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Generating Indicators of Disruptive Innovation Using Big Data

Brackin, Roger C; Jackson, Michael J.; Leyshon, Andrew; Morley, Jeremy G.; Jewitt, Sarah

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Authors

Roger C Brackin

Michael J. Jackson

Andrew Leyshon

Jeremy G. Morley

SARAH JEWITT SARAH.JEWITT@NOTTINGHAM.AC.UK
Professor of Human Geography and Development



Abstract

Technological evolution and its potential impacts are of significant interest to governments, corporate organizations and for academic enquiry; but assessments of technology progression are often highly subjective. This paper prototypes potential objective measures to assess technology progression using internet-based data. These measures may help reduce the subjective nature of such assessments and, in conjunction with other techniques, reduce the uncertainty of technology progression assessment. The paper examines one part of the technology ecosystem, namely, academic research and publications. It uses analytics performed against a large body of academic paper abstracts and metadata published over 20 years to propose and demonstrate candidate indicators of technology progression. Measures prototyped are: (i) overall occurrence of technologies used over time in research, (ii) the fields in which this use was made; (iii) the geographic spread of specific technologies within research and (iv) the clustering of technology research over time. An outcome of the analysis is an ability to assess the measures of technology progression against a set of inputs and a set of commentaries and forecasts made publicly in the subject area over the last 20 years. The potential automated indicators of research are discussed together with other indicators which might help working groups in assessing technology progression using more quantitative methods.

Citation

Brackin, R. C., Jackson, M. J., Leyshon, A., Morley, J. G., & Jewitt, S. (2022). Generating Indicators of Disruptive Innovation Using Big Data. Future Internet, 14(11), Article 327. https://doi.org/10.3390/fi14110327

Journal Article Type Article
Acceptance Date Nov 6, 2022
Online Publication Date Nov 11, 2022
Publication Date 2022-11
Deposit Date Nov 10, 2022
Publicly Available Date Mar 29, 2024
Journal Future Internet
Electronic ISSN 1999-5903
Peer Reviewed Peer Reviewed
Volume 14
Issue 11
Article Number 327
DOI https://doi.org/10.3390/fi14110327
Keywords Disruptive; Innovation; Technology; Assessment; Big Data; Unified Technology Pro-26 gression Modelling
Public URL https://nottingham-repository.worktribe.com/output/13460349
Publisher URL https://www.mdpi.com/1999-5903/14/11/327

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