Tourism demand forecasting using tourist-generated online review data

Mingming Hu; Hengyun Li; Haiyan Song; Xin Li; Rob Law

doi:10.1016/j.tourman.2022.104490

Tourism demand forecasting using tourist-generated online review data

Mingming Hu, Hengyun Li, Haiyan Song, Xin Li, Rob Law

Research output: Journal article publication › Journal article › Academic research › peer-review

65 Citations (Scopus)

Abstract

This study aims to forecast international tourist arrivals to Hong Kong from seven English-speaking countries. A new direction in tourism demand modeling and forecasting is presented by incorporating tourist-generated online review data related to tourist attractions, hotels, and shopping markets into the destination forecasting system. The main empirical findings indicate that tourism demand forecasting based on tourists’ online review data can substantially improve the forecasting performance of tourism demand models; specifically, mixed data sampling (MIDAS) models outperformed competing models when high-frequency online review data were included in traditional time-series models.

Original language	English
Article number	104490
Journal	Tourism Management
Volume	90
DOIs	https://doi.org/10.1016/j.tourman.2022.104490
Publication status	Published - Jun 2022

Keywords

Hong Kong
MIDAS
Online review
Social media data
Tourism demand forecasting

ASJC Scopus subject areas

Development
Transportation
Tourism, Leisure and Hospitality Management
Strategy and Management

More information

10.1016/j.tourman.2022.104490

http://hdl.handle.net/10397/94045

Cite this

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title = "Tourism demand forecasting using tourist-generated online review data",

abstract = "This study aims to forecast international tourist arrivals to Hong Kong from seven English-speaking countries. A new direction in tourism demand modeling and forecasting is presented by incorporating tourist-generated online review data related to tourist attractions, hotels, and shopping markets into the destination forecasting system. The main empirical findings indicate that tourism demand forecasting based on tourists{\textquoteright} online review data can substantially improve the forecasting performance of tourism demand models; specifically, mixed data sampling (MIDAS) models outperformed competing models when high-frequency online review data were included in traditional time-series models.",

keywords = "Hong Kong, MIDAS, Online review, Social media data, Tourism demand forecasting",

author = "Mingming Hu and Hengyun Li and Haiyan Song and Xin Li and Rob Law",

note = "Funding Information: This study is supported by Early Career Scheme from the Research Grants Council of the Hong Kong Special Administrative Region , China (Project No. 25500520 ) and the National Natural Science Foundation of China ( 71761001 ). ",

year = "2022",

month = jun,

doi = "10.1016/j.tourman.2022.104490",

language = "English",

volume = "90",

journal = "Tourism Management",

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AU - Hu, Mingming

AU - Li, Hengyun

AU - Song, Haiyan

AU - Li, Xin

AU - Law, Rob

N1 - Funding Information: This study is supported by Early Career Scheme from the Research Grants Council of the Hong Kong Special Administrative Region , China (Project No. 25500520 ) and the National Natural Science Foundation of China ( 71761001 ).

PY - 2022/6

Y1 - 2022/6

N2 - This study aims to forecast international tourist arrivals to Hong Kong from seven English-speaking countries. A new direction in tourism demand modeling and forecasting is presented by incorporating tourist-generated online review data related to tourist attractions, hotels, and shopping markets into the destination forecasting system. The main empirical findings indicate that tourism demand forecasting based on tourists’ online review data can substantially improve the forecasting performance of tourism demand models; specifically, mixed data sampling (MIDAS) models outperformed competing models when high-frequency online review data were included in traditional time-series models.

AB - This study aims to forecast international tourist arrivals to Hong Kong from seven English-speaking countries. A new direction in tourism demand modeling and forecasting is presented by incorporating tourist-generated online review data related to tourist attractions, hotels, and shopping markets into the destination forecasting system. The main empirical findings indicate that tourism demand forecasting based on tourists’ online review data can substantially improve the forecasting performance of tourism demand models; specifically, mixed data sampling (MIDAS) models outperformed competing models when high-frequency online review data were included in traditional time-series models.

KW - Hong Kong

KW - MIDAS

KW - Online review

KW - Social media data

KW - Tourism demand forecasting

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U2 - 10.1016/j.tourman.2022.104490

DO - 10.1016/j.tourman.2022.104490

M3 - Journal article

AN - SCOPUS:85122628099

SN - 0261-5177

VL - 90

JO - Tourism Management

JF - Tourism Management

M1 - 104490

ER -

Tourism demand forecasting using tourist-generated online review data

Abstract

Keywords

ASJC Scopus subject areas

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