A computational literature review of the technology acceptance model

An automated approach to the analysis of large bodies of literature is proposed.Analysis includes impact (citations), structure (co-authorships), and content (topic modeling of abstracts).The technology acceptance literature is reviewed using a fully automated method.Latent Dirichlet Allocation (LDA) is introduced.Further use cases include journal ranking and researcher analysis. 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.

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