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Record W4395477321 · doi:10.58812/wsis.v2i02.670

The Effect of Product and Service Diversification in Ecotourism Business Management on Beaches in Bali

2024· article· en· W4395477321 on OpenAlexaff
Ramdhan Kurniawan, Siska Jeanete Saununu, Iwan Harsono, Henny Budhysetia Dewi

Bibliographic record

VenueWest Science Interdisciplinary Studies · 2024
Typearticle
Languageen
FieldSocial Sciences
TopicCommunity-based Tourism Development and Sustainability
Canadian institutionsInstitut national de psychiatrie légale Philippe-Pinel
Fundersnot available
KeywordsDiversification (marketing strategy)BusinessEcotourismService (business)Product (mathematics)MarketingTourismEnvironmental resource managementProcess managementGeographyEconomics

Abstract

fetched live from OpenAlex

This research investigates the dynamic interplay between ecotourism, product and service diversification, and their impact on the beaches of Bali. Through a quantitative analysis involving 150 tourists and 150 local businesses, the study explores awareness levels, preferences, and economic outcomes associated with ecotourism practices. Descriptive statistics reveal a heightened awareness among tourists and a prevalent adoption of diversification strategies by local businesses. Correlation analysis establishes strong connections between tourist awareness, preferences for diversified destinations, and the positive correlation between business diversification and community involvement. Regression analysis underscores the economic advantages linked to businesses implementing diversification strategies. Comparative analyses shed light on subgroup differences, emphasizing the influence of awareness and diversification on preferences and economic impact. The discussion underscores the implications for sustainable ecotourism, emphasizing the role of collaboration among businesses, communities, and policymakers. The study contributes valuable insights to the discourse on sustainable tourism practices, offering actionable recommendations for the ecotourism industry on Bali's beaches.

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)0.005
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 score0.576
Threshold uncertainty score0.879

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.002
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0000.001
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 head0.022
GPT teacher head0.363
Teacher spread0.341 · 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

Citations0
Published2024
Admission routes1
Has abstractyes

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