Impact of Gamification on Consumers’ Favorability in Cause-Related Marketing Programs: Between-Subjects Experiments
Bibliographic record
Abstract
BACKGROUND: Successful cause-related marketing (CRM) campaigns can help companies stand out from their competitors; however, CRM may not have pleasant outcomes, even if it receives substantial investment. OBJECTIVE: This research aimed to investigate how gamified CRM projects influence consumers' favorability. METHODS: We introduced 3 different CRM projects in 3 different studies. Every project had 2 versions according to the level of gamification, and participants were randomly assigned into these 2 groups. Additionally, we used a 2 (gamification: lower, higher) 2 (rules presentation: without visual cues, with visual cues) between-subjects design to test the moderation role of rules presentation in gamified CRM projects. RESULTS: =14.57, P<.001). Moreover, we found that enjoyment played a mediation role between gamification and favorability (P<.001) in Study 2. In addition, the results of Study 3 indicated rules presentation in a gamified CRM program can moderate the indirect effect of gamification on favorability via enjoyment (index of the moderated mediation: 95% CI -1.12 to -0.10; for rules presentation with visual cues: 95% CI 0.69 to 1.40; for rules presentation without visual cues: 95% CI 0.08 to 0.83). CONCLUSIONS: Overall, this research contributes to the CRM literature and suggests gamification is an effective way of managing CRM campaigns.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".