Theory identity: A machine-learning approach

Kai R. Larsen, Dirk Hovorka, Jevin West, James Birt, James R. Pfaff, Trevor W. Chambers, Zebula R. Sampedro, Nick Zager, Bruce Vanstone

Research output: Chapter in Book/Report/Conference proceedingConference contributionResearchpeer-review

6 Citations (Scopus)


Theory identity is a fundamental problem for researchers seeking to determine theory quality, create theory ontologies and taxonomies, or perform focused theory-specific reviews and meta-analyses. We demonstrate a novel machine-learning approach to theory identification based on citation data and article features. The multi-disciplinary ecosystem of articles which cite a theory's originating paper is created and refined into the network of papers predicted to contribute to, and thus identify, a specific theory. We provide a 'proof-of-concept' for a highly-cited theory. Implications for cross-disciplinary theory integration and the identification of theories for a rapidly expanding scientific literature are discussed.

Original languageEnglish
Title of host publicationProceedings of the 47th Annual Hawaii International Conference on System Sciences, HICSS 2014
PublisherIEEE Computer Society
Number of pages10
ISBN (Print)9781479925049
Publication statusPublished - 2014
Event47th Hawaii International Conference on System Sciences, HICSS 2014 - Waikoloa, Waikoloa, HI, United States
Duration: 6 Jan 20149 Jan 2014


Conference47th Hawaii International Conference on System Sciences, HICSS 2014
Country/TerritoryUnited States
CityWaikoloa, HI


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