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

Indulska, Marta, Hovorka, Dirk S. and Recker, Jan (2012) Quantitative approaches to content analysis: Identifying conceptual drift across publication outlets. European Journal of Information Systems, 21 1: 49-69. doi:10.1057/ejis.2011.37

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Author Indulska, Marta
Hovorka, Dirk S.
Recker, Jan
Title Quantitative approaches to content analysis: Identifying conceptual drift across publication outlets
Journal name European Journal of Information Systems   Check publisher's open access policy
ISSN 0960-085X
Publication date 2012-01-01
Year available 2011
Sub-type Article (original research)
DOI 10.1057/ejis.2011.37
Open Access Status File (Author Post-print)
Volume 21
Issue 1
Start page 49
End page 69
Total pages 21
Editor Wynne W. Chin
Iris Junglas
José L. Roldán
Place of publication Hants, United Kingdom
Publisher Palgrave Macmillan
Language eng
Subject 1710 Information Systems
3309 Library and Information Sciences
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.
Keyword Unstructured data analysis
Quantitative semantic analysis
Text mining
Latent Semantic Analysis
Q-Index Code C1
Q-Index Status Confirmed Code
Institutional Status UQ
Additional Notes Attached pdf is an 'Author Copy'. Published online 30 August 2011. Special Issue: Quantitative Research Methodology.

Document type: Journal Article
Sub-type: Article (original research)
Collections: Official 2012 Collection
UQ Business School Publications
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Citation counts: TR Web of Science Citation Count  Cited 21 times in Thomson Reuters Web of Science Article | Citations
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Created: Tue, 04 Oct 2011, 23:49:44 EST by Karen Morgan on behalf of UQ Business School