EFFECTS OF SERVICE QUALITY, HOTEL TECHNOLOGY, AND PRICE FAIRNESS ON CUSTOMER LOYALTY MEDIATED BY CUSTOMER SATISFACTION IN HOTEL INDUSTRY IN CAMBODIA
Bibliographic record
Abstract
The hospitality industry plays a crucial role in contributing the country’s economy growth and, meanwhile, customer loyalty is widely regarded as important driving force for the hotel success. This study intends to discover the direct and indirect impacts of service quality, hotel technology and price fairness on customer loyalty via customer satisfaction for hotels in Cambodia. The study applies a quantitative method and conducts a non-probability survey of 500 customers accommodated in hotels located in five different selected city/provinces in Cambodia. With utilization of SPSS Amos version 23, all data are analyzed by structural equation modeling (SEM). The findings indicate that there are significantly positive relationships between service quality and price fairness on satisfaction as well as on customer loyalty, while price fairness acts as the most influencing factor and satisfaction itself has proven to be significant with customer loyalty. Furthermore, satisfaction partially mediates between service quality, price fairness and customer loyalty. However, hotel technology significantly influences on customer loyalty, yet does not on satisfaction. The research contributes to enriching the theoretical framework of customer loyalty in the hotel industry by its empirical insights. Practically, this study can assist hotel managers developing strategies for their customers retention by enhancing service quality, hotel technology and price fairness. Additionally, the government receives information from this study about the degree of customer loyalty in Cambodian hotels, which may be utilized to improve the government’s human capital training program and raise hotel performance.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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.
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".