The Age of Anxiety: Unpacking Technological Job Insecurity and Its Impact on Workplace Innovation
Bibliographic record
Abstract
As technology advances at an unprecedented pace, artificial intelligence tools like ChatGPT and Deep Seek are reshaping workplaces, sparking widespread fears of job displacement among employees. This phenomenon, termed Technological Job Insecurity (TJI), goes beyond concerns of machines replacing human roles—it encompasses anxieties about being outperformed by colleagues better equipped to navigate the digital age. While innovation remains critical in such challenging times, the psychological toll of TJI often dampens employees’ ability to think creatively and contribute new ideas. Drawing on Conservation of Resources (COR) Theory, this study examines how TJI erodes innovation performance by draining employees’ psychological resilience and resources. We identify Self-care Self-Efficacy (SCSE)—the ability to manage the impact of digital stress—as a vital mechanism that mitigates this negative effect, offering pathways for resource recovery and renewed focus. Additionally, we explore how Workplace Literacy Facilitation (WLF)—organizational strategies like proactive training and robust technical support—buffers the impact of TJI, creating environments where employees can thrive despite technological uncertainties. Findings from diverse industries provide insights into the interplay between fear, resilience, and innovation, urging organizations to prioritize both psychological well-being and collective support in the age of automation.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.006 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".