An empirical analysis of online price dispersion in the Italian airline industry

Abstract

Firms operating in the electronic marketplace set and adjust prices to affect demand and profitability. In service markets, such as airline markets, different prices are commonly offered by diverse firms to accommodate to a variety of market segments having particular sets of consumer attitudes. This variation in prices is the price dispersion and is based on market distinctiveness deriving from customer heterogeneity as well as the peculiar competition in the specific market arena. In this paper we use a panel dataset from the Italian airline market to investigate the role of competition and different online channels in the emergence of price dispersion. Specifically, we examine the unclear role of competition in price dispersion with novel data collected from different online channels, namely direct and Online Travel Agency (OTA) channels. We find that price dispersion is higher in routes where competition is higher even in presence of only one segment, namely the business segment. Our results also show that price dispersion significantly differs across different types of online channels.

Revenue models, in-app purchase, and the app performance: Evidence from Apple’s App Store and Google Play

“…Our findings suggest that in-app purchase is also a positive determinant of app revenue performance only in the Apple's App Store. As we have explained, this is because a segment with high willingness to pay is required to effectively practice price discrimination (Chen et al, 2014;Roma et al, 2014;Roma et al, 2015). While this segment is certainly present in Apple's App Store, it might not be well developed in Google Play, thus leading to a negative impact in terms of revenue performance in the latter store.…”

Understanding the price drivers of successful apps in the mobile app market

“…Such functional form has been shown to be effective for hedonic price estimation (Gandal, 1994;Brynjolfsson and Kemerer, 1996;Gallaugher and Wang, 2002;Roma et al, 2013) and in other related pricing studies (e.g., Roma et al, 2014Roma et al, , 2015. When the number of statistical units is quite large compared to the observation period (number of weeks), three basic regression models, namely pooled OLS, fixed effects and random effects models, are usually suggested (Wooldridge, 2002).…”

Understanding airline price dispersion in the presence of high-speed rail

“…There has been much literature examining the impact of the Internet on airline price dispersion. Roma et al (2015) note that the traditional microeconomic view would argue that the Internet and the e-commerce could foster competition and thereby led to pricing consistent with the prediction of the 'law of one price'. This view has received support in studies such as Clemons et al (2002), Baye et al (2004), Gerardi and Shapiro (2009) and Dominici (2009).…”

“…This view has received support in studies such as Clemons et al (2002), Baye et al (2004), Gerardi and Shapiro (2009) and Dominici (2009). However, Roma et al (2015) find that price dispersion differs greatly across different types of online channels. Price dispersion is found to be higher on the routes with higher competition.…”