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Record W4408236993 · doi:10.5267/j.jpm.2024.12.003

Innovative strategy: Utilizing social capital to develop green products

2025· article· en· W4408236993 on OpenAlexvenueno aff
Made Setini, Ida Bagus Udayana Putra, Ni Made Wahyuni, Daru Asih, Evi Triandini, I Putu Santikayasa

Bibliographic record

VenueJournal of Project Management · 2025
Typearticle
Languageen
FieldHealth Professions
TopicGlobal Healthcare and Medical Tourism
Canadian institutionsnot available
Fundersnot available
KeywordsSocial capitalBusinessIndustrial organizationCapital (architecture)Environmental economicsEconomicsSociologyGeographySocial science

Abstract

fetched live from OpenAlex

This research mainly focuses on developing health tourism in rural areas, especially in Bangli, Bali, which has excellent potential but faces various challenges. Through an approach that involves local community involvement, this research aims to create a tourism experience that supports physical and mental health while empowering local communities. The quantitative research method was used to collect data from 450 respondents in Subaya Village. The sample was taken with the criteria of understanding the concept of wellness tourism, having an empowerment group, and having acquired knowledge about designing wellness tourism-supporting products. After data collection, out of 450 questionnaires distributed, as many as 250 were returned, with 150 answers that met the respondents' criteria. The results of the analysis show that the integration of local cultural practices and natural resources can increase the attractiveness of destinations, as well as provide significant economic and social benefits for the community. The study also highlights the importance of eco-friendly products in enhancing the traveler experience, which can increase visitor satisfaction and loyalty. Through a sustainable health tourism model, it is hoped that synergy can be created between local economic development and environmental conservation.

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)0.002
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: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.456
Threshold uncertainty score0.490

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.003
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.111
GPT teacher head0.482
Teacher spread0.371 · 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 designNot applicable
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

Citations1
Published2025
Admission routes1
Has abstractyes

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