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Record W2888205088 · doi:10.5539/ijms.v10n3p30

Importance-Performance Analysis (IPA) of Service Quality for Virtual Reality Golf Center

2018· article· en· W2888205088 on OpenAlexvenueno aff
Jaeyoon Kwon, taerin chung

Bibliographic record

VenueInternational Journal of Marketing Studies · 2018
Typearticle
Languageen
FieldMedicine
TopicDiverse Approaches in Healthcare and Education Studies
Canadian institutionsnot available
Fundersnot available
KeywordsQuadrant (abdomen)CredibilityNoticeService qualityService (business)MarketingExploratory factor analysisKindnessBusinessQuality (philosophy)Customer satisfactionCenter (category theory)PsychologyMedicine

Abstract

fetched live from OpenAlex

The purpose of this study was examine service quality of virtual reality golf center using Importance and Performance Analysis (IPA), from September to November 2017, adult participants who participated in virtual reality golf center in Korea were selected as subjects. The collected data were analyzed and interpreted using SPSS program, frequency analysis, exploratory factor analysis, reliability analysis, and Importance-Performance Analysis. The results of this study were as follows. First, quadrant 1 included six items: convenient facilities provide the latest equipment, customized response, understanding the needs of customer, interior atmosphere, and modernized facilities. Second, quadrant 2 included five items: kindness of employees, employees’ expertise, resolve immediately if a problem occurs, quickly respond to customer needs, and employee credibility. Third, quadrant 3 included six items: customer individual interest, notice of service, employees’ dress and appearance, employees’ positive attitude, provide voluntary help, and promised time and service. Fourth, quadrant 4 included 3 items: provide safe service, thinking in terms of customer, and voluntary response.

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)0.004
metaresearch head score (Gemma)0.003
Version: codex-gemma-dda1882f352aValidation 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 score0.014
Threshold uncertainty score0.360

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.201
GPT teacher head0.470
Teacher spread0.269 · 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 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
Published2018
Admission routes1
Has abstractyes

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