The ‘romance of teams’: Toward an understanding of its psychological underpinnings and implications
Bibliographic record
Abstract
Although advocates of teamwork suggest that teams enhance performance, empirical evidence does not consistently, or robustly, support these claims. Still, a belief in the effectiveness of teams—among managers, employees, and the general lay population—seems very strong. What accounts for this ‘romance of teams’? In this paper, we offer a psychological answer to this question. We review evidence regarding the actual effectiveness of teams, in order to show that teams are not as effective as many believe them to be, and we argue that the romance of teams stems from the psychological benefits of group‐based activity. Specifically, we propose that team members experience both social‐emotional, and competence‐related, benefits, and we review an eclectic mix of research in support of this claim. We argue that these psychological benefits of teams lead people to assume that teams are ‘high performance’, thus, causing the romance of teams. Finally, we discuss potential implications of the romance for organizations, researchers, and employees.
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.
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 | 0.007 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.045 |
| Scholarly communication | 0.009 | 0.012 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.002 | 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".