-
Ding, Y.; Zhang, G.; Chambers, T.; Song, M.; Wang, X.; Zhai, C.: Content-based citation analysis : the next generation of citation analysis (2014)
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- Date
- 22. 8.2014 16:52:04
- Source
- Journal of the Association for Information Science and Technology. 65(2014) no.9, S.1820-1833
-
Tian, W.; Cai, R.; Fang, Z.; Geng, Y.; Wang, X.; Hu, Z.: Understanding co-corresponding authorship : a bibliometric analysis and detailed overview (2024)
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- Abstract
- The phenomenon of co-corresponding authorship is becoming more and more common. To understand the practice of authorship credit sharing among multiple corresponding authors, we comprehensively analyzed the characteristics of the phenomenon of co-corresponding authorships from the perspectives of countries, disciplines, journals, and articles. This researcher was based on a dataset of nearly 8 million articles indexed in the Web of Science, which provides systematic, cross-disciplinary, and large-scale evidence for understanding the phenomenon of co-corresponding authorship for the first time. Our findings reveal that higher proportions of co-corresponding authorship exist in Asian countries, especially in China. From the perspective of disciplines, there is a relatively higher proportion of co-corresponding authorship in the fields of engineering and medicine, while a lower proportion exists in the humanities, social sciences, and computer science fields. From the perspective of journals, high-quality journals usually have higher proportions of co-corresponding authorship. At the level of the article, our findings proved that, compared to articles with a single corresponding author, articles with multiple corresponding authors have a significant citation advantage.
- Source
- Journal of the Association for Information Science and Technology. 75(2023) no.1, S.3-23
-
Teo, H.-H.; Wang, X.; Wei, K.-K.; Sia, C.-L.; Lee, M.K.O.: Organizational learning capacity and attitude toward complex technological innovations : an empirical study (2006)
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- Abstract
- Recent studies have found organizational learning capacity to be a key factor in influencing organizational assimilation and exploitation of knowledge-intensive innovations. Despite its increasing importance, the impact of organizational learning capacity an technology assimilation is not well understood. Distilling from extant works an organizational learning and technology assimilation, this study identifies four components of organizational learning capacity, namely, systems orientation, organizational climate for learning orientation, knowledge acquisition and utilization orientation, and information sharing and dissemination orientation. The authors subject these components to structural equation modeling analyses to better understand their structure and dimensionality. The analyses strongly support the proposed four major dimensions underlying organizational learning capacity. Organizational learning capacity, as a higher-order factor, has a significant impact an attitude towards organizational adoption of knowledge-intensive innovations. Implications for practice and research are discussed.
- Source
- Journal of the American Society for Information Science and Technology. 57(2006) no.2, S.264-279
-
Jiang, Y.; Zheng, H.-T.; Wang, X.; Lu, B.; Wu, K.: Affiliation disambiguation for constructing semantic digital libraries (2011)
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- Abstract
- With increasing digital information availability, semantic web technologies have been employed to construct semantic digital libraries in order to ease information comprehension. The use of semantic web enables users to search or visualize resources in a semantic fashion. Semantic web generation is a key process in semantic digital library construction, which converts metadata of digital resources into semantic web data. Many text mining technologies, such as keyword extraction and clustering, have been proposed to generate semantic web data. However, one important type of metadata in publications, called affiliation, is hard to convert into semantic web data precisely because different authors, who have the same affiliation, often express the affiliation in different ways. To address this issue, this paper proposes a clustering method based on normalized compression distance for the purpose of affiliation disambiguation. The experimental results show that our method is able to identify different affiliations that denote the same institutes. The clustering results outperform the well-known k-means clustering method in terms of average precision, F-measure, entropy, and purity.
- Source
- Journal of the American Society for Information Science and Technology. 62(2011) no.6, S.1029-1041
-
Wang, X.; High, A.; Wang, X.; Zhao, K.: Predicting users' continued engagement in online health communities from the quantity and quality of received support (2021)
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- Abstract
- This article presents a rare insight into the migration of municipality record-keeping databases. The migration of a database for preservation purposes poses Online health communities (OHCs) have been major resources for people with similar health concerns to interact with each other. They offer easily accessible platforms for users to seek, receive, and provide supports by posting. Taking the advantage of text mining and machine learning techniques, we identified social support type(s) in each post and a new user's support needs in an OHC. We examined a user's first-time support-seeking experience by measuring both quantity and quality of received support. Our results revealed that the amount and match of received support are positive and significant predictors of new users' continued engagement. Our outcomes can provide insight for designing and managing a sustainable OHC by retaining users.
- Source
- Journal of the Association for Information Science and Technology. 72(2021) no.6, S.710-722
-
Wang, X.; Erdelez, S.; Allen, C.; Anderson, B.; Cao, H.; Shyu, C.-R.: Role of domain knowledge in developing user-centered medical-image indexing (2012)
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- Journal of the American Society for Information Science and Technology. 63(2012) no.2, S.225-241
-
Yang, B.; Rousseau, R.; Wang, X.; Huang, S.: How important is scientific software in bioinformatics research? : a comparative study between international and Chinese research communities (2018)
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- Journal of the Association for Information Science and Technology. 69(2018) no.9, S.1122-1133
-
Walsh, J.A.; Cobb, P.J.; Fremery, W. de; Golub, K.; Keah, H.; Kim, J.; Kiplang'at, J.; Liu, Y.-H.; Mahony, S.; Oh, S.G.; Sula, C.A.; Underwood, T.; Wang, X.: Digital humanities in the iSchool (2022)
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- Journal of the Association for Information Science and Technology. 73(2022) no.2, S.188-203
-
Wang, X.; Hong, Z.; Xu, Y.(C.); Zhang, C.; Ling, H.: Relevance judgments of mobile commercial information (2014)
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- Journal of the Association for Information Science and Technology. 65(2014) no.7, S.1335-1348
-
Tan, X.; Luo, X.; Wang, X.; Wang, H.; Hou, X.: Representation and display of digital images of cultural heritage : a semantic enrichment approach (2021)
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- Knowledge organization. 48(2021) no.3, S.231-247
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Wang, X.; Zhang, M.; Fan, W.; Zhao, K.: Understanding the spread of COVID-19 misinformation on social media : the effects of topics and a political leader's nudge (2022)
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- Journal of the Association for Information Science and Technology. 73(2022) no.5, S.726-737
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Wang, X.; Song, N.; Zhou, H.; Cheng, H.: Argument ontology for describing scientific articles : a statistical study based on articles from two research areas (2019)
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- Source
- Proceedings of the Association for Information Science and Technology 56(2019) no.1, S.855-857
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Song, N.; Cheng, H.; Zhou, H.; Wang, X.: Linking scholarly contents : the design and construction of an argumentation graph (2022)
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- Knowledge organization. 49(2022) no.4, S.213 - 235
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Reyes Ayala, B.; Knudson, R.; Chen, J.; Cao, G.; Wang, X.: Metadata records machine translation combining multi-engine outputs with limited parallel data (2018)
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- Journal of the Association for Information Science and Technology. 69(2018) no.1, S.47-59
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Wang, F.; Wang, X.: Tracing theory diffusion : a text mining and citation-based analysis of TAM (2020)
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- Journal of documentation. 76(2020) no.6, S.1109-1134
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Fang, Z.; Costas, R.; Tian, W.; Wang, X.; Wouters, P.: How is science clicked on Twitter? : click metrics for Bitly short links to scientific publications (2021)
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- Journal of the Association for Information Science and Technology. 72(2021) no.7, S.918-932
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Wang, X.; Duan, Q.; Liang, M.: Understanding the process of data reuse : an extensive review (2021)
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- Journal of the Association for Information Science and Technology. 72(2021) no.9, S.1161-1182
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Wang, X.; Song, N.; Zhou, H.; Cheng, H.: ¬The representation of argumentation in scientific papers : a comparative analysis of two research areas (2022)
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- Journal of the Association for Information Science and Technology. 73(2022) no.6, S.863-878
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Cui, Y.; Wang, Y.; Liu, X.; Wang, X.; Zhang, X.: Multidimensional scholarly citations : characterizing and understanding scholars' citation behaviors (2023)
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- Journal of the Association for Information Science and Technology. 74(2023) no.1, S.115-127
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Wang, X.; Lin, X.; Shao, B.: Artificial intelligence changes the way we work : a close look at innovating with chatbots (2023)
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- Source
- Journal of the Association for Information Science and Technology. 74(2023) no.3, S.339-353
