The benefits of transformational leadership for addressing workplace emotions after COVID-19 at a large multi-campus university
Bibliographic record
Abstract
The COVID-19 pandemic presented a series of challenges to organizations. Among those that successfully continued operating, the subsequent recovery period catalyzed pressure to redefine work structure after social distancing restrictions lifted. Here, we observed the benefits of transformational leadership in this historically unique context of organizational distress by applying an adjusted version of Ashkanasy and Dorris's (Ashkanasy and Dorris in Annu Rev Organ Psych Organ Behav 4:67-90, 2017) framework for workplace emotions to a large, multi-campus university. We quantitatively content-analyzed semi-structured interviews of more than 300 divisional leaders and their staff from across the organization. Interviews occurred in the months following the first semester of continuous in-person service delivery, when most employees returned to working in employer-operated space. Despite disproportionate emphasis on negative and self-focused emotions, negative emotions clustered in individuals' empathic recognition of others' emotions; though efforts to regulate those emotions proved scant. Positive emotions primarily emerged in response to local leadership efforts to mitigate the negative emotions of students, staff, and faculty. This data pattern suggests that individuals experienced negative emotions, recognized others' negative emotions, and appreciated leaders' interventions to ameliorate those negative emotions. Strategies reminiscent of transformational leadership therefore productively addressed the negative impact of workplace stress imposed by the pandemic, helping to facilitate compliance and enthusiasm with return-to-work efforts. The findings thus illustrate how a transformational style of leadership can address individuals' negative experiences during a period of pronounced existential stress.
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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 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.000 | 0.001 |
| 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 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".