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Record W2521815609 · doi:10.5539/ijms.v8n5p146

Brand Attachment on Service Loyalty in Banking Sector

2016· article· en· W2521815609 on OpenAlexvenueno aff
Mohammad Javad Taghipourian, Mahsa Mashayekh Bakhsh

Bibliographic record

VenueInternational Journal of Marketing Studies · 2016
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicCustomer Service Quality and Loyalty
Canadian institutionsnot available
Fundersnot available
KeywordsLoyaltyBusinessBrand loyaltyBrand managementService (business)MarketingAdvertisingConstruct (python library)Brand awarenessPsychologyComputer science

Abstract

fetched live from OpenAlex

<p>The purpose of this study is to examine the effects of brand attachment on service loyalty to the services provided by financial sector. As one of the extremely valuable assets of every firm is its brand, attachment creates a deep emotional link between the consumers and the brand such that it contributes to the success of brand management process. To this end, the effects of two dimensions of the construct (brand-self connection and brand prominence) on each of the dimensions of service loyalty would be explored. The questionnaire is based on Park et al. (2010) and Sudhahar et al. (2006). The results of structural equations modeling indicated that brand attachment had a significant positive effect on service loyalty. Furthermore, the existed a positive effect on the dimensions of brand attachment—i.e., brand-self connection and brand prominence—and all dimensions of loyalty—i.e., behavioral, attitudinal, cognitive, conative, affective, commitment, and trust). Among them, brand-self connection had the highest effect on cognitive loyalty, trust-based loyalty, and commitment-based loyalty while brand prominence was most effective on affective loyalty, cognitive loyalty, and trust-based loyalty.Because of the increase in the number of institutions in banking sector and the diversity of services they offer, banking managers can take the advantage of using the results of brand attachment's effect on the study variables and enhance the loyalty to their services.</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)0.003
metaresearch head score (Gemma)0.001
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 score0.084
Threshold uncertainty score0.371

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.001
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 head0.046
GPT teacher head0.317
Teacher spread0.271 · 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

Citations11
Published2016
Admission routes1
Has abstractyes

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