A Longitudinal Study on Quality of Experience (QoE) measures to predict customer’s Likelihood to Recommend (L2R) a service
Bibliographic record
Abstract
Models that predict satisfaction with a service over time need to consider the impact of emotions and remembered quality of experience in predicting overall attitudes towards a service. However, prior research on subjective quality of experience has typically focused on experiments conducted in a single session or over a short period of time. Thus, there is a gap between our understanding of instantaneous quality of experience and long-term judgments, such as overall satisfaction, and likelihood to recommend and likelihood to churn. The goal of the study reported here was to carry out a longitudinal study that would provide initial insights into how experiences of service quality over time are accumulated into memories that then drive longer term attitudes about the service. Our longitudinal study was carried out over a period of roughly 4 weeks with around 3 sessions per week. To facilitate the study, an online service was constructed that would let participants search through YouTube videos, and that added impairments (specified according to an overall experimental design) to the videos before they were played. Participants were asked to rate several measures, including Technical Quality, after each video was viewed. They were also asked to give overall impressions after each session of five videos had been viewed. The results were analyzed in terms of both sequencing effects within sessions. and memory effects that carried over between sessions.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | low |
| gpt | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | high |
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 | 0.006 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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, unvalidatedLabeled directly by 2 models reading the full record.
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".