Tracing theory diffusion: a text mining and citation-based analysis of TAM
ISSN: 0022-0418
Article publication date: 17 April 2020
Issue publication date: 11 February 2020
Abstract
Purpose
Theory is a kind of condensed human knowledge. This paper is to examine the mechanism of interdisciplinary diffusion of theoretical knowledge by tracing the diffusion of a representative theory, the Technology Acceptance Model (TAM).
Design/methodology/approach
Based on the full-scale dataset of Web of Science (WoS), the citations of Davis's original work about TAM were analysed and the interdisciplinary diffusion paths of TAM were delineated, a supervised machine learning method was used to extract theory incidents, and a content analysis was used to categorize the patterns of theory evolution.
Findings
It is found that the diffusion of a theory is intertwined with its evolution. In the process, the role that a participating discipline play is related to its knowledge distance from the original disciplines of TAM. With the distance increases, the capacity to support theory development and innovation weakens, while that to assume analytical tools for practical problems increases. During the diffusion, a theory evolves into new extensions in four theoretical construction patterns, elaboration, proliferation, competition and integration.
Research limitations/implications
The study does not only deepen the understanding of the trajectory of a theory but also enriches the research of knowledge diffusion and innovation.
Originality/value
The study elaborates the relationship between theory diffusion and theory development, reveals the roles of the participating disciplines played in theory diffusion and vice versa, interprets four patterns of theory evolution and uses text mining technique to extract theory incidents, which makes up for the shortcomings of citation analysis and content analysis used in previous studies.
Keywords
Acknowledgements
We would like to give special thanks to the reviewers and editors for their valuable comments. We thank Jiayue Ma, Lingzhi Yang, Tian Fang, Sisi Zhang, Wei Zhao, Xiaoyang Li, Xiaoyue Zhang and Yanan Hao for their work in data collection and corpus annotation. We also thank Jing Yang for his data support and Dr. Yujia Zhai for his suggestion on paper writing. This work was funded by the project of National Engineering Laboratory of Big Data Application Technology for Improving Government Governance Capability: “The Large-scale Intelligent Government Document Processing Technology based on NLP and Deep Learning”, National Social Science Fund of China: “Research on the Development Path and Construction of Information Science Discipline” (grant number: 17ZDA291), and National Natural Science Fund of China: “Research on the Organization and Mode of Modern Social Governance” (grant number: 71533002).
Citation
Wang, F. and Wang, X. (2020), "Tracing theory diffusion: a text mining and citation-based analysis of TAM", Journal of Documentation, Vol. 76 No. 6, pp. 1109-1134. https://doi.org/10.1108/JD-02-2020-0023
Publisher
:Emerald Publishing Limited
Copyright © 2020, Emerald Publishing Limited