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Knowledge mapping of tourism demand forecasting research.
Chengyuan Zhang1, Shouyang Wang2, Shaolong Sun3
1School of Economics and Management, Beihang University, Beijing 100191, China.
Tourism Management Perspectives
|August 25, 2020
Summary
This scientometric review maps global travel demand studies from 1999-2018. It highlights key research trends, influential contributors, and the growing impact of big data and machine learning on tourism forecasting.
Area of Science:
- Tourism and Hospitality Research
- Bibliometrics
- Data Science
Background:
- Global travel demand studies are crucial for economic planning.
- Understanding research trends enhances forecasting accuracy.
- A comprehensive knowledge map of this field is needed.
Purpose of the Study:
- To conduct a scientometric review of global travel demand studies from 1999-2018.
- To create knowledge maps of the field's structure, trends, and influential elements.
- To identify emerging research areas, particularly in big data and machine learning.
Main Methods:
- Scientometric review utilizing 388 bibliographic records.
- Co-citation analysis to identify influential works and researchers.
- Collaboration network analysis to map research partnerships.
- Emerging trends analysis to detect new research frontiers.
Main Results:
- Identified key disciplines, trending topics, and influential countries, institutions, publications, and researchers.
- Visualized the increasing integration of big data and machine learning techniques.
- Revealed a growing body of research focused on enhancing tourism demand forecasting.
Conclusions:
- The study provides a comprehensive overview of tourism demand forecasting research.
- It highlights the critical role of big data and machine learning in advancing the field.
- Offers guidance for researchers, operators, and decision-makers to improve forecasting accuracy.
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