Transactional Leadership and Innovative Behavior as Factors Explaining Emotional Intelligence: A Mediating Effect
Bibliographic record
Abstract
This research aimed to determine the mediating effect of innovative behavior on the relationship between transactional leadership and emotional intelligence in a SARS-CoV-2 context. During this period, behavioral issues among and between employees have been modified in a way that transactional leadership and innovative behavior were considered differently as factors explaining emotional intelligence. This research gap gave room for additional research to re-define the hypothesis of an existing mediating effect between these issues. In fact, as the empirical part of the research has proven, there is evidence of a mediating effect on the relationship of these variables in the sample used. A random sample of 403 owners of textile companies from the Gamarra Commercial Emporium in the district of La Victoria in Lima, Peru, was used to test the existing model regarding the factors explaining emotional intelligence. Data were evaluated by partial least squares structural equation modeling (PLS-SEM). It was determined that innovative behavior has a total mediating effect on the relationship between transactional leadership and emotional intelligence and that 15.9% of the variance of the emotional intelligence variable is explained by the model. This study theoretically contributes to the literature and provides empirical evidence of the relationship between the variables included in the model. Likewise, the model of the variables generated is useful both for the academic and business worlds; yet it must be strengthened and improved by adding more variables. This research contributes to deepening the understanding of the relationship between emotional intelligence, transactional leadership, and innovative behavior in the textile field during the SARS-CoV-2 period.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".