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Effect of Customer-Based Brand Equity on Brand Performance and Organizational Performance of Selected Manufacturing Companies in Nigeria

2025· article· W7117777054 on OpenAlexaff
Kyauta Nathan Bawa

Bibliographic record

VenueInternational Journal of Research and Innovation in Social Science · 2025
Typearticle
Language
FieldBusiness, Management and Accounting
TopicConsumer Behavior in Brand Consumption and Identification
Canadian institutionsInstitute on Governance
Fundersnot available
KeywordsBrand equityBrand managementSample (material)Quality (philosophy)Brand awarenessEmpirical researchPopulationCorporate branding

Abstract

fetched live from OpenAlex

This study investigates the influence of customer-based brand equity (CBBE) on brand performance and organisational performance in selected manufacturing companies in Nigeria. The target population comprised 1,500 consumers, from which a sample size of 379 was determined using the Taro Yamane formula. The research examined how the four dimensions of CBBE – brand trust, brand loyalty, brand associations, and perceived quality – affect both brand and organisational outcomes. Findings from regression analysis revealed that all four dimensions exert a positive and significant impact, with perceived quality and brand trust emerging as the most influential drivers of performance. These results underscore the strategic importance of brand management in enhancing competitiveness within Nigeria’s manufacturing sector. The study contributes to branding literature by offering theoretical insights, empirical evidence specific to the Nigerian context, and a methodological contribution through the integration of consumer-based and financial data. It concludes that strengthening customer-based brand equity is vital for sustaining organisational growth and enhancing shareholder value. Accordingly, the study recommends that Nigerian manufacturing firms should invest in strategies that build and reinforce brand equity to achieve long-term success.

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)0.006
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 score0.096
Threshold uncertainty score0.491

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0060.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0050.006
Science and technology studies0.0000.001
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 head0.038
GPT teacher head0.388
Teacher spread0.350 · 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

Citations0
Published2025
Admission routes1
Has abstractyes

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