Free trial or no free trial: Optimal software product design with network effects

Cheng, Hsing Kenneth; Tang, Qian

doi:10.1016/j.ejor.2010.01.014

Cited by 165 publications

(90 citation statements)

References 11 publications

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“…In these cases, the risk of demand cannibalisation for their paid products as a consequence of the introduction of a free version might be high, resulting in a significant price reduction for the paid product. More in general, some recent studies point out possible negative effects of free trial versions on the commercial product in presence of low product differentiation between them, e.g., when there are a few differences in terms of functionalities or the trial period is not sufficiently short (Bawa and Shoemaker, 2004;Smith and Telang, 2009;Cheng and Tang, 2010;Cheng and Liu, 2012). Therefore, it is important for developers to accurately design their free trial versions, reducing the degree of substitutability to a minimum.…”

Section: Trialability Effectmentioning

confidence: 99%

“…Both theoretical and empirical studies have examined the effects of using free trials or samples on variables, such as probability of purchase, price and profit, in case of physical goods (Scott and Yalch, 1980;Goering, 1985;Bawa and Shoemaker, 2004). More recently, some authors have investigated the effects of trialability in case of information goods (Gallaugher and Wang, 2002;Cheng and Tang, 2010;Cheng and Liu, 2012). Through a trial version users can test the product and resolve the uncertainty about its real value to them.…”

Section: Trialability Effectmentioning

confidence: 99%

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

Roma

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.

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Section: Trialability Effectmentioning

confidence: 99%

Section: Trialability Effectmentioning

confidence: 99%

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

Roma

Dominici

2016

IJEMR

View full text Add to dashboard Cite

show abstract

“…According to [7], the network effect is determined by two factors, namely, its intensity and the user-installed base. We define the intensity of network effect as and use the Bass model to express the user-installed base as shown in (1).…”

Section: Consumers' Utility Given Thatmentioning

confidence: 99%

“…As another important feature, an app's value is determined not only by its inherent value but also by the network effect of the products. When influenced by a positive network effect, the value of an app to each user increases along with the size of the installed base [6,7]. Therefore, if app providers take products' consumer value into account, then the consumers' perceived value and network effect are two key factors that impact the launch timing decision of the provider.…”

Section: Introductionmentioning

confidence: 99%

Mathematical Formulation and Analysis of the Optimal Launch Timing for Mobile Applications with Perceived Value and Network Effect

et al. 2017

Mathematical Problems in Engineering

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Successive release is a common strategy adopted by mobile app providers, and determining the launch timing of new app versions presents an important challenge to these providers. Network effect and consumers' perceived value are significant factors that influence the decisions of providers. By focusing on a monopoly market, we develop an optimization model that incorporates the two factors to determine the optimal launch timing of new versions of mobile apps. The model is solved by Lagrangian method, and the closed-form results indicate that the monopoly provider launches new app versions as soon as possible if the consumers' perceived value is not sufficiently high. Otherwise, the new version is launched after (or before) the sales of its former version reach maturity if the network effect is (or not) sufficiently high. Moreover, the monopoly app vendor delays the launch of a new version when the consumers enjoy a large network externality; however, the same vendor accelerates the release of upgrades if the consumers have a high perceived value of the app. This paper presents a novel mathematical formulation to analyze the launching policy of digital products.

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“…The previous studies only addressed a very simple model, which has one side supplier and other side customer (Eisenmann et al 2006, Rochet and Tirole 2006, Hagiu 2009, Cheng and Tang 2010. However, recently some case has experience a more complicated situation over that platform-based market.…”

Section: Introductionmentioning

confidence: 99%

Managing a Supply-Chain Based Two-Sided Market

Rong

Liu

Lin

2016

AMPROC

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Abstract:This paper develops a theoretical model of positive monopolist who organizes a two-sided market in the IP license industry. This model indicates, as a platform owner, they faced a new phenomenon that developers' side acts as a supply chain of firms instead of single firm. They have to deal with network effect between developers and customers, but also within the supply chain. We have developed models by adopting license fee and royalty fee to facilitate such network effects among supply chain and customers in the platform ecosystem. This article specifically enriches the two-sided platform by introducing supply-chain based side.Some practical implications also provide for platform owners who want to set up their platform ecosystem by licensing their IPs. This result could also be able to explain why Intel adopted a close supply chain in the PC industry while ARM adopted an open platform model in the mobile industry as a positive monopolist.

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Free trial or no free trial: Optimal software product design with network effects

Cited by 165 publications

References 11 publications

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

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

Mathematical Formulation and Analysis of the Optimal Launch Timing for Mobile Applications with Perceived Value and Network Effect

Managing a Supply-Chain Based Two-Sided Market

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