Where do customer loyalties really lie, and why? Gender differences in store loyalty
Bibliographic record
Abstract
Purpose The purpose of this paper is to examine gender differences in store loyalty and how those differences evolve with age. Design/methodology/approach Data for the study were collected in a survey of 32,054 shoppers in more than 50 grocery stores belonging to the same chain. In total, 20 satisfaction items were factor-analysed, resulting in four satisfaction factors. A logistic regression with store exclusivity as the dependent variable was then run to test the research hypotheses. Findings This study finds that men are more loyal than women to the store chain, while women are more loyal than men to individual stores. Women’s loyalty is more influenced by their satisfaction with interaction with store employees, while for men loyalty is more influenced by satisfaction with impersonal dimensions. Store loyalty increases with age, an effect that cannot be explained solely by declining mobility and cognitive impairment. Research limitations/implications This research examines declared behavioural practices rather than actual behaviour. However, in view of the high frequency of purchases in the retail category examined, and also because of the large sample of over 50 different stores, declared practices should be highly correlated with actual behaviour. Practical implications Results from satisfaction surveys should be interpreted differently for men and women. Loyalty programmes may want to adapt their approach, to incorporate gender differences into their loyalty reinforcing measures. Social implications This paper should also help to a better understanding of loyalty programs for both men and women, younger and older people. Originality/value This is the first demonstration from an in store customer survey that the shopping experience drives store loyalty differently for men and women.
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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 | 0.001 | 0.004 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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 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".