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Record W2302244575 · doi:10.5539/res.v8n2p71

Service Quality—Object of Business Excellence Measuring

2016· article· en· W2302244575 on OpenAlexvenueno aff
Miriam Jankalová

Bibliographic record

VenueReview of European Studies · 2016
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicCustomer Service Quality and Loyalty
Canadian institutionsnot available
FundersVedecká Grantová Agentúra MŠVVaŠ SR a SAV
KeywordsQuality (philosophy)Service qualityService (business)ExcellenceComputer scienceObject (grammar)Reliability (semiconductor)Quality management systemProcess managementQuality function deploymentKnowledge managementQuality managementMarketingBusinessArtificial intelligenceNew product development

Abstract

fetched live from OpenAlex

<p>The service quality as the object of business excellence measuring is more and more often mentioned with regard to corporate practice, but also at the theoretical level. Universal methods are applied in its evaluation that can aid evaluation systems taking into account the environment of service provision and personal quality of employees. The aim of this paper is to introduce the system of service quality measuring from the perspective of a customer. The paper is divided to following sections: theoretical background, aimed at approaches to define the notion of quality and dimensions of a service quality; research methodology, expert interviews, primary and secondary research and mathematical-statistical methods were applied; results, the outcome is the specification of dimensions of the service quality and the system of quality measuring is presented as a discriminatory function. The proposed index and dimensions and sub-dimensions defined within it form a basis for quantitative assessment of the achieved quality of service. It represents a methodology of creation of a system of service quality measuring; a model of multiple discriminatory analyse which enables the measuring of service quality through sub-dimensions with differentiated weight; a basis for creating an economic-statistical model of quantitative measuring of information.</p>

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)0.003
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: Systematic review · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.566
Threshold uncertainty score0.490

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.001
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 head0.141
GPT teacher head0.322
Teacher spread0.181 · 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 designSystematic review
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

Citations6
Published2016
Admission routes1
Has abstractyes

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