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Literature review is a crucial part of the research work, particularly a doctoral research, and the review quality is undoubtedly an engine for its efficiency and quality. The goal of literature reviews is “identifying, evaluating, and interpreting the existing body of recorded documents” (Fink, 2005). In fact, they are the stepping-stone toward finding an appropriate research issue in the doctoral work (Mongeau, 2008). However, performing literature reviews in the modern digital era raises serious challenges.
First, the current era witnesses a continually exploding digital information (Niazi, 2016) daily (Garner & Scott, 2013). Yet, performing literature review requires a “re-viewing” and “a written appraisal” of previous research work in a research field (Jesson, 2011). This task becomes more and more challenging (Aragón, 2013; Bornmann & Mutz, 2015) even for experienced researchers which can find it “quite intimidating…to keep up with and locate trends and hot topics in peer-reviewed work” (Niazi, 2016). In addition, this overload of information trades off rigour versus relevance (Booth, Papaioannou, & Sutton, 2012) whereas the doctoral research highly recommends a compromise between rigour, quality, time and efforts.
Second, the literature review procedure is very labor-intensive (Randolph, 2009) and lacks “a prescribed methodology” (Jesson, 2011). In fact, classical approaches often rely on “implicit, idiosyncratic methods of data collection and interpretation” (Mulrow & Cook, 1998). Also, they have commonly been accused of important bias and systematic inaccuracies (Booth et al., 2012) whereas success in science depends on reliable and unbiased understanding (McInerny et al., 2014).
The aforementioned factors result in a significant decreasing quality of literature reviews in dissertation and submitted manuscripts (Boote & Beile, 2005; Randolph, 2009) besides a great intimidation (Rowley & Slack, 2004) and anxiety (Alias & Suradi, 2008) among PhD students toward the literature review process.
Recently, the mapping of science has been an increasingly used tool to support the reviewing of literature by fulfilling the main function of literature reviews, i.e. identifying characteristics of existing research including patterns, themes and issues (Seuring & Müller, 2008; Webster & Watson, 2002). In fact, using bibliometric software, science mapping allows visualizing thematic, organizational and citations patterns. It can then represent an efficient educational technology tool that facilitates learning for the novice reviewer. Also, and by implementing appropriate bibliometric processes and resources, science mapping can improve the performance of the reviewer.
Science mapping is an important research area in bibliometrics (Cobo, López-Herrera, Herrera-Viedma, & Herrera, 2011b), which is a powerful research domain within the quantitative studies of science (Moed, Glänzel, & Schmoch, 2004, p. 20). According to Small (1999), Science mapping is “a spatial representation of how disciplines, fields, specialties, and individual documents or authors are related to one another”. From a technical perspective, a science map is a network made of nodes and edges. Nodes represent a specific element from research documents such as authors, articles, journals, keywords, or references. Edges are the “intellectual” relationship between the nodes (Cobo et al., 2011b). For example, when nodes represent keywords, edges connect them when documents have common keywords. According to Viedma-Del-Jesus et al. (2011), the more similar keywords documents have, the more likely they belong to the same higher research area (as cited in Börner, Chen, & Boyack, 2003).