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Analyzing Unstructured Text Data: Using Latent Categorization To Identify Intellectual Communities In Information Systems, Kai R. Larsen, David E. Monarchi, Dirk S, Hovorka, Christopher N. Bailey
Analyzing Unstructured Text Data: Using Latent Categorization To Identify Intellectual Communities In Information Systems, Kai R. Larsen, David E. Monarchi, Dirk S, Hovorka, Christopher N. Bailey
Dirk Hovorka
The Information Systems field is structured by the research topics emphasized by communities of journals. The Latent Categorization Method categorized and automatically named IS research topics in 14,510 abstracts from 65 Information Systems journals. These topics were clustered into seven intellectual communities based on publication patterns. The technique develops categories from the data itself, it is replicable, is relatively insensitive to the size of the text units, and it avoids many of the problems that frequently accompany human categorization. As such LCM provides a new approach to analyzing a wide array of textual data.
Explanation In Information Systems, Dirk S. Hovorka, Matt Germonprez, Kai R. Larsen
Explanation In Information Systems, Dirk S. Hovorka, Matt Germonprez, Kai R. Larsen
Dirk Hovorka
Explanation of observed phenomena is a major objective of both those who conduct and those who apply research in Information Systems (IS). Whereas explanation based on the statistical relationship between independent and dependent variables is a common outcome of explanatory IS research, philosophers of science disagree about whether statistical relationships are the sole basis for the explanation of phenomena. The purpose of this paper is to introduce an expanded concept of explanation into the realm of IS research. We present a framework based on the four principle explanation types defined in modern philosophy: covering-law explanation, statistical-relevance explanation, contrast-class explanation, and …