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Record W4387409822 · doi:10.1111/caim.12575

Lovable fools and creativity in teams

2023· article· en· W4387409822 on OpenAlexaff
Ma Jun, Bin Zhao, Jiani Yan, Christopher D. Zatzick

Bibliographic record

VenueCreativity and Innovation Management · 2023
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicJob Satisfaction and Organizational Behavior
Canadian institutionsSimon Fraser University
FundersNational Outstanding Youth Science Fund Project of National Natural Science Foundation of ChinaNational Natural Science Foundation of China
KeywordsCreativityTask (project management)PsychologySocial psychologyPsychological safetyApplied psychologyManagement

Abstract

fetched live from OpenAlex

Although employees with low task performance are typically perceived as outcasts in organizations, one group of such employees has the potential to generate positive outcomes. We seek to understand how lovable fools (i.e., individuals with low task performance and high contextual performance) influence creativity within teams. Specifically, we study the countervailing effects of increased psychological safety and decreased useful feedback as mediators of the relationship between the perceived existence of lovable fools in a team and individual and team creativity. We conduct two studies using individual and team‐level analyses to examine the hypothesized direct and indirect effects. Our findings support a positive relationship between perceived existence of lovable fools and individual/team creativity through increased psychological safety, even after accounting for the effects of reduced useful feedback provided by lovable fools. The theoretical and practical implications of these findings are discussed.

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)0.002
metaresearch head score (Gemma)0.008
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 score0.004
Threshold uncertainty score0.009

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.008
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0020.002
Scholarly communication0.0040.001
Open science0.0000.002
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.028
GPT teacher head0.268
Teacher spread0.240 · 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

Citations0
Published2023
Admission routes1
Has abstractyes

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