Report
Understanding and measuring consumer trust in the gambling industry - Full technical report
The understanding and measuring consumer trust in the gambling industry full technical report from The Gambling Commission.
Strengths and limitations
Yonder took care to ensure the survey findings, and the methodology behind the development of the trust question for the Gambling Survey for Great Britain (GSGB), were as robust as possible by employing a range of research methods as part of the overall project design. Some of the key strengths of the study are outlined.
Extensive stakeholder engagement
The study involved a considerable planning and scoping phase, including desk research around the concept of trust, analysis of existing consumer complaints data, and stakeholder engagement, for example consultation with the Gambling Commission’s Lived Experience Advisory Panel (LEAP). This was done to ensure that the design of the primary research was underpinned by relevant academic frameworks and considered the experiences of those with lived experience of gambling, and gambling-related harms. This meant the study has a strong and robust basis from which to develop primary research materials. Further detail of the scoping phase and stakeholder engagement are described in the Statement Selection Process chapter of this technical report.
Robust mixed-methods data collection
The research followed an iterative approach, combining exploratory qualitative research to understand the thematic drivers of trust, before a quantitative assessment to statistically validate statements with a broadly representative sample. This mixed-methods approach allowed for multiple data points for analysis, thus strengthening confidence in findings through triangulation.
In the qualitative phase, there was a broad range of views captured across different gambling audiences. Focus groups were beneficial for this study, as they allowed the capture of different views and for participants to interact with each other, harnessing greater insights than individual depth interviews.
In the quantitative phase, advanced statistical techniques were used to further validate and refine the statement selection, including the Maximum Difference (MaxDiff) survey tool, and factor analysis to decipher the relative importance of each statement in relation to drivers of overall trust (both described in more detail in the Statement Selection Process chapter). These processes gave the Commission confidence in the selection of shortlisted statements for the GSGB.
The final statements selected were then tested with a wide range of people who gambled and internal stakeholders to ensure that the wording used was comprehensible, easy to understand and relevant for different audiences completing the GSGB. This process is called “cognitive testing.” This was a valuable final step in validating the statements.
Limitations
There are some limitations to be noted within each phase of the project.
Qualitative research by nature relies on small sample sizes, meaning conclusions can only be directional, unless combined with corresponding quantitative data. Qualitative research heavily relies on the skills and experience of researchers, introducing potential researcher bias although this is mitigated against using thorough analysis techniques. Data is recorded and transcribed, and then analysis is undertaken thematically using a qualitative codeframe in excel.
All insights gathered from this study are based on self-reported behaviour rather than observed behaviour, meaning insight is limited to what participants felt comfortable revealing to Yonder in the research setting, which can also introduce a degree of bias.
Focus groups for this study took place in London and Birmingham only, so do not necessarily capture a wide range of views throughout Great Britain. Broad quotas were set during recruitment however to capture a broad range of views across age, gender, level of gambling experience, and risk of gambling harm using the PGSI scale.
Cognitive testing has similar limitations to qualitative research with regards to sample, in that it relies on interviewer accuracy, particularly when testing exact wording of statements in an online setting. Yonder overcame this limitation by sharing screens through Zoom or Teams, allowing participants to comment on the wording of statements as they were both read by the researcher and displayed on screen.
Cross-section quantitative survey research provides a valuable point-in-time read on attitudes and opinions across a specific sample of people, which is extrapolated out to the wider population group. One key limitation is that a cross-sectional survey does not always help to explain why respondents hold certain views.
The sample selection for the quantitative survey research also provides limitations in the collection of data and interpretation of results. As outlined in the methodology section of this report, the sample selected was those who have gambled online in the past 12 months, excluding National Lottery only players. As such, findings should not be treated as representative of all those who gamble, and caution should be taken in assessing the significance of sub-group differences such as gambling frequency and Problem Gambling Severity Index (PGSI) score, as these groups may also not be representative, and may represent a higher than natural proportion within the sample than they would in a survey of the general population.
The quantitative survey was conducted online, a standard method of market research in 2024. This survey was conducted with Yonder’s proprietory consumer panel, which is built to be high quality and representative of the United Kingdom
However, this mode of survey completion does have limitations, namely:
- reaching digitally disadvantaged respondents (this is likely to have been a very small risk for this project, given respondents had to be capable of gambling online as a pre-requisite for sampling)
- response bias, survey fatigue, flat-lining (selecting the same answer for all scale questions), and fraudulent answers.
Survey data is cleaned and reviewed by multiple researchers to ensure any incidences of the previous are removed from the dataset. Yonder’s Data Quality Charter guards against fraudulent responses and maximises data quality, via Research Defender technology on all panel sign ups, which uses digital fingerprint technology to combat click farms, bot traffic, and fraudulent respondents.
Further, this was a relatively long online survey, with an average completion time of 22 minutes. To counter respondent fatigue for the key part of the survey – the MaxDiff exercise – these questions were the first thing respondents saw after answering initial demographic questions.
Quantitative fieldwork took place in the run-up period before the 2024 general election. There is nothing explicit in the data to suggest this has had an impact since we do not have a reference point outside of this period to compare it to, and there is also nothing to suggest that the initial ‘Gamblegate’ report (opens in new tab) , which was published on the last day of data collection (12 June) impacted responses.
Maximum Difference (MaxDiff) is a survey tool that allows respondents to trade off certain statements against each other, to help determine drivers of trust. However, as it forces a trade-off, it does not provide us with an absolute measure of importance across the full set of statements. It also does not give information on the overall quality of the statements inputted into the survey, and whether they relate to trust. This was mitigated as far as possible for this project by the ingoing scoping and qualitative phase which helped Yonder and the Gambling Commission to form a broad understanding of the drivers of trust among consumers.
The tool also forces respondents to provide a definitive view, when shown 3 or more statements, as they are made to choose the one most and least important to them when considering trust. This limitation was mitigated through the introduction of 12 MaxDiff ‘screens’, ensuring that the statements were seen in a varying range of combinations, allowing Yonder to build an overall holistic picture of trust in the data reduction phase.
The GSGB output question set was limited by the need to be succinct and produce a brief list of statements to test in relation to trust. This was driven partly by the physical space available for the trust question on the paper copy of the GSGB, which is distributed using random sampling techniques for both online and paper surveys. There was also a desire to limit the number of statements included, so that they could feed into a succinct composite index score.
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Statement selection process - Trust technical report
Last updated: 23 July 2026
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