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Quantitative approaches to content analysis : identifying conceptual drift across publication outlets

Indulska, Marta, Hovorka, Dirk S., & Recker, Jan C. (2012) Quantitative approaches to content analysis : identifying conceptual drift across publication outlets. European Journal of Information Systems, 21(1), pp. 49-69.

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Abstract

Unstructured text data, such as emails, blogs, contracts, academic publications, organizational documents, transcribed interviews, and even tweets, are important sources of data in Information Systems research. Various forms of qualitative analysis of the content of these data exist and have revealed important insights. Yet, to date, these analyses have been hampered by limitations of human coding of large data sets, and by bias due to human interpretation. In this paper, we compare and combine two quantitative analysis techniques to demonstrate the capabilities of computational analysis for content analysis of unstructured text. Specifically, we seek to demonstrate how two quantitative analytic methods, viz., Latent Semantic Analysis and data mining, can aid researchers in revealing core content topic areas in large (or small) data sets, and in visualizing how these concepts evolve, migrate, converge or diverge over time. We exemplify the complementary application of these techniques through an examination of a 25-year sample of abstracts from selected journals in Information Systems, Management, and Accounting disciplines. Through this work, we explore the capabilities of two computational techniques, and show how these techniques can be used to gather insights from a large corpus of unstructured text.

Impact and interest:

5 citations in Scopus
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4 citations in Web of Science®

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ID Code: 47974
Item Type: Journal Article
Keywords: unstructured data analysis, quantitative semantic analysis, text mining, Latent Semantic Analysis
DOI: 10.1057/ejis.2011.37
ISSN: 0960-085X
Subjects: Australian and New Zealand Standard Research Classification > INFORMATION AND COMPUTING SCIENCES (080000) > INFORMATION SYSTEMS (080600) > Information Systems not elsewhere classified (080699)
Australian and New Zealand Standard Research Classification > COMMERCE MANAGEMENT TOURISM AND SERVICES (150000) > BUSINESS AND MANAGEMENT (150300) > Business Information Systems (150302)
Divisions: Past > QUT Faculties & Divisions > Faculty of Science and Technology
Past > Schools > Information Systems
Copyright Owner: Copyright 2012 Palgrave Macmillan
Deposited On: 10 Jan 2012 07:44
Last Modified: 02 Jul 2013 02:14

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