MétaCan
Menu
Back to cohort
Record W4310808884 · doi:10.32920/21685862

Key antecedents to the shopping behaviours and preferences of aging consumers: A qualitative study

2022· preprint· en· W4310808884 on OpenAlexaffabout
Osmud Rahman, Hong Yu

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldBusiness, Management and Accounting
TopicConsumer Retail Behavior Studies
Canadian institutionsToronto Metropolitan University
Fundersnot available
KeywordsClothingProduct (mathematics)PsychologyAffect (linguistics)MarketingCognitionQualitative researchService (business)Service providerAdvertisingBusinessSocial psychologySociologyPolitical science

Abstract

fetched live from OpenAlex

<p>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. 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. 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. 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. 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.</p>

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.544
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.007
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.104
GPT teacher head0.369
Teacher spread0.265 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations2
Published2022
Admission routes2
Has abstractyes

Explore more

Same topicConsumer Retail Behavior StudiesFrench-language works237,207