The effect of career adaptability on career planning in reaction to automation technology
Bibliographic record
Abstract
Purpose This study investigated employees’ career planning in preparation for the impact of manufacturing transformation triggered by automation technology. Built on career construction theory, the purpose of this paper is to conceptualize career planning as an attempt to integrate oneself into the social environment. In this process of integration, career adaptability is a critical psychological resource for adaptation to anticipated changes. Design/methodology/approach Through an online survey, 476 participants answered questions regarding the following aspects: perceptions of the threats and opportunities posed by automation technology; career adaptability, that is, career-related concern, control, curiosity, and confidence in adapting to occupational transitions; and career plans and actions to address the challenge, including short-term job crafting behaviors and long-term career adjustment plan. Findings The results showed that opportunity and threat perceptions were associated with one’s job crafting behavior and long-term career adjustment plan and such relationships were moderated by career adaptability and work experience relevant to automation technologies. Specifically, career adaptability is a psychological resource helping individuals deal with perceived challenges, while relevant work experience moderated one’s strategies to catch opportunities. Originality/value This study contributes to the understanding of psychosocial determinants for better career planning in the midst of the industrial revolution. Policies that aim to prepare workers for the upcoming social transition may benefit from this study to leverage adaptive and proactive behaviors at a societal level.
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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 | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".