Bibliographic record
Abstract
Loyalty is open studied topic within the retailing and marketing discipline. A strong and profitable base of loyal customers is an asset to any organization, and is one of the epitomes of success for a company. The flourishing of large, specialty niche retailers like Starbucks, Victoria Secret and Barnes & Noble are stellar success stories that thrive on their troop of staunch followers.\nPet retailing is a niche market which has its own interesting market characteristics. The emergence of the pet superstores in the 1990s, the like of PetSmart in the US, with 1,160 stores in the US and Canada, as well as Pets At Home in the UK with 266 stores to date, changes the competitive landscape for the traditional pet retailers. In Malaysia, a similar trend started a decade or so ago, with the influx of homegrown and regional large pet retail chain stores\nThis study aims to explore the antecedents of loyalty, benefits of loyalty and the types of loyalty that exist among the pet owners towards the special pet retail stores in Malaysia. The research is conducted via one-to-one interviews with a group of 19 pet owners.\nThe findings of the five key factors that engender loyalty and the three core types of loyalty benefits are compatible with previous studies on the frameworks and models for store loyalty, the antecedents of loyalty and its benefits.\nThe results from this study are useful to current and emerging pet retailers in Malaysia to understand and leverage on the key drivers of loyalty, and the benefits valued by their customers/pet owners. This, in turn, leads to growth in their businesses, and increase in profitability. Such knowledge and insights into the loyalty factors valued by the customers create a competitive edge for pet retailers to succeed in a highly competitive, maturing and low -differentiation buyer's market.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.009 | 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 teacher head, 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".