A computational literature review of the technology acceptance model
Michael J. Mortenson and
Richard Vidgen
International Journal of Information Management, 2016, vol. 36, issue 6, 1248-1259
Abstract:
A literature review is a central part of any research project, allowing the existing research to be mapped and new research questions to be posited. However, due to the limitations of human data processing, the literature review can suffer from an inability to handle large volumes of research articles. The computational literature review (CLR) is proposed here as a complementary part of a wider literature review process. The CLR automates some of the analysis of research articles with analyses of impact (citations), structure (co-authorship networks) and content (topic modeling of abstracts). A contribution of the paper is to demonstrate how the content of abstracts can be analyzed automatically to provide a set of research topics within a literature corpus. The CLR software can be used to support three use cases: (1) analysis of the literature for a research area, (2) analysis and ranking of journals, and (3) analysis and ranking of individual scholars and research teams. The working of the CLR software is illustrated through application to the technology acceptance model (TAM) using a set of 3,386 articles. The CLR is an open source offering, developed in the statistical programming language R, and made freely available to researchers to use and develop further.
Keywords: Literature review; Computational literature review; Topic models; Lda; Social network analysis; Co-authorship analysis; Citation analysis; Technology acceptance model; Journal ranking (search for similar items in EconPapers)
Date: 2016
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (17)
Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0268401216300329
Full text for ScienceDirect subscribers only
Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.
Export reference: BibTeX
RIS (EndNote, ProCite, RefMan)
HTML/Text
Persistent link: https://EconPapers.repec.org/RePEc:eee:ininma:v:36:y:2016:i:6:p:1248-1259
DOI: 10.1016/j.ijinfomgt.2016.07.007
Access Statistics for this article
International Journal of Information Management is currently edited by Yogesh K. Dwivedi
More articles in International Journal of Information Management from Elsevier
Bibliographic data for series maintained by Catherine Liu ().