An Exploration of Consumer Complaint Behavior towards the Hotel Industry: Case Study in Macao
Bibliographic record
Abstract
Macao has experienced positively exponential growth with the liberalization of the gaming industry in 2002. This profit-generating territory has attracted many international chain companies, such as Las Vegas Sands Corporation, Wynn Resorts Limited, MGM Resorts International, and Starwood Hotels and Resorts Worldwide, LLC, to establish their businesses in Macao. However, Macao is currently experiencing its worst downturn since 2002. Hotel operators should strive to continuously improving the services that they offer to survive the keen competitive environment. These operators should acquire feedback by encouraging and facilitating the complaint process to improve service quality and meet customer expectations. When customers encounter service failure, they engage in different coping strategies such as inertia, negative word-of-mouth, third party complaint, and voice (Kim, 2010). This study aims to explore the complaint behavior of customers toward the hotel industry in Macao. A qualitative approach is adopted with a sample of 30 respondents who have stayed in Macao hotels. Semi-structured questions are asked through in-depth interviews. The reasons for the complaints and complaint behavior of the customers have been identified, and recommendations are given based on the results of the analysis to provide insights for industry practitioners.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.003 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 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".