Let's work together, especially in the pandemic: finding ways to encourage problem-focused voice behavior among passionate employees
Bibliographic record
Abstract
Purpose This study seeks to unravel the relationship between employees' passion for work and their engagement in problem-focused voice behavior by identifying a mediating role of their efforts to promote work-related goal congruence and a moderating role of their perceptions of pandemic threats to the organization. Design/methodology/approach The research hypotheses were tested with quantitative data collected through a survey instrument administered among 158 employees in a large Portuguese-based organization that operates in the food sector, in the midst of the coronavirus disease 2019 (COVID-19) pandemic. The Process macro was applied to assess the moderated mediation dynamic that underpins the proposed theoretical framework. Findings Employees' positive work-related energy enhances their propensity to speak up about organizational failures because they seek to find common ground with their colleagues with respect to the organization's goals and future. The mediating role of such congruence-promoting efforts is particularly prominent to the extent that employees dwell on the threats that a pandemic holds for their organization. Practical implications The study pinpoints how HR managers can leverage a negative situation—employees who cannot keep the harmful organizational impact of a life-threatening virus out of their minds—into productive outcomes, by channeling positive work energy, derived from their passion for work, toward activities that bring organizational problems into the open. Originality/value This study adds to HR management research by unveiling how employees' attempts to gather their coworkers around a shared work-related mindset can explain how their passion might spur reports of problem areas, as well as explicating how perceived pandemic-related threats activate this process.
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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 | 0.006 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.005 | 0.004 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".