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Record W4400291066 · doi:10.3389/fpsyg.2024.1361616

Exploring the effects of negative supervisory feedback on creativity among research and development personnel: challenge or threat?

2024· article· en· W4400291066 on OpenAlexaff
Haihong Li, Jianwei Zhang, Muhammad Yaseen Bhutto, Myriam Ertz, Jie Zhou, Xingyu Xuan

Bibliographic record

VenueFrontiers in Psychology · 2024
Typearticle
Languageen
FieldPsychology
TopicCreativity in Education and Neuroscience
Canadian institutionsUniversité du Québec à Chicoutimi
FundersNational Social Science Fund of ChinaNatural Science Foundation of Shandong Province
KeywordsPsychologyCreativityApplied psychologySocial psychologyCognitive psychology

Abstract

fetched live from OpenAlex

Supervisory feedback to stimulate research and development (R&D) employee creativity is a management issue that concerns scholars and practitioners. However, there are divergences and contradictions regarding whether negative feedback promotes or hinders employee creativity. Integrating the feedback intervention and cognitive appraisal theories, we developed a double-edged sword model for negative supervisory feedback's influence on creativity. We tested the proposed model using a field sample of 513 R&D employees from seven science and technology enterprises. The results indicated that R&D employee challenge and threat appraisal moderated negative supervisory feedback's effect on prevention focus and the distal consequences for creativity. Individuals with high (low) levels of challenge (threat) appraisal have decreased prevention focus, thereby increasing their creativity when receiving negative supervisory feedback. In contrast, individuals with low (high) challenge (threat) appraisal have increased prevention focus, thereby decreasing their creativity when receiving negative supervisory feedback. These findings offer interesting implications for research on negative feedback and stimulation of science and technology R&D employee creativity in organizations.

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)0.001
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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.795
Threshold uncertainty score0.500

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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 head0.283
GPT teacher head0.426
Teacher spread0.143 · 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 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
Published2024
Admission routes1
Has abstractyes

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