Price dispersion, competition, and the role of online travel agents: Evidence from business routes in the Italian airline market

Electronic Commerce Research and Applications

Ragaglia

2016

Self Cite

a b s t r a c tIn this paper, we empirically examine how the revenue model (paid, free, or freemium) adopted for a given app affects the app revenue performance as measured by the app daily revenue rank. We also study the impact of in-app purchase on this measure of performance. Moreover, we study how such relationships are contingent upon the distribution platform where the app is marketed as well as the type of category to which the app belongs. We test our hypotheses relying on a large sample of top grossing apps from the two major app stores, namely Apple's App Store and Google Play. Our findings reveal that in the Apple's App Store, paid and freemium models are equivalent and both are more effective than the free model in terms of app revenue performance. On the other hand, in Google Play no significant differences between paid and free revenue models emerge, whereas the freemium model is shown to be less effective even than the free model. Moreover, while in-app purchase is shown to positively influence the app revenue performance in Apple's App Store, this effect is reversed in Google Play. Finally, the type of category is also shown to influence the effects of the revenue model and in-app purchase (the latter to a lesser extent) on the app performance.

Section: Implications For the Existing Literaturementioning

confidence: 99%

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

Electronic Commerce Research and Applications

Ragaglia

2016

Self Cite

“…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).…”

Section: Resultsmentioning

confidence: 99%

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

Dominici

2016

IJEMR

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Abstract:In this paper, we take the perspective of app developers. Specifically, based on a sample of top paid apps from three major app stores, i.e., App Store, Google Play, and Blackberry World, we construct a hedonic price model to examine the role of relevant factors in price formation in the app market. Our results suggest a strong evidence of two-sided market effects. In fact, the lower price charged for apps operating as two-sided markets reflect the strategy of subsidising users, due to the positive cross-side externalities they exert on valuable third parties. Surprisingly, the effects of trialability, in-app purchase and mechanisms to build reputation are not significant in the context of successful apps. Finally, we find weak evidence that developers of top paid apps prefer price skimming to penetration price strategies.

“…Second, even with regard to the random effect model, IVs different from those suggested by Borenstein and Rose (1994) and Gerardi and Shapiro (2009) and already adopted in this paper, might be utilised in the future. Also more recently, some studies have considered inverted U-shaped behaviour of price dispersion with competition (e.g., Dai et al, 2014;Roma et al, 2014). Third, from a broader perspective, collecting data samples relative to business trips with extended stay (i.e., more than one day) or even data from the leisure segment could really help increase the robustness of the main insights provided in this study.…”

Section: Managerial Implications and Conclusionmentioning

confidence: 91%

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

Zambuto

Dominici

2015

IJEMR

Self Cite

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.