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Record W3089097640 · doi:10.5430/rwe.v11n5p481

Effect of Knowledge Sharing and Digital Management to Performance on Ecotourism in Ranong Province, Thailand

2020· article· en· W3089097640 on OpenAlexvenueno aff
Witthaya Mekhum, Chonmapat Torasa

Bibliographic record

VenueResearch in World Economy · 2020
Typearticle
Languageen
FieldSocial Sciences
TopicDigital Marketing and Social Media
Canadian institutionsnot available
Fundersnot available
KeywordsEcotourismTourismBusinessMediationQuestionnaireInformation and Communications TechnologyMarketingTourist attractionGeographyComputer scienceSociology

Abstract

fetched live from OpenAlex

Ecotourism is the combination of ecosystem and tourism. Ecosystem tourism includes travel to destinations where flora, fauna, and cultural heritage are the primary attractions. Present study wants to establish the link between knowledge sharing, information and communication technology (ICT) and ecotourism performance with mediation of tourist attraction and digital management system among employees of ecotourism provider companies in Ranong province of Thailand. Data is collected through questionnaire survey method and via dropdown technique. Partial Least Square (PLS) is used in this study for data analysis. Results indicate that knowledge sharing related to ecotourism and ICT has positive significant impact on ecotourism performance and on tourist attraction and digital management system respectively. Tourist attraction has positive significant impact on ecotourism performance but not mediate the relation. Digital management system mediates the relation and also has the positive significant impact on ecotourism performance. Practitioners should focus on knowledge sharing variable, ICT and digital management system for increasing the ecotourism performance among ecotourism provider companies in Ranong province of Thailand.

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)0.000
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 score0.745
Threshold uncertainty score0.290

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.040
GPT teacher head0.352
Teacher spread0.312 · 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

Citations2
Published2020
Admission routes1
Has abstractyes

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