Modeling technical and service efficiency
A. George Assaf,
David Gillen and
Anna S. Mattila
Transportation Research Part B: Methodological, 2017, vol. 96, issue C, 113-125
Previous research on service failures, often measured by customer complaints, has not examined how organizations can measure or monitor their service efficiency. In this article, we introduce a new model that is suitable for measuring both service efficiency and technical efficiency when both bad outputs (i.e. service complaints) and good outputs (i.e. passenger trips and flights) are present. We develop our model with an output distance function, using Bayesian methods of inference organized around Markov chain Monte Carlo (MCMC). We illustrate our model with an application in the U.S. airline industry, an industry sector beset with service failures affecting both revenues and costs. We present the service inefficiency results of various US airlines and discuss the determinants of bad outputs in this industry. We also test whether our results are in line with market expectations by comparing the service efficiency estimates against the “American Customer Satisfaction Index” data.
Keywords: Service efficiency; Technical efficiency; Output distance function; U.S. airlines; Bayesian analysis; Markov chain Monte Carlo (search for similar items in EconPapers)
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