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Record W2953605947 · doi:10.5539/ass.v15n7p1

Effective Factors of Service Marketing Mix on Tourist Satisfaction: A Case Study

2019· article· en· W2953605947 on OpenAlexvenueno aff
Mahfuzur Rahman, Mohammad Shariful Islam, Md. Al Amin, Rebaka Sultana, Md. Imran Talukder

Bibliographic record

VenueAsian Social Science · 2019
Typearticle
Languageen
FieldSocial Sciences
TopicDiverse Aspects of Tourism Research
Canadian institutionsnot available
Fundersnot available
KeywordsCronbach's alphaMarketingTourismPsychologyService (business)Regression analysisNoveltyCustomer satisfactionSample (material)BusinessStatisticsSocial psychologyMathematicsGeography

Abstract

fetched live from OpenAlex

The core goal of the study is to examine the relationship between the service marketing mix and tourist satisfaction. The study also attempted to measures the impact of each element of service marketing on tourist satisfaction at Ahsan Manzil in Bangladesh. In order to attain the goal of the research, a good number of extant literature was reviewed and a structured questionnaire was developed to meet the research gap. Based on the studied variables non probabilistic convenience sampling method used to collect data from a sample of 250 respondents who visited the place and seven causal hypothesize was developed. Statistical measurement techniques employed for the study are descriptive, correlation, regression, ANOVA used and cronbach alpha measured the internal consistency of variables. Data analysis executed by using SPSS 20.0. The findings of the study revealed a positive linear relationship of all variables with tourist satisfaction except promotional activities. The novelty of the paper is that it exhibited the consequences of tourists’ satisfaction and dissatisfaction to guide decision-makers and to keep the specific focus on promotional activities.

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)0.004
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.289
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.002
Science and technology studies0.0020.001
Scholarly communication0.0000.001
Open science0.0010.000
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 head0.021
GPT teacher head0.351
Teacher spread0.331 · 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.

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

Citations14
Published2019
Admission routes1
Has abstractyes

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