Shopping well-being: the role of congruity and shoppers’ characteristics
Bibliographic record
Abstract
Purpose Although shopping well-being has become a focal construct in retail shopping studies, little is known about the key drivers of this construct. This study aims to further discern some of the key antecedents of shopping well-being by particularly focusing on the role of congruity. Furthermore, the study explores whether shoppers’ demographic characteristics moderate the effects of congruity on shopping well-being. Design/methodology/approach Data were collected from a survey of actual shoppers in two urban Canadian shopping malls via a mall intercept. Structural equation modeling using SmartPLS was conducted to validate the study’s model. Findings Functional congruity has a stronger effect than self-congruity on shopping well-being. Shoppers’ demographic variables do not generally act as moderators in the investigated linkages. Practical implications This study can help mall managers formulate better marketing programs that would ultimately enhance shopping well-being. Originality/value The study advances the retailing literature by putting forward a conceptual model that remedies identified shortcomings related to functional and self-congruity and establishes new linkages between functional congruity, self-congruity and shopping well-being. Furthermore, the study explores whether shoppers’ demographic variables moderate the effects of functional and self-congruity on shopping well-being.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".