Examining generational differences as a moderator of extreme‐context perception and its impact on work alienation organizational outcomes: Implications for the workplace and remote work transformation
Bibliographic record
Abstract
There is no doubt that extreme contexts (e.g., war zones and pandemics) represent substantial disruptions that force many companies to rethink the way they do business. With so much of the workforce now working remotely and concerns about resulting work alienation, we must ask this question: How can this be translated into the generational divide in workplaces based in extreme contexts? Using COVID-19 as an example trigger of extreme-context experience, therefore, we investigate generation as a moderator of the effects of extreme-context perception upon anxiety leading to alienation with subsequent behavioral outcomes on job insecurity, job satisfaction, and organizational citizenship behavior (OCB). A time-lagged survey procedure yielded 219 valid responses from a three-generation sample of employees working in multiple service organizations. The data were analyzed using partial least squares structural equation modeling (PLS-SEM). Our analysis suggested that intense extreme-context perception led to elevated anxiety and alienation, which, in turn, heightened job insecurity and worsened job satisfaction and OCB outcomes. Finally, during the experience of extreme-context times, generation was found to moderate our model, such that both Generation Y and Generation Z experienced higher anxiety due to extreme-context perception and hence higher job insecurity due to alienation compared with Generation X respondents. Our results endorse the criticality of implementing agile and generationally non-sectarian management for effectively functioning generationally diverse workforces in pandemic times.
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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) | 0.000 | 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".