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Halal tourism demand and firm performance forecasting: new evidence from machine learning

Author

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  • Zunaidah Sulong
  • Mohammad Abdullah
  • Mohammad Ashraful Ferdous Chowdhury

Abstract

This study forecasts both Halal tourism demand (HTD) and the financial performance of Halal tourism industry of Malaysia using machine learning. Based on the data over the period from 2009 to 2020, this study considered 338,233 tweets sentiments, and 11 Google trend keywords, firm-specific variables, and macroeconomic variables for HTD and financial performance forecasting. Out of 14 machine learning algorithms, this study found Bagged classification and regression trees method outperforms other forecasting models. The forecasting accuracy scores of HTD and firm financial performance models are 93.71% and 80.12%, respectively. The results reveal that internet data variables (Twitter & Google Trend) significantly contribute to the forecasting models. Evidently, our models functioned optimally during the COVID-19 pandemic. This study offers valuable insights for policymakers to devise sustainable Halal tourism.

Suggested Citation

  • Zunaidah Sulong & Mohammad Abdullah & Mohammad Ashraful Ferdous Chowdhury, 2023. "Halal tourism demand and firm performance forecasting: new evidence from machine learning," Current Issues in Tourism, Taylor & Francis Journals, vol. 26(23), pages 3765-3781, December.
    • Handle: RePEc:taf:rcitxx:v:26:y:2023:i:23:p:3765-3781
    DOI: 10.1080/13683500.2022.2145458
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