Exploring the impact of being perceived as a socially responsible organization on employee creativity
Bibliographic record
Abstract
Purpose The purpose of this paper is to provide insight into the emotional or affective mechanisms that underlie the relationship between employees’ perceptions of corporate social responsibility (CSR) and employee attitudes and behaviors. Drawing on affective events theory (AET), this study examines a sequential mediation model in which CSR perceptions influence positive affect (PA) at work which leads to employee engagement in the creative process that, in turn, affects employee creative behaviors. Design/methodology/approach Two-wave data were collected from a sample of employees working in the telecommunication sector in Egypt (N=208). The collected data were analyzed using structural equation modeling. Findings The study found a positive association between CSR perceptions and employee creative behaviors. The results also showed that CSR perceptions have induced PA, which, in turn, led to greater level of engagement in the creative process and eventually led employees to exhibit creative behaviors. Originality/value This is one of the first studies to use AET as a conceptual framework to explain the positive association between CSR and employee positive work outcomes (i.e. creativity). By integrating AET with CSR and employee outcomes literatures, this study contributes to the available knowledge regarding the affective or emotional mechanisms through which CSR perceptions could affect employee work behaviors.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".