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Record W2798206853 · doi:10.1177/1046496418767554

Initial Expectations of Team Performance: Specious Speculation or Framing the Future?

2018· article· en· W2798206853 on OpenAlexaff
Dustin J. Sleesman, John R. Hollenbeck, Matthias Spitzmüller, Maartje E. Schouten

Bibliographic record

VenueSmall Group Research · 2018
Typearticle
Languageen
FieldPsychology
TopicTeam Dynamics and Performance
Canadian institutionsQueen's University
Fundersnot available
KeywordsPsychologySpeculationFraming (construction)Social psychologyTask (project management)Context (archaeology)FinanceBusinessManagementEconomics

Abstract

fetched live from OpenAlex

This study demonstrates that the initial performance expectations of teams, formed even before members are very familiar with each other or the team’s task, are a key determinant of the team’s ultimate success. Specifically, we argue that such early formed beliefs determine the extent to which teams frame their task as a gain or loss context, which affects their orientation toward risk-taking. Our results suggest a self-fulfilling prophecy effect: Initial team performance expectations lead to the fulfillment of such expectations via risk-taking behavior. We also show that teams are less susceptible to this “risk-taking trap” to the extent that members have low avoidant or high dependent decision-making styles. We tested and found support for our predictions in a study of 540 individuals comprising 108 five-member teams working in a controlled environment. Our study contributes to theory on emergent states and decision biases in teams, and we offer a number of practical implications.

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 imitation

Not 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.

metaresearch head score (Codex)0.006
metaresearch head score (Gemma)0.039
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.006
Threshold uncertainty score0.031

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0060.039
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0010.003
Scholarly communication0.0030.004
Open science0.0010.001
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0030.000

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 head0.103
GPT teacher head0.426
Teacher spread0.324 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
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".

Quick stats

Citations3
Published2018
Admission routes1
Has abstractyes

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