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Impact of the COVID-19 Pandemic on the Revenue of the Catering Industry: Taiwan as an Example

Author

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  • Hsin-Chieh Wu
  • Tin-Chih Toly Chen
  • Syuan Yu Wang

Abstract

Due to the impact of the COVID-19 pandemic, people have reduced eating out, resulting in a severe drop in the revenue of the catering industry. Health risks have become a major factor affecting the revenue of this industry. Predicting the revenue of the catering industry during the COVID-19 pandemic will not only allow practitioners to adjust their business strategies, but also provide a reference for governments to formulate relief measures. To this end, this study proposes a fuzzy big data analytics approach in which random forests, recursive feature elimination, fuzzy c-means, and deep neural networks are jointly applied. First, random forests and recursive feature elimination are used to select the most influential factors. The data is then divided into clusters by fuzzy c-means. Subsequently, a deep neural network is built for each cluster to make predictions. The prediction results of individual clusters are then aggregated to improve prediction accuracy. The proposed methodology has been applied to forecast the revenue of the catering industry in Taiwan. The results of the experiment showed that the impact of new deaths on the revenue of the catering industry was far greater than the number of newly diagnosed COVID-19 cases.

Suggested Citation

  • Hsin-Chieh Wu & Tin-Chih Toly Chen & Syuan Yu Wang, 2024. "Impact of the COVID-19 Pandemic on the Revenue of the Catering Industry: Taiwan as an Example," SAGE Open, , vol. 14(2), pages 21582440241, May.
    • Handle: RePEc:sae:sagope:v:14:y:2024:i:2:p:21582440241241410
    DOI: 10.1177/21582440241241410
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    References listed on IDEAS

    as
    1. Taejung Park & Chayoung Kim, 2020. "Predicting the Variables That Determine University (Re-)Entrance as a Career Development Using Support Vector Machines with Recursive Feature Elimination: The Case of South Korea," Sustainability, MDPI, vol. 12(18), pages 1-11, September.
    2. Hsin-Chieh Wu & Yu-Cheng Wang & Tin-Chih Toly Chen, 2020. "Assessing and Comparing COVID-19 Intervention Strategies Using a Varying Partial Consensus Fuzzy Collaborative Intelligence Approach," Mathematics, MDPI, vol. 8(10), pages 1-23, October.
    3. Yu-Cheng Lin & Yu-Cheng Wang & Tin-Chih Toly Chen & Hai-Fen Lin, 2019. "Evaluating the Suitability of a Smart Technology Application for Fall Detection Using a Fuzzy Collaborative Intelligence Approach," Mathematics, MDPI, vol. 7(11), pages 1-21, November.
    4. Yu-Cheng Wang & Horng-Ren Tsai & Toly Chen, 2021. "A Selectively Fuzzified Back Propagation Network Approach for Precisely Estimating the Cycle Time Range in Wafer Fabrication," Mathematics, MDPI, vol. 9(12), pages 1-18, June.
    5. Min-Chi Chiu & Tin-Chih Toly Chen & Keng-Wei Hsu, 2020. "Modeling an Uncertain Productivity Learning Process Using an Interval Fuzzy Methodology," Mathematics, MDPI, vol. 8(6), pages 1-18, June.
    6. Yu-Cheng Wang & Tin-Chih Toly Chen, 2019. "A Partial-Consensus Posterior-Aggregation FAHP Method—Supplier Selection Problem as an Example," Mathematics, MDPI, vol. 7(2), pages 1-15, February.
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