Wilson, Charlie
(2021).
Social Influence and Disruptive Low Carbon Innovations Repeat Survey, 2020.
[Data Collection]. Colchester, Essex:
UK Data Service.
10.5255/UKDA-SN-855005
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)
This dataset was 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 the UK in 2019 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. These datasets are published on ReShare at URL = https://reshare.ukdataservice.ac.uk/854723/
A repeat survey was conducted with respondents from the previous 2019 UK sample. The survey was implemented by an international market research company between 23rd November and 20th December 2020. A total of n=1175 responses were collected. The survey responses were coded and cleaned by the project team. Both the repeat survey instrument and cleaned response data are made available here.
| Data creators: |
| Creator Name |
Affiliation |
ORCID (as URL) |
| Wilson Charlie |
University of East Anglia |
|
|
| Contributors: |
| Name |
Affiliation |
ORCID (as URL) |
| Pettifor Hazel |
University of East Anglia |
|
| Andrews Barnaby |
University of East Anglia |
|
| Vrain Emilie |
University of East Anglia |
|
|
| Sponsors: |
European Research Council
|
| Grant reference: |
678799
|
| Topic classification: |
Science and technology
Transport and travel
Society and culture
|
| Keywords: |
INNOVATION, CLIMATE CHANGE, CONSUMERS
|
| Project title: |
Social influence and disruptive low carbon innovations - SILCI
|
| Alternative title: |
SILCI
|
| Grant holders: |
Prof. Charlie Wilson
|
| Date published: |
24 Jun 2021 14:19
|
| Last modified: |
24 Jun 2021 14:19
|
| Collection period: |
| Date from: | Date to: |
|---|
| 23 November 2020 | 20 December 2020 |
|
| Country: |
United Kingdom |
| 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 survey was implemented online by the market research company, Dynata, in the UK. Survey responses were collected between 23rd November and 20th December 2020.
This was a repeat survey whereby respondents from the first wave (Main survey) were recontacted to participate. Question wording remained the same as the main survey to accurately capture changes in adoption status and influencing factors on the decision process. Additional questions were included in this repeat survey to capture insights on the impact of coronavirus on various survey topics, for example, domain activity, innovation information and social networks.
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 from the Main survey conducted in 2019, 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: |
UK survey data |
| Resource language: |
English |
|
| 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 final sample comprised n=1116 respondents. The average survey completion time was approximately 20 minutes.
|
| Rights owners: |
| Name |
Affiliation |
ORCID (as URL) |
| Wilson Charlie |
University of East Anglia |
|
|
| Contact: |
| Name | Email | Affiliation | ORCID (as URL) |
|---|
| Wilson, Charlie | charlie.wilson@uea.ac.uk | University of East Anglia | Unspecified |
|
| Notes on access: |
The Data Collection is available for download to users registered with the UK Data Service.
|
| Publisher: |
UK Data Service
|
| Last modified: |
24 Jun 2021 14:19
|
|
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