matter experts, information science, and computing and technology. The foundational structural elements of the ecosystem include funding, policies, access to existing data, community context, and technological infrastructure. The key elements of a healthy data ecosystem are clarity of purpose, user-focused design, sustainability, allied coproduction, and reciprocal interconnection. A healthier cultural data ecosystem means more collections and initiatives will have positive impacts for research, knowledge, and diverse communities, contributing positively to the broader information ecosystem and to society, at large.

Open Access (OA) publishing has progressed from an initial fringe idea to a still-growing, major component of modern academic communication. The proliferation of OA publishing presents a context to examine how new innovations and institutions develop. Based on analyses of 1,296,304 articles published in 83 OA journals, we analyze changes in the institutional status, gender, age, citedness, and geographical locations of authors over time. Generally, OA journals tended towards core-to-periphery diffusion patterns. Specifically, journal authors tended to decrease in high-status institutional affiliations, male and highly cited authors over time. Despite these general tendencies, there was substantial variation in the diffusion patterns of OA journals. Some journals exhibited no significant demographic changes, and a few exhibited periphery-to-core diffusion patterns. We find that although both highly and less-legitimate journals generally exhibit core-to-periphery diffusion patterns, there are still demographic differences between such journals. Institutional and cultural legitimacy—or lack thereof—affects the social and intellectual diffusion of new OA journals.

Open data as an integral part of the open science movement enhances the openness and sharing of scientific datasets. Nevertheless, the normative utilization of data journals, data papers, scientific datasets, and data citations necessitates further research. This study aims to investigate the citation practices associated with data papers and to explore the role of data papers in disseminating scientific datasets. Dataset accession numbers from NCBI databases were employed to analyze the prevalence of data citations for data papers from PubMed Central. A dataset citation practice identification rule was subsequently established. The findings indicate a consistent growth in the number of biomedical data journals published in recent years, with data papers gaining attention and recognition as both publications and data sources. Although the use of data papers as citation sources for data remains relatively rare, there has been a steady increase in data paper citations for data utilization through formal data citations. Furthermore, the increasing proportion of datasets reported in data papers that are employed for analytical purposes highlights the distinct value of data papers in facilitating the dissemination and reuse of datasets to support novel research.