From the algorithmic and emergent mindset to the heuristic mindset of reviewing literature
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From the algorithmic and emergent mindset to the heuristic mindset of reviewing literature Adasa Nkrumah Kofi Frimpong1 · Ping Li1 · Millicent Amoah1 · Md Altab Hossin1 Accepted: 23 October 2020 © Springer Nature B.V. 2020
Abstract Studies on literature review (LR) methodology appear scant. Researchers have outlined common challenges associated with the LR process. Traditionally, conducting a LR entails paraphrasing existing studies. Researchers have failed to address the emergent nature of literature and how to deal with this challenge effectively. We analyzed the challenges of conducting a LR of published articles. Also, we collected data from postgraduate students in China. Using the five-level Qualitative Data Analysis and the learning heuristics of the translation process, we matched the units of analytical tasks of the LR at the strategy level to their selected and constructed components at the tactics level. A post-test analysis shows that this model explains 72% of the variance in the new strategy . Keywords Educational research · Discourse analysis · Literature review · Qualitative data analysis · Challenges of literature review · Postgraduate students
1 Introduction Researchers conduct a literature review (LR) to inform and/or justify the choice of research questions, theoretical foundation, and chosen methodology. The LR establishes the topic’s importance, introducing the background information needed to understand the subject. A LR is vital to evaluate the depth and breadth of the research related to the issue. Traditional and manual approaches to reviewing literature have proven ineffective, as they are often conducted in an ad hoc manner, lack thoroughness, and do not usually follow a specific methodology (Snyder 2019). The ineffectiveness of these traditional approaches can result from the unstructured and semi-structured nature of LR methodology. Moreover, qualitative methods require a detailed understanding of a topic’s processes, theories, and experiences. Large amounts of information, in non-numeric forms, * Adasa Nkrumah Kofi Frimpong [email protected] Ping Li [email protected] 1
School of Management and Economics, Center for West African Studies, University of Electronic Science and Technology of China, No. 2006, Xiyuan Ave, West Hi‑Tech Zone, Chengdu, Sichuan 611731, P.R. China
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are gathered on the issue under investigation. As a result, it is difficult to vividly explore, describe, and compare different literature sources, especially for pattern analysis and visualization, to evaluate and build on theory. It has become necessary to develop better information management (record, sort, match, link, and visualize) to amalgamate and derive meaning from diverse data sources. Researchers want to better answer their research questions without losing track of the different sources they have gathered. They need to perform the research without missing essential points, especially when there is a change in the study aspect. They are int
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