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Record W3176053984 · doi:10.5539/ijps.v13n2p33

Visitor Satisfaction and Tourist Attraction Image

2021· article· en· W3176053984 on OpenAlexvenueno aff
B O Y Marpaung, Felicia Tania

Bibliographic record

VenueInternational Journal of Psychological Studies · 2021
Typearticle
Languageen
FieldSocial Sciences
TopicDiverse Aspects of Tourism Research
Canadian institutionsnot available
FundersCHIST-ERAUniversitas Sumatera UtaraAgencia Nacional de Investigación e Innovación
KeywordsTourismVisitor patternDestination imagePerceptionTourist attractionMarketingService qualityService (business)AdvertisingQuality (philosophy)Perspective (graphical)BusinessDestinationsTourist destinationsGeographyPsychologyComputer science

Abstract

fetched live from OpenAlex

Tourist destination image is formed through tourists' perception, which is significantly influenced by interrelated factors related to tourists and the tourist destination itself, i.e., travel motivation, service quality, and tourist satisfaction. The object of this research is the Parapat city in Simalungun Regency North Sumatra Province of Indonesia. Parapat in Simalungun Regency, as a high potential tourist destination in North Sumatera Province of Indonesia, must be able to create a positive image to increase the number of tourist visits. The purpose of this study is to analyze the direct and indirect influence of tourists’ motivation to travel, service quality, and tourist satisfaction towards the image of Parapat. This study suggests a tourist development strategy through the formation of Parapat positive destination image from tourist’s perspective, hence will provide benefits for Parapat tourism stakeholders in developing and managing tourist destinations by improving the quality of existing functional aspects.

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)0.001
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.706
Threshold uncertainty score0.414

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
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 head0.099
GPT teacher head0.502
Teacher spread0.402 · 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

Citations4
Published2021
Admission routes1
Has abstractyes

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