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Record W2129708897 · doi:10.5539/ibr.v5n3p107

Investigating Service Quality Initiatives of Pakistani Commercial Banks

2012· article· en· W2129708897 on OpenAlexvenueno aff
Shaukat Ali Raza, Shahid A. Zia, Syed Abir Hassan Naqvi, Asghar Ali

Bibliographic record

VenueInternational Business Research · 2012
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicCustomer Service Quality and Loyalty
Canadian institutionsnot available
Fundersnot available
KeywordsCronbach's alphaSERVQUALReliability (semiconductor)Service qualityEmpathyTest (biology)Quality (philosophy)Service (business)BusinessVariance (accounting)Sample (material)Simple random sampleScale (ratio)MarketingPsychologyStatisticsOperations managementMathematicsSocial psychologyAccountingGeographyEconomicsMedicineEnvironmental health

Abstract

fetched live from OpenAlex

The study investigated the service quality initiatives taken by Pakistani commercial banks in Lahore based on the perceptions of 447 respondents, selected by using multistage random sampling technique, through SERVQUAL scale which was found reliable at 0.866 Cronbach’s alpha. Mean scores, alphas, and correlations were calculated. One-Sample t-test, Independent Samples t-test, and One-way ANOVA were employed for significance and variance analysis. The study concluded that customers, employees, and managers respectively were not satisfied with the overall service quality provided by the Pakistani banks in terms of five sub-scales of service quality. However, tangibles were relatively at top whereas assurance was at the lowest position. Reliability and empathy were at almost similar level and banks failed in their responsiveness. Pakistani banks need to revisit their quality initiatives and focus on responsiveness, assurance, reliability, empathy, and tangibles in order of priority to ensure the set standards of service quality.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.004
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.013
Threshold uncertainty score0.025

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.211
GPT teacher head0.436
Teacher spread0.224 · 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 source (direct Gemma or distilled Codex), 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

Citations10
Published2012
Admission routes1
Has abstractyes

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