Playing in the backstore: interface gamification increases warehousing workforce engagement
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it