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Record W4400429917 · doi:10.1080/21568316.2024.2366402

Corporate Social Responsibility and Sustainable Tourism in Tourist Destinations: A Qualitative Case Study from Türkiye

2024· article· en· W4400429917 on OpenAlex
David A. Fennell, Kadir Çakar, Nurullah Cihan Ağbay, İsmail Uzut

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueTourism Planning & Development · 2024
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicOrganizational Strategy and Culture
Canadian institutionsBrock University
Fundersnot available
KeywordsTourismCorporate social responsibilityTourist destinationsQualitative researchDestinationsSustainable tourismBusinessSocial responsibilityPublic relationsPolitical scienceSociologySocial science

Abstract

fetched live from OpenAlex

This study investigated the critical success factors for Türkiye’s competitiveness in sustainable tourism practices. The study is based on data collected from face-to-face qualitative interviews with key tourism stakeholders in Antalya (n = 15) and Istanbul (n = 15), Türkiye’s most popular and most visited cities. A qualitative multiple-case study approach was employed as a research design, and content analysis was used to analyze the data using an abductive technique. The findings confirm that an all-inclusive system creates the main obstacle to achieving sustainable tourism and corporate social responsibility (CSR) in Antalya, with overtourism being a main sustainability challenge in Istanbul. Further, it was found that the dependency between tour operators and hotels hinders the development of alternative tourism, subsequently causing barriers to the sustainability of destinations. Moreover, technology was proposed as the most influential driver in integrating sustainable tourism, CSR, and awareness.

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.

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.302
Threshold uncertainty score0.903

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.044
GPT teacher head0.307
Teacher spread0.263 · 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