This box is not visible in the printed version.
The understanding and measuring consumer trust in the gambling industry full technical report from The Gambling Commission.
Published: 23 July 2026
Last updated: 23 July 2026
This version was printed or saved on: 23 July 2026
Online version: https://www.gamblingcommission.gov.uk/report/understanding-and-measuring-consumer-trust-in-the-gambling-industry-full-technical-report
This report details the technical approach taken towards a comprehensive mixed-methodology study conducted by Yonder Consulting in collaboration with the Gambling Commission as part of the Consumer Voice Research Programme.
The study was designed to assess various aspects of consumer trust of the gambling industry, to inform the Commission of key areas on which to focus to ensure the gambling industry is fair, transparent and open. The research was underpinned by the Commission’s Licensing Objectives as set out in the Gambling Act 2005 (opens in new tab) , its Corporate Strategy, and the Path to Play model.
The primary aim was to gain a comprehensive understanding of drivers of consumer trust towards the gambling industry. The study also assessed how different consumer sub-groups – including those with heightened engagement with gambling – differed in their views on trust.
As well as building the Commission’s consumer understanding, this study had 3 output objectives:
This report focuses on the development process, pilot and validation for the trust question, as well as providing a suite of recommendations for future-proofing the question for future iterations of the GSGB.
The full project report Exploring drivers of consumer trust in gambling has a comprehensive description of the multi-phased methodology designed to meet this project’s objectives.
A short overview has been provided in a timeline.
1 The Lived Experience Advisory Panel (LEAP) provides expert independent advice to the Commission based on its members' personal lived experience of gambling harms.
Qualitative analysis collects non-numerical data, focusing on exploring the depth and complexity of a subject, using methods such as interviews and focus groups to capture rich, contextual insights. This type of research emphasises subjective meaning, patterns, and interpretations rather than measurable or generalisable outcomes. While qualitative research provides nuanced understandings of the how and why it does not aim to produce statistical or broadly generalisable findings.
Quantitative analysis involves the processing of numerical data, focusing on a summary of results garnered over a large sample of people, usually designed to be representative of the target population (such as, those who gamble online). It focuses on measuring variables and using statistical techniques to determine relationships, trends, or causal connections, often through methods like surveys. It does not typically explore the depth, complexity, or subjective meanings behind behaviours or experiences.
Problem Gambling Severity Index (PGSI): the PGSI consists of 9 items and each item is assessed on a 4-point scale: never, sometimes, most of the time, almost always. Responses to each item are given the following scores:
When scores to each item are added up, a total score ranging from 0 to 27 is possible.
Maximum Difference (MaxDiff) is a specialised instrument used in market research surveys to determine the relative importance or preference of items, such as product features, statements, or attributes. The tool presents respondents with a series of choice sets, each containing a subset of items from the total list under consideration. For each choice set, respondents are asked to select the item they find most important or appealing and the one they find least important or appealing. The MaxDiff survey tool includes features for randomising the presentation of choice sets to minimise any bias from the order in which the items are presented. Data processing provides an aggregated importance score based on all respondents.
Factor analysis is a statistical method designed to distill a large number of statements into a smaller set of variables that are more manageable and understandable. This technique detects the attributes from the data that align to underlying imaginary constructs called factors and assigns a score to each attribute which explains how strongly it aligns with that factor.
Participants: we use the term “participants” when referring to the people who took part in the qualitative research (including Cognitive testing).
Respondents: we use the term “respondents” when referring to the people who took part in the quantitative research.
Consumers: the term ‘consumer(s)’ is used where analysis is a combination of qualitative and quantitative methods and/or when describing behaviour in more general terms.
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.
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.
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.
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:
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.
Yonder conducted a desk research phase, analysing existing models and frameworks relating to consumer trust to inform the scope of the exploratory qualitative research.
This framework identifies drivers of trust in government institutions. This framework informs research on how gambling companies can build trust by ensuring fair play, clear and accessible terms and conditions, and high ethical standards in advertising and customer relations. It helps guide questions into how perceptions of integrity and fairness in gambling influence customer confidence, especially in mitigating potential gambling harms.
This model highlights reliability, transparency, empathy, and competency as critical in establishing consumer trust in the financial sector. It explores different dimensions of trust, including interpersonal, propensity to take risk, and institutional trust, all of which can be applied to the gambling sector.
This examines corporate reputation based on public perception, customer experience, and social responsibility, assessing companies on authenticity, societal impact, and alignment with consumer expectations. The framework contributes to how a gambling company's reputation is shaped by its commitment to responsible gambling practices, customer service quality, and communication. It provides a basis for understanding consumer responses to ethical practices and social responsibility in gambling, emphasising the importance of reputation in building long-term trust.
This survey measures global trust across sectors and institutions, focusing on competence, ethical behaviour, and transparency, and highlighting trends in consumer trust. This tool can help benchmark gambling industry trust levels against other sectors and informs research on how broader societal attitudes influence trust in gambling companies, providing guidance on effective communication and transparency.
Yonder explored further published sources of trust in the following sectors:
Finally, Yonder also reviewed and took guidance from:
Thematic analysis of these sources was conducted via a framework developed in Microsoft Excel, where key common findings were drawn to focus on the most pertinent areas for exploration. This analysis was intended to provide thorough immersion into readily available literature and adjacent category best practise to guide theme and stimulus development for forthcoming primary research phases but not to introduce any ranking or rating of framework elements.
Following desk research, 4 overarching themes were identified relevant to the gambling industry, which formed the basis of areas to explore and validate in primary research.
These were:
More detail of these themes are included in the published narrative report.
1 OECD Trust in Government Survey Framework (opens in new tab).
2 Moin et al, Introducing a composite measure of trust in financial services, The Service Industries Journal, 2021.
3 Edelmen Trust Barometer (opens in new tab).
Following desk research, the Gambling Commission’s Lived Experience Advisory Panel (LEAP) was convened to investigate the 4 identified factors influencing trust, both positively and negatively, within and surrounding the gambling sector. A 90-minute online group discussion was held with 9 LEAP panel members, facilitated by Yonder.
The objective of the panel discussion was to conduct an exploration of trust dynamics, ensuring that the research captured a wider spectrum of experiences from people who engage in gambling activities. This included individuals across different gambling experiences, including those at risk of gambling-related harms due their own or someone else's gambling. By bringing together diverse perspectives, the panel aimed to develop insights that would contribute to more effective and sensitive approaches to trust-building within the sector.
When considering trust in the gambling industry, the following themes were highlighted by LEAP, which built on similar findings found in the desk research relating to transparency and customer interaction, and helped to inform the qualitative phase.
Clarity and transparency: Clear, straightforward communication and transparent terms and conditions (T&Cs) are essential for building trust, helping prevent misunderstandings that can erode confidence.
Empathy and human interaction: Customer support that demonstrates empathy, especially during challenging situations, fosters a positive perception of a company’s commitment to its users’ wellbeing.
Responsiveness and accountability: Operators or regulators that admit mistakes promptly and resolve issues efficiently build a reputation for reliability and accountability, which strengthens consumer trust.
Reliability and reputation: Consistent service and a strong market presence, supported by positive reviews or certifications, increase trust.
Using the themes identified in the desk research and LEAP session, Yonder designed a qualitative discussion guide and undertook research in the form of focus groups to further explore the themes and to assist the development of broad ‘trust statements’ for the quantitative phase.
Yonder moderated 4 face-to-face focus groups, each 2 hours long. 6 participants took part in each group, amounting to a total of 24. Focus groups took place in 2 regions in England: London and Birmingham. The sample frame was designed to capture a broad range of views across those who gamble and sociodemographic characteristics. This included a range of participant age, gambling settings, gambling frequency, and products used:
The sample was also monitored to ensure a spread of sociodemographic representation:
Attitudinal, behavioural and awareness metrics were also monitored, including:
The focus groups explored thematic drivers of trust, through themes identified by the desk research and LEAP discussions in the scoping phase as well as some stimulus material designed to prompt further insight around the issues identified (for example, news stories relating to gambling regulation).
Following the qualitative focus groups, an interim report was delivered outlining the key findings, with a particular focus on statements to explore for quantitative assessment.
In order to inform the development of specific statements for quantitative assessment, a workshop was held to run through the interim report, focusing on the specific codeframe developed that captured the most pertinent themes. The qualitative phase made several recommendations on key themes to test within the survey, relating to the importance of regulation, fairness of game play, operator marketing activity and advertising, regulation, and industry reputation. More detail on the qualitative findings can be found in the published narrative report.
Following the qualitative phase, an initial list of statements was developed for quantitative assessment. A total of 25 statements were developed for testing in the quantitative survey, covering the thematic findings. This was iterative process, with several drafts of the statements produced in conjunction with the Gambling Commission and wider consultation with LEAP.
Statements were developed in conjunction with the themes identified in the qualitative research, with specific wording refined from the Gambling Commission’s Licensing Objectives as set out in the Gambling Act 2005, and its Corporate Strategy, and the Path to Play model to inform appropriate statements. Statements were bucketed into 6 distinct categories. These categories were:
An online survey was developed and conducted via Yonder Data Solutions proprietary online panel. The sample for the survey was 1,000 Great British adults aged 18 and over who had participated in any gambling activity in the past 12 months, to ensure that questions were posed to a relevant audience. Given the nature of the statements, it was decided that those who only used lottery products (such as National Lottery draws or scratchcards) would not be included in the survey sample.
Quotas were set on age, gender, work status and region to ensure the sample was designed to be broadly representative of the GB population. Weighting was then applied to the final achieved sample to ensure the key demographic groups are proportionally reflected in the final data, improving its accuracy and representativeness. A full list of quotas and weighting schemes are provided in the appendices. Fieldwork took place between 10 to 12 June 2024.
To inform the most important drivers of trust, the survey tool needed to incorporate 2 separate measures of these statements. One question was required to understand the relative importance of different themes, while the other was required to measure participant attitudes towards these trust themes, that would later be used for a factor analysis.
Two key questions were developed, one explicitly in relation to trust in the gambling industry, and the other more generally of how the gambling industry is perceived among consumers using an attitudinal Likert scale.
For the trust specific question, respondents were asked the following:
We’d now like to show you a series of statements relating to factors which might influence your level of trust in the gambling industry. You will see these statements over the next several screens. For each set of statements, we would like you to select which you think is most important and which you think is least important, in terms of trusting the gambling industry.
For this question, to ascertain the most pertinent themes that drive trust in the gambling industry and their relative importance, a Maximum Difference (MaxDiff) exercise was conducted. This technique is particularly effective for evaluating a set of statements by forcing respondents to make trade-offs between them.
In the MaxDiff exercise, respondents were presented with subsets of the 25 statements about trust. Each subset typically contains a smaller number of statements (for example, 3 to 4 at a time). For each subset, respondents indicate which statement they feel is the most important and which is the least important. This forced-choice approach ensures that respondents cannot give equal importance to all statements in any subset, compelling them to make a clear prioritisation. Each statement is shown multiple times in different combinations, ensuring robust data collection and minimising the influence of context or chance.
The data generated from these choices are analysed using advanced statistical models to derive a score for each statement. These scores, reflect the relative importance of each statement. The scores are then indexed at a total level, for ease of interpretation (in this case, between 1 to 10), providing a clear hierarchy of statements and themes.
Importantly, MaxDiff scores are relative rather than absolute. For example, if 1 statement has a score of 8 and another has a score of 2, this indicates that the first statement is considered 4 times more important than the second. These scores do not represent absolute levels of importance but highlight how much more or less influential one theme is compared to another in driving trust. This indexing method is particularly valuable in the context of identifying trust drivers in the gambling industry, where there are nuances and issues are multi-faceted. Results from the Max Diff test can be found in the published narrative report.
A second question was asked of respondents in relation to their attitudes towards the performance of the gambling industry:
Still thinking about gambling industry, to what extent do you agree or disagree with the following statements?
This question was asked in relation to 23 of the 25 statements. Only 2 statements were not included in this question. The first was “Gambling companies are licensed in Great Britain”, and the second was “Gambling companies are a familiar or a reputable name”. These statements were omitted from the performance question due to the more objective or factual nature of these questions, meaning that they were not suitable for the attitudinal Likert scale.
Wording of the remaining 23 statements was tweaked very slightly for this question, in order to fit the Likert agree or disagree scale used but remained fundamentally comparable to those used in the MaxDiff exercise. A full list of the statements used in the MaxDiff exercise can be found in the appendices.
Other survey questions were also used as a means of sub-analysis for the key questions. This included key sociodemographic questions, gambling activities, PGSI, and an attitudinal ‘gambling literacy’ question to measure general perceptions of the risks of gambling. A link to the full question set including the gambling literacy question can be found in the appendices.
Upon completion of the survey, further data interrogation was undertaken via a factor analysis of the statements used in the performance question.
Factor analysis is a statistical method used to simplify complex data by grouping related items into a smaller set of categories or variables. This simplification works by identifying patterns in the responses. It looks at how different statements relate to each other, finding groups of items that people tend to respond to in similar ways. These groups are classed as factors, and they represent the underlying motivations or themes that drive the responses. This survey incorporated 23 statements relating to trust factors, and the factor analysis showed how these statements associate with each other, and which factors have the strongest bearing on answers given with the themes identified. Instead of dealing with all 23 statements individually, the analysis highlights the most notable drivers of trust in a robust way.
Using a combination of the Max Diff scores, factor analysis scores and consideration of meaningful sub-group differences for each exercise, each statement was analysed using an index spreadsheet to consider the merits and justification for inclusion or exclusion in the final list of statements considered to holistically reflect the most pertinent drivers of trust. A workshop took place in order to refine the list of statements down to 10 for inclusion the GSGB survey. This process considered statements that had the highest Max Diff scores and highest importance scores using the factor analysis, alongside a qualitative assessment of statements that were particularly important, for example due to their distinct nature. It was also considered that representation from each thematic category should be included in the final list of statements to ensure coverage against the Commission’s licencing objectives.
A final step taken to refine the selected list of statements was cognitive testing (sense checking) with a range of people who gamble, and with four members from the Gambling Commission’s Lived Experience Advisory Panel (LEAP). Meetings also took place with internal stakeholders to ensure there was clear understanding of the purpose of these statements and what they were intended to capture.
The purpose of this step was to ensure that the wording of the statements selected was clearly comprehended by the key target audience, broadly reflective of the gambling population that will be asked these questions in the GSGB survey. This informed prioritisation of the final statements selected. Full details of the objectives of cognitive testing can be found in the appendices.
The sample frame for the cognitive testing (as shown in Appendix E) was developed to ensure a range of views across those who gamble, including National Lottery only players who were not the initial focus of the online survey.
Cognitive testing participants were shown each of the 10 shortlisted statements, each in turn. Participants were asked to give feedback on these statements. Crucially, participants were not asked how they would answer them or their views, but the clarity of the wording, comprehension and language appropriateness for the audience.
For example, the cognitive testing helped to uncover that there was a wide range of interpretation of the word “manipulation” in the statement “Gambling companies are free from corruption and manipulation” by participants. Therefore, the final statement did not include the word “manipulation”.
Similarly, the cognitive testing helped to understand optimal sentence structure to ensure comprehension of statements. The statement “Effective measures are in place to protect young and vulnerable people when gambling” was deemed to be confusing because of the conflation with young and vulnerable people as 1 group, rather than 2 distinct groups. Therefore, the statement was amended slightly to “Effective measures are in place to protect young, and other vulnerable, people when gambling.” This subtle change helped to provide clarity on the final selected statement.
Results from the cognitive phase were analysed thematically, and a workshop took place to make final minor amendments to the statements informed by participants recommendations.
Following the cognitive testing phase, 8 statements were finalised and selected for inclusion in the GSGB survey, asked of all of those who had gambled:
Thinking about all activities you have engaged in (such as betting, casino, bingo and lottery), both online and in person, how much do you agree or disagree with the following statements?
Answer options:
This report continues in the Pilot and validation exercise section.
Following the development of the new ‘Trust question’ above it was piloted on Wave 4 of Year 2 (2024), of the online Gambling Survey for Great Britain (GSGB). After analysis of the first set of data from the GSGB, the Commission came to Yonder with the need to understand why there was a large proportion of ‘don’t know’ responses than expected across the statements being tested, as well as a need to understand whether the same trends appeared when surveying Yonder’s Y-Live panel.
To understand this in more detail, Yonder fielded a quantitative survey to a sample of 1,500 people who had gambled in the last 12 months (excluding National Lottery only players). This sample size was chosen to ensure readable base sizes for follow-up questions concerning each of the statements, based on estimated fallout of those responding ‘Don’t know’ calculated using data from the Commission’s GSGB; the last 12 months' timeframe was also used to reflect that used in the GSGB.
Firstly, respondents answered a series of questions about their demographic characteristics, as well as their gambling activity, for routing purposes. Respondents would then answer the ‘Trust question’, to sense check the nationally representative results from the GSGB against those from Yonder’s proprietary panel. Across the sample of 1,500 people who had gambled in the last 12 months (excluding National Lottery only), 43 percent selected ‘Don’t know’ for at least one statement.
Those who had selected a “Don’t know” response were then shown follow-up questions to delve deeper to understand why they had selected a ‘don’t know’ response. The approach here was 2-fold, with an initial mini-qualitative question intended to allow respondents to go into more depth about their response, and a prompted multi-code question with potential reasons, which included confusion or misunderstanding concerning each ‘Trust question’ statement. For the latter, the multi-code lists were designed in collaboration with the Commission.
To limit respondent burden and repetitiveness, a least fill approach was employed to ensure a maximum of 4 follow-up questions (1 mini-qualitative, and 3 multi-code prompted lists) were seen per participant. Where a participant had said ‘Don’t know’ in response to four or more ‘Trust question’ statements, the least fill ensured that they were not asked about the same statement in the mini-qualitative exercise, and the multi-code prompted list.
The mini-qualitative exercise asked participants to imagine they were explaining why they were unsure about the statement in question as if they were speaking in a thread and/or forum online or having a conversation with a friend who gambles. After they provided an open-text response detailing what they would say, a smart AI probe tool was used to follow up for further detail. The AI probe was instructed to ‘understand how the statement can be refined so it made sense to as many people as possible, as well as digging deeper into why people answered ‘Don’t know’. It was instructed to ‘probe for extra detail on how the statement might be improved – if they didn’t understand a certain word and/or phrase, could this be switched out?’ If the API did not pick up sufficient detail to include a tailored follow-up, the default probe ‘Which part of the sentence, if any, do you think is unclear?’ was used.
The mini-qualitative approach elicited useful qualitative feedback within the survey environment, allowing the Commission to get in-depth feedback on statements without needing to speak with participants via interviews or groups.
AI probing can sometimes garner more honest insights as respondents do not qualify their responses as much as they perhaps would with a human interviewer
Yonder’s ringfenced panel of people who gamble can provide insights from a more involved perspective, which can then be applied to the broader audience on the GSGB.
In keeping with the foundational samples for this programme of research, 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 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.
Our sample size for this survey was 1,500. When filtering down to those that responded, ‘don’t know’, and were willing to give a verbatim response, this left base sizes in the 75 to 90 region for the mini-qualitative verbatims. Although larger base sizes are unlikely to have led to different thematic analysis, they would have given more weight to findings.
Burden on the respondent in this survey had potential to be high, with multiple mini-qualitative questions. It is also inherently difficult to describe why you don’t know something. To mitigate this burden, a least fill approach was used so respondents did not need to answer more than four follow-up questions.
To analyse the verbatim data from the mini-qualitative exercise, Yonder’s proprietary AI summary dashboard was used. This dashboard takes the initial verbatim responses for each statement and provides the user with the top 10 themes that are emerging, alongside a short overall summary. The relevant verbatim responses are also shown on screen to enable human validation of the AI thematic ranking and summaries.
Yonder’s research team used this dashboard alongside supplementary analysis of verbatims and follow-up responses to present the ‘top three’ emergent themes for each ‘Trust question’ statement, and assign a statement clarity score out of 5 to each, ranging from 1 for ‘Room for improvement’ to 5 for ‘Strong’.
Scores were assigned as follows:
| Statement | Score | Comments |
|---|---|---|
| Gambling companies are held accountable by a regulator if their conduct falls short of standards | 4 | Statement should be left as is – there is a potential to add examples of enforcement, but this would come with risk of priming |
| Gambling companies are free from corruption | 5 | No specific recommended change |
| Gambling games and machines are fair and free of errors | 2 | ‘Fairness’ was thought to be a particularly subjective phrase, which could be further refined to focus on a specific aspect of the gambling experience |
| Activities, offers and odds are clear, easy to understand and not misleading | 4 | No specific recommended change |
| Effective measures are in place to protect young, and other vulnerable people when gambling | 4 | Statement should be left as is – there is a potential to add examples of measures in place, but this would come at risk of priming |
| Gambling companies have effective checks in place before they allow gambling (ID, financial) | 5 | No specific recommended change |
| Gambling promotions and incentives do not encourage excessive gambling | 4 | Prompting respondents to consider what ‘excessive’ looks like to them may elicit less don’t know responses, but this statement is broadly strong |
| It is quick and easy to withdraw and collect my winnings | 4 | Prompting respondents to consider what ‘quick’ looks like to them may elicit less don’t know responses, but this statement is broadly strong |
Responses to the multi-code question lists were used to reinforce these findings, by picking out issues with a clear proportional lead versus others (for example, for those that said they ‘don’t know’ whether ‘gambling companies are free from corruption’, they were overwhelmingly more likely to say it was because they ‘didn’t know if gambling companies are corrupt or not’ (84 percent) verses not knowing ‘how gambling companies can be corrupt (8 percent), or not knowing ‘what is meant by corruption’ (4 percent)).
The multi-code findings validated Yonder’s understanding that the notable proportions answering ‘Don’t know’ was due to a lack of awareness or engagement with a specific element of the gambling experience, rather than a lack of understanding of the statement itself.
To that end, Yonder recommended adding a ‘Not applicable’ code across all statements, to afford respondents without direct personal experience a more accurate response option.
Additionally, Yonder recommended adding qualifying text to the ‘Trust question’ text itself, along the lines of “please answer your personal impression, even if you can’t be certain whether it is truly the case”, to ensure respondents answered based on interpretation, rather than solely relying on absolute knowledge.
The outcome of the validation exercise demonstrated similar trends as the GSGB survey and assured the Commission that the trust statements themselves are fit for purpose. The revised question was also shared with NatCen, who administer the GSGB for their review, and further suggestions were made to ensure the question would work effectively when placed within the survey.
NatCen recommended reframing the opening to the ‘Trust question’ to ensure that respondents were answering the question based on their own views and experiences rather than perceptions or opinions drawn from elsewhere. They also recommended streamlining the statement on activities, odds and offers to make it easier for respondents to answer. Finally, the question was split into two shorter questions due to space restrictions on the paper copies of the questionnaire. Yonder were consulted on these final changes and after full consideration, the Commission accepted all recommendations, which were implemented ready for the 2026 fieldwork of the GSGB.
This version of the trust question was added to both the online and paper GSGB survey during 2025:
The next question relates to consumer trust in the gambling industry.
Thinking about all the activities you have engaged in (such as betting, bingo and lottery), both online and in person, how much do you agree or disagree with the following statements?
Answer options:
This version of the trust question was added to both the online and paper GSGB survey at the beginning of 2026. The question is still classed as being in development while further analysis and assessment takes place.
The next question asks for your thoughts on how gambling companies operate.
Based on your own personal views and experiences of the gambling activities you have engaged in (such as betting, bingo and lottery), both online and in person, even if you take part occasionally/infrequently.
How much do you agree or disagree with the following statements?
Answer options:
And how much do you agree or disagree with the following?
Answer options:
From the 8 statements detailed previously, 5 were chosen to form a composite trust index score. They were:
While 8 statements were put forward for the Gambling Survey for Great Britain (GSGB) in total, these 5 were chosen as the core statements for the composite trust index calculation owing to their broad thematic spread, and their high levels of importance in the Maximum Difference (MaxDiff) exercise, both of which meant they were thought to be an accurate way of developing an index to track ‘trust’ over time. The additional three statements were included for broader Gambling Commission understanding, allowing them to maintain awareness of emerging themes concerning trust in the industry.
A composite index was chosen as it allows the Commission to aggregate multiple responses into a single metric to give them a holistic view of trust in the industry. This is particularly pertinent given the broad nature of the drivers of trust, as demonstrated earlier in this report and in the accompanying full report.
As these statements will be posed to respondents on an agreement scale in the GSGB, the index works by assigning a ‘score’ to each statement based on the response given. These scores are shown in the table.
| Response | Score |
|---|---|
| Agree strongly | 100 |
| Agree slightly | 80 |
| Neither agree nor disagree | 60 |
| Disagree slightly | 40 |
| Disagree strongly | 20 |
| Don't know | 0 |
A composite index score is then attributed at the respondent level, based on an average of their statement-by-statement scores. So, for example, if a respondent selected ‘Agree strongly’ for three statements, and ‘Disagree strongly’ for the other 2, the calculation would look as follows:
If a respondent selects ‘Don’t know’, then that statement does not receive a score for their respondent-level index, and the index is calculated by taking an average of the remaining statements. An example of this is available, using the same responses as before, except for ‘Don’t know’ instead of the final ‘Strongly disagree’.
If ‘Don’t know’ is the response for 3 or more statements per respondent, then that respondent does not contribute to the index score at the overall dataset-level.
It is recommended that bi-annual reviews of consumer trust are conducted to ensure the question set stays relevant and reflects the evolving landscape of the gambling industry. This involves not only maintaining core themes that track established trust factors but also monitoring the emergence of new themes or changes in consumer expectations. To stay ahead of these shifts, regular qualitative research could be conducted with key stakeholders such as the Lived Experience Advisory Panel (LEAP) panel, and consumer groups that interact with the gambling industry. Their insights will provide an up-to-date understanding of the factors influencing trust, particularly in response to changes in regulation, societal attitudes, and industry practices.
Stakeholder engagement could extend beyond research participants to include industry experts, regulators, and consumer advocacy groups. Their perspectives can help identify optional or emerging statements that may require tracking in the future.
For instance, themes such as the role of emerging technologies and artificial intelligence in gambling operations, the use of unlicensed sites or the implementation of enhanced financial risk checks could become increasingly important over time. Monitoring these trends ensures that the survey set remains forward-looking and reflective of issues that impact trust.
One of the strengths of the core trust index score is its ability to provide consistent and reliable measurements over time. The current statement index has been built using a robust assessment of the most important factors driving trust. Altering the core statements too frequently risks introducing noise into the data, which can undermine the ability to track trends effectively. Changes to the index should therefore be made cautiously and only when there is strong evidence that new themes are both critical and enduring. A measured approach ensures that the index reflects evolving trust dynamics without sacrificing its long-term trackability. When changes are necessary, a repeat of tools like Maximum Difference (MaxDiff) and factor analysis could be employed to test the relative importance of new themes against the existing factors. This ensures that any updates to the index are evidence-based and add meaningful value. At the same time, optional or emerging statements can be tested separately to gauge their relevance before inclusion in the survey set, or integration into the core index.
The primary aim of this study was to give an overview of the most pertinent drivers of trust in the gambling industry for those who gamble. Future use of the data could deep dive into how the index score and individual statements within it differ for different consumer audiences. For example, there may be value in an assessment of the most important trust factor for those with a higher propensity to gamble, or with a higher Problem Gambling Severity Index (PGSI) score. Data from the Gambling Survey for Great Britain (GSGB) survey will provide robust sample sizes to analyse different audiences with greater confidence.
The Exploring Drivers of Consumer Trust in Gambling published report is available.
| Group | Target (percentage) |
|---|---|
| Male | 55% |
| Female | 45% |
| Ages 18 to 24 | 9% |
| Ages 25 to 34 | 18% |
| Ages 35 to 44 | 20% |
| Ages 45 to 54 | 19% |
| Ages 55 to 64 | 16% |
| Ages 65 years and over | 19% |
| Working | 66% |
| Not working | 34% |
| Scotland | 8% |
| North East England | 5% |
| North West England | 13% |
| Yorkshire and Humber | 10% |
| West Midlands | 9% |
| East Midlands | 6% |
| Wales | 5% |
| East England | 8% |
| London | 16% |
| South East England | 11% |
| South West England | 8% |
Exploring drivers in consumer trust data tables
The statements on regulation and accountability were:
The statements on protection and safety measures were:
The statements on transparency and fairness were:
The statements on advertising and promotion were:
The statements on customer experience and support were:
The statements on reputation were:
1. Comprehension:
2. Response Options:
3. Question Bias or Sensitivity:
4. Gambling Literacy and Social Bias: Did consumers find the question easy or difficult to answer? What factors contributed to their experience?
5. Recall Difficulty, Fatigue, or Bias: How did consumers arrive at their answers? How far back in time were they recalling to respond to the question?
| Depth Number | Age | Gender | SEG (including 1 to 2 x Ethnic Minority) | PGSI |
|---|---|---|---|---|
| 1 to 2 | Frequent gamblers (at least a few times a month) | x2 Male x2 Female |
1 x B 1 x C1 1 x C2 1 x D |
2 x PGSI 8 and above |
| 3 to 4 | Frequent gamblers (at least a few times a month) | x2 Male x2 Female |
1 x B 1 x C1 1 x C2 1 x D |
7 x 0 to 7 PGSI |
| 5 to 9 | Occasional gamblers (less than once per month) | Minimum x2 Male Minimum x2 Female |
1 to 2 x B 1 to 2 x C1 1 to 2 x C2 1 to 2 x D |
7 x 0 to 7 PGSI |
| 10 to 12 | Those who use National Lottery or scratchcards only | Minimum x1 Male Minimum x1 Female |
1 to 2 x BC1 1 to 2 x C2D |
0 PGSI |