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  • × author_ss:"Zhang, Y."
  1. Zhang, M.; Zhang, Y.: Professional organizations in Twittersphere : an empirical study of U.S. library and information science professional organizations-related Tweets (2020) 0.04
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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.
  2. Zhang, Y.; Kudva, S.: E-books versus print books : readers' choices and preferences across contexts (2014) 0.03
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    Abstract
    With electronic book (e-book) sales and readership rising, are e-books positioned to replace print books? This study examines the preference for e-books and print books in the contexts of reading purpose, reading situation, and contextual variables such as age, gender, education level, race/ethnicity, income, community type, and Internet use. In addition, this study aims to identify factors that contribute to e-book adoption. Participants were a nationally representative sample of 2,986 people in the United States from the Reading Habits Survey, conducted by the Pew Research Center's Internet & American Life Project (http://pewinternet.org/Shared-Content/Data-Sets/2011/December-2011--Reading-Habits.aspx). While the results of this study support the notion that e-books have firmly established a place in people's lives, due to their convenience of access, e-books are not yet positioned to replace print books. Both print books and e-books have unique attributes and serve irreplaceable functions to meet people's reading needs, which may vary by individual demographic, contextual, and situational factors. At this point, the leading significant predictors of e-book adoption are the number of books read, the individual's income, the occurrence and frequency of reading for research topics of interest, and the individual's Internet use, followed by other variables such as race/ethnicity, reading for work/school, age, and education.
  3. Zhang, Y.; Zhang, G.; Zhu, D.; Lu, J.: Scientific evolutionary pathways : identifying and visualizing relationships for scientific topics (2017) 0.03
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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.
  4. Xu, H.; Bu, Y.; Liu, M.; Zhang, C.; Sun, M.; Zhang, Y.; Meyer, E.; Salas, E.; Ding, Y.: Team power dynamics and team impact : new perspectives on scientific collaboration using career age as a proxy for team power (2022) 0.01
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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.
  5. Zhang, Y.; Xu, W.: Fast exact maximum likelihood estimation for mixture of language model (2008) 0.00
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    Source
    Information processing and management. 44(2008) no.3, S.1076-1085
  6. Zhang, Y.: ¬The influence of mental models on undergraduate students' searching behavior on the Web (2008) 0.00
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    Source
    Information processing and management. 44(2008) no.3, S.1330-1345
  7. Zhang, Y.: Complex adaptive filtering user profile using graphical models (2008) 0.00
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    Source
    Information processing and management. 44(2008) no.6, S.1886-1900
  8. Tenopir, C.; Wang, P.; Zhang, Y.; Simmons, B.; Pollard, R.: Academic users' interactions with ScienceDirect in search tasks : affective and cognitive behaviors (2008) 0.00
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    Source
    Information processing and management. 44(2008) no.1, S.105-121
  9. Zhang, Y.: Understanding the sustained use of online health communities from a self-determination perspective (2016) 0.00
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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.
  10. Zhang, Y.: ¬The impact of Internet-based electronic resources on formal scholarly communication in the area of library and information science : a citation analysis (1998) 0.00
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    Date
    30. 1.1999 17:22:22
  11. Zhang, Y.: Developing a holistic model for digital library evaluation (2010) 0.00
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    Abstract
    This article reports the author's recent research in developing a holistic model for various levels of digital library (DL) evaluation in which perceived important criteria from heterogeneous stakeholder groups are organized and presented. To develop such a model, the author applied a three-stage research approach: exploration, confirmation, and verification. During the exploration stage, a literature review was conducted followed by an interview, along with a card sorting technique, to collect important criteria perceived by DL experts. Then the criteria identified were used for developing an online survey during the confirmation stage. Survey respondents (431 in total) from 22 countries rated the importance of the criteria. A holistic DL evaluation model was constructed using statistical techniques. Eventually, the verification stage was devised to test the reliability of the model in the context of searching and evaluating an operational DL. The proposed model fills two lacunae in the DL domain: (a) the lack of a comprehensive and flexible framework to guide and benchmark evaluations, and (b) the uncertainty about what divergence exists among heterogeneous DL stakeholders, including general users.
  12. Zhang, Y.; Jansen, B.J.; Spink, A.: Identification of factors predicting clickthrough in Web searching using neural network analysis (2009) 0.00
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    Date
    22. 3.2009 17:49:11
  13. Zhang, Y.; Liu, J.; Song, S.: ¬The design and evaluation of a nudge-based interface to facilitate consumers' evaluation of online health information credibility (2023) 0.00
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    Date
    22. 6.2023 18:18:34
  14. Zhang, Y.; Wu, M.; Zhang, G.; Lu, J.: Stepping beyond your comfort zone : diffusion-based network analytics for knowledge trajectory recommendation (2023) 0.00
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    Date
    22. 6.2023 18:07:12