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

Scientific knowledge forms the foundation of technological innovation, yet how to effectively facilitate its translation into applied technologies remains underexplored. Existing research has largely focused on measuring the linkage between science and technology, while overlooking the institutional mechanisms that can actively promote this process. Open Access (OA), by improving the accessibility and visibility of scientific outputs, presents a promising pathway to accelerate knowledge diffusion. This study investigates whether and how journal transitions to OA foster the uptake of scientific knowledge in technological domains, using agriculture as a representative field. We compile a panel dataset of 299 agricultural journals (1990–2024) and employ a multi-period Difference-in-Differences (DID) framework. The results show that journal transitions to full and immediate OA significantly increase the number of patent citations per article. Mechanism analysis further reveals that OA enhances social media attention and early scholarly usage, thereby facilitating the translation of research into patents. These findings highlight OA as not merely a tool for improving scholarly access, but also a strategic driver for research translation into technological innovation.

Understanding the mechanisms driving the distribution of scientific citations is a key challenge in assessing the scientific impact of authors. We investigate the influence of the preferential attachment rule (PAR) in this process by analyzing individual citation events from the DBLP dataset and two Scopus-based datasets, enabling us to estimate the probability of citations being assigned preferentially. Our findings reveal that, for the aggregated dataset, PAR dominates the citation distribution process, with approximately 70% of citations adhering to this mechanism. However, analysis at the individual level shows significant variability, with some authors experiencing a greater prevalence of preferential citations, particularly in the context of external citations. In contrast, self-citations exhibit notably different behavior, with only 20% following PAR. We also demonstrate that the prominence of PAR increases with an author's citability (average citations per paper), suggesting that more citable authors are preferentially cited, while less-cited authors experience more random citation patterns. Furthermore, we show that self-citations may influence bibliometric indices, such as the h-index. Our results confirm the distinct dynamics of self-citations compared to external citations, raising questions about the mechanisms driving self-citation patterns. These findings provide new insights into citation behaviors and highlight the limitations of existing approaches.

Advertisements have always been a part of our cultural heritage, and this also applies to online web advertisements. Unlike print ads, there are serious technical challenges involved in archiving and successfully replaying ads displayed in web pages. To explore these challenges, we created a small dataset of 250 web ads. Ultimately, we collected and archived 279 ads, which we classified into 5 different categories: combination ads, image ads, embedded web page ads, video ads, and text-only ads. In our sample, combination ads were the most prevalent and, not surprisingly, text-only ads were the easiest to archive and replay. During the course of this study, we encountered five major problems in archiving and replaying the web ads. We detail the issues uncovered and provide suggestions for ameliorating the replay of some web ads, in addition to other dynamically loaded embedded web resources.

Data storytelling combines communication and visualization techniques with storytelling principles to convey insights meaningfully and engagingly. This paper introduces the AI-DIKW framework, an extension of the Story Data–Information–Knowledge–Wisdom (S-DIKW) hierarchy, designed to support the co-creation of data-driven stories by combining human expertise with Generative AI. The AI-DIKW framework extends the S-DIKW model through the application of the journalistic 5-Ws, which guide Generative AI to provide consistent and contextually relevant outputs. This study describes how Generative AI can contribute to the iterative construction of a story's key components while ensuring alignment with ethical principles. Applying Generative AI at the AI-Data stage involves extracting insights from the data. Applying Generative AI at the AI-Information stage means enriching the insight with relevant context, while at the AI-Knowledge stage, Generative AI helps the data storyteller to enrich the story with meaningful next steps, anchored to a selected ethical framework. Finally, at the AI-Wisdom stage, Generative AI helps the data storyteller to tailor the story to specific audiences. At each stage of the AI-DIKW framework, Generative AI acts as a co-designer that helps the data storyteller to frame and edit the story. The article also describes a case study that applies the concepts described practically.

Mixed-initiative interaction is a key feature of Conversational Information Seeking (CIS), where both users and agents actively participate in dialogue. Given the potential risks associated with agent-initiative interactions, a CIS process model is needed to guide when and how the agent should take the initiative. Conversation analysis was employed to derive a speech act framework from the 37,268 utterances in the Wizard of Search Engine dataset. Lag sequential analysis was then used to identify adjacency pairs of speech acts with significant transition probabilities. This study identified six user acts, including querying, assessing, elaborating, reformulating, instructing, and deciding, and seven agent acts, including answering, inquiring, checking, eliciting, soliciting, informing, and offering. These acts were connected based on their adjacency, resulting in the CIS process model. This model comprises one fundamental querying-answering sub-process and four optional sub-processes: need negotiation, process collaboration, result evaluation, and query elicitation. The robustness of this model was evaluated on another CIS dataset, ConvSearch, with new behavioral patterns emerging. This study identifies the multi-stage, iterative, and dynamic nature of mixed-initiative user–agent interaction in CIS, offers methodological insights into CIS data collection, annotation, and analysis, and provides guidance for the development and evaluation of CIS agents.

Global research policies, often driven by political agendas rather than academic expertise, generate pressure on local entities to conform to global standards. This is particularly the case for universities seeking international relevance, which must address Open Research Data (ORD) principles. Our study examines the strategic decisions that university boards must make in adopting ORD, and explores the developing role of data stewards as key facilitators in day-to-day data governance. Drawing on the first-hand experience of a professional data steward at a research-intensive Swiss university, we illustrate in four situations how power dynamics and knowledge gaps complicate the reconciliation of ORD ideals with local operational realities. In response, we advocate a strategic shift to an integrated data stewardship model. We also propose strategies to empower data stewards by increasing the visibility of ORD in research projects, promoting task flexibility, reducing bureaucratic constraints, and setting realistic, incremental goals. We further recommend adapting global terminology to local contexts, harmonizing processes, and proactively promoting ORD. Ultimately, our efforts emphasize the specificity of universities as expert organizations and complement traditional education and training initiatives. In this way, we aim to pave the way for a more effective and holistic implementation of ORD and ultimately other global policies.

Phenomenon-based classification (PBC) systems are gaining renewed interest for their potential to facilitate interdisciplinary research. This paper acknowledges the practical advantages highlighted by proponents but critically examines the underlying philosophical assumptions concerning the nature of “phenomena.” Drawing on insights from the philosophy of science, semiotics of information, and domain analysis, this paper argues that PBC is caught in a foundational paradox: its ambition to classify phenomena irrespective of context is implicitly essentialist, while its method for defining those phenomena is necessarily constructivist, relying on social consensus. This unexamined tension between its goals and its practice is a significant theoretical weakness. Understanding this philosophical conflict is crucial for developing more epistemologically robust and truly interoperable knowledge organization systems.

Digital literacy programs (DLPs) implemented by community-anchored organizations have become a key focus toward achieving digital equity. However, there is a notable lack of quantitative empirical research that measures DLPs' effectiveness in supporting the achievement of tangible benefits of digital literacy (DL), beyond simply acquiring it. Our study examines gaps in DLP outcomes (known as the third-level digital divide) by considering program participants' differing technology readiness. We surveyed 305 Maryland residents (in the United States) with experience in different DLPs (e.g., one-on-one or group instruction) provided by libraries, schools, community centers, and non-profits. Results from hierarchical multiple regression analyses revealed that frequent DLP use, combined with high motivation for initial technology adoption and high autonomy in technology behavior, leads to greater progress in digital empowerment and life betterment as DL gain increases. However, a lack of autonomy negates the effects of DLP use and the resulting DL gain. We also find that those struggling with technology unaffordability and anxiety compensate for these deficits through frequent DLP use, experiencing progress in digital empowerment as they gain more DL. We demonstrate the role of DLPs in preventing further exclusion among digitally marginalized individuals, advancing the digital divide discourse and offering practical recommendations to strengthen DLP impact.

Many academic libraries aim to improve services for and interactions with patrons and stakeholders who are neurodivergent, that is, those who have neurocognitive functions that differ from what is considered normal. To support this aim, numerous scholarly works have focused on neurodivergence in academic libraries, but such works have yet to be reviewed and synthesized to guide future practice and research. Therefore, a scoping review of 47 relevant scholarly publications was conducted to identify the main topics, recommendations for practice, and remaining knowledge gaps. Existing research on the topic has concerned itself with neurodivergent staff, users, and their respective needs and experiences with library services and library spaces. Recommendations are identified to better meet the needs of stakeholders—both neurodivergent and neurotypical—by improving accessibility and tailoring library instruction and services, for example, in customizing library spaces, integrating universal design for learning into library instruction, and providing social support for neurodivergent students. Gaps remain around conditions other than autism and around how intersectional demographic factors affect library accessibility for neurodivergent users. Following these results, we suggest possible directions for future research.

Electronic healthcare records (EHRs) contain healthcare inequities that impede equitable patient care, frequently due to subtle and unconscious biases ingrained in medical language. These biases, when documented in EHRs, can significantly impact subsequent medical decisions and the quality of care. Leveraging priming theory in psychology, we introduce a novel natural language processing application that simulates cognitive activation to enhance the detection of subtle EHR biases. This approach transitions our focus from mere technical integration to comprehending the psychological underpinnings of bias perception within a machine learning framework. Moreover, employing a data-driven strategy, we investigate potential correlations between patient demographics—like age, gender, and race—and the prevalence of subtle biases through analyzing model performance, attention shifts, and word contributions. Conducted on the MIMIC database, our research demonstrates that simulating cognitive memory activation improves the model's capability to detect and classify biased language. Additionally, our findings indicate a potential association between specific biased expressions and patient demographic traits. Our work deepens the understanding of discrimination mechanisms in healthcare and underscores the value of cognitive psychological theories in crafting AI systems aimed at social welfare.