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Record W2208486633 · doi:10.1108/ijsms-16-01-2014-b003

Customer productivity in technology-based self-service of virtual golf simulators

2014· article· en· W2208486633 on OpenAlexfundno aff
Taehee Kim, Hyo-Min Seo, Min Cheol Kim, Kyung-Ro Chang

Bibliographic record

VenueInternational Journal of Sports Marketing and Sponsorship · 2014
Typearticle
Languageen
FieldDecision Sciences
TopicTechnology Adoption and User Behaviour
Canadian institutionsnot available
FundersSamsungUniversity of British ColumbiaSungkyunkwan University
KeywordsProductivityBusinessMarketingService (business)Customer satisfactionReuseTertiary sector of the economyEngineeringEconomics

Abstract

fetched live from OpenAlex

Boosting productivity in the service sector is a key priority for promoting long-term growth. To have customers perform certain tasks normally undertaken by employees is an important means to improving productivity. Technological innovation has influenced business practices for several decades and many service firms, including sports service firms, are now utilising technology extensively to reduce the use of labour. This study investigates how the user's perception of technology-based self-service (TBSS)affects customer productivity and how the customer productivity evaluated by TBSS influences the customer's intentions to reuse in relation to a virtual golf simulator - a successful and seriously played game in Korea.

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)0.017
metaresearch head score (Gemma)0.009
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.034
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0170.009
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.030
GPT teacher head0.327
Teacher spread0.297 · 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.

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

Citations11
Published2014
Admission routes1
Has abstractyes

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Same venueInternational Journal of Sports Marketing and SponsorshipSame topicTechnology Adoption and User BehaviourFrench-language works237,207