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Record W1785689944 · doi:10.5267/j.msl.2015.8.005

Effects of online marketing on Iranian ecotourism industry: Economic, sociological, and cultural aspects

2015· article· en· W1785689944 on OpenAlexvenueno aff
Arash Riasi, Shirin Pourmiri

Bibliographic record

VenueManagement Science Letters · 2015
Typearticle
Languageen
FieldSocial Sciences
TopicDiverse Aspects of Tourism Research
Canadian institutionsnot available
Fundersnot available
KeywordsEcotourismSociologyMarketingTourismBusinessPolitical science

Abstract

fetched live from OpenAlex

This study intends to evaluate the impacts of implementing online marketing strategies on Iranian ecotourism industry. The study had eight hypotheses which were designed based on economic, sociological, and cultural aspects of Iranian ecotourism industry. The results indicate that the expansion of online marketing increased the number of foreign tourists who visited Iran’s natural tourist attractions, while it had no significant impact on the number of domestic tourists who visited these tourist attractions. The results also indicated that online marketing did not increase the amount of investment in Iranian ecotourism industry. Additionally, online ecotourism marketing did not have a significant influence on the total number of travels to Iran’s historical tourist attractions. Respondents believe implementing online marketing strategies in Iranian ecotourism industry helps Iran expand its cultural tourism in rural areas and it expands its hospitality industry; they also believe that online marketing helps to create more jobs in Iranian ecotourism industry and to improve working conditions in this industry.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.005
Threshold uncertainty score0.015

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0040.000

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 head0.035
GPT teacher head0.328
Teacher spread0.293 · 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 source (direct Gemma or distilled Codex), 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

Citations55
Published2015
Admission routes1
Has abstractyes

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