Identifying the target market and how they define value: Innovative Fitness Langley
Bibliographic record
Abstract
This study explored the similarities and differences in the consumer segmentation of two Innovative Fitness (IF) locations, Langley, BC and West Vancouver, BC to determine which market-segment(s) IF Langley should target. In addition this study explored what values are meaningful to consumers when choosing a fitness facility and how these values should be communicated to consumers through the IF brand. The data was derived from a questionnaire, applying characteristics of the SERVQUAL instrument and Chelladurai and Chang's systems view of dimensions of quality in fitness clubs and from focus group discussions. Demographic results showed that although both locations were relatively similar in many ways there were significant differences in income and post-secondary education levels. Customer value and quality data revealed two significant and consistent factors in choosing a fitness facility, the quality of the training and staff knowledge. However, customer satisfaction levels reported by IF Langley customers of training quality and staff knowledge were lower than expected. This lack of congruency was speculated to be due to price sensitivity among other service gaps. When choosing a fitness facility brand was not identified as a prominent value factor although positive correlations with the IF brand did emerge as customers experienced IF Langley. --Leaf ii.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".