The Impact of the Severe Acute Respiratory Syndrome (SARS) on International Airline Demand in Asia Pacific
Bibliographic record
Abstract
The aim of this dissertation is to analyse the impact of exogenous factors on efficiency of airlines based in the Asia Pacific using the Data Envelopment Analysis (DEA) approach. In measuring the efficiency of different airlines, the effects of the environment (exogenous factors) and the effects of productive efficiency are isolated. Exogenous factors refer to essentials outside the control of the firms while productive efficiency signifies the individual airline’s profitability state of affairs through a lengthy period of time. The focal point of this study revolves around the epidemic period of Severe Acute Respiratory Syndrome (SARS), the widespread which put the global airline industry and especially the Asia Pacific, into turmoil from February 2003, and lasted almost 6 months into the year. In June 2003, the height of the SARS pandemic saw passenger boardings at major hubs dwindled significantly, threatening even the most profitable airlines to file for bankruptcy. Because SARS mostly affected travel hubs such as Hong Kong, Taiwan, Bangkok, Singapore and outside of Asia Pacific, Canada, airlines included in this study are concentrated on these few airport hubs. Representing the airline industry within the Northeast Asia (NEA) region are Cathay Pacific Airways (CX) of Hong Kong and China Airlines (CI) of Taiwan. In Southeast Asia (SEA), airlines involved in this study are Singapore Airlines (SQ) and Thai Airways International (TG). Also, the basis of this study is to ascertain of these four most affected airlines, which counter the impact of SARS more efficiently and how long the lagging effect SARS has on these airlines. According to Yu (1998), there are two approaches to account for the effects of exogenous factors:\n1) Stochastic Frontier Method: a one-step procedure which includes exogenous variables directly in estimating the efficiency measures;\n2) Data Envelopment Analysis: a two-step approach which firstly estimates the relative gross efficiencies using both inputs and outputs and then, analyses the effects of exogenous variables on the gross efficiency.\nIn this study, however, the Data Envelopment Analysis technique will be employed in ascertaining the efficiency of these selected airlines in Asia Pacific during turbulent times. A comparison will be carried out between these four major carriers to establish their relative efficiency to rebound from the unprecedented impact of SARS. It will be seen how the fastest growing region, the Asia Pacific, can counter adverse effect of exogenous factors and continue with the upward trends in both passenger mass and cargo traffic while most established airlines in America and Europe spiraled downwards in times of crisis. The Malmquist DEA methods are subsequently adopted to calculated indices of total factor productivity (TFP) change, scale efficiency change,technological change, technical efficiency change and pure technical efficiency change.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".