Unveiling the knowledge structure of technological forecasting and social change (1969-2020) through an NMF-based hierarchical topic model

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Abstract

This article examines the knowledge structure of the journal Technology Forecasting and Social Change (TFSC) from its inception in 1969 until 2020. In this paper we argue that the structure of knowledge in the field of technological forecasting is more complex than a topic model -- a bag of words -- can in fact effectively reveal. Therefore we propose and demonstrate a hierarchical model that selectively combines topics at varying levels of generality. The resultant analysis, which is based on non-negative matrix factorization, reveals four distinct branches of technology forecasting work, composed of seven distinct topics. Each topic and branch are examined individually through a detailed examination of terms and keywords. Representative works and authors in each of the branches are also identified. The method enables the examination of the complex structure of knowledge in a scientific journal in a succinct representation. The resultant analysis can assist future researchers, enabling them to better position their work, and to better identify the key references across the various subject silos.
Original languageEnglish
Article number121277
Number of pages35
JournalTechnological Forecasting and Social Change
Volume174
Early online date25 Oct 2021
DOIs
Publication statusE-pub ahead of print - 25 Oct 2021

Keywords

  • topic models
  • technological forecasting
  • historical trends
  • machine learning

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