Abstract
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 language | English |
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Title of host publication | Proceedings of the 47th Annual Hawaii International Conference on System Sciences, HICSS 2014 |
Publisher | IEEE Computer Society |
Pages | 4639-4648 |
Number of pages | 10 |
ISBN (Print) | 9781479925049 |
DOIs | |
Publication status | Published - 2014 |
Event | 47th Hawaii International Conference on System Sciences, HICSS 2014 - Waikoloa, Waikoloa, HI, United States Duration: 6 Jan 2014 → 9 Jan 2014 |
Conference
Conference | 47th Hawaii International Conference on System Sciences, HICSS 2014 |
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Country/Territory | United States |
City | Waikoloa, HI |
Period | 6/01/14 → 9/01/14 |