Bibliographic record
Abstract
Purpose The purpose of this paper is to offer a reconceptualization of the critical incident technique (CIT) and affirm its utility in management and organization studies. Design/methodology/approach Utilizing a case study from a leadership context, the paper applies the CIT to explore various leadership behaviours in the context of nonprofit boards in Canada. Semi-structured critical incident interviews were used to collect behavioural data from 53 participants – board chairs, board directors, and executive directors – from 18 diverse nonprofit organizations in Alberta, Canada. Findings While exploiting the benefits of a typicality of events, in some instances the authors were able to validate aspects of transformational leadership theory, in other instances the authors found that theory falls short in explaining the relationships between organizational actors. The authors argue that the CIT potentially offers the kind of “thick description” that is particularly useful in theory building in the field. Research limitations/implications Drawing on interview material, the authors suggest that incidents can be classified based on frequency of occurrence and their salience to organizational actors, and explore the utility of this distinction for broader theory building purposes. Practical implications Principally, the paper proposes that this method of investigation is under-utilized by organization and management researchers. Given the need for thick description in the field, the authors suggest that the approach outlined generates exceptionally rich data that can illuminate multiple organizational phenomena. Social implications The role of nonprofit boards is of major importance for those organizations and the clients that they serve. This paper shed new light on the leadership dynamics at the top of these organizations and therefore can help to guide improved practice by those in board and senior management positions. Originality/value The CIT is a well-established technique. However, it is timely to revisit it as a core technique in qualitative research and promote its greater use by researchers. In addition, the authors offer a novel view of incidents as typical, atypical, prototypical or archetypal of organizational phenomena that extends the analytical value of the approach in new directions.
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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 | 0.053 | 0.102 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.012 | 0.009 |
| Science and technology studies | 0.005 | 0.017 |
| Scholarly communication | 0.016 | 0.014 |
| Open science | 0.005 | 0.013 |
| Research integrity | 0.005 | 0.008 |
| Insufficient payload (model declined to judge) | 0.009 | 0.005 |
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".