Exemplary followership. Part 1: refining an instrument
Bibliographic record
Abstract
Purpose The overall purpose of this paper is to determine to what extent organizational citizenship behaviors predict followership behaviors within medical organizations in the USA. This is the first part of a two-part article. Part 1 will refine an existing followership instrument. Part 2 will explore the relationship between followership and organizational citizenship. Design/methodology/approach Part 1 of this survey-based empirical study used confirmatory factor analysis on an existing instrument followed by exploratory factor analysis on the revised instrument. Part 2 used regression analysis to explore to what extent organizational citizenship behaviors predict followership behaviors. Findings The findings of this two-part paper show that organizational citizenship has a significant impact on followership behaviors. Part 1 found that making changes to the followership instrument provides an improved instrument. Research limitations/implications Participants in this study work exclusively in the health-care industry; future research should expand to other large organizations that have many followers with few managerial leaders. Practical implications As organizational citizenship can be developed, if there is a relationship between organizational citizenship and followership, organizations can provide professional development opportunities for individual followers. Managers and other leaders can learn how to develop organizational citizenship behaviors and thus followership in several ways: onboarding, coaching, mentoring and career development. Originality/value In Part 1, the paper contributes an improved measurement for followership. Part 2 demonstrates the impact that organizational citizenship behavior can play in developing high performing followers.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".