Determinants of SMEs employees’ creativity and their impact on innovation at workplac
Bibliographic record
Abstract
Employees being prolific in the workplace is equally essential to survive in the global job market. The study projected to identify various driving factors of employee creativity and proposed a theoretical model and further investigated the impact of independent variables on employee innovation. Using the survey questionnaire, primary data from 246 small and medium enterprises (SME) employees of Saudi Arabian organization was collected. Exploratory factor analysis (EFA) underlined the identified determinants into six causal factors namely training and brainstorming session (TBS), employee recognition and reward (ERR), resources and fund allocation (RFA), employee competencies (EC), workplace environment (WE) and management support (MS). Subsequently, multiple linear regression analysis was conducted to examine the influence of these factors on employee innovation (EI). Findings showed the regression model was significant and explained 84.2% of the variance on the dependent variable. Also, all six constructs resulted significant positive relationships with EI. Results showed EC has the highest positive relation with EI. The study demonstrated a better working environment, collaboration, team spirit, work autonomy, and morale will reinforce creativity with the support of management and allocation of resources and funds. Lack of these elements will ruin the work culture and thereby the workplace innovation. Though the results are presented from a small group and with no generalization, the study sheds light on many employees in creating a competitive edge and flourish with new opportunities by encountering uncertainties using new approaches at work. The findings are consistent and the study reconfirms the literature. The study also provides some indirect inferences to organizational human resource (HR) policymakers. Further, the limitations and future research scope were also well demonstrated.
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 imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".