Advergames and Consumer Behaviour: A Quantitative Comparative Analysis of the United Kingdom and Saudi Arabia
Bibliographic record
Abstract
Background/Aim: People from different cultures share different thoughts, values, and morals, and the activities they engage in reflect their backgrounds. Advergames are an innovative way to create relationships with your customers. For instance, advergames can be used as a communication tool between your business and the targeted customer to promote your brand. This study focuses on advergames and consumer behaviour, with the aim of investigating the similarities between Saudi Arabia (SA) and the United Kingdom (UK). Methods: This study used a quantitative approach, with participants chosen from different educational institutions in SA and the UK. A total of 500 participants were shortlisted, and questionnaires were designed and distributed among them to gather data. The questionnaire responses were measured on a seven-point Likert scale. The data were then analysed using the Statistical Package of the Social Sciences version 23.0 (SPSS). Descriptive statistical analysis using means and standard deviations was run to determine the relationship between various constructs, such as persuasiveness, level of experience, brand familiarity, and advergame design, and consumer behaviour. Results: The study found that the brand familiarity and persuasiveness of advergames influenced consumer behaviour. Furthermore, the design of advergames was found to influence consumer behaviour. The study also predicted that the level of experience with the gameplay would influence consumer behaviour. Conclusion: The study found that consumer behaviour is influenced by persuasiveness, level of experience, brand familiarity, and advergame design in the UK and SA. It is suggested that game developers and marketing experts dive right into the consumer black box to learn more about consumer choices, preferences, and comfort zones.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".