Examination of the Role of Positive Leadership Mindset in Mitigating the Effects of Crises on Organizations
Bibliographic record
Abstract
Maintaining a positive mindset as a leader (or a positive leadership mindset) has proved to be important for leadership effectiveness and may be even more important in times of crisis because such a mindset contributes to organizational survival and resilience. In this study, we examined whether a positive leadership mindset helped to mitigate the harmful effects of the COVID-19 crisis on an organization and if so, how. To discover the answer, we conducted an empirical study involving 165 participants who worked in various organizations operating in Canada via a survey posted on LinkedIn just after the pandemic. As this study relates to crisis management, we conducted it in reference to the COVID-19 crisis. Results revealed that a positive leadership mindset can constructively impact leaders’ innovative behavior. We found that leaders’ innovative behavior did not have a significant relationship with the effects of the COVID-19 crisis on organizations; however, the use of information and communication technology (ICT) and the provision of psychological support to employees moderated this relationship, as leaders’ innovative behavior negatively influenced the effects of the COVID-19 crisis when ICT and psychological support to employees were used to a high degree. The implications of these findings for both theory and practice, as well as for the direction of future research, are provided.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".