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Mapping digital businesses with big data: Some early findings from the UK

Nathan, M; Rosso, A; (2015) Mapping digital businesses with big data: Some early findings from the UK. Research Policy , 44 (9) pp. 1714-1733. 10.1016/j.respol.2015.01.008. Green open access

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Abstract

Governments around the world want to develop their ICT industries. Researchers and policymakers thus need a clear picture of digital businesses, but conventional datasets and typologies tend to lag real-world change. We use innovative ‘big data’ resources to perform an alternative analysis for all active companies in the UK, focusing on ICT-producing firms. Exploiting a combination of observed and modelled variables, we develop a novel ‘sector-product’ approach and use text mining to provide further detail on key sector-product cells. We find that the ICT production space is around 42% larger than SIC-based estimates, with around 70,000 more companies. We also find ICT employment shares over double the conventional estimates, although this result is more speculative. Our findings are robust to various scope, selection and sample construction challenges. We use our experiences to reflect on the broader pros and cons of frontier data use.

Type: Article
Title: Mapping digital businesses with big data: Some early findings from the UK
Open access status: An open access version is available from UCL Discovery
DOI: 10.1016/j.respol.2015.01.008
Publisher version: http://dx.doi.org/10.1016/j.respol.2015.01.008
Language: English
Additional information: This version is the author accepted manuscript. For information on re-use, please refer to the publisher’s terms and conditions.
Keywords: Big Data, Text mining, ICTs, Digital economy, Industrial policy, Firm-level analysis,
UCL classification: UCL
UCL > Provost and Vice Provost Offices > UCL BEAMS
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of the Built Environment
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of the Built Environment > Centre for Advanced Spatial Analysis
URI: https://discovery.ucl.ac.uk/id/eprint/10086280
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