Bibliographic record
Abstract
Online Travel Agencies (OTAs) have profoundly influenced the hospitality and tourism industry, necessitating an understanding of the evolution of OTA research. This study employs bibliometric analysis to examine the progression, thematic structure, and future directions of OTA research from 2000 to February 2024. By analyzing publication trends, the study illustrates the increasing significance of OTA research, evidenced by its growing volume and acceleration rate. Three dominant themes—satisfaction, management and service quality, and consumer behavior—were identified, alongside five key co-citation clusters, including online reviews and eWOM, research methods and theory development, and channel distribution strategies. Co-word analysis revealed shifting focal points in OTA research, transitioning from loyalty (2005) to trust (2012) and behavior (2021). Co-word analysis further revealed shifting trends in OTA research, from loyalty (2005-2012) to trust (2012-2021) and behavior (mid-2021 onwards). Thematic analysis identified pivotal intellectual milestones, with revenue management and pricing strategy emerging as themes of high centrality but low knowledge development, emphasizing their importance yet underdeveloped state. This research recommends exploring collaborations between OTAs and hospitality stakeholders, including contracts, commissions, convergent marketing, and co-branding, while incorporating advancements in artificial intelligence and digital transformation. Therefore, future studies are recommended to incorporate these advanced analytical methods to present the most up-to-date ideas. In light of changing consumer behaviors driven by trends like post-COVID-19 risk aversion, digital transformation, and health consciousness, this study encourages research into subthemes such as solo travel and smart purchasing. This comprehensive analysis synthesizes two decades of OTA literature, offering a holistic view of its knowledge structure and progression. It provides valuable insights for both academia and practitioners by presenting an integrated overview of OTA research, identifying gaps, and proposing strategic directions. This study advances OTA research in hospitality and tourism, paving the way for future investigations of its industry impact.
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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 | 0.014 | 0.070 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.210 | 0.277 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.010 | 0.008 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".