Generating Indicators of Disruptive Innovation Using Big Data

• Published in: Future internet Vol. 14; no. 11; p. 327
• Main Authors: Brackin, Roger C., Jackson, Michael J., Leyshon, Andrew, Morley, Jeremy G., Jewitt, Sarah
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01.11.2022
• Subjects: assessment; Automation; Big Data; Clustering; disruptive; Disruptive innovation; Indicators; innovation; Internet; Product development; R&D; Research & development; Scientific papers; Smartphones; Technology; Technology assessment; Technology utilization; Trends; unified technology progression modelling
• ISSN: 1999-5903; 1999-5903
• DOI: 10.3390/fi14110327

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.