The integrated role of adaptive leadership, sense of empathy and communication transparency: trust building in corporate communication during the pandemic
Bibliographic record
Abstract
Purpose The COVID-19 pandemic has generated a significant impact on several aspects of public relations practice. By adopting the theoretical framework of adaptive leadership, this research is designed to explore how an organization’s top leadership can support related adaptive action in strategic communication. Particularly, we hope to explore whether the application of adaptive leadership could facilitate a higher level of communication transparency as well as deliver a sense of caring and empathy in COVID-19 communication. Design/methodology/approach An international online survey was designed and conducted for this study. The first sample consists of 776 full-time communication professionals in the United States of America with 435 women (56.1%) and 341 men (43.9%). The second sample consists of 268 full-time communication professionals in Canada, with 110 women (41.0%) and 158 men (59.0%). The two samples were merged into the final sample of 1,044 for the data analysis. Findings Results confirmed that the perceived impact of the COVID-19 pandemic increased communication professionals’ challenges in building trust. It also drives adaptive changes in their coping actions in strategic communication. More importantly, the top leadership within the organization played a key role in this adaptive leadership environment by demonstrating commitment to transparency in COVID-19 communication and delivering a sense of empathy during the pandemic. Originality/value This study contributes to our understanding of strategic communication in times of crisis, such as the COVID-19 global pandemic, by focusing on the adaptive nature of communication leadership. More importantly, our study confirms the important roles of two leadership attributes (i.e. the sense of empathy and the commitment to communication transparency) in supporting adaptive leadership, which eventually influences trust building.
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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 | 0.008 | 0.035 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.005 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.003 |
| 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".