Examining the Connectorship Scale: Factor Structure and Correlations With Self-Efficacy and Extraversion
Bibliographic record
Abstract
The Connectorship Scale was designed to assess how leaders connect with their followers and is described to measure eight dimensions: social interactivity, dependability, positive communication, presenting oneself, storytelling ability, belief in networking, tangible introduction, and belief in the importance of online networking. This study explores the scale properties and confirmatory factor analyses (CFA) of the Connectorship Scale and examines how the scale scores correlate with self-efficacy and extraversion based on responses from 454 (52% women) adult business students. The internal consistency estimates suggested that one of the subscales, positive communication, was unreliable; we therefore excluded that subscale from further analyses. A CFA of the seven-factor model suggested good fit once two pairs of error terms were allowed to correlate. Self-efficacy and all facets of extraversion positively correlated with six of the seven connectorship subscales, the exception being the tangible introduction scale. The results raise concern about the positive communication subscale from the Connectorship Scale but do support the use of the other seven subscales for research about engaged and effective 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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".