Bibliographic record
Abstract
Purpose: The main objective of this study was to explore the impact of online reviews on hotel performance. Methodology: The study adopted a desktop research methodology. Desk research refers to secondary data or that which can be collected without fieldwork. Desk research is basically involved in collecting data from existing resources hence it is often considered a low cost technique as compared to field research, as the main cost is involved in executive’s time, telephone charges and directories. Thus, the study relied on already published studies, reports and statistics. This secondary data was easily accessed through the online journals and library. Findings: The findings revealed that there exists a contextual and methodological gap relating to the impact of online reviews on hotel performance. Preliminary empirical review revealed that online reviews serve as a potent mechanism through which potential guests gather information, make booking decisions, and form perceptions about hotels. These findings underscore the need for hotels to adopt a proactive approach to online reputation management, engage with guest feedback, and leverage online reviews as a strategic tool for improving their performance in an increasingly competitive hospitality industry. As travelers continue to rely on online reviews to guide their choices, the role of online feedback in shaping the success and sustainability of hotels is likely to remain a critical area of study and strategic focus in the years to come. Unique Contribution to Theory, Practice and Policy: The Social Influence theory, the Information Asymmetry theory and the Expectancy Disconfirmation Theory may be used to anchor future studies on hotel performance. The study recommended for active reputation management, credible review sources, leverage positive reviews, sentiment analysis tools, tailored management responses, long term reputation building amongst others. These recommendations aim to help hotels harness the positive influence of online reviews on their performance while actively managing and mitigating the potential negative impacts of unfavorable feedback. By adopting a proactive and guest-centric approach to online reputation management, hotels can optimize their performance in the competitive hospitality industry.
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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 | 0.005 | 0.051 |
| 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 | 0.000 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".