The Impact of Gamification on Customer Engagement in Small and Medium Enterprises
Bibliographic record
Abstract
This study investigates the impact of social media gamification techniques on Small and Medium-Sized Enterprises (SMEs) in Indonesia, focusing on point-based rewards, leaderboards, and achievement badges. By analyzing data from 162 Indonesian SMEs using Partial Least Squares Structural Equation Modeling (PLS-SEM), the research reveals several key findings. Point-based rewards and leaderboards significantly boost customer engagement and loyalty by motivating customers to interact more frequently with the business, enhancing their overall experience and satisfaction. As customers accumulate points and see their names on leaderboards, their sense of competition and accomplishment drives continued participation and loyalty. Achievement badges play a crucial role in encouraging initial customer participation by providing immediate recognition of customer efforts, which motivates further interaction. While badges may not have as strong an impact on long-term loyalty as point-based rewards and leaderboards, they are effective in initially engaging customers. The study highlights the importance of tailoring gamification strategies to the specific needs and behaviors of SME customers to maximize social media engagement efforts. By doing so, businesses can effectively enhance customer retention and loyalty. Furthermore, this research fills a gap in the literature by providing measurable insights into the impact of gamification on SMEs, offering practical recommendations for businesses aiming to improve customer engagement through social media. The findings emphasize the effectiveness of customized gamification techniques and offer actionable strategies for fostering long-term customer loyalty, contributing to the broader understanding of social media’s role in SME growth and sustainability.
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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 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".