Technological progress and biases in airline productivity: A network MPI analysis under external disruptions

This study aims to investigate the significant role of technological progress and its biases among different factors and outputs within a two-stage network production technology structure in influencing productivity changes in airlines. To address variations in productivity under external disruptions and market fluctuations, this study develops a novel network Malmquist Productivity Index (MPI) decomposition model, identifying both inter-group biases between inputs and outputs and intra-group biases among input factors and outputs. Analyzing data from 2015 to 2021 for 16 global airlines, the study measures productivity changes and their components within the network production framework. The technological change is decomposed to analyze the bias characteristics of input factors such as labor and operational expenses, intermediate products like available seat kilometers and available freight ton kilometers, and output factors including revenue passenger kilometers and revenue freight ton kilometers. The findings indicate that efficiency improvements and technological progress are primary drivers of productivity growth, while output-biased technological change limits this growth. Efficiency improvements, increased magnitude of technological change, and input-biased technological change in the service stage contribute to overall productivity gains, mitigating the negative impact of efficiency deterioration and reduced technological change in the production stage. Notably, productivity regressed during 2020–2021, reflecting operational disruptions caused by external shocks such as the COVID-19 pandemic. These results suggest that targeted technological advancements and efficiency improvements in specific production and service stages can significantly influence overall productivity. The study provides strategic insights for airlines to navigate disruptions, emphasizing the importance of understanding and managing technological biases to enhance productivity.