A semantic similarity adjusted document co-citation analysis: a case of tourism supply chain
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A semantic similarity adjusted document co‑citation analysis: a case of tourism supply chain Kamal Sanguri1 · Atanu Bhuyan1 · Sabyasachi Patra1 Received: 2 December 2019 © Akadémiai Kiadó, Budapest, Hungary 2020
Abstract Document co-citation analysis (DCA) is employed across various academic disciplines and contexts to characterise the structure of knowledge. Since the introduction of the method for DCA by Small (J Am Soc Inf Sci 24(4):265–269, 1973) a variety of modifications towards optimising its results have been proposed by several researchers. We recommend a new approach to improve the results of DCA by integrating the concept of the document similarity measure into it. Our proposed method modifies DCA by incorporating the semantic similarity using latent semantic analysis for the abstracts of the top-cited documents. The interaction of these two measures results in a new measure that we call as the semantic similarity adjusted co-citation index. The effectiveness of the proposed method is evaluated through an empirical study of the tourism supply chain (TSC), where we employ the techniques of the network and cluster analyses. The study also comprehensively explores the resulting knowledge structures from both the methods. The results of our case study suggest that the clustering quality and knowledge map of the domain can be improved by considering the document similarity along with their co-citation strength. Keywords Document co-citation analysis · Network analysis · Cluster analysis · Tourism supply chain · Latent semantic analysis
Introduction Document co-citation analysis (DCA) uses citation frequency as the measure of the semantic similarity among two documents (Small 1973). Two documents are said to be co-cited if they are both cited in a different document. The strength of this association depends upon the frequency or count of the co-citation; higher the count, higher is the strength of association among the co-cited documents (Vanraan 1990). Proposed independently by Small (1973) and Marshakova (1973), DCA helps in revealing the structure of knowledge in an academic field. It is considered as a useful measure that may vary with time as the research in the field evolves (Trujillo and Long 2018). Gmür (2003) summarised the state of the methodological approaches in DCA since its * Kamal Sanguri [email protected] 1
Indian Institute of Management Kashipur, Kundeshwari, Kashipur, Uttarakhand 244713, India
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Vol.:(0123456789)
Scientometrics
inception. The author argues that two schools of thought dominate the field: the micro approach intends to analyse the evolution of academic fields retrospectively, and the macro approach is primarily focused on the overall assembly of the academic field. DCA is usually employed in various academic areas to understand their knowledge subdomains, and associations among them. Thus, enabling the effective capturing of the evolution and variations in the academic or scientific field (Nerur et al. 2008). Notwithstanding the acceptance of DCA i
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