An Efficient Method for Detecting and Extracting Nomological Networks

Posted: 13 Jun 2015

See all articles by Jingjing Li

Jingjing Li

University of Virginia - McIntire School of Commerce

Kai R. Larsen

Leeds School of Business; Information Systems Group; Gallup

Ahmed Abbasi

University of Virginia - McIntire School of Commerce

Date Written: June 12, 2015

Abstract

The accumulated literature base in the behavioral sciences represents the IS discipline’s greatest source of knowledge, and yet the same literature has grown beyond human comprehension. An experiment is conducted showing the inability of experts to retrieve relevant constructs using full-text search. To address this inability to access the body of theoretical behavioral science research we propose a novel IT artifact built on an information extraction approach to nomological network discovery. Based on the design science paradigm we develop a three-step process for extraction and assembly of nomological networks proceeding through article download, hypothesis extraction, variable extraction, and finally to variable integration. Rule-based vs. machine learning algorithms are evaluated and compared to determine the best approach for the extraction steps. A dataset of all the relevant behavioral studies from two top journals in Information Systems and Psychology is used to evaluate the approach in comparison to expert decisions, leading into a discussion of limitations and possible extensions.

Keywords: Information extraction, Nomological networks, Inter-Nomological network, INN, Theories, Variables, Social science, Behavioral science, Construct proliferation.

Suggested Citation

Li, Jingjing and Larsen, Kai R. and Abbasi, Ahmed, An Efficient Method for Detecting and Extracting Nomological Networks (June 12, 2015). Available at SSRN: https://ssrn.com/abstract=2617893 or http://dx.doi.org/10.2139/ssrn.2617893

Jingjing Li (Contact Author)

University of Virginia - McIntire School of Commerce ( email )

P.O. Box 400173
Charlottesville, VA 22904-4173
United States

Kai R. Larsen

Leeds School of Business; Information Systems Group ( email )

995 Regent Dr.
Boulder, CO 80309-0419
United States

Gallup ( email )

901 F St NW
Washington, DC 20004
United States

Ahmed Abbasi

University of Virginia - McIntire School of Commerce ( email )

P.O. Box 400173
Charlottesville, VA 22904-4173
United States

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