The Linkage Between Turkish Managers’ Leadership Orientations and Their Innovativeness Feature: An Empirical Study
Bibliographic record
Abstract
The dual issues of leadership features and innovativeness constitute an important component of the relatedliterature along with the studies focusing on the connections between these two. The current study considers thisconnection with the notion that innovativeness should be included as a feature of a leader, but it also movesfurther by trying to understand how leadership orientations and innovativeness are patterned together within theleadership concept. To this end, the authors of the current study collect data from the top managers of businessesin the Istanbul Leather Organized Industrial Zone and perform inferential analyses. It is discovered thatleadership orientations have three and that innovativeness has five distinct factors. A structural equation modelthat includes all of these factors together under the concept of leadership is proposed. Although all of theinnovativeness factors are found to be integrated within this model, only one factor for leadership orientations –people orientation – can be integrated within the model. In other words, innovativeness can entirely be includedwithin the proposed model of leadership, but leadership orientations can only partially be included. Most of theinnovativeness factors are positively and moderately related to leadership, albeit assertiveness is notconsiderably favored. Overall, the emphasis appears to be on people oriented and innovative leadership.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".