Date of Award

Spring 1-1-2011

Document Type

Dissertation

Degree Name

Doctor of Philosophy (PhD)

Department

Computer Science

First Advisor

James Martin

Second Advisor

Kai Larsen

Third Advisor

Tamara Sumner

Abstract

This study examines automated approaches to discovering behavioral knowledge that are encoded as constructs in social and behavioral science disciplines. To date, constructs relationships are ordinarily revealed through laborious psychometric methods, but this study has shown that it is possible to extract these relationships through automated computational approaches. By building on text similarity measures from prior literature, we are able to predict construct relationships through construct name, definition and items. The predicted relationships were woven into an interlock system to demonstrate construct interplays, even though they have not been studied. The construct interlock could be seen as a theory map to understand human decision-making. Two use cases were presented to demonstrate the efficacy of the proposed measures: measuring the root constructs in UTAUT and visualizing network of construct perceived usefulness. The encouraging results showed that the proposed measures could dramatically expedite theory development, at the same time also expedite progression of human science.

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