The Effect of Gamification on Employee Boredom and Performance<sup>*</sup>
Bibliographic record
Abstract
ABSTRACT This study investigates the effect of gamification on employee boredom and performance in a repetitive work process. In video games, loot is unpredictable, intermittent rewards used to motivate players to repeat boring actions. In a 2 × 1 laboratory experiment, I examine how gamification, featuring nonmonetary loot point rewards, may impact boredom and performance. I find that individuals have mixed opinions. On the one hand, they recognize the emotional value of gamification and find the repetitive work process more attractive. On the other hand, they experience a violation of fairness even though the point rewards do not impact their monetary payoff. My findings help reconcile the seemingly contradictory predictions from two sets of motivation theories. While some conventional theories (e.g., equity theory, expectancy theory, and agency theory) suggest that unpredictable rewards negatively affect motivation, both the reinforcement theory of motivation and findings from neuroscience research indicate a bright side to those rewards. Due to the countervailing effects, I do not find a significant difference in either boredom or performance between conditions. My results show that when gamifying repetitive work processes with unpredictable rewards such as loot points, managers need to address fairness concerns while maintaining the motivational properties of gamification.
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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 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".