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Sustaining growth for innovative new enterprises: UK firm data
Creator
Sensier, M, University of Manchester
Gök , A, University of Manchester
Shapira, P, University of Manchester
Study number / PID
851779 (UKDA)
10.5255/UKDA-SN-851779 (DOI)
Data access
Restricted
Series
Not available
Abstract
To select the group of UK firms we initially searched in the FAME database (available from the University of Manchester Library) with keywords relating to the green goods sector, please see the publication Shapira, et al (2014, in Technological Forecasting & Social Change, vol. 85, pp. 93-104) for further details on the keywords. This database contains anonymized firm data from a sample of UK firms in the green goods production industry. We combine data from structured sources (the FAME database, patents and publications) with unstructured data mined from firm's web-sites by saving key words in text and summing up counts of these to create additional explanatory variables for firm growth. The data is in a panel from 2003-2012 with some observations missing for firms. We collect historical data from firm's web-sites available in an archive from the Wayback machine.This project probes the growth strategies of innovative small and medium-size enterprises (SMEs). Our research focuses on emerging green goods industries that manufacture outputs which benefit the environment or conserve natural resources, with an international comparative element involving the UK, the US, and China.
The project investigates the contributions of strategy, resources and relationships to how innovative British, American, and Chinese SMEs achieve significant growth. The targeted technology-oriented green goods sectors are strategically important to environmental rebalancing and have significant potential (in the UK) for export growth. The research examines the diverse pathways to innovation and growth across different regions. We use a mix of methodologies, including analyses of structured and unstructured data on SME business and technology performance and strategies, case studies, and modelling. Novel approaches using web mining are pioneered to gain timely information about enterprise developmental pathways. Findings from the project will be used to inform management and policy...
Terminology used is generally based on DDI controlled vocabularies: Time Method, Analysis Unit, Sampling Procedure and Mode of Collection, available at CESSDA Vocabulary Service.
Methodology
Data collection period
01/01/2012 - 31/12/2014
Country
United Kingdom
Time dimension
Not available
Analysis unit
Organization
Universe
Not available
Sampling procedure
Not available
Kind of data
Numeric
Text
Data collection mode
We collected the financial information on the UK firms by downloading Companies House data from the FAME database available through the University of Manchester Library (see http://www.library.manchester.ac.uk/searchresources/databases/f/). Grant information on companies came from the Technology Strategy Board. Patent information was from the Derwent database and publication information was from the Web of Science. The Consumer Price index was from the Office for National Statistics (http://www.ons.gov.uk/ons/rel/cpi/consumer-price-indices/index.html). The Human Resources in Science and Technology variable was from the Eurostat database (http://ec.europa.eu/eurostat/data/database).Unstructured data was mined from firm's web-sites. The UK Intellectual Property Office has clarified that the data mining we are doing and the way we are doing it is permissible. See: https://www.gov.uk/government/uploads/system/uploads/attachment_data/file/375954/Research.pdf