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Record W2773134559 · doi:10.5430/afr.v7n1p179

Assessing the effect of Waiting Time Management Strategies on Waiting Time Satisfaction among Bank Customers in Ghana

2017· article· en· W2773134559 on OpenAlexvenueno aff
Joseph Mbawuni, Nimako Gyasi Simon

Bibliographic record

VenueAccounting and Finance Research · 2017
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicConsumer Retail Behavior Studies
Canadian institutionsnot available
Fundersnot available
KeywordsContext (archaeology)Customer satisfactionMarketingStructural equation modelingBusinessService qualityQuality (philosophy)Compensation (psychology)Retail bankingFinancial servicesBanking industryService (business)Conceptual modelEconomicsFinanceComputer sciencePsychology

Abstract

fetched live from OpenAlex

This paper empirically assesses the effect of waiting time management strategies on consumer waiting time satisfaction (WTS) in bank institutions in an emerging economy, using Ghana banking industry as the research context. Drawing from relevant banking and financial marketing literature, a conceptual framework was developed and tested using empirical data from a cross-sectional survey of 480 sampled customers of commercial banks in Ghana. Data were analysed using partial least squares structural equation modelling (PLS-SEM). The findings indicate that, with the exception of apology for delays, the key factors that influence consumer WTS are perceived compensation, waiting environment, quality of delay information and customer mind-engagement strategies. The findings offer important theoretical and managerial implications to scholars and practitioners in the banking service context. This paper provides an initial study into waiting time management in financial services context in Sub-Saharan Africa.

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)0.005
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies, Scholarly communication
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.322
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0020.000
Scholarly communication0.0020.002
Open science0.0000.001
Research integrity0.0000.001
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 head0.037
GPT teacher head0.347
Teacher spread0.309 · 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

Citations3
Published2017
Admission routes1
Has abstractyes

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