Play at Work: An Integrative Review and Agenda for Future Research
Bibliographic record
Abstract
Play has gained increasing interest among progressive-minded managers as an important driver of motivation and productivity in work contexts. Despite its popularity in contemporary organizations, there is little consensus in the academic literature about the role of play in the workplace. This review organizes and synthesizes the current state of knowledge of play at work in order to gain a more comprehensive understanding of what play at work is, when individuals engage in play at work, and the effects of workplace play on work outcomes. First, we review existing definitions of play and their limitations. We then introduce a recent conceptualization of play in adulthood that defines play based on three core features and discuss its relevance in the workplace. Second, we review theoretical perspectives on play and extant empirical research on the antecedents and consequences of play at work, organizing it according to three levels of analysis. Third, we propose a promising agenda for future research by focusing on a number of important issues that have emerged from our review of existing work. These issues are organized into two sections: refining and extending the current research on play, and generating novel ideas and new research directions on unexplored areas of inquiry. We believe this review makes important and timely contributions to the research on play at work by providing comprehensive analysis of the diverse and fragmented literature on play in the workplace.
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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 | 0.008 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.009 | 0.010 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.006 | 0.009 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 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".