Wilson, Charlie
(2021).
Social Influence and Disruptive Low Carbon Innovations, 2019.
[Data Collection]. Colchester, Essex:
UK Data Service.
10.5255/UKDA-SN-854723
SILCI investigates low carbon disruptive innovations. This project conducts empirical research to understand what are potentially disruptive low carbon innovations, what novel attributes they offer users, and what impact might their widespread adoption have on emissions.
As well as identifying and characterising disruptive low carbon innovations across sectors and applications, SILCI is interested in how and why they are adopted, and so how they spread. Information exchanged through social networks, through online activity, and through physical activity in neighbourhoods influences people’s behaviour. Social influence plays an important role in diffusing innovations. SILCI explores what role social influence plays in the diffusion of disruptive low carbon innovations and how these diffusion processes can be accelerated to help reduce emissions.
Data description (abstract)
These two datasets were collected as part of the SILCI project (‘Social influence and disruptive low carbon innovations’). The SILCI project ran from 2016 - 2021 at the Tyndall Centre for Climate Change Research, University of East Anglia and was funded by the European Research Council (ERC) through the Starting Grant #678799. Further details on the SILCI project and related publications can be found at: http://www.silci.org.
The SILCI project explored disruptive low carbon innovations and how they spread through processes of social influence.
As part of the SILCI project, a national online survey was conducted in both the UK and Canada to understand consumers' perceptions, communication behaviour, and adoption propensity towards a wide range of low-carbon innovations in four different consumer domains: transport, food, homes and energy. The survey instrument was developed and tested by the project team. The survey was implemented by an international market research company in the UK between 2nd July – 3rd September 2019 and in Canada between 11th October – 14th November 2019. A total of n=3014 responses were collected in the UK and n=3352 in Canada. The survey responses were coded and cleaned by the project team. Both the survey instrument and cleaned response data are made available here.
Data creators: |
Creator Name |
Affiliation |
ORCID (as URL) |
Wilson Charlie |
University of East Anglia |
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Contributors: |
Name |
Affiliation |
ORCID (as URL) |
Pettifor H. |
University of East Anglia |
|
Andrews B. |
University of East Anglia |
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Vrain E. |
University of East Anglia |
|
Axsen J |
Simon Fraser University |
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Sponsors: |
European Research Council
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Grant reference: |
ERC Starting grant 678799
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Topic classification: |
Transport and travel Society and culture
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Keywords: |
INNOVATION, CLIMATE CHANGE, CONSUMERS
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Project title: |
Social influence and disruptive low carbon innovations (SILCI)
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Alternative title: |
SILCI
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Grant holders: |
Charlie Wilson
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Project dates: |
From | To |
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1 September 2016 | 31 May 2021 |
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Date published: |
15 Mar 2021 17:26
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Last modified: |
15 Mar 2021 17:32
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Collection period: |
Date from: | Date to: |
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2 July 2019 | 14 November 2019 |
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Country: |
United Kingdom, Canada |
Spatial unit: |
No Spatial Unit |
Data collection method: |
The survey instrument was developed by the research team to measure consumers' perceptions, communication behaviour, individual characteristics and adoption propensity towards 16 low-carbon consumer innovations in four different domains: transport, food, homes and energy. The survey also asked questions about the mainstream consumption activity in each of the four domains.
The research team iteratively tested and refined the instrument for clarity and comprehensibility prior to implementation. The survey was implemented online by the market research company, Dynata, in the UK and Canada. Survey responses were collected in the UK between 2nd July – 3rd September 2019 and in Canada between 11th October – 14th November 2019.
Sampling design in each country consisted of two stages. First, the 'core' survey sample of ~2500 adults (aged 18 or over) nationally representative on key socio-economic indicators (age, gender, household income). Second, the 'boost' sample of ~500 respondents selected by choice-based and quota sampling to purposively target adopters of innovations which had not achieved the quota of 100 adopters from the ‘core’ sample.
The survey instrument was structured in nine blocks of questions:
1) Adoption - on respondents' current experience with 16 different innovations and four mainstream activities (in each of four domains)
2) Domain Activity - on respondents' current behaviour in one particular domain (transport, food, homes, energy)
3) Domain Innovativeness - on respondents' propensity to adopt innovations in one particular domain
4) Innovation Familiarity - on respondents' familiarity with one particular innovation (or one mainstream activity)
5) Innovation Attributes - on respondents' perceptions of the attributes of one particular innovation (or one mainstream activity)
6) Innovation Information - on respondents' information-seeking and social influence on one particular innovation (or one mainstream activity)
7) Social Network - on respondents' social network position and role
8) Personal Characteristics - on respondents' personality, lifestyle and values
9) Personal Situation - on respondents' circumstances, living conditions, and socio-economics.
All respondents answered question block 1 (Adoption) about their current experience of having or using the 16 innovations, and questions from block 2 about their experience of the four mainstream activities. Based on their responses, respondents were allocated to a survey variant corresponding to one particular innovation or mainstream activity (as there are 16 innovations and four corresponding mainstream activities, this resulted in 20 survey variants). Respondents then answered question blocks 4-6 on the specific innovation or mainstream activity and blocks 2-3 on its corresponding domain. For example, if a respondent was allocated to Carsharing, they answered questions on ‘transport’ for block 2-3 and ‘carsharing’ for blocks 4-6. All respondents then answer question blocks 7-9.
Response options consisted of Likert-type ordinal scales (1-5), including item scales measuring values, personality, innovation attributes, social influence categorical response options. There was also one continuous response option measuring adoption propensity on a scale of 1-100. |
Observation unit: |
Individual |
Kind of data: |
Numeric, Text |
Type of data: |
Cross-national survey data |
Resource language: |
English |
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Data sourcing, processing and preparation: |
The market research company implemented an initial set of quality control measures to identify and remove possible respondents with low cognitive engagement in the survey questions and low familiarity of the innovations. Control measures included: (i) straight line responses on blocks of questions; (ii) inappropriate or irrelevant open-ended responses revealing a lack of understanding of questions; (iii) contradictory responses on identical but inversely-framed questions; (iv) unrealistically fast survey completion times; (v) response selection ‘never heard of’ for >=14 innovations or ‘don’t know’ for >= 5 innovations.
Upon receiving the datasets, the research team conducted similar quality control measures but for additional sets of questions, as well as an extra quality check to identify any errors in the allocation algorithm. A total of n = 7 respondents from the UK and n=7 respondents from Canada failed the researcher’s quality checks and were removed. The final samples comprised n=3007 respondents in the UK and n=3345 respondents in Canada. The average survey completion time was approximately 20 minutes.
In order to identify potentially false positive adopters of innovations, open ended text responses asking for examples of corresponding innovations were manually examined and classified using web search and other validation of real-world innovation. New dummy variables were created for each innovation to identify adopters of innovations with wrong examples removed (found at the end of the dataset and codebook labelled ‘NWE’).
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Rights owners: |
Name |
Affiliation |
ORCID (as URL) |
Wilson C |
University of East Anglia |
|
|
Contact: |
Name | Email | Affiliation | ORCID (as URL) |
---|
Wilson, C. | charlie.wilson@uea.ac.uk | University of East Anglia | Unspecified |
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Notes on access: |
The Data Collection is available for download to users registered with the UK Data Service.
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Publisher: |
UK Data Service
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Last modified: |
15 Mar 2021 17:32
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