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Record W4317426028 · doi:10.3389/fpsyg.2023.1042911

The role of error risk taking and perceived organizational innovation climate in the relationship between perceived psychological safety and innovative work behavior: A moderated mediation model

2023· article· en· W4317426028 on OpenAlexafffund
Ahmed Elsayed, Bin Zhao, Abd El-mohsen Goda, Ahmed Elsetouhi

Bibliographic record

VenueFrontiers in Psychology · 2023
Typearticle
Languageen
FieldHealth Professions
TopicOccupational Health and Safety Research
Canadian institutionsSimon Fraser University
FundersSocial Sciences and Humanities Research Council of Canada
KeywordsPsychologyMediationPerceived organizational supportModerated mediationSocial psychologyPsychological safetyRisk perceptionOrganisation climateWork (physics)Structural equation modelingApplied psychologyOrganizational commitmentPerceptionComputer science

Abstract

fetched live from OpenAlex

To better understand how to motivate innovative work behavior (IWB) at the individual level in organizations, we investigate the link between perceived psychological safety and IWB and the role of error risk taking and perceived organizational innovation climate in this study. In particular, we hypothesize a moderated mediation model in which (a) perceived psychological safety is positively related to IWB, (b) error risk taking mediates the positive relationship between perceived psychological safety and IWB, and (c) perceived organizational innovation climate strengthens the positive link between error risk taking and IWB and the mediated link between perceived psychological safety and IWB via error risk taking. We tested the hypothesized model using data collected from 315 full-time employees working at six information and communication technology companies in a high-technology business district of Egypt. The findings largely support our hypotheses. We conclude by discussing the theoretical and 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 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)0.004
metaresearch head score (Gemma)0.003
Version: codex-gemma-dda1882f352aValidation 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 score0.035
Threshold uncertainty score0.757

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.004
Science and technology studies0.0010.000
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 head0.146
GPT teacher head0.485
Teacher spread0.339 · 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

Citations36
Published2023
Admission routes2
Has abstractyes

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