IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v15y2023i18p13582-d1237639.html
   My bibliography  Save this article

A Lasso and Ridge-Cox Proportional Hazard Model Analysis of Thai Tourism Businesses’ Resilience and Survival in the COVID-19 Crisis

Author

Listed:
  • Supareuk Tarapituxwong

    (Faculty of Management Sciences, Chiang Mai Rajabhat University, Chiang Mai 50200, Thailand)

  • Namchok Chimprang

    (Center of Excellence in Econometrics, Faculty of Economics, Chiang Mai University, Chiang Mai 50200, Thailand)

  • Woraphon Yamaka

    (Center of Excellence in Econometrics, Faculty of Economics, Chiang Mai University, Chiang Mai 50200, Thailand)

  • Piangtawan Polard

    (Faculty of Management Sciences, Chiang Mai Rajabhat University, Chiang Mai 50200, Thailand)

Abstract

This study aims to investigate the factors contributing to the survivability of Thai tourism businesses during the COVID-19 pandemic. In December 2021, a comprehensive survey was conducted among 400 tourism businesses across Thailand, coinciding with the heightening impact of the ongoing COVID-19 crisis. The study explores the perceptions of tourism businesses regarding the impact of COVID-19 and its influence on their chances of survival. To address this issue, the study employs the Lasso and Ridge Cox proportional hazards models. The findings reveal several significant factors. Firstly, businesses located in the Southern region, operating without physical premises and generating a substantial annual net income, face a lower risk of failure. Secondly, implementing strategies that prioritize consistent working hours and regular schedules, and reducing reliance on part-time employees, positively contribute to survival chances. Additionally, governments can effectively monitor high-risk businesses based on entrepreneurs’ perception of failure risk and offer targeted assistance. Moreover, businesses targeting domestic tourists and engaging in import and export activities within their supply chains demonstrate higher survivability rates. The availability of raw materials and entrepreneurs’ anticipation of a longer recovery time also play crucial roles in business survival. Government relief measures, such as tax relief and reduced Social Security Fund contributions, effectively increase the probability of business survival. Finally, timely adaptations and support within the initial period of from six months to a year are essential for building resilience in the face of challenges.

Suggested Citation

  • Supareuk Tarapituxwong & Namchok Chimprang & Woraphon Yamaka & Piangtawan Polard, 2023. "A Lasso and Ridge-Cox Proportional Hazard Model Analysis of Thai Tourism Businesses’ Resilience and Survival in the COVID-19 Crisis," Sustainability, MDPI, vol. 15(18), pages 1-22, September.
  • Handle: RePEc:gam:jsusta:v:15:y:2023:i:18:p:13582-:d:1237639
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/15/18/13582/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/2071-1050/15/18/13582/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Woraphon Yamaka & Paravee Maneejuk & Rungrapee Phadkantha & Wiranya Puntoon & Payap Tarkhamtham & Tatcha Sudtasan, 2023. "Survival and Duration Analysis of MSMEs in Chiang Mai, Thailand: Evidence from the Post-COVID-19 Recovery," Mathematics, MDPI, vol. 11(4), pages 1-21, February.
    2. Manuel Adelino & Song Ma & David Robinson, 2017. "Firm Age, Investment Opportunities, and Job Creation," Journal of Finance, American Finance Association, vol. 72(3), pages 999-1038, June.
    3. Aljughaiman, Abdullah A. & Nguyen, Tam Huy & Trinh, Vu Quang & Du, Anqi, 2023. "The Covid-19 outbreak, corporate financial distress and earnings management," International Review of Financial Analysis, Elsevier, vol. 88(C).
    4. Gémar, Germán & Moniche, Laura & Morales, Antonio J., 2016. "Survival analysis of the Spanish hotel industry," Tourism Management, Elsevier, vol. 54(C), pages 428-438.
    5. Hui Zou & Trevor Hastie, 2005. "Addendum: Regularization and variable selection via the elastic net," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 67(5), pages 768-768, November.
    6. Muhammad Umar Farooq & Amjad Hussain & Tariq Masood & Muhammad Salman Habib, 2021. "Supply Chain Operations Management in Pandemics: A State-of-the-Art Review Inspired by COVID-19," Sustainability, MDPI, vol. 13(5), pages 1-33, February.
    7. Hui Zou & Trevor Hastie, 2005. "Regularization and variable selection via the elastic net," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 67(2), pages 301-320, April.
    8. M Arashi & M Roozbeh & N A Hamzah & M Gasparini, 2021. "Ridge regression and its applications in genetic studies," PLOS ONE, Public Library of Science, vol. 16(4), pages 1-17, April.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Tutz, Gerhard & Pößnecker, Wolfgang & Uhlmann, Lorenz, 2015. "Variable selection in general multinomial logit models," Computational Statistics & Data Analysis, Elsevier, vol. 82(C), pages 207-222.
    2. Oxana Babecka Kucharcukova & Jan Bruha, 2016. "Nowcasting the Czech Trade Balance," Working Papers 2016/11, Czech National Bank.
    3. Carstensen, Kai & Heinrich, Markus & Reif, Magnus & Wolters, Maik H., 2020. "Predicting ordinary and severe recessions with a three-state Markov-switching dynamic factor model," International Journal of Forecasting, Elsevier, vol. 36(3), pages 829-850.
    4. Hou-Tai Chang & Ping-Huai Wang & Wei-Fang Chen & Chen-Ju Lin, 2022. "Risk Assessment of Early Lung Cancer with LDCT and Health Examinations," IJERPH, MDPI, vol. 19(8), pages 1-12, April.
    5. Margherita Giuzio, 2017. "Genetic algorithm versus classical methods in sparse index tracking," Decisions in Economics and Finance, Springer;Associazione per la Matematica, vol. 40(1), pages 243-256, November.
    6. Nicolaj N. Mühlbach, 2020. "Tree-based Synthetic Control Methods: Consequences of moving the US Embassy," CREATES Research Papers 2020-04, Department of Economics and Business Economics, Aarhus University.
    7. Wang, Qiao & Zhou, Wei & Cheng, Yonggang & Ma, Gang & Chang, Xiaolin & Miao, Yu & Chen, E, 2018. "Regularized moving least-square method and regularized improved interpolating moving least-square method with nonsingular moment matrices," Applied Mathematics and Computation, Elsevier, vol. 325(C), pages 120-145.
    8. Dmitriy Drusvyatskiy & Adrian S. Lewis, 2018. "Error Bounds, Quadratic Growth, and Linear Convergence of Proximal Methods," Mathematics of Operations Research, INFORMS, vol. 43(3), pages 919-948, August.
    9. Mkhadri, Abdallah & Ouhourane, Mohamed, 2013. "An extended variable inclusion and shrinkage algorithm for correlated variables," Computational Statistics & Data Analysis, Elsevier, vol. 57(1), pages 631-644.
    10. Lucian Belascu & Alexandra Horobet & Georgiana Vrinceanu & Consuela Popescu, 2021. "Performance Dissimilarities in European Union Manufacturing: The Effect of Ownership and Technological Intensity," Sustainability, MDPI, vol. 13(18), pages 1-19, September.
    11. Candelon, B. & Hurlin, C. & Tokpavi, S., 2012. "Sampling error and double shrinkage estimation of minimum variance portfolios," Journal of Empirical Finance, Elsevier, vol. 19(4), pages 511-527.
    12. Susan Athey & Guido W. Imbens & Stefan Wager, 2018. "Approximate residual balancing: debiased inference of average treatment effects in high dimensions," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 80(4), pages 597-623, September.
    13. Andrea Carriero & Todd E. Clark & Massimiliano Marcellino, 2022. "Specification Choices in Quantile Regression for Empirical Macroeconomics," Working Papers 22-25, Federal Reserve Bank of Cleveland.
    14. Kim, Hyun Hak & Swanson, Norman R., 2018. "Mining big data using parsimonious factor, machine learning, variable selection and shrinkage methods," International Journal of Forecasting, Elsevier, vol. 34(2), pages 339-354.
    15. Shuichi Kawano, 2014. "Selection of tuning parameters in bridge regression models via Bayesian information criterion," Statistical Papers, Springer, vol. 55(4), pages 1207-1223, November.
    16. Yize Zhao & Matthias Chung & Brent A. Johnson & Carlos S. Moreno & Qi Long, 2016. "Hierarchical Feature Selection Incorporating Known and Novel Biological Information: Identifying Genomic Features Related to Prostate Cancer Recurrence," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 111(516), pages 1427-1439, October.
    17. Chuliá, Helena & Garrón, Ignacio & Uribe, Jorge M., 2024. "Daily growth at risk: Financial or real drivers? The answer is not always the same," International Journal of Forecasting, Elsevier, vol. 40(2), pages 762-776.
    18. Enrico Bergamini & Georg Zachmann, 2020. "Exploring EU’s Regional Potential in Low-Carbon Technologies," Sustainability, MDPI, vol. 13(1), pages 1-28, December.
    19. Qianyun Li & Runmin Shi & Faming Liang, 2019. "Drug sensitivity prediction with high-dimensional mixture regression," PLOS ONE, Public Library of Science, vol. 14(2), pages 1-18, February.
    20. Jung, Yoon Mo & Whang, Joyce Jiyoung & Yun, Sangwoon, 2020. "Sparse probabilistic K-means," Applied Mathematics and Computation, Elsevier, vol. 382(C).

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jsusta:v:15:y:2023:i:18:p:13582-:d:1237639. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.