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Record W2620698989 · doi:10.5539/jsd.v10n3p14

Management Perceptions of Organizational Service Quality Practices

2017· article· en· W2620698989 on OpenAlexvenueno aff
Syahri Nehru Husain, Yasir Syam Husain

Bibliographic record

VenueJournal of Sustainable Development · 2017
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicCustomer Service Quality and Loyalty
Canadian institutionsnot available
Fundersnot available
KeywordsSERVQUALEmpathyService qualityConfirmatory factor analysisReliability (semiconductor)Dimension (graph theory)PerceptionService (business)BusinessSample (material)Quality (philosophy)PsychologyOrganizational cultureMarketingApplied psychologySocial psychologyPublic relationsPolitical science

Abstract

fetched live from OpenAlex

The research purposed to investigate the management perception of organizational service quality practices. The study conducted in Institution of One-Stop Service of Southeast Sulawesi. Using SERVQUAL Instruments including tangibility; reliability; responsiveness; assurance; and empathy, customers were interviewed and filling a questionnaire. The sample size of 150 was selected purposively, but only 116 samples were analyzed. Data was analyzed with using confirmatory factor analysis and then the results were compared with using performance importance analysis (PIA). This research found that dimensions of responsiveness; reliability; and empathy was the main factor of organizational service quality. Otherwise, tangibility and assurance were not an important dimension for organizational service quality. This research limited on the perception of the customer of public services. This finding indicated that there were differences organizational service quality practices from the other sector and country. The study suggested that organizational service quality practices should have reliability; responsiveness; and empathy on the customer.

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)0.003
metaresearch head score (Gemma)0.007
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 score0.004
Threshold uncertainty score0.014

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.007
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0020.001
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.034
GPT teacher head0.306
Teacher spread0.272 · 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

Citations1
Published2017
Admission routes1
Has abstractyes

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