Analysis of Gen Z's Readiness to Leverage AI in Green Jobs
Bibliographic record
Abstract
As an environmental issues enthusiast and tech-savvy generation, Gen Z is poised to benefit from green and digital transitions by utilizing AI in their preferred green jobs. This study uses a qualitative approach to describe Gen Z's readiness to use AI in green jobs based on the Readiness for Organizational Change theory. The study employed purposive sampling to interview 19 Gen Z employees in green jobs (academia, business, community, and public sectors), supported with literature reviews. The research examines readiness through four key aspects: appropriateness, management support, change efficacy, and personal valence. It also analyzes Gen Z’s perceptions of AI’s importance, benefits, uses, and the challenges in the application. The findings show that Gen Z employees view AI as essential for enhancing work efficiency and productivity, though they face some challenges. Various organizational approaches to AI adoption highlight that AI integration is not just technological, but also cultural. This research offers insights for organizations to create an enabling environment to use AI effectively. Furthermore, this study encourages the organization’s management to gain a deeper understanding of Gen Z employees’ application of AI in green jobs to support their workforce in adapting to technological advancements.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".