Performance measurement of 6 main public airports of Thailand : The impact of pre- and post- COVID-19 pandemic crisis

Name: Nattawat Benjaparn

keyword: Air transportation

; Performance measurement

; Linear programming

; Data envelopment analysis (DEA)

; Malmquist total factor productivity index (MPI)

; Simar and Wilson bootstrapping regression

; Aviation industry

Abstract: Tourism is the main factor to drive the GDP of Thailand since 1995. The revenue from the tourism sector is more than 15 percent of GDP every year since 2015. The components to support the sustainable development of Tourism are infrastructure, hotel industry, favorable government policy, and trade fairs. This thesis focuses only on infrastructure as the airport sector by measuring the full performances of the 6 main public airports in Thailand. These airports include Suvarnabhumi Airport (BKK) in Samut Prakan, Don Mueang International Airport (DMK) in Bangkok, Phuket International Airport (HKT) in Phuket, Chiang Mai International Airport (CNX) in Chiang Mai, Hat-Yai International Airport (HDY) in Songkhla, and Mae Fah Luang-Chiang Rai International Airport (CEI) in Chiang Rai. These airports have handled more than 80 percent of all passengers in Thailand’s airports every year since 2008. All airports are operated by the Airports of Thailand Public Company Limited, one of the biggest Thai Companies. The thesis contains 2 major analysis parts. The first part analyses the full performance measurement of the airports between 2007 to 2020 by employing the data envelopment analysis (DEA), Malmquist total factor productivity index (MPI), and Simar and Wilson Bootstrapping regression. The DEA model is employed to measure the technical efficiency scores of the airports. The MPI model measures productivity growths of the airports by decomposing into the technical efficiency change (TEC) and technical change (TC). The Simar-Wilson model employs to test which micro and macro factors will affect the airports’ efficiency scores. The findings of this part report the airport hubs can perform better than non-airport hubs. Both percent of international passenger and low-cost carriers (LCCs) movements increased the airports’ efficiency scores. The macro shocks of the global financial crisis between 2008 to 2009 and the COVID-19 pandemic in 2020 declined the performances of the airports. The airports had the trends of technical efficiency and total factor productivity progress since 2007, but these values had dropped more than 40 percent in 2020. The second part forecasts the recovery period of the airports after the COVID-19 occurred in 2020 for the next 10 years by predicting the efficiency scores and productivity changes of the airports between 2021 to 2030. The findings of this part report that every airport except BKK and HDY will spend at least 6 years to perform the same as in 2019. In the post-Covid-19 period, taking the advantage of new technologies will be the main factor to drive productivity growth. In contrast, the airports tend to regress in the working system in labor. Lastly, the findings in thesis can suggest various policies to the airports for transforming the traditional airports into smart airports within a few years after the pandemic. The policies include setting the new working systems such as Agile, Lean, and Talent density and adopting new technologies such as the internet of things (IOT), big data, and artificial intelligence (AI) to improve TEC and TC, respectively. These suggestions will help the airports in reducing the time for transformation

Thammasat University. Thammasat University Library

Address: BANGKOK

Email: preserv@tu.ac.th

Name: Supawat Rungsuriyawiboon

Role: advisor

Created: 2021

Modified: 2023-06-09

Issued: 2023-06-09

วิทยานิพนธ์/Thesis

application/pdf

DOI: 10.14457/TU.the.2021.1008

eng

DegreeName: Master of Economics

Level: Master's Degree

Descipline: Economics

Grantor: Thammasat University

©copyrights Thammasat University

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