How the company interrelated factors increase business with existing customers with customer hotel experience as a moderator variable: Empirical study in the hotels
Bibliographic record
Abstract
The study aimed to clarify the most important interconnected factors in the tourism sector “especially in hotels” that contribute to enhancing the interaction of current customers of hotels operating in tourism sector in light of the presence of the customer’s hotel experience as a moderator variable, through an empirical study on hotels, based on an analysis of the opinions of a sample of hotels customers in this sector. The study has been done depending on a questionnaire designed for this purpose and the study employs SmartPLS to analyze the data collected. The study found that the variables: employee efficiency, improving service quality, re-engineering business processes, and enhancing customer relationship management affect the business output generated by current customers of hotels, but the impact of these factors varies on the customers repurchase intention. The factor (Employee’s competencies- EC) has the greatest degree of correlation with an impact coefficient of (-0.227), while the lowest degree of correlation is for the factor (Reengineering business process- RBP) with an impact coefficient of (-0.034). By adopting the variable (CSE) as a moderator variable, the relationships between the independent factors and the dependent factor showed a significant change from the inverse relationship to the direct relationship. The study also showed, through the coefficient of determination (16.1%), that the selected independent factors can be developed either by expanding the sample or by selecting other simple factors or a composite of more than one factor. The moderator variable showed a negative effect on the relationship between the dependent factor (CR) and both factors (ERM and ISQ), while its effect was positive on the relationship between the dependent factor and the other independent factors (RBP and EC).
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".