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Record W4417187949 · doi:10.69554/omtw3795

‘We are locally owned’: Measuring and understanding the brand value of local ownership

2025· article· en· W4417187949 on OpenAlexaff
C. Clifton Eason, John P. Bentley, Scott J. Vitell, Melissa Cinelli, Nathan Kirkpatrick

Bibliographic record

VenueJournal of brand strategy · 2025
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicConsumer Retail Behavior Studies
Canadian institutionsBrock University
Fundersnot available
KeywordsPreferenceBrand preferenceRelevance (law)Construct (python library)Willingness to payValue (mathematics)Measure (data warehouse)Quality (philosophy)Selection (genetic algorithm)Store brand

Abstract

fetched live from OpenAlex

The ‘buy local’ consumer phenomenon has continued to grow in recent years. While consumers’ attitudes towards locally produced products have been well-studied, consumer preferences for locally owned stores (LOSs) have received little research attention. Therefore, the purpose of this research was to understand the potential relevance of localness as an element of a firm’s overall brand by: (a) developing and validating a construct to measure the preference for patronising LOSs, (b) identifying antecedents of the preference, (c) evaluating the prevalence of the preference and (d) testing the performance of the new measure in a study to determine whether consumers with this preference are willing to pay a premium at LOSs. The results indicate that localness may be an important brand attribute to emphasise, as a considerable proportion of consumers consider a store being locally owned to be an important store selection attribute. Three antecedents of the local shopping preference (LSP) are identified, and the localness preference maintains a strong positive relationship with one’s willingness to pay (WTP) a premium at a LOS over what similar merchandise would cost at a national chain (NC). This article is also included in The Business & Management Collection which can be accessed at https://hstalks.com/business/.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.169
Threshold uncertainty score0.395

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.071
GPT teacher head0.259
Teacher spread0.188 · 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.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations0
Published2025
Admission routes1
Has abstractyes

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