| 胡媛,艾文华,胡子祎,胡昌平.高校科研人员数据需求管理影响因素框架研究[J].中国图书馆学报,2019,45(4):104~111 |
| 高校科研人员数据需求管理影响因素框架研究 |
| Research on the Influencing Factors Framework of Data Demand Management of University Scientific Researchers |
| 投稿时间：2019-01-08 修订日期：2019-06-17 |
| DOI： |
| 中文关键词: 数据需求管理 科研人员 影响因素 扎根理论 框架构建 |
| 英文关键词: Data demand management Researchers Influencing factors Grounded theory Framework construction |
| 基金项目:本文系国家社会科学基金重大项目“云环境下国家数字学术资源信息安全保障体系研究”(编号14ZDB168)的研究成果之一 |
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| 中文摘要: |
新型科研范式下,科研模式的变革对高校科研人员的数据需求管理提出了更高的要求,探究高校科研人员数据需求管理的影响因素,可为科研人员数据需求的有效引导与管理提供参考。本文通过焦点会议、个人访谈和网络数据获取原始资料,运用扎根理论方法探索高校科研人员数据需求管理的影响因素,最终得到67项初始概念、24项范畴和8项主范畴。在此基础上逐步归纳提炼出由用户、服务、情境和技术4项维度组成的数据需求管理影响因素的USCT模型分析框架,同时进一步分析了各项主范畴对数据需求管理的内在关联机制及作用关系。结果表明,用户个人特质、个性化服务以及任务情境对科研数据需求管理的影响最为直接；用户感知、知识服务、移动信息情境、技术应用以及技术匹配会对数据需求管理产生间接影响。图6。表3。参考文献49。
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| 英文摘要: |
The environment and pattern of scientific research are developing towards digital, open and community oriented, and influenced by the increasingly diversified data resources and the mature of various methods. All these make the researchers demand becoming more complicated and deepened. The evolution of scientific research puts forward higher requirement for the data demand management of collegial researchers.
We constructed an analytical framework of the influencing factors of data demand management for scientific researchers based on the grounded theory. The interaction of key elements involved in the framework was analyzed, aiming to reveal the influence mechanism of key factors on researchers data demand management.
This research is composed of four parts: research problem generation, data collection, data processing, and theory construction. Staged collection method was used in data collection mainly through personal in depth interview and focus group meeting, and the data we collected was supplemented and verified by the data and comments in various scientific research platforms. Data standardization and reduction was carried out by the qualitative analysis software Nvivo 12.0, and the original data was coded in three level: open coding, spindle coding and selective coding.
The USCT framework we constructed contains 8 main categories: personal ability characteristic, user perception, personalized service, knowledge service, task context, mobile information context, technology application and technology fit, involving 24 categories and 67 initial concepts. All categories and concepts were generalized into 4 layers: key layer (user dimension), guarantee layer (service dimension), driven layer (context dimension) and foundation layer (technology dimension). In the subsequent analysis of interactive relationship and internal correlative mechanism between main categories and data demand management, we found personal ability characteristic of user,
personalized service
and task context to be the most direct impact factor on data demand management, while user perception,
knowledge service, mobile information context, technology application and technology fit work in an indirect way.
The USCT model framework has several positive effects: 1) it contributes to the construction and perfection of the conceptual system and framework of scientific research data demand management; 2) it provides a reliable guiding analysis model for the construction of a science data service platform to promote scientific research and innovation.
We innovatively constructed an analysis framework for the influencing factors of data demand management of university researchers. This framework organically integrates “user” (scientific researcher, subject librarian), “services”, “context” and “technology” into one system. We attempt to guide and develop the researcher's scientific data needs under their knowledge environment through computer information technology, and ultimately guarantee the provision of suitable service.
Further demonstration and service practices are needed to verify and perfect the theory. Follow up studies can take “user context service technology” as the main line for empirical research and explore the external features and internal path mechanisms of data demand management in all domains. 6 figs. 3 tabs. 49 refs.
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