Bibliographic record
Abstract
Purpose Many studies have investigated consumers’ loyalty to businesses situated in the local area, in the community, the region or in the same country. However, the effect of loyalty to the state in which the consumer resides has received little attention. This paper aims to propose the concept of home-state attachment (HSA) and develop models of its antecedents and its effects on criterion variables such as loyalty to local business. Design/methodology/approach After refinement of the measure of HSA, the authors conduct two studies (n = 202 and n = 201) among residents of two different Canadian provinces (states). They estimate the models, which include both formative and reflective indicators, using structural equation modeling. Findings The results of both studies show that HSA can be distinguished from related constructs like consumer ethnocentrism (CET). HSA has a strong effect on loyalty to local businesses, independent of the effect of CET, testifying to its importance. HSA also affects other criterion variables, with loyalty to local business playing a mediational role. Originality/value This paper shows that HSA, a social-identity-based motivation for local patronage, is an important but largely overlooked determinant of loyalty to local businesses. The robustness of the results over two studies suggests that appeals to consumers based on this motivation may enhance the effectiveness of marketing programs.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".