Bibliographic record
Abstract
My major research paper (MRP) focuses on the service design of WestJet’s domestic check-in space at Toronto International Pearson Airport. In the context of this micro space, service design refers to all of the touch points or points of contact between the customer and the organization designed into this space. This includes anything that communicates with the customer in this space to direct their behavior. My central research question is: how does the service design of the domestic check-in space at WestJet affect customer behavior? In exploring this question, I examined two main aspects: (1) service design and (2) customer behavior. Service design theory is concerned with managing customers’ experience of service quality through the design of services. I observed how customers experienced the service design of the check-in space through their visible behaviors and reconstructed a service blueprint or map of each step in the check-in service with which to track these behaviors. This allowed me to identify variances between customers’ actual behaviors and the desired customer behaviors in the check-in space. I also conducted a series of interviews with select WestJet employees to understand the service objectives of the check-in space and the strategic objectives of the organization. An analysis of the self-service route of the check-in space indicates that some sub-touch points are not positioned at natural decision points for customers. This is despite the fact that the sub-touch points are designed to supply customers with information to make decisions at each major touch point in the check-in service. Consequently, actual customer behaviors vary from WestJet’s desired customer behaviors in the self-service route of the check-in space. These findings suggest that there are nuisances in the design of the check-in service that are impeding WestJet’s service objectives and resulting in inconsistent and potentially confusing customer experiences.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.013 | 0.008 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".