The influence of project leadership style, innovative work behavior, and openness to experience on employee performance
Bibliographic record
Abstract
The purpose of this study is to analyze the relationships between project leadership style and performance, innovation work behavior and performance, and openness to experience and performance. A quantitative research method was employed, and the analysis was conducted using Partial Least Squares–Structural Equation Modeling (PLS-SEM) via SmartPLS software version 4.0.9.9. PLS-SEM was chosen for its suitability in analyzing complex models with multiple constructs and pathways, particularly in exploratory research contexts. The study surveyed 430 SME employees, selected through snowball sampling based on the accessibility of reliable participants—those who had already completed the questionnaire. PLS-SEM is especially effective for handling small to medium sample sizes, non-normal data distributions, and both reflective and formative constructs. It also prioritizes maximizing the explained variance of dependent variables and is well-suited for testing relationships between latent constructs. A five-point Likert scale was used to measure respondents’ agreement with statements related to the variable indicators, ranging from 1 (strongly disagree) to 5 (strongly agree). The data analysis included validity testing, reliability testing, significance testing, and hypothesis testing. The results indicate that project leadership style has a positive and significant relationship with performance. Similarly, innovation work behavior and openness to experience both show positive and significant associations with performance.
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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 | 0.001 | 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 | 0.000 |
| 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".