Data-Driven Business Analytics for the Tourism Industry in the UK: A Machine Learning Experiment Post-COVID

Authors: Obogo, J.U. and Adedoyin, F.F.

Journal: Proceedings - 2021 IEEE 23rd Conference on Business Informatics, CBI 2021 - Main Papers

Volume: 2

Pages: 78-86

ISBN: 9781665420693

DOI: 10.1109/CBI52690.2021.10058

Abstract:

The use of data-driven business analytic models has had a significant impact on several sectors of the economy. In the UK, the tourism industry has contributed significantly to the economy. The contribution of tourism to the UK economy is estimated to be £145.9 billion (7.2%) of UK GDP. Regardless of its economic value, tourism is also one of the most vulnerable sectors, as it is susceptible to natural disasters, civil unrest, crisis, and pandemics, all of which can fully shut down the industry. Hence, an accurate and reliable tourism demand forecast is important. Apart from COVID-19, no other occurrence in modern history has had such a broad impact on the economy, industries, everyone and businesses in the world (Galvani et al., 2020). However, with the impact of COVID19 on the industry, it is imperative to reassess potential recovery plans for the UK economy, particularly for local tourism businesses. Macroeconomic data is collected over many source markets for the UK and a machine learning algorithm is tested to assess the future of the industry.

http://eprints.bournemouth.ac.uk/36295/

Source: Scopus

Data-Driven Business Analytics for the Tourism Industry in the UK: A Machine Learning Experiment Post-COVID

Authors: Adedoyin, F.

Conference: 2021 IEEE 23rd Conference on Business Informatics (CBI)

Dates: 1-3 September 2021

DOI: 10.1109/CBI52690.2021.10058

Abstract:

The use of data-driven business analytic models has had a significant impact on several sectors of the economy. In the UK, the tourism industry has contributed significantly to the economy. The contribution of tourism to the UK economy is estimated to be £145.9 billion (7.2%) of UK GDP. Regardless of its economic value, tourism is also one of the most vulnerable sectors, as it is susceptible to natural disasters, civil unrest, crisis, and pandemics, all of which can fully shut down the industry. Hence, an accurate and reliable tourism demand forecast is important. Apart from COVID-19, no other occurrence in modern history has had such a broad impact on the economy, industries, everyone and businesses in the world (Galvani et al., 2020). However, with the impact of COVID19 on the industry, it is imperative to reassess potential recovery plans for the UK economy, particularly for local tourism businesses. Macroeconomic data is collected over many source markets for the UK and a machine learning algorithm is tested to assess the future of the industry.

http://eprints.bournemouth.ac.uk/36295/

Source: Manual

Data-Driven Business Analytics for the Tourism Industry in the UK: A Machine Learning Experiment Post-COVID

Authors: Obogo, J.U. and Adedoyin, F.F.

Conference: 2021 IEEE 23rd Conference on Business Informatics (CBI)

ISBN: 978-1-6654-2069-3

ISSN: 2378-1971

Abstract:

The use of data-driven business analytic models has had a significant impact on several sectors of the economy. In the UK, the tourism industry has contributed significantly to the economy. The contribution of tourism to the UK economy is estimated to be £145.9 billion (7.2%) of UK GDP. Regardless of its economic value, tourism is also one of the most vulnerable sectors, as it is susceptible to natural disasters, civil unrest, crisis, and pandemics, all of which can fully shut down the industry. Hence, an accurate and reliable tourism demand forecast is important. Apart from COVID-19, no other occurrence in modern history has had such a broad impact on the economy, industries, everyone and businesses in the world (Galvani et al., 2020). However, with the impact of COVID19 on the industry, it is imperative to reassess potential recovery plans for the UK economy, particularly for local tourism businesses. Macroeconomic data is collected over many source markets for the UK and a machine learning algorithm is tested to assess the future of the industry.

http://eprints.bournemouth.ac.uk/36295/

https://ieeexplore.ieee.org/xpl/conhome/9610156/proceeding

Source: BURO EPrints