Employees’ reactions toward COVID-19 information exposure: Insights from terror management theory and generativity theory.
Bibliographic record
Abstract
As the coronavirus disease (COVID-19) has imposed significant risks to our health and affected our social and economic order, information on COVID-19 becomes readily accessible via various mass media and social media. In the current research, we aim to understand the impacts of employees' exposure to COVID-19 information on their workplace behaviors. Integrating Terror Management Theory (TMT; Becker, 1973; Greenberg et al., 1986) with Generativity Theory (Erikson, 1963, 1982), we proposed and investigated two psychological mechanisms (i.e., death anxiety and generativity-based death reflection) that account for the effects of employees' COVID-19 information exposure on their work withdrawal and helping behaviors toward coworkers. We also examined organizational actions [internal and external corporate social responsibility (CSR) activities] that served as a context for employees to make sense of their COVID-19 information exposure. We conducted two studies with samples of full-time employees (N1 = 278; N2 = 382) to test our predictions. Results in both studies showed that employees' exposure to COVID-19 information was positively related to their death anxiety and generativity-based death reflection, which in turn predicted their work withdrawal and helping behaviors, respectively. Further, employees' perceived internal CSR of their organization mitigated the positive association between COVID-19 information exposure and their death anxiety, weakening the positive indirect effect of COVID-19 information exposure on their work withdrawal. Our study offers new insights to the understanding of work and employment in the COVID-19 pandemic and sheds light on how individuals' death-related experiences shape work-related behaviors. (PsycInfo Database Record (c) 2021 APA, all rights reserved).
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 teacher head, 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".