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Record W4285189818 · doi:10.5267/j.uscm.2022.3.012

Mediating mechanism of customer satisfaction on customer relationship management implementation and customer loyalty among consolidated banks

2022· article· en· W4285189818 on OpenAlexvenueno aff
Adams Adeiza, Mohammed Sani Abdullahi, Fadi Abdel Muniem Abdel Fattah, Olawole Fawehinmi, Noor Azizi Ismail, Marina Arnaut, Osaro Aigbogun, Ibraheem Salisu Adam, Amauche Ehido

Bibliographic record

VenueUncertain Supply Chain Management · 2022
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicCustomer Service Quality and Loyalty
Canadian institutionsnot available
Fundersnot available
KeywordsBusinessNonprobability samplingCustomer satisfactionCustomer relationship managementOperationalizationLoyaltyLoyalty business modelMarketingStructural equation modelingCustomer retentionService qualityOrder (exchange)Customer advocacyKnowledge managementService (business)Computer scienceFinance

Abstract

fetched live from OpenAlex

The aim of this study is to investigate the mediating mechanism of customer satisfaction (CS) on customer relationship management (CRM) and customer loyalty (CL) among Nigerian consolidated banks. This paper used a survey research design, and the study unit of analysis consists of selected customers among Nigerian consolidated banks. This study used a purposive sampling technique whereby structured questionnaires were used to collect data from 750 customers of the 5 focused banks in Kano State, Nigeria. Partial least square–structural equation modelling (PLS-SEM) was used to evaluate the study hypotheses. The outcome of the study revealed that CRM has a significant effect on CL while CS partially mediates CRM and CL relationship. This paper provides substantial results to practitioners to realize the role of developing a CRM strategy in the Nigerian banking industry. In line with that, the management of the banks should build sound CRM components such as process fit, customer information quality and information system support to deliver sound services in order to operate and compete in the banking ecosystem effectively. This paper has made a substantial contribution to the body of knowledge in the CS, CL, and CRM literature by operationalizing it within the Nigerian banking industry.

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)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.744
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.002
Science and technology studies0.0010.000
Scholarly communication0.0000.001
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.000

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 head0.021
GPT teacher head0.266
Teacher spread0.245 · 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.

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

Citations10
Published2022
Admission routes1
Has abstractyes

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