A large scale knowledge integration leading to human decision making

Abstract

For the first time natural language processing approaches are applied on a large scale to psychometric methods. Psychometric methods have been applied in hundreds of thousands of published studies. This study examines automated approach 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 measure 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. We visualizing network of construct on a very well studied information system 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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