Using Natural Language Processing Techniques to Tackle the Construct Identity Problem in Information Systems Research

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The growing number of constructs in behavioral research presents a problem to theory integration, since constructs cannot clearly be discriminated from each other. Recently there have been efforts to employ natural language processing techniques to tackle the construct identity problem. This paper compares the performance of the novel word-embedding model GloVe and different document projection methods with a latent semantic analysis (LSA) used in recent literature. The results show that making use of an advantage in document projection that LSA has over GloVe, performance can be improved. Even against this advantage of LSA, GloVe reaches comparable performance, and adjusted word embedding models can make up for this advantage. The proposed approach therefore presents a promising pathway for theory integration in behavioral research.

Original languageEnglish
Title of host publicationProceedings of the 53rd Annual Hawaii International Conference on System Sciences, HICSS 2020 : Knowing What We Know: Theory, Meta-analysis, and Review
EditorsTung X. Bui
Number of pages10
Place of PublicationHonolulu
PublisherUniversity of Hawaiʻi at Mānoa
Publication date07.01.2020
Pages5675-5684
ISBN (electronic)978-0-9981331-3-3
Publication statusPublished - 07.01.2020
EventHawaii International Conference on System Sciences - HICSS 2020 - University of Hawai'i at Manoa, Hawai, United States
Duration: 07.01.202010.01.2020
Conference number: 53
https://scholarspace.manoa.hawaii.edu/handle/10125/63576

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