‘We are locally owned’: Measuring and understanding the brand value of local ownership
Bibliographic record
Abstract
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/.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".