Data-driven decision making in graduate students’ research topic selection. Cognitive processes and challenging factors
| DOI | https://doi.org/10.1108/AJIM-01-2019-0019 |
| Date | 16 September 2019 |
| Pages | 657-676 |
| Published date | 16 September 2019 |
| Author | Qiao Li,Ping Wang,Yifan Sun,Yinglong Zhang,Chuanfu Chen |
| Subject Matter | Library & information science,Information behaviour & retrieval,Information & knowledge management,Information management & governance,Information management |
Data-driven decision making in
graduate students’research
topic selection
Cognitive processes and challenging factors
Qiao Li, Ping Wang and Yifan Sun
Wuhan University School of Information Management, Wuhan, China
Yinglong Zhang
University of North Carolina at Chapel Hill School of Information and Library Science,
Chapel Hill, North Carolina, USA, and
Chuanfu Chen
Wuhan University School of Information Management, Wuhan, China
Abstract
Purpose –With the advent of the intelligent environment, as novice researchers, graduate students face
digital challenges in their research topic selection (RTS). The purpose of this paper is to explore their
cognitive processes during data-driven decision making (DDDM) in RTS, thus developing technical and
instructional strategies to facilitate their research tasks.
Design/methodology/approach –This study developes a theoretical model that considers data-driven
RTS as a second-order factor comprising both rational and experiential modes. Additionally, data literacy and
visual data presentation were proposed as an antecedent and a consequence of data-driven RTS, respectively.
The proposed model was examined by employing structural equation modeling based on a sample of
931 graduate students.
Findings –The results indicate that data-driven RTS is a second-order factor that positively affects the level
of support of visual data presentation and that data literacy has a positive impact on DDDM in RTS.
Furthermore, data literacy indirectly affects the level of support of visual data presentation.
Practical implications –These findings provide support for developers of knowledge discovery systems,
data scientists, universities and libraries on the optimization of data visualization and data literacy
instruction that conform to students’cognitive styles to inform RTS.
Originality/value –This paper reveals the cognitive mechanisms underlying the effects of data literacy and
data-driven RTS under rational and experiential modes on the level of support of the tabular or graphical
presentations. It provides insights into the match between the visualization formats and cognitive modes.
Keywords Data literacy, Data-driven decision making, Data-driven knowledge discovery,
Dual-process theories, Research topic selection, Visual data presentation
Paper type Research paper
1. Introduction
Selecting a research topic is the first step of research. Researchers identify topics in various
areas that they want to explore during the process of research topic selection (RTS), and the
essential parts of this process are matching personal interests with existing knowledge and
identifying knowledge gaps (Avan, 2000; Bhatti et al., 2012). As Albert Szent Györgyi, the
1937 Nobel laureate in Physiology or Medicine, highlighted, “discovery consists of seeing
what everybody has seen, and thinking what nobody has thought”(Yorks and Nicolaides,
2006). To make informed RTS decisions, researchers need to apply appropriate approaches
to the analysis of useful resources to discover knowledge. Aslib Journal of Information
Management
Vol. 71 No. 5, 2019
pp. 657-676
© Emerald PublishingLimited
2050-3806
DOI 10.1108/AJIM-01-2019-0019
Received 5 February 2019
Revised 21 May 2019
Accepted 19 June 2019
The current issue and full text archive of this journal is available on Emerald Insight at:
www.emeraldinsight.com/2050-3806.htm
The authors would like to acknowledge reviewers, editors, and all participants for their contribution to
the improvement of this study. This study is supported by grants from the National Natural Science
Foundation of China under the agreement 71774121 and 91546124.
657
Data-driven
decision
making
Although many factors influence graduate students’RTS decisions such as experience
(Mosyjowski et al., 2017), interest and advice from teachers or classmates (Wang and Park,
2015), obtaining knowledge by reviewing literature is found to be an essential capacity
(Tang and Gan, 2005) and central work of students’RTS. However, students may be
overwhelmed by the plethora of theories,terms or methodologies due to cognitive challenges
associated with the literature review (Levy and Ellis, 2006); thus, it is difficult for them to
identify topics of interest (Click, 2018). For students who struggle with RTS, the beginning
and further development of research can be hampered by ambiguous research topics.
The “fourth paradigm”proposed by Jim Gray in 2007 (Bell et al., 2009) introduced a data
exploration approach that provided unprecedented opportunities to reduce the cognitive
burden associatedwith knowledge discovery and to transform practice in RTS. In thefield of
conservation science, for example, text-mining tools were used to help researchers identify
emerging research topics and research gaps based on data analysis rather than literature
reviews (Westgate et al., 2015). The above challenges and opportunities indicate the need to
develop a novel data-driven paradigm for RTS decision making.
Data-driven RTS draws upon data as a resource and uses data-driven knowledge discovery
as the analytical approach to guide RTS decision making. During this data-driven decision
making (DDDM) process, knowledge discovery in databases (KDD) techniques and tools are
integrated and applied to discover research statuses, trends and gaps hidden in data from
literature, researchers and their community and real-world data and thus inform researchers’
RTS (Uramoto et al., 2004; Jiang and Zhang, 2016). For example, Essential Science Indicators
provides researchers who are interested in pursuing promising topics in certain research
areas with the identification and visualization of research fronts, based on a cluster analysis
of Clarivate Analytics-indexed highly cited papers data (Clarivate Analytics, 2019).
The advantages of data-driven RTS can be summarized as follows: first, it reduces cognitive
load and provides data-based evidence for RTS decisions. Students can identify research topics
that are able to meet practical demands and demonstrate the application value of topics based
on evidence obtained by the analysis of real-world data rather than the media coverage or
personal observation. Second, an analysis of interdisciplinary research data reveals associations
among disciplines, which helps to reduce cognitiveandcollaborativechallengesassociated with
interdisciplinary research (Leahey et al., 2017). Third, data are not only a resource but also an
interdisciplinary research object containing numerous promising topics ( Jin et al.,2015),thus
providing novel research opportunities. Finally, the application of intelligent tools contributesto
integrating knowledge that is isolated in massive literature and data silos (Attwood et al., 2010),
thus helping students discover useful knowledge hidden in data and informing their RTS.
Graduate students face challenges related to data literacy, visual data presentation and
cognition in data-driven RTS. This may reduce their confidence and comprehension of data
analytics and their trust in the application of data analytics to inform decisions (Zhou and Chen,
2018). The increasing volumes of data go beyond researchers’capacity to validate, analyze,
visualize, store and curate data (Collins, 2010), thus making data literacy more challenging.
Graduate students have several difficulties in searching (He et al., 2011), analyzing and
interpreting data (Click, 2018), which makes it difficult for them to use data and data analytics
for RTS decisions. Additionally, Wright (2007) found that graduate students’methods of
making sense of data differ from those of experienced researchers; thus, it is difficult for
students to understand and then follow “expert”guidelines for data analysis. This finding
reinforces the need to develop data literacy strategies for graduate students. However,
researchers pay more attention to developing information literacy strategies that can support
traditional RTS (Lee and Laverty, 2014), thus leaving a gap in data literacy strategies in the
contextofgraduatestudents’data-driven RTS. In terms of human cognition, based on
cognitive-experiential self-theory (Epstein, 1994) and dual-process theories, both rational and
experiential modes of information-processing affect decision making (Khatri et al.,2018)
658
AJIM
71,5
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