隨著全球化與國際經貿活動之發展，航空運輸已成為支撐國際旅運與全球物流體系之重要基礎設施，其營運效率與資源配置表現對整體經濟發展具有關鍵影響。然而，航空公司屬於高度資本密集且營運結構複雜之產業，傳統單一績效指標已難以全面反映其經營表現。此外，COVID-19 疫情對航空產業造成重大衝擊，使客運與貨運需求呈現不同變動趨勢，進一步凸顯區分不同產出面向進行效率分析之必要性。
本研究以 2021 年至 2024 年間 17 家國際航空公司為研究對象，蒐集其公開財務與營運資料，並採用動態SBM DEA模型（Slacks-Based Measure Data Envelopment Analysis）進行效率評估。
實證結果顯示，多數航空公司於疫情後呈現效率回升趨勢，但不同公司之效率表現仍存在顯著差異。整體而言，客運效率普遍高於貨運效率，顯示貨運業務在需求波動與營運不確定性方面具有較高風險。此外，大型航空公司通常在運能配置與產出規模上具備優勢，而新興或低成本航空公司則透過精簡資源配置維持營運彈性。

Air transportation plays a vital role in facilitating global mobility, international trade, and economic development. As one of the most capital-intensive industries, airlines require substantial investments in aircraft, infrastructure, human resources, and operational facilities while operating under highly competitive and dynamic market conditions. Consequently, improving operational efficiency has become an important objective for airline managers, policymakers, and researchers. However, airline performance cannot be adequately evaluated using a single financial or operational indicator because airlines simultaneously consume multiple resources and generate diverse outputs through passenger and cargo transportation. Furthermore, airline operations are influenced by various external factors, including economic fluctuations, fuel price volatility, regulatory policies, technological developments, and market competition, making efficiency evaluation increasingly complex.
The COVID-19 pandemic dramatically changed the global aviation industry by causing an unprecedented decline in passenger demand, disrupting international travel, and altering cargo transportation patterns. Although the industry has gradually recovered since 2021, airlines have demonstrated different recovery speeds and operational performances due to variations in business models, fleet structures, resource allocation, and strategic responses. These changes have highlighted the limitations of traditional efficiency evaluation methods that focus solely on aggregate operational outcomes without considering heterogeneous business characteristics or dynamic changes over time.
Accordingly, this study evaluates airline operational efficiency from a multidimensional perspective by distinguishing passenger and cargo operations while incorporating dynamic efficiency changes across multiple periods. The study aims to provide a more comprehensive understanding of airline operational performance during the post-pandemic recovery period and to offer useful managerial implications for improving resource allocation, operational strategies, and long-term competitiveness under an increasingly uncertain business environment.

Badanik, B., Remenysegova, R., & Kazda, A. (2023). Sentimental Approach to Airline Service Quality Evaluation. Aerospace, 10(10), Article 883.
Borsati, M., & Fageda, X. (2024). Airline price responses in the face of demand shocks: European lessons from the COVID-19 pandemic. Transportation Research Part E-Logistics and Transportation Review, 186, Article 103537.
Choi, K., Lee, D., & Olson, D. (2015). Service quality and productivity in the US airline industry: a service quality-adjusted DEA model. Service Business, 9(1), 137–160.
Chen, Z., Wanke, P., Antunes, J., & Zhang, N. (2017). Chinese airline efficiency under CO2 emissions and flight delays: A stochastic network DEA model. Energy Economics, 68, 89–108.
Cui, Q., & Yu, L. (2021). A Review of Data Envelopment Analysis in Airline Efficiency: State of the Art and Prospects. Journal of Advanced Transportation, 2021, Article 2931734.
Davras, Ö., & Davras, G. (2025). Uncovering the asymmetric effects of airline service attributes on passenger satisfaction: Evidence from Pegasus Airlines in Turkey. Research in Transportation Business and Management, 63, Article 101488.
Greer, M. (2016). AIRLINE MERGERS IN THE UNITED STATES SINCE 2005: WHAT IMPACT HAVE THEY HAD ON AIRLINE EFFICIENCY? In J. D. Bitzan, J. H. Peoples, & W. W. Wilson (Eds.), Airline Efficiency (Vol. 5, pp. 161–195).
Gualini, A., Zou, L., & Dresner, M. (2023). Airline strategies during the pandemic: What worked? Transportation Research Part a-Policy and Practice, 170, Article 103625.
Ganji, S., Tirkolaee, E., Bandari, S., & Ajirlu, S. (2025). Incorporation of prospect theory into double-frontier cross-efficiency: a case study of Iranian airline. International Jo
Hu, J.-L., & Wang, S.-C. (2006). Total-factor energy efficiency of regions in China. Energy. policy, 34(17), 3206-3217.
Hermoso, R., Latorre, M., & Martinez-Nuñez, M. (2019). Multivariate Data Envelopment Analysis to Measure Airline Efficiency in European Airspace: A Network-Based Approach. Applied Sciences-Basel, 9(24), Article 5312.
Hien, N., Long, N., Liem, V., & Luu, D. (2024). Customer Responses to Airline Service Failure: Perspectives from Expectation Disconfirmation Theory. Sage Open, 14(2), Article 21582440241248334.
Hu, X., Sy, M., & Wu, L. (2025). Dynamic Optimality of Airline Fuel Cost Hedging. Journal of Derivatives, 33(2), 28–51.
Inci, G., & Polat, S. (2025). Efficiency Analysis and Classification of an Airline's Email Campaigns Using DEA and Decision Trees. Information, 16(11), Article 969.
Jaroenjitrkam, A., Kotcharin, S., & Maneenop, S. (2023). Corporate resilience to the COVID-19 pandemic: Evidence from the airline industry. Asian Journal of Shipping and Logistics, 39(4), 26–36.
Khezrimotlagh, D., & Kaffash, S. (2025). Navigating Efficiency: Insights from One-Stage and Two-Stage DEA Modeling in the Airline Industry. Annals of Operations Research, 351(2), 1451–1487.
Losa, E.T., Arjomandi, A., Dakpo, K.H., Bloomfield, J., (2020). Efficiency comparison of airline groups in Annex 1 and non-Annex 1 countries: a dynamic network DEA approach. Transp. Policy 99, 163-174.
Li Y, Lin T-Y, Chiu Y, Lin S-N,Chang T-H (2021). Impact of alliances and delay rate on airline performance. Manage Decis Econ. 2021;42:1607–1618.
Lee, C., & Leung, E. (2024). Designing Predictive Models for Customer Recommendations During COVID-19 in the Airline Industry. Ieee Transactions on Engineering Management, 71, 13274–13284.
Lee, J., & Yoon, S. (2024). Growing stakeholderism in the airline industry after the COVID-19 pandemic. Journal of Air Transport Management, 116, Article 102574.
Lee, J., Yu, J., Radic, A., & Han, H. (2024). Uncovering air traveler purchase behavior: Influence of airline goods product characteristics towards repurchase intention. Journal of Retailing and Consumer Services, 79, Article 103858.
Li, Y., Zheng, J., & Zhang, Y. (2024). Airline environmental efficiency measures through a non-convex meta-frontier DEA model. Energy Efficiency, 17(8), Article 103.
Merkert, R., & Hensher, D. (2011). The impact of strategic management and fleet planning on airline efficiency - A random effects Tobit model based on DEA efficiency scores. Transportation Research Part a-Policy and Practice, 45(7), 686–695.
Merkert, R., & Pearson, J. (2015). A Non-parametric Efficiency Measure Incorporating Perceived Airline Service Levels and Profitability. Journal of Transport Economics and Policy, 49, 261–275.
Maneenop, S., & Kotcharin, S. (2020). The impacts of COVID-19 on the global airline industry: An event study approach. Journal of Air Transport Management, 89, Article 101920.
Murugesan, R., Rekha, A., Nitish, N., & Balanathan, R. (2024). Forecasting airline passengers' satisfaction based on sentiments and ratings: An application of VADER and machine learning techniques. Journal of Air Transport Management, 120, Article 102668.
Omrani, H., Fahimi, P., & Emrouznejad, A. (2022). A common weight credibility data envelopment analysis model for evaluating decision making units with an application in airline performance. Rairo-Operations Research, 56(2), 911–930.
Rashid, A., See, K., & Yu, M. (2024). Measuring airline efficiency using a dynamic network data envelopment analysis in the presence of innovation capital. Technological Forecasting and Social Change, 206, Article 123457.
Ragab, H., Polo-Peña, A., & Mahrous, A. (2024). The effect of airline service quality, perceived value, emotional attachment, and brand loyalty on passengers' willingness to pay: The moderating role of airline origin. Case Studies on Transport Policy, 18, Article 101313.
Raad, N., Fick, J., Shah, N., & Srinivas, S. (2025). From voices to value: Leveraging online reviews to benchmark airline efficiency with GenAI-assisted text analytics and DEA. Journal of the Operational Research Society.
Seth, B., Saxena, P., & Arora, S. (2021). Efficiency Assessment of Indian Passenger Airlines Using Two-Stage Efficiency Model: Non Traditional DEA Approach. International Journal of Modelling and Simulation, 41(5), 351–358.
Sezgen, E., Mason, K., & Mayer, R. (2023). Airline brand management: A practical perspective to brand management in the airline industry. Research in Transportation Business and Management, 49, Article 100985.
Sahin, I., & Ates, S. (2025). The relationship between fleet structures and business models of combined airline cargo companies: a comparison with apriori algorithm. International Journal of Shipping and Transport Logistics, 21(1), 128–151.
Sun, X., Zheng, C., Wang, K., & Wandelt, S. (2025). The art of airline cessation: A comprehensive analysis based on airline ceases reported between the years 2010 and 2023. Journal of Air Transport Management, 124, Article 102748.
Sun, X., Zheng, C., Fu, X., Dresner, M., Zhang, A., & Wandelt, S. (2026). Global airline productivity analysis for the year 2022. Journal of Air Transport Management, 133, Article 102955.
Tone, K., & Tsutsui, M. (2010). An epsilon-based measure of efficiency in DEA – A third pole of technical efficiency. European Journal of Operational Research, 207(3), 1554-1563. https://doi.org/10.1016/j.ejor.2010.07.014
Wang, X., Zheng, J., Tang, L., & Luo, Y. (2023). Recommend or not? The influence of emotions on passengers? intention of airline recommendation during COVID-19. Tourism Management, 95, Article 104675.
Wang, K., Wan, Y., Jiang, C., & Gu, H. (2025). Sourcing strategies for disruption recovery: Empirical evidence from the US airline industry. Transport Policy, 167, 145–156.
Wendel, L., Albers, S., & Dewulf, W. (2025). Towards a typology of airline vertical strategies. Journal of Air Transport Management, 127, Article 102810.
Wu, X., Wang, K., Fu, X., Dong, K., Sun, X., & Oum, T. (2025). How does COVID-19 pandemic affect airline's route choice and market contact? - Full-service carriers vs. low-cost carriers in China. Transportation Research Part a-Policy and Practice, 191, Article 104291.
Xu, Y., Wandelt, S., & Sun, X. (2024). Airline scheduling optimization: literature review and a discussion of modelling methodologies†. Intelligent Transportation Infrastructure, 3, Article liad026.
Zhang, A., & Zhang, Y. (2021). Is it time for an integrated aviation market in Northeast Asia? An airline performance perspective. Transport Policy, 110, 161–169.
Zhang, H., Wu, W., Jiang, Y., & Chen, X. (2024). Flight delay propagation in the multiplex network system of airline networks. Physica a-Statistical Mechanics and Its Applications, 648, Article 129883.