Navigating Job Insecurity: Insights and Strategies for the Future of Work
Bibliographic record
Abstract
Global political instability, economic fluctuations, health pandemics, and rapid technological advancements have contributed to an increased sense of job insecurity among workers worldwide. Against this backdrop, this symposium brings together five evidence-based presentations, laying the foundation for the future of job-insecurity research. The first two presentations focus on non-traditional behavioral outcomes of job insecurity by examining its relationships with territorial behaviors and knowledge hiding behaviors. Meanwhile, they leverage novel theorical frameworks to understand the underlying mechanisms of job insecurity and propose different moderators in altering the consequences of job insecurity. To cope with job insecurity from one’s full-time job, the third presentation examines whether side hustles may reduce the initial level and the slope of job insecurity trajectory. Building on the growing usage of robots, the fourth presentation examines how, why, and when robot (physical and psychological) anthropomorphism (i.e., human-like appearance and autonomy) may impact employee perceived job insecurity. The final presentation develops a new conceptualization of job insecurity — technology-induced job insecurity, and examines whether, how, and when it may directly and indirectly impact employee in-role behaviors and organizational citizenship behaviors via burnout. Together, this symposium presents innovative research findings aimed at understanding and addressing the persistent problem of job insecurity. Job Insecurity and Territorial Behaviors: Conservation of Resources and Social Identity Theories Author: Lixin Jiang; U. of Auckland Author: Prithviraj Chattopadhyay; Cambridge Judge Business School Author: Elizabeth George; Cambridge Judge Business School Job Insecurity and Knowledge Hiding: Self-serving Cognitions and Psychological Climate Author: Yan Tu; Central China Normal U. Author: Lixin Jiang; U. of Auckland The Dynamic Impact of Side Hustle Stability on Full-Time Job Insecurity in the Gig Economy Author: Linwei Gan; Chinese U. of Hong Kong Author: Guohua Huang; Hong Kong Baptist U. Author: Kenneth S Law; Chinese U. of Hong Kong Robot Anthropomorphism and Employee Job Insecurity: Perceived Control and Zero-sum Digital Mindset Author: Yan Tu; Central China Normal U. Author: Po Hao; Northwest U., China Author: Lixin Jiang; U. of Auckland Author: Lirong Long; Huazhong U. of Science and Technology Job Preservation and Strain Effects of Technology-Induced Job Insecurity on Job Performance Author: Tahira M. Probst; Washington State U. Vancouver Author: Laura Petitta; Sapienza U. of Rome Author: Valerio Ghezzi; Sapienza U. of Rome Author: Claudio Barbaranelli; Sapienza U. of Rome
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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 | 0.010 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.013 | 0.026 |
| Scholarly communication | 0.026 | 0.023 |
| Open science | 0.003 | 0.013 |
| Research integrity | 0.008 | 0.010 |
| Insufficient payload (model declined to judge) | 0.010 | 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".