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Team-level Resources: The Answer to Today's Organizational Challenges

2021· article· en· W3183649046 on OpenAlexaboutno aff
Jennifer Feitosa, Eleni Georganta, Jan B. Schmutz, Meinald T. Thielsch

Bibliographic record

VenueAcademy of Management Proceedings · 2021
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicCollaboration in agile enterprises
Canadian institutionsnot available
Fundersnot available
KeywordsAgile software developmentTeamworkVirtuality (gaming)PsychologySociologyAnimationManagementComputer scienceArtVisual arts

Abstract

fetched live from OpenAlex

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)

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.831
Threshold uncertainty score0.652

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.002
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.032
GPT teacher head0.244
Teacher spread0.212 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreEmpirical

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".

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Citations0
Published2021
Admission routes1
Has abstractyes

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