Transactive Memory Systems, Temporary Teams, and Conflict: Innovativeness During a Hackathon
Bibliographic record
Abstract
The transactive memory system has been studied extensively, yet we still know little about how it influences the effectiveness of temporary teams. Additionally, little is known about the boundary conditions of the well-established benefits of transactive memory systems on team performance. Our primary goal in this study is to build and test a theory that investigates the influence of a transactive memory system on the performance of temporary teams while accounting for conditional effects of both task and relationship conflict. On the surface, a transactive memory systems perspective may seem incompatible with temporary teams. Transactive memory systems typically require time or team member familiarity to develop. However, team members on temporary teams often are selected because of their expertise, not team member familiarity, and often must quickly and effectively operate under time and outcome pressures. We present a theory that suggests transactive memory systems should have a meaningful influence on temporary teams, and its effect is accentuated in the presence of task conflict and attenuated in the presence of relationship conflict. We test our theory using a sample of 202 teams participating in the Global Game Jam, the world's largest hackathon devoted to designing and developing games within a 48-h period. In addition to implications for literatures on transactive memory systems and temporary teams, our study adds to a growing literature providing practical advice and insight regarding hackathons, a pervasive source of innovation and idea generation.
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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 | 0.005 | 0.030 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".