Knowledge creation dynamics of technological forecasting and social change special issues

Rohail Ashraf*, Muhammad Asif Khan, Rafique Ahmed Khuhro, Zeeshan Ahmed Bhatti

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

17 Citations (Scopus)
51 Downloads (Pure)

Abstract

Technological Forecasting and Social Change (TFSC) is a leading peer-reviewed journal that addresses issues at the intersection of technology and society. A major strategy of the journal is to actively solicit and publish Special Issues (SIs). These SIs were first launched in 1979 to highlight and solicit manuscripts from the “hot” emerging issues of the discipline. This paper aims to analyze SIs and to highlight it's impact on TFSC as compared to Regular Issues (RIs). Using bibliometric analysis, this study first establishes that SIs have a higher impact on the field than RIs when evaluated based on average citations per manuscript, percentage of ‘hot’ papers, and the rate of citations per annum. The study then identifies leading actors (authors, affiliated institutions, and countries) and journals (knowledge inflow/outflow) that have contributed to the success of TFSC-SIs. Finally, using bibliographic coupling, seven thematic clusters of TFSC's SIs were identified. These clusters were compared with the knowledge clusters developed by Singh et al. (2020) for the entirety of TFSC journals, and four clusters unique to SIs were identified i.e. (Climate Change & Energy, Entrepreneurship and Innovation, Sustainability and, Social Media & Internet of Things). It is observed that these unique SI clusters have received disproportionate attention during the last decade and are likely to influence the future trajectory of the journal.
Original languageEnglish
Article number121663
Number of pages15
JournalTechnological Forecasting and Social Change
Volume180
Early online date12 Apr 2022
DOIs
Publication statusPublished - 1 Jul 2022

Keywords

  • Knowledge structures
  • Knowledge dynamics
  • Bibliometrics
  • Bibliographic coupling
  • Knowledge clusters

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