The impact of gamification features on customer brand engagement: A study in entertainment mobile apps
Bibliographic record
Abstract
This study aims to develop a conceptual model to investigate the influence of the main features of gamification (entertainment, interactivity, innovation, and sociability) on customers' brand engagement in terms of emotional, cognitive, and behavioural dimensions on SnappQ as the online social and entertaining application. A survey employing researcher-developed-questionnaire was conducted by collecting data from 233 customers using SnappQ in Iran. The structural equation modelling method was employed to investigate research hypotheses. The findings of this study revealed that gamification features have a significant relationship with customer brand engagement. Following the findings, employing gamified services and gamification features in online social and entertaining platforms help managers to satisfy customers' psychological needs, and this leads them to have to learn the manner of managing their customers to engage them profitably. The findings help marketing managers/practitioners to understand how to enhance customers' motivation to engage with the desired brand by designing different gamification features.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.003 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".