The Implementation of Integrated Multichannel Services in the Hospitality Sector in Vietnam
Bibliographic record
Abstract
The research streams on multichannel integration (MCI) in the hospitality sector recently caught the attention of academics and practitioners. However, knowledge and understanding of integrated multichannel services are still unfamiliar to enterprises, especially small and medium-sized enterprises (SMEs) and enterprises in developing countries like Vietnam. Since this topic is limitedly exploited in the hospitality industry, the paper explores the opportunities and challenges for implementing integrated multichannel services in the hospitality sector in Vietnam based on the service science perspective. In the context of emerging digital technology and changing consumer behaviour today, an exploratory study on integrated multichannel services of hotels was conducted on eight hotel managers, eight online travel agencies (OTA), and sixteen domestic tourists. The results show the variety of channels of hotels can reach customers thanks to integrated multichannel services. However, the current situation of channel integration (between the direct and the indirect channel of the hotel through online travel intermediaries) is currently inconsistent. For this reason, challenges related to integrated services, promotion and price, and information access in the channel integration of the selected hotels have been explored. Since then, several solutions are suggested to accelerate MCI in the hospitality sector to stimulate demand for domestic tourism.
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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 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".