Page 171 - Journal of Library Science in China, Vol.45, 2019

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170 Journal of Library Science in China, Vol.11, 2019

participation of humanities scholars as a process, we also suggest engaging humanities scholars by
understanding their motivation as well as information needs and information behavior at each stage
of the participation process. In so doing, humanities scholars can make better use of information
technologies in their research.

Research on the influencing factors framework of data demand management
of university scientific researchers

〇a ∗
HU Yuan , AI Wenhua, HU Ziyi & HU Changping
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 become 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 factors 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 development

* Correspondence should be addressed to HU Yuan, Email: hyuan@whu.edu.cn, ORCID: 0000-0003-0149-4287