Team-level Resources: The Answer to Today's Organizational Challenges
Bibliographic record
Abstract
To advance our understanding of the mechanisms required to support teams under today's unstable and unpredictable conditions, this symposium explores team-level resources under various external (high perceived team virtuality, adopted agile methodology) and internal challenging conditions (membership change, high team diversity, autonomous technology as a team member). Bringing together five high-quality empirical papers that use a range of methodologies, we present evidence on emergent states (e.g., mutual trust, psychological safety) and team processes (e.g., feedback seeking behavior, coordination) as important team-level resources in various team types and contexts. Building on our findings, organisations can create teamwork-supportive conditions and environments to reduce stress, improve processes, and increase effectiveness. Are all virtual teams created equally? Work characteristics and Team Perceived Virtuality Presenter: Patricia Costa; UCP - Católica Lisbon School of Business & Economics Presenter: Lisa Handke; Freie U. Berlin Presenter: Thomas Alexander O'Neill; U. of Calgary Unpacking the relationship between psychological safety and feedback seeking in agile teams Presenter: Jan B. Schmutz; ETH Zürich Presenter: Mirko Antino; Instituto U. de Lisboa (ISCTE-IUL) Presenter: Denniz Dönmez; Swisscom AG When a Team Member Leaves: Adapting to Compositional Disruptions Presenter: Jennifer Feitosa; Claremont McKenna College Presenter: Alicia Davis; Claremont Graduate U. Presenter: Fabrice Delice; Brooklyn College, City U. of New York Presenter: Reggie Romain; Accenture Crisis Management Teams during the COVID-19 pandemics: Demands and Resources Presenter: Meinald Thielsch; U. of Muenster Presenter: Stefan Röseler; U. of Münster Presenter: Julia Kirsch; U. of Münster Presenter: Christoph Lamers; State Fire Service Institute NRW Presenter: Guido Hertel; U. of Muenster Affective Team Trust in Human-Agent Teams: The Importance of Benevolence Presenter: Eleni Georganta; TUM School of Management, Technical U. of Munich Presenter: Anna-Sophie Ulfert; Goethe U. Presenter: Lilian Marie Friedrich; TUM School of Management, Technical U. of Munich Presenter: Katharina Piehlmeier; Ludwig Maximilian U. of Munich (LMU)
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How this classification was reachedexpand
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".