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Record W2152755595 · doi:10.5539/ijms.v6n2p46

Role of Geography in the Relative Salience of the Antecedents of Cruise Passengers' Satisfaction

2014· article· en· W2152755595 on OpenAlexvenueno aff
Amit Bhatnagar, Amita Bhadauria, Sanjoy Ghose

Bibliographic record

VenueInternational Journal of Marketing Studies · 2014
Typearticle
Languageen
FieldSocial Sciences
TopicCruise Tourism Development and Management
Canadian institutionsnot available
Fundersnot available
KeywordsCruiseSalience (neuroscience)MarketingBusinessValue (mathematics)EntertainmentService (business)AdvertisingComputer sciencePolitical scienceEngineering

Abstract

fetched live from OpenAlex

While it is quite straightforward to determine the factors that satisfy cruise passengers, such as dining, entertainment, etc., it is a challenge to determine their relative importance. A cruise firm with limited resources needs to concentrate its resources chiefly on those aspects of the cruise travel experience that its customers value more. Unfortunately the findings in the academic literature about the relative importance of the determinants of cruise passengers’ satisfaction provide little guidance as they rarely converge across studies. We offer an explanation for this lack of convergence among the different studies about the relative importance of determinants. We believe that cruise customers who go to different locations form different market segments, with their own unique preferences. The differences in previous studies are due to the data being collected from different locations. We collect a unique dataset from an online website about cruise reviews, and use it to provide empirical support for our explanations. We find that while all consumers prefer value for money the most, the second most important factor is public rooms for tourists headed to Alsaka, cabins for the ones headed to Mediterranean, and service for the ones headed to Caribbean. The findings of this study would be of value to the management of cruise companies in refining advertising message, provision of different cruise services, etc.

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)0.003
Version: codex-gemma-dda1882f352aValidation 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 score0.039
Threshold uncertainty score0.327

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
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 head0.013
GPT teacher head0.314
Teacher spread0.301 · 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

Citations1
Published2014
Admission routes1
Has abstractyes

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