Emotion as soft power in organisations
Bibliographic record
Abstract
Purpose Adopting the critical sensemaking (CSM) lens to the micro-level interaction between leader and employees, the article offers a theoretically informed example of leading with soft power and positive emotions that blurs boundaries in democratic organisations. Design/methodology/approach The research methodology involves videography and interpretive analysis of video-recorded interactions that combines focused ethnography with video analysis. The analysis focuses on face-to-face meeting interactions between a leader and employees in a small service firm. Findings The findings illustrate how restoring the sense of the democratic organisation is an accumulating and complex phenomenon where explicit and implicit organisational rules and changing identity positions are enacted by constructing affective loyalties, moral and reflex emotions that serve as soft power capacities helping the leader and employees to enact meanings attached to a democratic rather than hierarchical organisation. Practical implications The article provides new insight for human resources practitioners and leaders who want to build resilient organisations and pay attention to shared, distributed and relational leadership practices, co-creative work and collective decision-making processes. Originality/value The power explored in previous sensemaking studies has been power over, which is most often associated with the negative aspects of power, such as domination and suppression, in the pursuit of specific performance. The applications of videography method linking ethnography and interpretive analysis of video-recorded interactions are still rare in organisation studies.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.022 |
| Scholarly communication | 0.010 | 0.006 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 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".