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Customer Perception towards Service Delivery of Finance Companies in Mahendranagar

2024· article· en· W4411669240 on OpenAlexaff
Premlata Singh

Bibliographic record

VenueSudurpaschim Spectrum · 2024
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicCOVID-19 Pandemic Impacts
Canadian institutionsWestern University
Fundersnot available
KeywordsBusinessService delivery frameworkPerceptionService (business)Customer serviceMarketingFinancePsychology

Abstract

fetched live from OpenAlex

Customer perception encompasses how individuals select, organize, and interpret information to form a coherent understanding of a brand or service. This study examines customer perceptions regarding the service delivery of finance companies in Mahendranagar. The primary objectives are to evaluate the impact of security, analyze employee behavior, assess the role of trust and reliability, and determine the effect of accessibility on customer perceptions. The research involved 140 participants from diverse professional backgrounds, using a 24-item questionnaire. Data were collected through convenience sampling, ensuring representation across different genders, ages, and occupations. The study highlighted four critical aspects of financial service delivery— accessibility, trust and reliability, employee behavior, and security—that significantly shape customer perceptions. These service quality dimensions are essential in influencing customer satisfaction. The findings suggest that trust and reliability exert the most considerable effect on customer perceptions compared to other factors. This research provides valuable insights for finance companies in Mahendranagar, offering guidance on improving service delivery by focusing on these key factors to enhance customer satisfaction and loyalty.

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)0.000
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 categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.635
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.002

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 head0.029
GPT teacher head0.249
Teacher spread0.220 · 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; both teacher heads agree on what is shown here.

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
Published2024
Admission routes1
Has abstractyes

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