The effect of culture dimension in digitalization era on the complaint behavior in hotel industry
Bibliographic record
Abstract
To conduct business in the global market in the era of digitalization, hotels need to pay more attention to the complaint behavior of guests with different cultures to adjust their methods of handling these complaints. The purpose of this study is to analyze the influence of Hofstede's five cultural dimensions on the complaint behavior of guests. This study also offers strategic solutions for hoteliers in facing various kinds of complaint behavior from guests with different cultures. This research was conducted on tourists who have stayed in five-star hotels in Badung Regency – Bali, with a total sample of 110 respondents. The data were col-lected through questionnaires. The data analysis was performed using the structural equation model (SEM) with the partial least square (PLS) approach. The results of this study indicate that the power distance cultural dimension has a significant influence on public action and private action. Uncertainty avoidance, individualism versus collectivism, and long term versus short term orientation dimension have a significant influence on public action and private action. The culture of masculinity versus femininity has a significant influence on private action and no action.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".