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Record W2901164151 · doi:10.1108/jcm-10-2017-2416

Home-state attachment and its effects

2018· article· en· W2901164151 on OpenAlexaffabout
Mrugank V. Thakor, Susan Reid, Rui Chen

Bibliographic record

VenueJournal of Consumer Marketing · 2018
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicCustomer Service Quality and Loyalty
Canadian institutionsBishop's UniversityConcordia University
Fundersnot available
KeywordsLoyaltyMarketingOriginalityStructural equation modelingFormative assessmentSituatedBusinessRobustness (evolution)Loyalty business modelValue (mathematics)Social psychologyPsychologyCreativityComputer science

Abstract

fetched live from OpenAlex

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.

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)0.003
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 score0.342
Threshold uncertainty score0.474

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
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 head0.015
GPT teacher head0.258
Teacher spread0.243 · 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

Citations2
Published2018
Admission routes2
Has abstractyes

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