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Record W4221125411 · doi:10.5539/hes.v12n2p84

The Understanding about Cultural Intelligence of Cabin Crew from Thailand's International Airlines

2022· article· en· W4221125411 on OpenAlexvenueno aff
Dech-siri Nopas, Wirathep Pathhumcharoenwattana, Archanya Ratana-Ubol

Bibliographic record

VenueHigher Education Studies · 2022
Typearticle
Languageen
FieldSocial Sciences
TopicInternational Student and Expatriate Challenges
Canadian institutionsnot available
Fundersnot available
KeywordsCrewDiversity (politics)Cultural diversityPublic relationsBusinessAviationPsychologyCrew resource managementAeronauticsSociologyMarketingEngineeringPolitical scienceLaw

Abstract

fetched live from OpenAlex

In the airline business, cabin crew are the employees who have direct contact with passengers. They are in an occupational group that generally has to face many difficulties in the workplace. In addition to their responsibilities to ensure the safety of the flight, one particular issue that creates tremendous stress for cabin crew is when they have to serve passengers from other different cultures. This study explores the understanding of cabin crew from the international airlines concerning cultural intelligence. The key informant of 12 cabin crew were selected from the international airlines in Thailand. Interview approach was used to collect the data using an in-depth interview form which was then analyzed by using content analysis. The findings revealed 1) there is the importance of knowing cultural intelligence for cabin crew from the international airlines, 2) it is not just the skills to deal with the passengers but everyone in workplace, 3) everyone is different, 4) their experiences matter, 5) we should think, learn, plan, and act, 6) we also should embrace and adapt the Thai cultures that have already existed to the world, 7) it is important to learn knowledge through the diversities and differences, 8) and also learn the knowledge through experiences, 9) we should realize the awareness of cultural diversity and difference. 10) we should expand the perspective since the world is bigger than you think, 11) we should discuss and learn the shared understanding and involvement in cultural diversity and difference for every cabin crew, and 12) we should identify the appropriate actions to effectively work and deal with people from different cultural backgrounds. The study summarized, then proposed the findings of overall understanding about cultural intelligence of cabin crew to Thailand’s international airlines organization. The researchers proposed the outcomes to the research institutions, the academic institutions, and the airline organizations in Thailand and around the world.

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)0.002
metaresearch head score (Gemma)0.005
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.039
Threshold uncertainty score0.077

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0050.007
Scholarly communication0.0060.003
Open science0.0010.003
Research integrity0.0010.003
Insufficient payload (model declined to judge)0.0020.000

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 head0.226
GPT teacher head0.451
Teacher spread0.224 · 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 designQualitative
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

Citations3
Published2022
Admission routes1
Has abstractyes

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