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{{short description|Norwegian-American academic}}
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'''Kai R. Larsen''' is a Norwegian-American academic and management information systems scholar. He is an Associate Professor of Information Systems at the Leeds School of Business at the [[University of Colorado]]-Boulder.

== Education and career ==

Larsen received his undergraduate and masters training computer science in Norway; he received his Diplomkandidat in computer science from the NHI College of Computer Science - in 1992 and his Candidatus Magisterii in [[Software Engineering]] from The National College for Teachers of Commerce in 1994. In 2000, he received his PhD in Information Science from SUNY-Albany, the State [[University of New York]]{{Disambiguation needed|date=August 2020}} in Albany.  He joined the University of Colorado-Boulder in 2000.

== Research interests ==

Larsen studies information systems (IS)—primarily a behavioral science—that employ psychometric approaches to understand and predict technology adoption and use focuses on creating information technology (IT) artifacts to solve societal problems. His research falls in three primary areas: construct identifies detection, developing tools for literature reviews, and understanding patterns of survey response by semantic relationships among the questions asked in a survey. 

In social science “construct” is a latent or unobservable property that cannot be completely captured by some single operational measure -- like “perceived ease of use” of technology or “trust” in a technology vendor.  Sometimes researchers use the same label with different meanings (the jingle fallacy), or different labels with the same meanings (the jangle fallacy). A jangle fallacy is reinventing the wheel but using new terms so that this is not obvious Larsen and Bong developed machine learning methods using Natural Language Processing to find when two papers were studying the same underlying theoretical relationships, even if they were using different terms. <ref>{{Cite journal|last1=Larsen|first1=Kai R.|last2=Bong|first2=Chin How|year=2016|title=A Tool for Addressing Construct Identity in Literature Reviews and Meta Analyses|url=https://www.researchgate.net/publication/287878556|journal=MIS Quarterly|volume=40|issue=3|pages=529–A20|via=EBSCOhost|doi=10.25300/misq/2016/40.3.01}}</ref>

Because researchers use terms inconsistently, scholars engaged in literature reviews miss many papers that are actually relevant. Larsen, Hovorka, West, and Dennis developed a cloud system that begins with a researcher-supplied set of theory-initiating papers for a given theory or area of research and combines citation analysis and NLP to evaluate the usefulness of the article to a literature review.<ref>Larsen, K. R., Hovorka, D. S., West, J. D., and Dennis, A. R. 2019. "Understanding the Elephant: A Discourse Approach to Corpus Identification for Theory Review Articles," Journal of the Association for Information Systems).</ref> Li, Larsen, and Abbassi developed “TheoryOn” [http://theoryon.org/], a tool to facilitate literature reviews across fields that may use different terms for similar constructs. <ref>Li, J., Larsen, K. R., and Abbasi, A. in press. "Theoryon: Designing a Construct-Based Search Engine to Reduce Information Overload for Behavioral Science Research," MIS Quarterly).
</ref> This invention received the INFORMS <ref>[http://witsconf.org/informs-award/ ISS Design Science Award]</ref>. 

==References==
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