Search (42 results, page 2 of 3)

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Abstract

Designing effective consumer health information systems requires deep understanding of the context in which the systems are being used. However, due to the elusive nature of the concept of context, few studies have made it a focus of examination. To fill this gap, we studied the context of consumer health information searching by analyzing questions posted on a social question and answer site: Yahoo! Answers. Based on the analysis, a model of context was developed. The model consists of 5 layers: demographic, cognitive, affective, situational, and social and environmental. The demographic layer contains demographic factors of the person of concern; the cognitive layer contains factors related to the current search task (specifically, topics of interest and information goals) and users' cognitive ability to articulate their needs. The affective layer contains different affective motivations and intentions behind the search. The situational layer contains users' perceptions of the current health condition and where the person is in the illness trajectory. The social and environmental layer contains users' social roles, social norms, and various information channels. Several novel system functions, including faceted search and layered presentation of results, are proposed based on the model to help contextualize and improve users' interactions with health information systems.

Source

Journal of the American Society for Information Science and Technology. 64(2013) no.6, S.1158-1172

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Abstract

Searches for specific factual health information constitute a significant part of consumer health information requests, but little is known about how users search for such information. This study attempts to fill this gap by observing users' behavior while using MedlinePlus to search for specific health information. Nineteen students participated in the study, and each performed 12 specific tasks. During the search process, they submitted short queries or complete questions, and they examined less than 1 result per search. Participants rarely reformulated queries; when they did, they tended to make a query more specific or more general, or iterate in different ways. Participants also browsed, primarily relying on the alphabetical list and the anatomical classification, to navigate to specific health topics. Participants overall had a positive experience with MedlinePlus, and the experience was significantly correlated with task difficulty and participants' spatial abilities. The results suggest that, to better support specific item search in the health domain, systems could provide a more "natural" interface to encourage users to ask questions; effective conceptual hierarchies could be implemented to help users reformulate queries; and the search results page should be reconceptualized as a place for accessing answers rather than documents. Moreover, multiple schemas should be provided to help users navigate to a health topic. The results also suggest that users' experience with information systems in general and health-related systems in particular should be evaluated in relation to contextual factors, such as task features and individual differences.

Source

Journal of the Association for Information Science and Technology. 65(2014) no.1, S.53-68

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Abstract

Sustained use of an information source is sometimes important for achieving an individual's long-term goals, such as learning and self-development. It is even more important for users of online health communities because health benefits usually come with sustained use. However, little is known about what retains a user. We interviewed 21 participants who had been using online diabetes communities in a sustained manner. Guided by self-determination theory, which posits that behaviors are sustained when they can satisfy basic human needs for autonomy, competence, and relatedness, we identified mechanisms that help satisfy these needs, and thus sustain users in online health communities. Autonomy-supportive mechanisms include being respected and supported as a unique individual, feeling free in making choices, and receiving meaningful rationales about others' decisions. Competence-cultivating mechanisms include seeking information, providing information, and exchanging information with others to construct knowledge. Mechanisms that cultivate relatedness include seeing similarities between oneself and peers, receiving responses from others, providing emotional support, and forming small underground groups for closer interactions. The results suggest that, like emotions, information and small group interactions also play a key role in retaining users. System design and community management strategies are discussed based on these mechanisms.

Source

Journal of the Association for Information Science and Technology. 67(2016) no.12, S.22842-2857

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Abstract

Twitter is utilized by many, including professional businesses and organizations; however, there are very few studies on how other entities interact with these organizations in the Twittersphere. This article presents a study that investigates tweets related to 5 major library and information science (LIS) professional organizations in the United States. This study applies a systematic tweets analysis framework, including descriptive analytics, network analytics, and co-word analysis of hashtags. The findings shed light on user engagement with LIS professional organizations and the trending discussion topics on Twitter, which is valuable for enabling more successful social media use and greater influence.

Source

Journal of the Association for Information Science and Technology. 71(2020) no.4, S.491-496

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Abstract

The extant literature demonstrates that the age-related digital divide prevents older adults from enhancing their quality of life. To bridge this gap and promote active aging, this study explores the interplay between social networks and older adults' use of information and communication technology (ICT). Using an action-oriented field research approach, we offered technical help (29 help sessions) to older adult participants recruited from western China. Then, we conducted content analysis to examine the obtained video, audio, and text data. Our results show that, first, different types of social networks significantly influence older adults' ICT use in terms of digital skills, engagement, and attitudes; however, these effects vary from person to person. In particular, our results highlight the crucial role of a stable and long-term supportive social network in learning and mastering ICT for older residents. Second, technical help facilitates the building and reinforcing of such a social network for the participants. Our study has strong implications in that policymakers can foster the digital inclusion of older people through supportive social networks.

Content

Beitrag in: JASIST special issue on ICT4D and intersections with the information field. Vgl.: https://asistdl.onlinelibrary.wiley.com/doi/10.1002/asi.24700.

Source

Journal of the Association for Information Science and Technology. 74(2023) no.12, S.1437-1448

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Abstract

The rapid growth of the non-English-speaking Internet population has created a need for better searching and browsing capabilities in languages other than English. However, existing search engines may not serve the needs of many non-English-speaking Internet users. In this paper, we propose a generic and integrated approach to searching and browsing the Internet in a multilingual world. Based an this approach, we have developed the Chinese Business Intelligence Portal (CBizPort), a meta-search engine that searches for business information of mainland China, Taiwan, and Hong Kong. Additional functions provided by CBizPort include encoding conversion (between Simplified Chinese and Traditional Chinese), summarization, and categorization. Experimental results of our user evaluation study show that the searching and browsing performance of CBizPort was comparable to that of regional Chinese search engines, and CBizPort could significantly augment these search engines. Subjects' verbal comments indicate that CBizPort performed best in terms of analysis functions, cross-regional searching, and user-friendliness, whereas regional search engines were more efficient and more popular. Subjects especially liked CBizPort's summarizer and categorizer, which helped in understanding search results. These encouraging results suggest a promising future of our approach to Internet searching and browsing in a multilingual world.

Footnote

Teil eines Themenheftes zu: Information seeking research

Source

Journal of the American Society for Information Science and Technology. 55(2004) no.9, S.818-831

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Abstract

Information quality (IQ) is key to users' satisfaction with information systems. Understanding what IQ means to users can effectively inform system improvement. Existing inquiries into self-tracking data quality primarily focus on accuracy. Interviewing 20 consumers who had self-tracked health indicators for at least 6 months, we identified eight dimensions that consumers apply to evaluate self-tracking data quality: value-added, accuracy, completeness, accessibility, ease of understanding, trustworthiness, aesthetics, and invasiveness. These dimensions fell into four categories-intrinsic, contextual, representational, and accessibility-suggesting that consumers judge self-tracking data quality not only based on the data's inherent quality but also considering tasks at hand, the clarity of data representation, and data accessibility. We also found that consumers' self-tracking data quality judgments are shaped primarily by their goals or motivations, subjective experience with tracked activities, mental models of how systems work, self-tracking tools' reputation, cost, and design, and domain knowledge and intuition, but less by more objective criteria such as scientific research results, validated devices, or consultation with experts. Future studies should develop and validate a scale for measuring consumers' perceptions of self-tracking data quality and commit efforts to develop technologies and training materials to enhance consumers' ability to evaluate data quality.

Source

Journal of the Association for Information Science and Technology. 73(2022) no.6, S.879-891

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Abstract

A cross-level model based on the information processing perspective and trait activation theory was developed and tested in order to investigate the effects of individual-level epistemic motivation and team-level task reflexivity on three different individual contribution behaviors (i.e., adding, deleting, and revising) in the process of knowledge creation on team wikis. Using the Hierarchical Linear Modeling software package and the 2-wave data from 166 individuals in 51 wiki-based teams, we found cross-level interaction effects between individual epistemic motivation and team task reflexivity on different knowledge contribution behaviors on wikis. Epistemic motivation exerted a positive effect on adding, which was strengthened by team task reflexivity. The effect of epistemic motivation on deleting was positive only when task reflexivity was high. In addition, epistemic motivation was strongly positively related to revising, regardless of the level of task reflexivity involved.

Source

Journal of the Association for Information Science and Technology. 70(2019) no.5, S.448-461

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Abstract

Conversational interfaces are increasingly popular as a way of connecting people to information. With the increased generative capacity of corpus-based conversational agents comes the need to classify and filter out malevolent responses that are inappropriate in terms of content and dialogue acts. Previous studies on the topic of detecting and classifying inappropriate content are mostly focused on a specific category of malevolence or on single sentences instead of an entire dialogue. We make three contributions to advance research on the malevolent dialogue response detection and classification (MDRDC) task. First, we define the task and present a hierarchical malevolent dialogue taxonomy. Second, we create a labeled multiturn dialogue data set and formulate the MDRDC task as a hierarchical classification task. Last, we apply state-of-the-art text classification methods to the MDRDC task, and report on experiments aimed at assessing the performance of these approaches.

Source

Journal of the Association for Information Science and Technology. 72(2021) no.12, S.1477-1497

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Source

Journal of the American Society for Information Science. 51(2000) no.1, S.57-68

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Abstract

Purpose - This study aims to investigate the effects of different search and browse features in digital libraries (DLs) on task interactions, and what features would lead to poor user experience. Design/methodology/approach - Three operational DLs: ACM, IEEE CS, and IEEE Xplore are used in this study. These three DLs present different features in their search and browsing designs. Two information-seeking tasks are constructed: one search task and one browsing task. An experiment was conducted in a usability laboratory. Data from 35 participants are collected on a set of measures for user interactions. Findings - The results demonstrate significant differences in many aspects of the user interactions between the three DLs. For both search and browse designs, the features that lead to poor user interactions are identified. Research limitations/implications - User interactions are affected by specific design features in DLs. Some of the design features may lead to poor user performance and should be improved. The study was limited mainly in the variety and the number of tasks used. Originality/value - The study provided empirical evidence to the effects of interaction design features in DLs on user interactions and performance. The results contribute to our knowledge about DL designs in general and about the three operational DLs in particular.

Theme

Information Gateway

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Abstract

Whereas traditional science maps emphasize citation statistics and static relationships, this paper presents a term-based method to identify and visualize the evolutionary pathways of scientific topics in a series of time slices. First, we create a data preprocessing model for accurate term cleaning, consolidating, and clustering. Then we construct a simulated data streaming function and introduce a learning process to train a relationship identification function to adapt to changing environments in real time, where relationships of topic evolution, fusion, death, and novelty are identified. The main result of the method is a map of scientific evolutionary pathways. The visual routines provide a way to indicate the interactions among scientific subjects and a version in a series of time slices helps further illustrate such evolutionary pathways in detail. The detailed outline offers sufficient statistical information to delve into scientific topics and routines and then helps address meaningful insights with the assistance of expert knowledge. This empirical study focuses on scientific proposals granted by the United States National Science Foundation, and demonstrates the feasibility and reliability. Our method could be widely applied to a range of science, technology, and innovation policy research, and offer insight into the evolutionary pathways of scientific activities.

Source

Journal of the Association for Information Science and Technology. 68(2017) no.8, S.1925-1939

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Abstract

The Information Schools, also referred to as iSchools, have a unique approach to data science with three distinct components: human-centeredness, socially responsible, and rooted in context. In this position paper, we highlight and expand on these components and show how they are integrated in various research and educational activities related to data science that are being carried out at iSchools. We argue that the iSchool way of doing data science is not only highly relevant to the current times, but also crucial in solving problems of tomorrow. Specifically, we accentuate the issues of developing insights and solutions that are not only data-driven, but also incorporate human values, including transparency, privacy, ethics, fairness, and equity. This approach to data science has meaningful implications on how we educate the students and train the next generation of scholars and policymakers. Here, we provide some of those design decisions, rooted in evidence-based research, along with our perspective on how data science is currently situated and how it should be advanced in iSchools.

Source

Journal of the Association for Information Science and Technology. 72(2021) no.6, S.793-796

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Abstract

Power dynamics influence every aspect of scientific collaboration. Team power dynamics can be measured by team power level and team power hierarchy. Team power level is conceptualized as the average level of the possession of resources, expertise, or decision-making authorities of a team. Team power hierarchy represents the vertical differences of the possessions of resources in a team. In Science of Science, few studies have looked at scientific collaboration from the perspective of team power dynamics. This research examines how team power dynamics affect team impact to fill the research gap. In this research, all coauthors of one publication are treated as one team. Team power level and team power hierarchy of one team are measured by the mean and Gini index of career age of coauthors in this team. Team impact is quantified by citations of a paper authored by this team. By analyzing over 7.7 million teams from Science (e.g., Computer Science, Physics), Social Sciences (e.g., Sociology, Library & Information Science), and Arts & Humanities (e.g., Art), we find that flat team structure is associated with higher team impact, especially when teams have high team power level. These findings have been repeated in all five disciplines except Art, and are consistent in various types of teams from Computer Science including teams from industry or academia, teams with different gender groups, teams with geographical contrast, and teams with distinct size.

Source

Journal of the Association for Information Science and Technology. 73(2022) no.10, S.1489-1505

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Abstract

Many disciplines, including the broad Field of Information (iField), offer Data Science (DS) programs. There have been significant efforts exploring an individual discipline's identity and unique contributions to the broader DS education landscape. To advance DS education in the iField, the iSchool Data Science Curriculum Committee (iDSCC) was formed and charged with building and recommending a DS education framework for iSchools. This paper reports on the research process and findings of a series of studies to address important questions: What is the iField identity in the multidisciplinary DS education landscape? What is the status of DS education in iField schools? What knowledge and skills should be included in the core curriculum for iField DS education? What are the jobs available for DS graduates from the iField? What are the differences between graduate-level and undergraduate-level DS education? Answers to these questions will not only distinguish an iField approach to DS education but also define critical components of DS curriculum. The results will inform individual DS programs in the iField to develop curriculum to support undergraduate and graduate DS education in their local context.

Source

Journal of the Association for Information Science and Technology. 74(2023) no.6, S.641-662

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Journal of the American Society for Information Science and technology. 52(2001) no.8, S.628-654

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Source

Journal of the Association for Information Science and Technology. 71(2020) no.10, S.1179-1191

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Source

Journal of the American Society for Information Science and Technology. 59(2008) no.8, S.1331-1346

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Source

Journal of the Association for Information Science and Technology. 65(2014) no.8, S.1695-1706

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Source

Journal of the Association for Information Science and Technology. 71(2020) no.5, S.553-567