Business models in the Mobile Application Industry:
How can mobile application providers utilize business models
to increase app success?
Tim van der Hoff S1646346
University of Groningen Faculty of Economics and Business
MSc, Business Administration Strategy & Innovation
Master Thesis April 16, 2013
Thesis supervisor: Dr. Florian Noseleit
Abstract
The purpose of this study was to investigate the business models that mobile application providers employ, and to assess the influence of those business models on the success of mobile applications. A literature review was conducted to analyze the configurations of value creation; value delivery and value capture mechanisms of the business models. This research measured the success of three value capture mechanisms: purchasing price, display advertisements and in-app purchases. The top-grossing 150 mobile applications in both the App Store and Google Play were analyzed. Descriptive research was used to establish platform differences in mobile application business models. Probit analyses were performed to measure the influence of business model elements on app success. The results of the study indicated that Apple and Google Play had a similar distribution of business models. None of business models were found to influence app success in Google Play. In the App Store, the pricing mechanism was found to negatively influence app success. This influence was found to be non-linear, suggesting the existence of consumer pricing thresholds.
Table of Contents 1. Introduction 4 1.1 Business Models 4 1.2 Research Question 5 2. Literature Review 7 2.1 Mobile Applications 7 2.2 Mobile Platforms 7 2.2.1 Profit models 8 2.3 App Pricing 9 2.3.1 App functionality 9 2.3.2 Marginal costs 9 2.3.3 Search costs 10 2.3.4 Pricing thresholds 10
2.4 Mobile Application Business Models 11
2.5 Value Creation 12 2.6 Value Delivery 13 2.7 Value Capture 14 2.7.1 Price-‐related strategies 14 2.7.2 In-‐App Purchases 17 2.7.3 Display Advertising 19 2.8 App Success 20 3. Research Design 22 3.1 Data Collection 22 3.2 Measures 23 3.2.1 Application price 23 3.2.2 In-‐app purchases 23 3.2.3 Display advertisement 23 3.2.4 Content type 23 3.2.5 Application Success 24 3.3 Data Analysis 25 4. Results 26 4.1 Descriptive Statistics 26 4.1.1 Content 27 4.1.2 Pricing 27 4.1.3 In-‐app purchases 29 4.1.4 Advertisements 30
4.2 Business Model Elements and App Success 31
5. Discussion 38
5.1 How Application Providers Utilize Business Models 38
5.1.1 Pricing 38
5.1.2 Advertising 38
5.1.3 In-‐app purchases 39
5.1.4 Platforms and distribution of value capture mechanisms 39
5.2 Value Capture Mechanisms and App Success 39
5.2.2 Pricing thresholds 40 5.2.3 Advertising 41 5.2.4 In-‐app purchases 41 5.2.5 Content 41 5.3 Practical Contributions 41 5.4 Academic Contributions 42 6. Conclusions 43
6.1 Answering the Research Question 43
6.2 Limitations and Suggestions for Further Research 44
1. Introduction
1.1 Business Models
For long, strategists have acknowledged that technological innovation in itself does not guarantee profits (Teece, 1988). Value appropriation, therefore, has been a point of much concern. Over the years, multiple sources of value appropriation have been identified. Earlier literature focused on enterprise strategy, business strategy, resource allocation, appropriability regimes or managerial ability (Chesbrough and Rosenbloom, 2002). Yet, recent literature has identified the significance of business models in capturing the value of innovations.
Business models have become a central theme in innovation related literature, especially in the literature concerning information goods and the digital economy. The origins of ‘business models’ can be traced back to computing and system modeling specialists who used the concept to refer to computer simulations of business processes that increased in as the complexity of the environment increased (Sako, 2012). Business models describe the configurations of the value creation; value delivery and value capture mechanisms of a firm (Teece, 2010). As such, they are directly related to the financial performance of individual firms (Malone et al., 2006).
1.2 Research Question
The introduction of software applications (apps) for mobile devices is a more recent change in the technological environment is. This change was triggered by innovations in computing power of mobile devices and an increasing user adoption of Internet services for mobile devices. Mobile apps build upon the innovations of the Internet, hence, much of the literature that applies to the Internet and the digital economy applies to mobile apps as well. However, the mobile industry is different from other digital industries. It is an industry where the supply and demand of application providers and users is aggregated and governed by platform owners. The platform owners limit the possible business models that application providers can pursue. In addition, it is an industry with price sensitive consumers.
Literature has focused predominantly on business models for platforms and architectures (Muller, Kijl and Martens, 2011; Yu and Deng, 2011; Cusumano, 2010; Holzer and Ondrus, 2009). Some authors have addressed business models from the perspective of the individual software or mobile application provider. Mahadevan (2000) analyzed Internet business models under different market structures and segments. He identified six revenue streams that are unique to internet-based business: revenue from online seller communities; advertising; variable pricing strategies; free offering and disintermediation of the supply-chain. Clemons (2009) focused on how Internet applications could be monetized. He divided applications between those that sold content or provided access to content and established that there was an untapped potential for new business models in Internet applications. Bergvall-Kareborn and Howcroft (2011) analyzed how application developers respond and adapt to platform business models. They found that developers were motivated by the opportunity to work on leading edge technological platforms, but that drawbacks were associated with control and influence on the platform.
Though these authors focused at business models or individual application providers, none of them focused on business models for application providers in the context of the mobile industry. For instance, we do not know which business models exist and how well they perform. Hence, three central questions remain unaddressed: “What are the business models in the mobile application industry?” “How are the business models used?” and “How do the business models relate to the success of mobile applications?” This research aims to fill that gap by answering the following research question:
2. Literature Review
This chapter will cover the literature review of the thesis. The goal of this chapter is to explain the context of the mobile application industry, to explain the existing business models and to identify potential influences on app success. The chapter is organized in the following way. The first three sections will provide an explanation of (1) mobile applications, (2) the mobile platforms that are discussed in this paper and (3) the pricing of mobile applications. The fourth section will explain the concept business models. Business models consist of value creating; value delivering and value capture mechanisms. The mechanisms will be discussed in subsequent order. The chapter will conclude with explaining how business models in the mobile application industry may relate to the success of mobile applications.
2.1 Mobile Applications
Mobile applications, more commonly termed mobile apps, are software applications available for smartphones or mobile devices. Apps require operating systems that support standalone software and can connect to the Internet. The creators of apps are named developers or application providers. The terms are used interchangeably throughout this research. There are two types of apps: native apps and mobile web apps (Gahran, 2011). Native apps are separate programs that can be downloaded from mobile app stores and that are stored on mobile devices. Native apps are specific to one type of operating system. Mobile web apps run within a mobile device’s Internet browser, are not stored on a mobile device and are not specific to one type of operating system. This research regards the analysis of native apps.
2.2 Mobile Platforms
Microsoft and Research in Motion (RIM). Apple and Google have become the market leaders by creating self-sustaining platforms with critical masses of providers and users (Lin and Ye, 2009; Visionmobile, 2011). The platforms have different profit models. Apple is said to be a ‘closed’ system that relies on sales revenue, while Google is an ‘open’ system that relies primarily on advertising income.
2.2.1 Profit models
Apple's OS, named iOS, is a proprietary operating system. Apple does not license iOS to other mobile device manufacturers but uses it as a means of control over its platform. The iPhone, Apple’s smartphone, and its OS are an integrated package (Lin and Ye, 2009). Apple has been able to create a high-end smartphone that delivers a superior user experience. Apple’s strategy is to target the high-end market segment and to profit from device and application sales (Kenney and Pon, 2011). Apple imposes a premium cost on handset buyers for purchasing the hardware with the software preinstalled (Lin, Li and Whinston, 2011) and takes a 30% share of sales revenue generated by the applications (Apple, 2012).
Google on the other hand freely shares its operating system, named Android, with handset manufacturers. It formed the Open Handset Alliance (OHA) together with members including Samsung, Motorola and LG. This alliance, led by Google, is a business consortium of technology and mobile firms that are committed to advancing open standards for mobile devices. The members of OHA have agreed to develop products that support the Android Platform (Bergvall-Kareborn and Howcroft, 2011).
Apple (IOS) Google (Android)
Openness Closed Open Source
Positioning High-end market segment Every market segment Profit models Application sales
Handset sales
Application sales Advertising Table 1. Platform strategies for Apple and Google.
2.3 App Pricing
This section will discuss the pricing of apps. Three influences on app prices will be discussed: app functionality, marginal costs of digital goods and search costs. Consumers of apps are believed to be price sensitive. Price sensitivity constitutes how sensitive consumer demand is to a change in price. The low average prices of apps indicate that app consumers are price sensitive. Apple offers approximately 780.000 apps at an average price of $1.50. Over 55% of these apps can be used free of charge and 22.5% is priced less than $1 (148apps). Likewise, Google offers over 70% of its 720.000 apps for free (Reyvaldi, 2012). App price sensitivity leads to pricing thresholds where demand disproportionally drops for apps with prices that exceed the pricing threshold.
2.3.1 App functionality
The most obvious source of price dispersion is product heterogeneity (Smith, Bailey and Brynjolfsson, 1999): if the products are different, then it is not surprising that the prices are different as well. Apps are often categorized as either gaming or utility apps. Among the two, price dispersion can be observed. The average price of a utility app in the App Store is $1.60 while the average price of game apps is $0.91 (148apps). Therefore, the content of an app can be seen as prime influencer of app pricing.
2.3.2 Marginal costs
2.3.3 Search costs
Platforms can indirectly influence app pricing, even though the platforms allow the mobile application providers to determine app prices. Thus, app pricing may be dependent on the platform where the app is provided for. There are two areas in which platforms can influence app pricing. In aggregation: by bringing together the demand of many consumers and the products of many suppliers. In search: by making information accessible to consumers and suppliers (Bailey, 1998).
There are costs associated with finding out the utility and value of the product. These costs are called search costs (Nelson, 1970). By providing product and pricing information an app store can reduce information asymmetries and lower search costs (Bakos, 1991). If search costs are effectively lowered, markets become efficient (Diehl, Kornish and Lynch, 2003). Smith, Bailey and Brynjolfsson (1999) argue that consumers in efficient markets are more sensitive to small changes in price as long as substitute products exist.
App stores bring together the supply of apps but also act as a sorting tool by screening through the alternatives and recommending a selection of applications they believe the consumer might be interested in (Alba et al., 1997). This is called the smart agent function. The apps that are selected by the smart agent tend to be alike in quality. Because consumers can easily find other apps that suit their preferences, and because the selected apps are similar in terms of overall quality, consumers become less willing to pay a premium for their preferred option (Kaul and Wittink, 1995). If an app store were to perform its task of providing information inadequately by letting informational inefficiencies prevail, the market should become more monopolistic. In such instances buyers often buy from the first seller they visit (Bailey, 1998). Consequently, because demand is inelastic, there is no incentive for application providers to lower prices (Bailey, 1998).
2.3.4 Pricing thresholds
moment content is not for free anymore. Hence, asking just a ‘penny’ can stop consumers in their price tracks (Figure 1).
Figure 1. The relationship between price and demand for digital goods, termed the penny gap. Taken
from Kopelman (2007).
Though the actual pricing threshold of app consumers may not necessarily be zero, the Penny gap is illustrative of the difficulties that mobile application providers are faced with. While they are free to determine the prices of their apps, overall market dynamics may dictate that it is most effective not to set a price at all and that it is difficult to get paid for apps. This research will analyze the existence of pricing threshold and provide recommendations for price setting.
2.4 Mobile Application Business Models
This thesis adopts view of Teece (2010) that business models describe the configurations of (1) value creation; (2) value delivery and (3) value capture mechanisms of a firm (Teece, 2010). Business models can influence the financial performance of firms (Barney, 1991). Malone et al. (2006) find that some business models can financially outperform others. However, their study was conducted in relation to the US economy. To the knowledge of the author, firm performance has not yet been linked to business models for digital goods, let alone for mobile applications.
The mobile application industry provides a fairly unique situation. In this industry, the owners of mobile platforms restrict the application providers in the value creation; value delivery and value capture mechanisms. Value creation and value delivery are fixed (there is one imposed method) but there are several possibilities to capture value. Mobile platform owners allow mobile application providers to (1) determine the prices for their apps, (2) include in-app purchases and (3) include advertisements in their apps (Apple, 2012; Android, 2012; Android 2012a). Since value creation and delivery are fixed and variations can only occur in the value capture, it is most important to determine the influence of the value capture mechanisms on app success. For this reason, the primary focus of this research constitutes the value capture mechanisms. The next sections will discuss the value creation and delivery. Then, a more elaborate discussion of the value capture mechanisms will follow.
2.5 Value Creation
While there are minor differences in the requirements that the platforms set for application development, the underlying principle is the same. The platform imposes the tools that are required for value creation, makes them freely accessible for everyone who wishes to develop an app on that particular platform, and requires a small fee for publication of the app. As such, the value creation mechanism is the same for all business models.
2.6 Value Delivery
Mobile platforms have created marketplaces for mobile applications. These marketplaces are commonly called app stores or portals. Apple’s app store is called the ‘App Store’ and Google’s app store is called ‘Google Play’. The app stores are critical to the distribution of mobile apps because they act as intermediaries that bring together application providers and handset owners. The process of the distribution of mobile apps is depicted in Figure 2.
Figure 2. The process of app distribution and payment. Taken from Holzer and Ondrus (2009).
1. The developer creates an application and uploads it on the application marketplace; 2. The consumer downloads the app from the app store;
3. The app store receives the consumer’s payment; 4. The developer receives its share of the payment.
platform and much like value creation, value delivery mechanisms are similar for all the business models in the mobile application industry.
2.7 Value Capture
Platform owners govern and restrict value creation and value delivery. They also restrict value capture by prescribing the value capture mechanisms that mobile application providers can implement. There are three value capture mechanisms that mobile application providers can choose from. They can (1) determine the prices for their apps, (2) include in-app purchases and (3) include advertisements in their apps (Apple, 2012; Android, 2012; Android 2012a). This chapter will discuss what these value capture mechanisms entail and how application providers can implement them.
2.7.1 Price-related strategies
As discussed, it is difficult to determine appropriate app prices because there are several factors that potentially influence optimal app pricing. Many application providers have moved towards business models involving asking no purchase price in order to appeal to a larger group of users. At the same time, there is a realization that these users eventually have to pay for content in order for the developer to make a profit. Price-related strategies can be categorized as freemium strategies and paid strategies.
Freemium strategies
Mobile applications that do not require a purchase before installation can be divided into two categories: (1) completely free and (2) freemium. A completely free strategy is employed when an app is published and not monetized in any way (Barros, 2011). It is best used when an app is meant to serve as a promotional tool and the application provider is not looking to generate revenue with its app.
Freemium products may be supported with advertisements (Wilson, 2006; Higbee, 2011). Apps that are solely ad-supported cannot be considered freemium as they lack the option to purchase complementary features, which is essential to the freemium model. Anderson (2009) distinguishes between four types of freemium models:
• Time limited: provides users with full access for a certain period of time, after which one has to quit using the product or pay to continue use;
• Feature limited: provides the user with free features and premium features that can be accessed once the premium content is purchased.
• Seat limited: entails free full use for some numbers of the products or services for some people. Once this number has been reached new users have to pay for full service.
• Customer type limited: occurs when a firm decides to have some categories of consumers use the product for free while having others pay for the same service.
Mobile applications that are based on a freemium strategy predominantly adopt feature limitations. Application providers publish an app that can be downloaded and used for free. Once the application is installed, users can buy access to premium content or features from within the application through ‘in-app purchases’ (Bonnington, 2012).
Figure 3 displays a freemium strategy with feature limitations. In its original form the feature limited freemium strategy exists of a (1) free core that is available to everyone, (2) a premium core that can be accessed through purchase and (3) add-ons or adjunct services that can be purchased. Nonetheless, freemium strategies have evolved to often consist of just a free core with related add-ons that can be implemented in combination with the free core. For example, a free gaming app that requires ‘energy’ to be played, which can either be purchased or replenished by waiting for a certain period of time, contains no clearly defined premium core.
There are some difficulties associated with the execution of freemium strategies. Given the nature of digital goods and the existence of consumer pricing thresholds, one could expect that business models revolving around free content would outperform those revolving around paid content, but conversion rates from freemium strategies are believed to be low (Teece, 2010). The first difficulty arises when a developer has to decide which feature should be free, and which feature should be considered premium content or add-ons (Needleman and Loten, 2012). When an app provider gives too much of its content away for free, users may not feel the need to purchase additional items. When a provider does not give enough content away for free, users may not experience enough of the product to be convinced to spend money at the application. Secondly, a freemium approach requires a trade-off between growing the free user base and generating sales revenue (Pujol, 2010). Finding the right balance between the two aspects may prove difficult and may potentially harm revenues. Furthermore, due to an abundance of freemium applications, users that do not use freemium applications on a consistent basis are not easily compelled to spend their money on an app. Likewise, users that abandon a freemium application after a couple of weeks cannot be easily monetized.
Paid App Strategies
need to ask a low enough price to get people to buy the application. Another hindrance for paid strategies is that consumers expect updates in content, and expects these updates to be for free. Over time, the initial selling price may no longer cover the costs of these updates.
2.7.2 In-App Purchases
In-app purchases (IAP) entail the purchase of extra content from within a mobile application. The IAP is a business model element that can be used in conjunction with a freemium or paid strategy. Three categories of IAP have been identified: virtual goods, version upgrades and subscriptions. An analysis of the categories follows.
Virtual goods
Virtual goods are used to enhance the use of an app. They give the user an incentive to access bonus features or advance faster (Ruud, 2011). Selling virtual goods embarks upon switching costs: if the consumer switches to another application, he or she loses the investment that was made in virtual goods (Clemons, 2009). Two types of virtual goods exist:
• Durable goods: virtual goods that provide a permanent benefit after purchase. An example would be unlocking additional levels that can be accessed indefinitely in a mobile gaming app;
• Consumable goods: virtual goods that are deleted upon use and require a repurchase for consequent use. An example would be in-game currency.
Often durable goods cannot be accessed unless the user first buys the virtual currency of a particular app. Consumable goods come in two forms: (1) a virtual currency that can be exchanged for temporary or permanent benefits or (2) a form of energy that is required to use an application. Activities within the application require a certain amount of energy. Energy levels typically restore automatically. Users can decide to wait for energy levels to slowly refill or to purchase new energy, thus resuming activities within the app without hindrances.
Version upgrades
that can be unlocked through purchase. Upgrades may not necessarily be restricted to free applications (Barros, 2011). Version upgrades provide a permanent benefit and are non-replenishable. It may prove difficult to distinguish between durable goods and version upgrades. In this research I make the distinction based on the following difference: a version upgrade provides access to the premium core of a product while durable goods will be considered add-ons.
Subscriptions
The in-app purchase of subscriptions relies on monetization through a subscription fee. Developers use subscriptions to provide up-to-date content of the news, but may also extend to other service related applications (Barros, 2011). There are two types of subscriptions:
• Auto-renewing subscriptions: subscriptions that renew automatically after the expiration of the contract;
• Non-renewing subscriptions: subscriptions that require the user to purchase the service again for further use after expiration of the contract.
Category Types Description
Version upgrades Version upgrades Upgrades product to premium version
Virtual Goods Durables Goods that last permanently Consumables Goods that are deleted upon use Subscriptions Non-renewing Non-renewing subscriptions
Auto-renewing Auto-renewing subscriptions Table 2. Overview of in-app purchase characteristics.
While in-app purchases may be applied to both freemium and paid strategies there is a challenge associated with in-app purchases: people may feel deceived when they find out they have to purchase content through in-app purchases. Therefore it should be clear to the user beforehand that in-app purchases are included.
2.7.3 Display Advertising
Display advertising - also called an ad strategy - capitalizes on an application’s usage rather than application sales (Higbee, 2011; Ruud, 2011). Display advertisements continually generate revenue over the lifetime of an application. A continual inflow of revenue reduces the risk that the cost of updates will exceed the revenue generated over the lifetime of the product. The value of advertisements is determined by the ‘effective cost per thousand impressions” (eCPM) an app is receiving. In this regard, the number of ads a user will see is dependent on the time the user spends using the app (Dalu, 2010). Display advertising thrives when large numbers of users use an application for extended periods of time. For this reason, apps targeted at a niche audience may not generate meaningful advertising revenue. There are two broad types of ad strategies:
• House ads: unpaid ads that promote a developers’ mobile property; • Banners: a hypertext link with a graphic element.
extra revenue generated by advertisements and the potential loss of purchases. Freemium strategies that use advertising are compatible with consumer’s expectations that everything online should be available for free, but it still is unclear how to combine advertising with content most effectively (Priest, 2009). What is more, users are generally reluctant to pay for applications that also employ advertising strategies.
Advertising revenues have been falling (Clemons, 2009). Moreover, display advertisements commonly bring along inconveniences: (1) consumers may not want to see ads and experience ads as being intrusive (Cassavoy, 2012; Clemons, 2009); (2) ads take up screen space of mobile devices, which already is limited; (3) ads cause substantial battery drainage (Nichols, 2012) and (4) consumers may not trust ads (Clemons, 2009).
2.8 App Success
As seen in the previous sections, the business models for mobile application providers can be summarized in three steps.
1. Create value by developing an app using software development tools that are provided by the platform.
2. Deliver value by making the app available for download in the app store of the platform.
3. Capture value by sales, in-app purchases or advertisements.
3. Research Design
This chapter will discuss the design of this research. It comprises of three sections: data collection, measures and data analysis. The sources of the data, sample size, collection methods and statistical analyses will be discussed.
3.1 Data Collection
The research entails quantitative and categorical data. As the research entails the success of business models, it makes sense to analyze a sample of mobile applications that are considered successful. Data on successful applications are available for the App Store and Google Play in the form of ranking systems. The two platforms have similar ranking systems (Appshopper, 2012; Google Play, 2012):
• Top free applications: ranks applications with a price of zero, based on total number of downloads;
• Top paid applications: ranks applications with a price that is greater than zero, based on number of downloads;
• Top grossing applications: ranks applications that have the highest combined revenue from sales price and in-app purchases.
I analyzed the top-grossing rankings for two reasons. First, the top-grossing list contains free and paid applications. Second, an app’s ranking in the top-grossing list is directly related to the ability of an app to generate sales revenue vis-à-vis other apps. The #1 position is held by the app that generates most sales revenue that day, the #2 position is held by the app that generates second-most sales revenue and so on. The caveat of the top-grossing list is that it does not take into consideration revenue from advertising. Applications that solely rely on advertising income are therefore not represented in the data.
3.2 Measures
Because the value creation and value delivery mechanisms are similar for every business model, they are less relevant in comparing the success of the different business models. Hence, business models were measured according to the measures that were identified in the literature review: application price; in-app purchases, display advertisements. Because the literature review revealed price asymmetries among app functionality, content type was added as a fourth measure. A brief description of each measure follows.
3.2.1 Application price
The prices of mobile applications are used to distinguish between freemium and paid monetization strategies. Applications with a selling price of zero are monetized through freemium monetization strategies, and applications with a selling price higher than zero are monetized with a paid app monetization strategy. Furthermore, the influence of price on success will be assessed. Prices could be found directly in the application portals.
3.2.2 In-app purchases
This research identified four types of in-app purchases: consumables; durables; version upgrades and subscriptions. The App Store provided information about the availability of in-app purchases. The types of in-in-app purchases were not directly determined by the App Store but could be accurately assessed by analyzing the individual in-app purchases. Google Play did not provide information on the availability of in-app purchases in its apps. I relied on information from product descriptions, app-reviews and developer websites, which were abundantly available, to determine the availability and the type of in-app purchases.
3.2.3 Display advertisement
Information regarding the inclusion of display advertisements was not provided by the platforms directly. I depended on app-reviews, product descriptions and Internet forum discussions. Additionally, company website of ‘Softonic’ (http://en.softonic.com) often provided information about the inclusion of mobile advertisements.
3.2.4 Content type
the aggregate of travel and navigation app categories. Media is aggregate of music, photo and video applications and utility is an aggregate of the remaining applications.
3.2.5 Application Success
The top-grossing ranking exhibits the underlying goal of business models: generating revenue. For this reason, this research analyzes application rankings. Difficulties arise when success is measured according to ranking because consumer’s attribution of application value is not proportionally divided among ranking positions. Top ranked applications generate disproportionally more revenue than lower ranked applications. This is in line with Carare’s (2011) observation that application buyers attribute a higher value (in dollars) to apps that were previously ranked higher. Carare (2011) observed a slight ‘bump’ in value attribution after the 25th
ranked application (Figure 4). This observation is likely connected to the increased visibility the first 25 applications obtain in app stores.
Figure 4. Consumers attribute a higher value to apps that were previously ranked higher. Taken from
Carare (2011).
Following the ‘bump’, I divided the sample into two groups: the extremely successful group and the (less) successful group. Note that every app in the top-grossing list can be considered successful and that I am merely distinguishing between the very successful and the rest of the successful apps. The extremely successful group consists of the top 25 grossing applications on either application store. The less successful group consists of the rest of the applications.
and 50 applications. All tests performed dramatically worse than the original test, demonstrating that there is a structural difference between the top 25 applications and the rest of the applications in the top-grossing list. Therefore, the chosen size of the success group appears to be correct.
3.3 Data Analysis
4. Results
This chapter consists of two sections. The first section, with the descriptive statistics, will examine how pricing, in-app purchases; display advertising and content are distributed. Herein, it will seek the answer to “How are the business models used?” The second section constitutes a platform-based comparison of how the monetization elements influence the success of an app. In that, it will seek the answer to “How do the business models relate to the success of mobile applications?”
4.1 Descriptive Statistics
The descriptive statistics of the data set are displayed in Table 3, Table 4 and Table 5.
App Store Google Play Total Percentage
Travel 17 14 31 10.33%
Games 84 82 166 55.33%
Media 11 13 24 8%
Utility 38 41 79 26.33%
Total 150 150 300 100%
Table 3. Overview of application content types.
App Store Google Play
Count 150 150
Minimum €0 €0
Maximum €89.99 €76.03
Mean €4.49 €5.65
Std. dev. €12.93 €12.38
Mean paid app €7.92 €10.46
Std. dev. paid app €16.41 €15.32
App Store Google Play Total Percentage
Freemium 65 69 134 44.67%
Paid 85 81 166 55.33%
Total 150 150 300 100%
In-app purchases 103 89 192 64%
Without in-app purchases 47 61 108 36%
Total 150 150 300 100%
Display ads 18 29 47 15.67%
Without display ads 131 121 252 84%
Missing 1 - 1 .33%
Total 150 150 300 100%
Table 5. Overview of app monetization strategies.
The data set contains 300 applications spread evenly between the App Store and Google Play. Information on the inclusion of advertisements was missing for one app. This value was omitted in further analyses on display advertisements. The sample contained more apps with in-app purchases than freemium apps, signifying that at least some percentage of paid applications included in-app purchases.
4.1.1 Content
The app stores had very similar distribution of content. Games accounted for more than half of the applications. A chi-square test was used to test for the statistical significance of the difference in distribution of content on the platforms (X2
(1, N = 300) = .595, p = .898). As significance levels exceeded the pre-specified alpha level, we cannot say that there is a significant difference in content between the two platforms.
4.1.2 Pricing
selling price was 89.99 euro. App prices in the App Store (M=4.49, SD=12.93) were found to be 1.16 euro lower on average than app prices in Google Play (M=5.65, SD=12.83). An independent t-test was conducted to test for app price differences between two application stores. Equal variances were assumed using the Levene’s test (F= .374, p= .541). Though the mean difference was considerable, it did not differ statistically across application stores (T (298)=.625, p=.43). The results suggest that no statistically significant difference in average app prices exists between the application stores. Price means and standard deviations per content type are exhibited in Figure 5.
Figure 5. Pricing of content types.
Figure 6. Distribution of freemium and paid strategies in content types.
In the App Store, 65 apps (43.3%) were monetized with freemium strategies and 85 apps (56.7%) were monetized with paid strategies. In Google Play, 69 apps (46%) were monetized with freemium strategies and 81 apps (41%) with paid strategies (Table 5). A chi-square test was used to test for the statistical significance of the difference in freemium versus paid strategies across the platforms (X2
(1, N = 300) = 0.216, p = .642). Results illustrate that we cannot say that there are statistically significant differences in the distribution of paid and freemium strategies between the platforms.
Paid apps (N=166) were selected. Paid app prices in the App Store (M=7.92, SD=16.41) were on average 2.54 euro lower than app prices in Google Play (M=10.46, SD=15.32) (Table 4). An independent t-test was used to test for the significance of the app price differences (T (164) =-1.027, p=.306). Equal variances were assumed using the Levene’s test (F= .062, p=.804). Though the difference in average paid app prices is considerable, it is not significant. The results suggest that there is no statistically significant difference in paid app prices between the application stores.
4.1.3 In-app purchases
Figure 7. Overview of in-app purchase categories in content types.
The four types of in-app purchases were not distributed evenly across the types of content (Figure 7). Consumables were almost exclusively found in games. Furthermore, durables and subscriptions accounted for a large share of the in-app purchases in travel and utility apps. In the App Store 103 apps (34.33%) included in-app purchases. In Google Play 89 apps included in-app purchases (29.67%). A chi-square test was used to test for the statistical significance of the difference in use of in-app purchases between the platforms (X2
(1, N = 300) = 2.836, p = .059). The results indicate that we cannot say there are statistically significant differences in the use of in-app purchases between the platforms.
4.1.4 Advertisements
Figure 8. Distribution of advertisements in content types.
In the App Store, 18 apps (12.1%) of the apps contain display advertisements. In Google Play, 29 apps (19.3%) of the apps contain display advertisements. A chi-square test was used to test for the statistical significance of the difference in use of display advertisements between the platforms (X2
(1, N = 299) = 2.968, p = .085). As significance levels exceed the pre-specified alpha level, we cannot say that there is a significant difference in the use of display advertising between the two platforms.
4.2 Business Model Elements and App Success
Dependent Variable: Success Independent variable b-coef Z Sig. 95% C.I.
In-app purchases 0.020 (.399) 0.05 0.960 -.762 .802 Price -0.132 (.065) -2.03 0.043 -.260 -.004 Price squared 0.003 (.001) 2.40 0.016 .000 .005 Advertisements 0.134 (.376) 0.36 0.721 -.603 .871 Games 4.337 (.263) 16.49 .000 3.822 4.853 Travel 4.734 (.507) 9.31 .000 3.739 5.731 Utility 4.232 (.292) 14.48 .000 3.660 4.805 Constant -5.405 (.669) -8.08 .000 -6.717 -4.093 Number of obs. Pseudo R2 Log pseudolikelihood Wald chi2 Prob>chi2 149 0.159 -55.347 967.99 .000 Note: Standard errors in parentheses.
Table 6. Probit analysis results for success in the App Store.
apps. Marginal effects were computed to measure how much price influences the success of an app (Table 7).
Dependent Variable: Success Independent variable Marginal effect Z Sig. 95% C.I.
In-app purchases .003 (.070) .05 .960 -.133 .140 Price -.023 (.012) -2.00 .045 -.046 -.000 Price squared .000 (.000) 2.33 .020 .000 .001 Advertisements .023 (.066) .36 .721 -.105 .152 Games .738 (.070) 10.55 .000 .601 .875 Travel .964 (.010) 92.12 .000 .944 .985 Utility .960) (.021) 46.84 .000 .920 1.000
Note: Standard errors in parentheses.
Table 7. Marginal effects for app success in the App Store.
The marginal effect for price was -.023. This signifies that, for an increase of price with 1 euro, the likelihood of success for an app decreases with 2.3 percent points on average.
Dependent Variable: Success
Price at: Margin Z Sig. 95% C.I.
0 .228 (.040) 5.73 .000 .150 .307 0.79 .204 (.032) 6.41 .000 .142 .267 3.99 .129 (.028) 4.66 .000 .075 .183
Note: Standard errors in parentheses.
Table 8. Predictive margins for the App Store at the price level of 0 .79 and 3.99.
Table 8 demonstrates that increasing the price from 0 to .79 decreases the likelihood of app success by 2.4%. This indicates that free and low-priced strategies are comparable in their influence on application success. A sharp drop in success following a small increase in price, as expected by the theory of the penny gap, was not found. Increasing the price from .79 to 3.99 decreased the likelihood of app success in a more substantial manner: 7.9%. An app of 3.99 is nearly twice as unlikely to be successful as a free app is. As such, the effect of price on success is non-linear. The negative influence of price on success is small at first, but as price increases success is affected in a stronger fashion.
Dependent Variable: Success
Price at: Margin Z Sig. 95% C.I.
0 .228 (.040) 5.73 .000 .150 .307 0.79 .204 (.032) 6.41 .000 .142 .267 2.39 .162 (.026) 6.17 .000 .111 .214
Note: Standard errors in parentheses.
Table 9. Predictive margins for the App Store at the price level of 0 .79 and 2.39.
difference in the likelihood of success between a free app and an app of 2.39 is 6.6%. The underlying principle is comparable to the previous analysis: the influence of price on success is small at first but then becomes larger. A similar probit regression analysis was performed for Google Play. The results are presented in Table 10.
Dependent Variable: Success Independent variable b-coef Z Sig. 95% C.I.
In-app purchases .544 (.354) 1.54 .124 -.150 1.237 Price -.036 (.043) -0.84 .401 -.120 .048 Price squared .001 (.001) 1.28 .202 .000 .002 Advertisements -.450 (.347) -1.29 .196 -1.131 .231 Games 4.641 (.208) 22.28 .000 4.232 5.049 Travel 4.789 (.611) 7.84 .000 3.591 5.986 Utility 3.753 (.430) 8.74 .000 2.911 4.595 Constant -5.724 (.738) -7.76 .000 -7.171 -4.278 Number of obs Pseudo R2 Log pseudolikelihood Wald chi2 Prob>chi2 150 .147 -57.635 746.50 .000 Note: Standard errors in parentheses.
Table 10. Probit analysis results for success in Google Play.
success of mobile applications in Google Play. Once more, games, travel apps and utility apps are more successful than media apps. To test the linearity of the influence of price in Google Play, predictive margins were again computed. I generated the predictive margins at the 25, 50 and 75 percentile cut points of the price mean for the entire sample (n=300), and for Google Play (n=150). For the entire sample the cut points were similar to those of the first analysis. For Google Play the cut points were 0, 1.24 and 4.75.
Dependent Variable: Success
Price at: Margin Z Sig. 95% C.I.
0 .208 (.046) 4.50 .000 .117 .298 0.79 .202 (.040) 5.01 .000 .123 .281 3.99 .178 (.027) 6.69 .000 .126 .230
Note: Standard errors in parentheses.
Table 11. Predictive margins for the Google Play at the price level of 0 .79 and 3.99.
Table 11 demonstrates that in Google Play, the difference in the likelihood of app success between a free app and an app of .79 is .6%. The difference in the likelihood of app success is more substantial between app prices of .79 to 3.99: 2.4%. The difference in the likelihood of app success between a free app and an app of 3.99 was 3%.
Dependent Variable: Success
Price at: Margin Z Sig. 95% C.I.
0 .208 (.046) 4.50 .000 .117 .298 1.24 .198 (.037) 5.32 .000 .125 .271 4.75 .173 (.026) 6.53 .000 .121 .225
Note: Standard errors in parentheses.
5. Discussion
In this chapter, I will discuss the results of this study. Section 5.1 discusses how mobile application providers utilize business models. Section 5.2 will discuss how business model elements influence the success of mobile applications.
5.1 How Application Providers Utilize Business Models
As seen in the literature review, the business models for mobile application providers can be analyzed on the basis of the value capture mechanisms. This section will analyze those mechanisms and determine how application providers currently use them.
5.1.1 Pricing
Judging from the price means it becomes apparent that large asymmetries exist among the types of content. Large mean value differences across content types signify that consumers have different willingness to pay for different types of apps. This thesis argued that apps ought to be divided based on the content of the app instead of addressing the industry as a whole. Following Bailey, Smith and Brynjolfsson (1999), price dispersion across all four categories shows that the categories are different.
App prices in the top-grossing list are higher than average app prices and are not at marginal cost levels. From this point of view it becomes evident that the media and games categories are closer to marginal cost levels than the utility and travel categories. It is not unlikely that gaming and media apps are more price-competitive than utility and travel apps. Still, pricing differences can be observed even between games and media apps. Games have low average prices because the freemium applications push average app prices downward. Media apps have low average prices because application providers pursue lower-priced paid app strategies.
5.1.2 Advertising
advertising strategies revolve around engaging users for a longer period of time as the number of ads a user will see is dependent on the time the user spends using the app (Higbee, 2011; Ruud, 2011; Dalu, 2010). Games are presumably most suited for engaging users for longer periods of time. Therefore, freemium and advertising strategies may be more prevalent in games.
5.1.3 In-app purchases
In-app purchases are a popular method of monetizing content. Mobile application providers do not exclusively use them in freemium applications. The type of in-app purchase that application providers incorporate largely depends on the content of an app. In-app purchase consumables are the foremost method of monetizing games whereas subscriptions and durables are mostly applied in the other content types. This contributes to the view that apps should be addressed based on the content, and that developers make strategic differences based on the content of an app.
5.1.4 Platforms and distribution of value capture mechanisms
Platforms are not different in the distribution of content; price; in-app purchases and advertisements. This is particularly interesting with regard to freemium and advertising strategies on Google Play. Given that Google thrives by sheer user numbers and not primarily by application income, one might expect freemium and advertisement supported applications to be dominant on Google Play. As this research shows, this is not true. In a similar vein, given that Apple’s mobile device is a premium product bought by users who are believed to be less price sensitive, one may expect paid app strategies and in-app purchases to be included more often in the App Store. As such, the expected influences of platform strategies were not visible in the business models. Hence, platform strategy does not appear to influence the business models that are pursued by successful application providers.
5.2 Value Capture Mechanisms and App Success
This section will discuss how the value capture mechanisms influence the success of apps.
5.2.1 Price
markets are more sensitive to price as long as substitute products are available. Moreover, they argue that market efficiency is caused by the reduction of information asymmetries. Thus, a possible explanation for the price sensitivity of consumers in the App Store is that (1) substitute products are available and that (2) the App Store adequately provides product and pricing information to consumers. Price elasticity offers an incentive to mobile application providers to lower prices. Therefore, if mobile application providers are looking to enhance the success of their applications, prices in the App Store will eventually move toward marginal cost levels.
On Google Play, success is unaffected by price. Following the same logic, there is either (1) an absence of substitute products or (2) Google Play does not adequately provide product and pricing information to consumers. Looking at the total supply of applications on Google Play and the similarities in content with the App Store, it seems unlikely that there is an absence of substitute products. This implies that Google Play falls short on providing product and pricing information to consumers. In an app store where there is no price elasticity, there is no incentive for application providers to lower prices (Bailey, 1998). Therefore, it seems unlikely that application providers on Google Play will lower prices. With an incentive to lower prices in the App Store and the absence of such an incentive in Google Play, price differences between the App Store and Google Play may become apparent in the future.
5.2.2 Pricing thresholds
There is no lower-pricing threshold in the App Store. The App Store does have an upper-pricing threshold after which demand for an app falls at a disproportionate rate (Sangman, Gupta and Lehmann, 2001; Ofir, 2004). The impact of an increase in price on success, going from free to the next cheapest price, is small, but further increasing the price results in greater decreases in success. As a result, free and cheap applications have a higher likelihood of success than expensive apps. To enhance the likelihood of success, free and low-price strategies should be employed.
Store, the likelihood of success of free apps and apps that cost 79 cents is relatively similar. Demand drops significantly for prices higher than 79 cents. Rather than speaking of a penny gap, there appears to be a price threshold of 79 cents in the App Store, after which it becomes difficult to convince consumers to pay for apps. For this reason we cannot speak of a threshold at the point of just asking a penny: the penny gap does not exist in the App Store. If the price of an app does not influence the success of an app, this is evidence against the penny gap in itself. Hence, the results from Google Play also disprove the existence of a penny gap.
5.2.3 Advertising
Cassavoy (2009) and Clemons (2009) argued that advertisements annoy users. Yet, it appears that advertisements do not trouble users to the extent that the success of an app is negatively impacted. This research measured success based on sales revenue. Advertising income is an additional source of revenue that complements sales revenue. The success of apps is not affected by advertisements. Accordingly, if sales revenue is unaffected by advertisements, application providers should include advertisements to boost total revenue. Yet, as previously discussed, games may be the only content type that can engage users long enough to make advertisements worthwhile.
5.2.4 In-app purchases
In-app purchases by themselves are not related to the success of apps. If application providers in the App Store reduce prices to the optimal price level of zero, then inevitably in-app purchases will have to be included to guarantee sales revenue.
5.2.5 Content
Games, travel and utility apps have a higher likelihood of success than media apps. Combined with the large price asymmetries between content types, it is apparent that a broader distinction than just games and the rest of applications is required to study apps.
5.3 Practical Contributions
providers that the pricing of apps should be tailored to the platform that the app is published on. In the App Store, a free or low-priced pricing structure should be chosen because consumers are price sensitive. On Google Play the pricing structure does not statistically influence the success of an app. Therefore providers for Google Play may be more flexible in the pricing structures they choose for their apps. In addition, it demonstrates to mobile app providers that the type of monetization element they employ should fit the content of their app.
Currently mobile application providers in the top-grossing list use similar value capture mechanisms, regardless of the platform for which they provide apps. Given the influence of price on the success of apps and given the existence of pricing threshold in the App Store this behavior is suboptimal. Developers in the App Store can maximize the success of their applications by implementing freemium or low-priced strategies. The success of apps is unrelated to business models on Google Play. Hence, business models cannot be utilized to improve the success of apps on Google Play.
5.4 Academic Contributions
6. Conclusions
The thesis was based upon the premise that business models are of vital importance to firm success. In the light of a change in the technological environment, it aimed to answer three questions: (1) “What are the business models in the mobile application industry?” and, (2) “How are the business models used?” and “How do the business models relate to the success of mobile applications?” A literature review was conducted to assess the context of the mobile application industry and to determine the business models that are available to application providers. Business models consist of the configuration of value creation; value delivery and value capture mechanisms. The main findings of the literature review were that the platforms, on which the mobile applications are sold, restrict the business models that can be used by mobile application providers, so that only a small number of business models are available. The value creation mechanisms are confined to using development tools that are prescribed by the platform and widely available to every person who whishes to develop applications. The value delivery mechanisms are restricted to selling apps in the app stores that are integrated in the platform and ceding 30 percent of sales revenue. The value capture mechanisms can be based on three elements: price, advertisements and in-app purchases. Because the value creation and delivery mechanisms are similar for every application developer, differences in business models can only occur in the value capture mechanisms.
The research analyzed the 150 apps that generated the highest sales revenue in the App Store and on Google Play. The analysis of the descriptive statistics revealed that there were no differences in business models between the platforms. Key findings included that (1) advertisements are primarily used in games and that (2) the type of in-app purchases depends on the content of an application. The results of the thesis indicated that business models can affect the success of apps in the App Store, but cannot in Google Play. Price was found to be the only influencer of success. In the App Store, a pricing threshold exists after which it becomes difficult to convince users to pay for the app. For this reason, business models that are based on no, or on low purchase prices should be pursued.
6.1 Answering the Research Question
Application providers in the App Store can reduce prices to increase the success of their applications. On Google Play, business model elements do not influence the success of mobile applications. Advertisements can be included in games to generate additional revenue, as advertisements do not influence the success of an app and additional revenue can be generated.
6.2 Limitations and Suggestions for Further Research
The generalizability of the findings is one of the concerns of this thesis. This research relied solely on top-grossing app ratings. While this provides a clear indication of app success as measured by sales revenue, analyzing other selection of apps can provide a broader understanding on success. Examples include the top-free and the top-paid rankings, or a random sample from the total population of apps. Performing longitudinal research, rather than collecting data at a fixed point in time, could further improve generalizability. Moreover, this thesis approximated profitability and success of mobile applications based on application ranking, yet, provided no financial data on either sales revenue or profitability. Introducing financial measures would allow the researcher to gain further insight in the distribution of revenues, after which conclusions can be drawn on monetization strategies and profitability. A third limitation of this research entails the role of advertising income. The measurements of sales revenue and success did not account for advertising income. Advertising income should be added to sales revenue to approach the true profitability of apps.
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