ArticleTourism management perspectives2020
Knowledge mapping of tourism demand forecasting research.
Article in Tourism management perspectives, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
3 citing papers in PubMed.
- Research Landscape of Artificial Intelligence and e-Learning: A Bibliometric Research.Frontiers in psychology · 2022Article
- Roles and Research Trends of Neuroscience on Major Information Systems Journal: A Bibliometric and Content Analysis.Frontiers in neuroscience · 2022Article
- Exploration of Social Benefits for Tourism Performing Arts Industrialization in Culture-Tourism Integration Based on Deep Learning and Artificial Intelligence Technology.Frontiers in psychology · 2021Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Utilizing a scientometric review of global trends and structure from 388 bibliographic records over two decades (1999-2018), this study seeks to advance the building of comprehensive knowledge maps that draw upon global travel demand studies. The study, using the techniques of co-citation analysis, collaboration network and emerging trends analysis, identified major disciplines that provide knowledge and theories for tourism demand forecasting, many trending research topics, the most critical countries, institutions, publications, and articles, and the most influential researchers. The increasing interest and output for big data and machine learning techniques in the field were visualized via comprehensive knowledge maps. This research provides meaningful guidance for researchers, operators and decision makers who wish to improve the accuracy of tourism demand forecasting.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.