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Record W2903898960 · doi:10.5430/bmr.v7n4p46

The Impact of Brand Identification, Brand Equity, Brand Reputation on Brand Loyalty: Mediating Role of Brand Affect in Pakistan

2018· article· en· W2903898960 on OpenAlexvenueno aff
Tayyaba Mahmood, Shumaila Qaseem, Qazi Muhammad Ali, Hafiz Fawad Ali, Asad Afzal Humayon, Amna Gohar

Bibliographic record

VenueBusiness and Management Research · 2018
Typearticle
Languageen
FieldSocial Sciences
TopicDigital Marketing and Social Media
Canadian institutionsnot available
Fundersnot available
KeywordsBrand equityBrand managementBrand loyaltyReputationBrand awarenessBusinessBrand extensionAdvertisingMarketingAffect (linguistics)Corporate brandingVariablesMediationPsychologyMathematicsPolitical science

Abstract

fetched live from OpenAlex

Brand loyalty has been an important factor for quite a while in marketing structure of the organizations. Organizations tend to achieve brand loyalty through employing different strategies and by engaging customers to produce a sense of belongingness among them regarding the brand. One of the major sector that has shown a great trend and growth in recent era is the apparel industry of Pakistan. However, little studies have tried to conceptualize and check the influence of brand related concepts on the brand loyalty in Pakistan. Current study has tried to check the influence of Brand Reputation, Brand Identification, Brand Equity and Brand Effect on Brand Loyalty. An important contribution of the study was to consider the brand affect as the mediating variable among the Brand Reputation, Brand Identification, Brand Equity and Brand Loyalty. 180 questionnaires were distributed among the students of the universities and the 166 valid questionnaires were considered for the data analysis giving use response rate of 92%. Correlational and regression analysis was used to check the influence of the independent variables on the dependent variable while package of Preacher and Hayes 2016 was used for the mediation analysis. Limitations and Future Directions have been discussed in the study as well.

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)0.010
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.880
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0100.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.001
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 head0.050
GPT teacher head0.476
Teacher spread0.426 · 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

Citations5
Published2018
Admission routes1
Has abstractyes

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