Playing in the backstore: interface gamification increases warehousing workforce engagement
Bibliographic record
Abstract
Purpose In a warehouse setting, where hourly workers performing manual tasks account for more than half of total warehouse expenditure, a lack of employee engagement has been directly linked to company performance. In this article, the authors present a laboratory experiment in which two gamification elements, goal setting and feedback, are implemented in a wearable warehouse management system (WMS) interface to examine their effect on user engagement and performance in an item picking task. Both implicit (neurophysiological) and explicit (self-reported) measures of engagement are used, allowing for a richer understanding of the user's perceived and physiological state. Design/methodology/approach This experiment uses a within-subject design. Two experimental factors, goals and feedback, are manipulated, leading to three conditions: no gamification condition, self-set goals and feedback and assigned goals and feedback. Twenty-one subjects participated (mean age = 24.2, SD = 2.2). Findings This article demonstrates that gamification can successfully increase employee engagement, at least in the short-term. The integration of self-set goals and feedback game elements has the greatest potential to generate long-term intrinsic motivation and meaningful engagement, leading to greater employee engagement and performance. Originality/value This article explores the underlying effects of gamification through two of the most prominent motivational theories (self-determination theory [SDT] and goal-setting theory) and one of the leading employee engagement models (job demands-resource model [JD-R[ model). This provides a theory-rich interpretation of the data, which allows to uncover the motivational pathways by which gamification affects engagement and performance.
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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 | 0.001 | 0.004 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 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".