Research and analysis

Technical Report, Attitudes and Awareness Survey, 2024

Published 2 October 2024

1. Introduction

1.1 Background to the survey

The department for Science, Innovation and Technology’s (DSIT) priorities include ensuring that new and existing technologies are safely developed and deployed across the UK.  To this end, the department initiated monitoring of several topics relating to emerging technologies, personal data protection, and digital platforms via a population survey.

Following an open competition, YouGov were contracted to design and deliver a survey capable of generating nationally representative estimates for adults (aged 18 or over) in the UK, disaggregated by key demographic characteristics.

1.2 Survey objectives

The aims of the survey were to assess the following amongst the general UK population, disaggregated into key demographic groups and ITL 1 regions: 

• the level of adoption and awareness of blockchain and immersive virtual worlds  

• attitudes towards pricing on digital platforms 

• behaviours regarding personal data control 

2. Questionnaire

2.1 Questionnaire development

The initial questionnaire was designed by DSIT and reviewed by YouGov to ensure the questions were compatible with online delivery of the survey.

As this was the first time the survey has been run, cognitive testing was carried out between the 1 and 12 of February 2024 on all questions. This consisted of interviews with 20 respondents, lasting 45 minutes each. Feedback from these interviews was used to improve response accuracy by adding further explanatory text and additional response options.

The revised questionnaire was piloted between the 26th and 27th February. No issues with length, routing, question performance or respondent comprehension were identified.

2.2 2024 Questionnaire

The full text of the questionnaire is included in Annex A.

3. Sampling and fieldwork

3.1 Sample design

Respondents were selected from the YouGov online panel to provide a representative sample for the UK and key demographic groups.  Specifically, a quota system was used based on age, gender, ethnicity, region and social class to select members of the online panel to be part of the survey sample.  As the sample was drawn from a large pool of potential respondents (over 2.5 million UK adults), the achieved sample was reasonably balanced (see section 4 for a comparison of target and achieved quotas). 

Nevertheless, users should be aware of the risk of self selection bias as the panel consists of those who have volunteered to participate in online research in return for a small financial incentive.

3.2 Fieldwork procedure

Sample members were invited to complete the survey via an email which contained a link to the YouGov online platform.  All sample members received the same email, and participation was limited to those selected into the sample i.e. it was not possible to access the survey without an invitation.  Respondents were required to complete all questions. 

3.3 Fieldwork performance

The survey was open for responses from the 27th of February to the 8th March, 2024.  It achieved a valid response rate of 99.2%; of 5,106 invitees, only 40 were removed due to failing attention checks or providing incomplete responses, leaving 5,066 in the final dataset.

3.4 Survey length

As demographic data were already available for all panel members, survey questions were restricted to the topics of interest to the department, reducing the survey length.  The median time to complete the survey was 4 minutes 42 seconds, and three quarters of respondents completed the survey in 6 minutes 45 seconds or less.

4. Data processing

Data were initially processed by YouGov to transform the raw responses from survey participants to a clean dataset. This involved coding of responses, e.g. converting age data in years to banded values, and calculating the weight to be applied to each respondent to ensure the overall estimates are representative of the UK population

Further processing was then performed by DSIT on the cleaned dataset.  This was predominantly aggregation to generate summary statistics, and application of disclosure control.

More detail on the weighting, statistical production, and quality control processes is given in the following sections.

4.1 Weighting

Although stratification can be used to generate a sample that has a broadly similar demographic profile to the population of interest, the match is unlikely to be exact and may deteriorate if not all sample members complete the survey successfully.  Where certain groups are under or over sampled relative to the general population, weighting can be used to adjust the impact of responses from members of those groups on the aggregated figures by assigning them a higher or lower weight, respectively.   

The target percentages for the 2024 survey weighting scheme are summarised in the table below.

Table 4.1. Target percentages for each demographic group to ensure a nationally representative weighting scheme

	Target (%)	Achieved (%)
Age and Gender
Men: 18-34	14%	15%
Men: 35-54	16%	17%
Men: 55+	19%	17%
Women: 18-34	13%	14%
Women: 35-54	17%	18%
Women: 55+	21%	19%
Social Grade
AB	22%	23%
C1	31%	31%
C2	21%	20%
DE	26%	25%
Ethnic background
Any white ethnic background	89%	90%
Multiple ethnic background	1%	1%
Any Asian background	6%	5%
Any black ethnic background	3%	2%
Any other ethnic background	1%	1%
Region
North East	4%	4%
North West	11%	11%
Yorkshire and the Humber	8%	9%
East Midlands	7%	7%
West Midlands	9%	9%
East of England	9%	9%
London	13%	13%
South East	14%	14%
South West	8%	9%
Wales	5%	5%
Scotland	8%	8%
Northern Ireland	3%	3%

If the sample is very different from the general population, it may be necessary to use very large weights.  This can introduce bias and decrease the reliability of results due to over-reliance on a small number of responses.  In this survey the weights applied varied between 0.78 and 1.48.

The extent of the difference between the sample and general populations can be assessed using the weighting efficiency.  The weighting efficiency can take any value between 0 and 100%, with 100% denoting a perfect match between sample and population.  For this survey, the weighting efficiency was 99.02%, indicating a relatively good match and low distortion from weighting.

4.2 Statistical production

Summary statistics were produced by aggregating the weights assigned to the relevant respondents for each intersection of question response and demographic group, or “cell”.  These were divided by the total weight for each demographic group to estimate the proportion of that demographic group who gave each question response.

Where the number of respondents for a particular cell was too low, the value was removed to prevent the potential disclosure of respondents’ identities (primary disclosure) and the reporting of unreliable values.  Where the suppressed value could have been calculated by differencing from the total, the value in another cell in that group has also been removed (secondary disclosure).  

4.3 Quality control

Production of the estimates was carried out by one member of staff in R.  To ensure an independent evaluation, another member of staff validated the estimates by dual running the production in Excel. 

• Primary and secondary disclosure control was checked by counting the number of suppressed values for each row where values could be deduced by differencing. The count should be either zero, or at least two for suppressed values.
• To ensure correctness of the original analysis when constructing demographic breakdowns, figures were produced in another application (Microsoft Excel) by a different member of staff. These cross-tabulations were checked against the original analysis produced in R for any variance in results.
• Formatting of the entire results workbook was checked against Government Analysis Function guidance, including:
  • Accessibility
  • Formatting
  • Shorthand and symbols
  • Ethnicity ordering
  • Geographical ordering

6. Annex A: 2024 questionnaire

The full text of the 2024 survey is as follows:

This survey is on the topic of data and technology. 
 
Your YouGov account will be credited with 50 points for completing the survey. 
 
We have tested the survey and found that, on average it takes around 5 minutes to complete. This time may vary depending on factors such as your Internet connection speed and the answers you give. 
 
Please click the forward button below to continue. 

Base: All 
Question type: Grid 
#row order: randomize 

[QPURCHASE] Have you ever bought goods or services from big technology companies?  
 
This includes goods or services made and provided directly by big technology companies including Alphabet (Google), Apple, Meta (Facebook), Amazon and/or Microsoft. 
 
Please note this does NOT include other parties using the mentioned big technology companies’ platforms to sell their own products or services.  
 

[QPURCHASE_1] Bought GOODS made by big technology companies

[QPURCHASE_2] Bought SERVICES provided by big technology companies

1. Yes, in the last 12 months
2. Yes, but not in the last 12 months
3. Never bought before
4. Not sure/ don’t know

Base: All 
Question type: Grid 
#row order: randomize 

[Q1] To what extent do you think the prices of goods and services from big technology companies represent value for money?  
 
Again, please think about goods or services made and provided directly by big technology companies including Alphabet (Google), Apple, Meta (Facebook), Amazon and/or Microsoft.  
 Please note this does NOT include other parties using the mentioned big technology companies’ platforms to sell their own products or services.  
 

[Q1_1] GOODS made by big technology companies

[Q1_2] SERVICES provided by big technology companies

1. Very good value for money
2. Good value for money
3. Neither good nor poor value for money
4. Poor value for money
5. Very poor value for money
6. Don’t know

Base: All 
Question type: Grid 
#row order: randomize 

[Q2] To what extent do you agree or disagree with the following statements? 
 
Please note by personal data we mean any information that relates specifically to you, including data that is automatically generated when you browse or use an app or site. For example, your IP address, purchase history, web activity.  
 
[Q2_1] I UNDERSTAND how my personal data is used by big technology companies.

[Q2_2] I am SATISFIED how my personal data is used by big technology companies.

1. Strongly agree
2. Somewhat agree
3. Neither agree nor disagree
4. Somewhat disagree
5. Strongly disagree
6. Don’t know

Base: All 
Question type: Multiple 
#row order: randomize except 8 and 9

[Q3] What, if any, actions have you taken to increase control of your personal data in the last 12 months? Please select all that apply.  
 
Again, by personal data we mean any information that relates specifically to you, including data that is automatically generated when you browse or use an app or site. For example, your IP address, purchase history, web activity.  

1. Stopped using a product or
2. Switched to an alternative product or service which provides greater control of personal data
3. Deleted cookies from my browser – Cookies are small data files that store information about your preferences and allow websites to identify you when you visit again.
4. Rejected additional cookies when prompted – Cookies are small data files that store information about your preferences and allow websites to identify you when you visit again.
5. Used a VPN (Virtual Private Network) – A VPN can allow users to hide their personal data and IP address
6. Read the terms and conditions of a product/ service relating to personal data
7. Checked/ changed privacy settings/ policy for relevant apps/ devices/ sites
8. Other (open [Q3_OTHER]) [open] please specify
9. None of these – I have not taken action in the last 12 months

Base: Those who have not taken any action to increase control of personal data 
Question type: Multiple 
#row order: randomize except 7, 8 and 98 which are final options 
#Question display logic:  
If [Q3] - None of these – I have not taken action in the last 12 months is selected [if 9 in Q3]  

[Q3_NOACTION] Can you tell us why you’ve not taken action in the last 12 months? Please tick all that apply. 

1. Lack of alternative services/products to switch to
2. Not sure how to
3. Too much time or effort
4. It wouldn’t make a difference
5. It would worsen my experience using the product/service
6. It is a role for a government or regulator
7. It has not occurred to me
8. Sharing personal data benefits my user experience
9. It is a role for the company
10. Other (open [Q_NOACTION_OTHER]) [open] please specify
11. No need to – I am happy with the level of security over my personal data
12. Don’t know

Question type: Text 

We would now like to ask you about new technologies. 

 Question type: Text 

The first type of technology we’d like to ask you about is Distributed Ledger Technology (DLT). 
 
This is a way of working with digital data that is distributed across multiple locations. An example of a DLT is a blockchain, which records information. The information is then distributed across a network of computers like the world wide web. This makes it difficult to change, or hack the information. A common use of DLT is for crypto assets such as NFTs (Non-Fungible Tokens). 

Base: All 
Question type: Single 

[Q_DLT_AWARE] Before taking this survey, had you heard of Distributed Ledger Technology (DLT)? 

1. Yes, heard of it and know what it is
2. Yes, heard of it but don’t know what it
3. No, I hadn’t heard of it

Base: Those have heard of DLT 
Question type: Single 
#Question display logic:  
If [Q_DLT_AWARE] - Yes, heard of it and know what it is or Yes, heard of it but don’t know what it is, is selected [if Q_DLT_AWARE in [1,2]]  

[Q_DLT_INTEREST] And which of these best describes your interest in the use of Distributed Ledger Technology (DLT)? 

1. I already use it
2. I am interested in using it in the next twelve months
3. I am not interested in using it in the next twelve
4. Not sure

Question type: Text 

The next type of technology we’d like to ask you about is immersive technology.  
 
By immersive technology we mean the use of devices that immerse users in digitally generated worlds. This can be either through adding digitally generated images to the physical world (augmented reality, e.g. Pokémon Go) or by placing users inside entirely digitally generated worlds (virtual reality, e.g. as seen in the BBC show Your Home Made Perfect).  
 
Some further examples of immersive technology include (but are not limited to) virtual worlds in gaming, virtual tours of places or buildings or using digital images in addition to the physical world in healthcare or skills training.  
   

Base: All 
Question type: Single 

[Q_IMMERSIVE_AWARE] Before taking this survey, had you heard of immersive technology? 

1. Yes, heard of it and know what it
2. Yes, heard of it but don’t know what it is
3. No, I hadn’t heard of it

Base: Those have heard of immersive technology 
Question type: Single 
#Question display logic:  
If [Q_IMMERSIVE_AWARE] - Yes, heard of it and know what it is or Yes, heard of it but don’t know what it is, is selected [if Q_IMMERSIVE_AWARE in [1,2]]  

[Q_IMMERSIVE_INTEREST] And which of these best describes your interest in using immersive technology? 

1. I already use it
2. I am interested in using it in the next twelve months
3. I am not interested in using it in the next twelve months
4. Not sure