A tale of two airports: How Ontario and Oakland international airports are boosting the employee experience to enhance the overall customer experience
Bibliographic record
Abstract
Customer Experience professionals are faced with the challenge of enhancing and supporting environments that they rarely control, which can often lead to gaps in the desired level of service along the passenger journey. Airport leadership teams must recognise that the key to closing these gaps is largely based on employee experience and how it correlates to the overall guest experience. Understanding the importance of organisational health and workplace culture are paramount to developing a positive work environment in which employees can thrive. One of the challenges in enhancing the employee experience is determining how to link multiple work cultures and employee groups within a single airport campus to ensure a seamless experience for passengers. Additionally, airport leaders must anticipate and adapt to the needs of a new digitally based work culture that is arising amid a global pandemic. A healthy employee experience founded on organisational trust, transparency and an engaged workforce is essential to overcoming organisational challenges in any environment. However, by agreeing that employee experience is critical to delivering customer experience, airport leaders and tenant partners can work together to improve communication and promote a healthy work culture. More research can be done on best practices for uniting multiple organisations operating in a common environment. This paper discusses the importance of the employee experience as it relates to organisational health and workplace culture and explores how two airports in California are working to boost the employee experience to enhance overall customer experience.
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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.011 | 0.006 |
| Scholarly communication | 0.009 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".