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 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 | 0.005 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.012 | 0.001 |
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".