Productivity and Efficiency Measurement of Airlines (eBook)

In today's competitive environment, airlines are doing everything they can to improve efficiency and productivity. Productivity and Efficiency Measurement of Airlines: Data Envelopment Analysis using R identifies and explains sources of airline efficiency and helps achieve these goals through the use of state-of-the-art measurement techniques. Each chapter measures airline performance through the data envelopment analysis (DEA) model and other DEA variants. This book thoroughly discusses topics such as cost and revenue efficiency performance, carbon emissions performance management, and complex airline data analysis, employing appropriate models for each. Model methodologies are also discussed. The in-depth coverage is useful for all audiences, including students with a basic understanding of models, researchers and airline operators and management. Productivity and Efficiency Measurement of Airlines: Data Envelopment Analysis using R provides R codes to help readers generate results and quantify efficient practices. These results provide airline decision-makers with the essential information they need to create better policies and avoid underperforming practices. - Thoroughly summarizes key DEA measurement models for productivity and efficiency ofairlines - Guides users in generating airline performance results using DEA model and its variants - Features R codes useful for generating empirical results, and best practices, promoting qualitypolicy and management decisions

Boon Lee is Associate Professor in economics at the Queensland University of Technology, Faculty of Business and Law, in Brisbane, Australia. His primary research interest focuses on eco-efficiency, efficiency and productivity analysis with a focus on key service industries, such as the airline industry and higher education. He is experienced in the application of data envelopment analysis (DEA) in measuring efficiency of firms and has published several articles on efficiency and productivity of airlines, including in Elsevier'sJournal of Air Transport Management. He has widely published in international journals including Journal of Development Economics, Energy Economics, Tourism Management, OMEGA, Economics of Education Review, Journal of Productivity Analysis, Agricultural Economics, Ecological Economics, Land Use Policy and Journal of Transport Economics and Policy. He is a certified facilitator of the LEGO® SERIOUS PLAY® method and materials. He is Senior Fellow of the Higher Education Academy (HEA) and AssociateFellow of the HEA (Indigenous).

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In today's competitive environment, airlines are doing everything they can to improve efficiency and productivity. Productivity and Efficiency Measurement of Airlines: Data Envelopment Analysis using R identifies and explains sources of airline efficiency and helps achieve these goals through the use of state-of-the-art measurement techniques. Each chapter measures airline performance through the data envelopment analysis (DEA) model and other DEA variants. This book thoroughly discusses topics such as cost and revenue efficiency performance, carbon emissions performance management, and complex airline data analysis, employing appropriate models for each. Model methodologies are also discussed. The in-depth coverage is useful for all audiences, including students with a basic understanding of models, researchers and airline operators and management. Productivity and Efficiency Measurement of Airlines: Data Envelopment Analysis using R provides R codes to help readers generate results and quantify efficient practices. These results provide airline decision-makers with the essential information they need to create better policies and avoid underperforming practices. - Thoroughly summarizes key DEA measurement models for productivity and efficiency ofairlines- Guides users in generating airline performance results using DEA model and its variants- Features R codes useful for generating empirical results, and best practices, promoting qualitypolicy and management decisions