Key antecedents to the shopping behaviours and preferences of aging consumers
Bibliographic record
Abstract
Purpose The purpose of this paper is to gain a deeper understanding of how income, cognitive age, physiological change and life-changing events may affect older consumers’ shopping behaviours and preferences. Design/methodology/approach In-depth semi-structured interview was employed for this study. In total, 13 informants were recruited in Toronto, including 11 females and 2 males aged between 51 and 80 years. Content analysis and holistic interpretation were employed for data analysis. Findings According to the findings, price was a major concern to many informants regardless of their income level. The relationship between “feel age”, “look age”, or even “health age”, are not always positively correlated. The vast majority of the informants preferred shopping at the brick-and-mortar stores over online shopping. Some informants experienced difficulties or challenges in finding clothing that fit well due to the change of their body shapes. In addition, many informants needed to adjust their personal needs and buying priorities to cope with their changing personal situations and social roles. Practical implications Other than the price and mobility issues, older consumers encounter different challenges when they shop for different products. It is imperative for retailers, service providers and product developers to understand the older consumers’ changing needs, aspirations and challenges through diverse perspectives – the transition of social roles, physiological change and life-changing events. Originality/value Many prior studies are merely focused on one topic (e.g. cognitive age) or product category (e.g. clothing). Through this multidimensional and mixed categorical approach, new knowledge and insights can be generated and added to the current body of research.
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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 | 0.005 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".