“THERE’S AN APP FOR THAT!”

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“THERE’S AN APP FOR THAT!”

Explaining the success of apps

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“THERE’S AN APP FOR THAT!”

Explaining the success of apps

by

Joost W.P. Frenay

University of Groningen Faculty of Economics and Business

First supervisor: Florian Noseleit Second supervisor: Thijs Broekhuizen

Master Thesis

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ABSTRACT

Smartphone apps have become a billion dollar business since they found their way through central app stores and on our phones in 2008. Apps like 'Angry Birds' have been downloaded millions of times making it a very interesting business commercially. However, little research has been done in the academic world about this topic. This thesis aims to fill this gap by means of an exploratory study about the success of apps. The results will give insights in the success of apps and the success factors of an individual app. Furthermore, differences in app usage of users of the three biggest smartphone platforms will be examined; iPhone, Android and BlackBerry. First, existing literature was reviewed that covered relevant concepts and looked at similar industries as current literature about apps is virtually non-existent: subjects that were discussed were network effects, (hardware and software) adoption literature, the technology acceptance model, business models and information goods. Second, a survey was conducted under 99 smartphone users and showed that iPhone users are the most app centered users, followed by Android and BlackBerry users. Price was the most important aspect users take into account, followed by rating. Constructs of the Technology Acceptance Model were also found important for the adoption of apps: perceived usefulness, perceived ease of use and enjoyment were all found relevant to users‟ perception of apps. While social, news and game apps are the most popular apps, games are the most commercially viable apps of the three. The „freemium‟ business model (consumers download an app for free and can purchase upgrades or additional functions in-app) or offering the app for €0.79 brings forth the most successful apps.

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PREFACE

Groningen, November 16th, 2011 The idea for this research originated around January when I was required to hand in a Master Thesis proposal for the Field Course of my Master Strategy & Innovation. The writing of a proposal was an exercise to prepare us for when we actually started with our thesis. Desperately searching for a topic, a friend of mine told me I should not think too hard about it as the proposal was not „the real deal‟. As we stood in an elevator, both with phones in our hands, he said; “Why don‟t you write something about smartphones? They‟re innovative products and we all know you like gadgets and technology, right?” Within 5 minutes I came to the topic of „apps‟ and decided that it would suffice for now as a topic for my practice proposal. Two months later, I got rid of my BlackBerry and got an iPhone and plunged myself in the world of apps. Looking at successful apps like „Angry Birds‟, I then realized that this might be an interesting topic for my real thesis. And the rest, as they say, is history.

It has been a long road for me but it looks like I finally managed to graduate, albeit with the help from some others whom I hereby would like to thank. First, I would like to thank my supervisor Florian. He helped me greatly with structuring the research and provided useful feedback so that I could continue on the right track. Furthermore, I would like to thank my good friends Tim, who is responsible for finding my research topic, and Kevin, who provided me a crash course SPSS and who allowed me to spend many days at his office working on my thesis. Also a word of thanks to my sister Anne-Roos, who enthusiastically helped me „recruit‟ respondents for my questionnaire and my girlfriend Charlotte, who kept me motivated during these last months. Last but not least, I would especially like to thank my parents for letting me make my own decisions during my time in Groningen and of course for sponsoring me all these years.

Thanks to you all!

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1. INTRODUCTION

“You can’t just ask customers what they want and then try to give that to them. By the time you get it built, they’ll want something new.” Steve Jobs, 1955 – 2011

1.1 The coming of apps

Suddenly there were „apps‟ available for our smartphones. In their short time since their inception, apps have nestled themselves in our society. It has only been three years since the app revolution began and already there is a real App-industry and a real App-economy. How did it all start? Since the introduction and widely adoption of smartphones, the use and possibilities of mobile internet grew immensely. Although the first smartphone, IBM‟s Simon, already was released in 1993 (Florida Times-Union newspaper, 1993), it was the early 2000s when smartphones were optimized for wireless e-mail with the coming of Symbian OS (operating system maintained by Nokia) and BlackBerry (from Research In Motion). It was however since the introduction of Apple‟s iPhone in 2007 that the adoption of smartphones and the use of mobile internet grew rapidly. West and Mace (2010) argue that the success of the iPhone is thanks to “Apple‟s conception of the mobile internet as being another modality of the existing wired internet and its leveraging of existing systems competencies” and that the promise to deliver “real internet” on a mobile phone was a core part of Apple‟s strategy. In any case, Apple‟s notion of people‟s need for mobile internet was correct (or, if the need was not apparent, Apple created that need successfully, as late and former Apple CEO Steve Jobs‟ quote illustrates).

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8 launched Android Market for their Android OS in October 2008, RIM launched BlackBerry App World on April 1st 2009 followed by Nokia‟s Ovi Store in May, Palm‟s App Catalog in June and Windows Marketplace for Mobile in October. In the past two years both the number of apps available and number of downloads exploded. At the beginning of 2010, 120.000 apps were available and over 3 billion had been downloaded (Apple, 2010). That number more than doubled over the last year; more than 300.000 available apps and over 7 billion downloads, generating over $1 billion revenue for the app developers. As of the 22nd of January 2011, Apple reported that 10 billion apps have been downloaded (Apple Insider, 2010)(Apple, 2011).

These numbers are enormous and are just from apps for the iPhone and iPod. Purcell et al. (2010), employed at The Nielsen Company and The Pew Research Center, have done research about the use of apps; “Every metric we capture shows a widening embrace of all kinds of apps by a widening population, states Roger Entner, coauthor of the report and senior vice president at Nielsen. “It‟s … not too early to say that this is an important new part of the technology world.”

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1.2 Problem definition

It is clear that apps have made a huge and lasting impact on the mobile phone industry. It is believed that apps will become as big as the internet and as popular among consumers as websites are today. The revenue stream of over $1 billion says it all. However, because of the hype created with Apple‟s App Store, "many developers rushed to market with their apps and with the continuing growth of apps more than 90% of the developers will fail", says Ilja Laurs, chief executive of GetJar, a leading independent application store. The library of available apps becomes too large. Furthermore, the app worlds are a hit-driven environment: no matter the quality or originality of the content, an app that reaches the top of the charts cannot maintain that position indefinitely. Laurs: “Many developers are realizing that it‟s hard to reach a sustainable business in a catalogue environment because it's a hit-driven environment” (BBC, 2009).

It is a very interesting new economy which has its own rules yet, strikingly, little to no academic research has been conducted about this topic. This thesis takes a first step and tries to fill that gap by performing an exploratory study that tries to explain:

1. The success of apps

2. Success factors of an individual app

3. The differences in app usage among different smartphone users.

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2. LITERATURE REVIEW

The fact that the success of apps is fairly recent is the reason that literature on the subject is still limited. Nevertheless, to give structure to this research it is important to obtain a clear picture of what the industry looks like and to study existing literature about industries and topics that are comparable with apps or share certain characteristics. This will be done in this part of the thesis and is structured as follows: first, definitions of smartphones and apps will be given. Secondly, the structure of the market will be explained. Then, literature about network externalities will be discussed. Fourth, the topic of price and costs for information goods will be examined and finally the adoption of software and technology will be looked into.

2.1 Smartphone and apps: definitions

First however, a definition for apps and smartphones has to be given. For both smartphones and apps the cell phone industry lacks standard, widely shared definitions of smartphones and what is and is not considered an “app”. After searched and compared several variations of the definitions the following are being used for this research: “A cellular telephone with built-in applications and Internet access. Smartphones provide digital voice service as well as text messaging, e-mail, Web browsing, still and video cameras, MP3 player and video and TV viewing. In addition to their built-in functions, smartphones can run myriad applications, turning the once single-minded cellphone into a mobile computer” (PC Magazine, 2010).

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2.2 Market structure

The distribution of apps takes place via application portals. Applications portals are places where developers can publish their apps and where consumers can buy and download them from and come in two variations: decentralized and centralized portals.

With decentralized portals, developers can freely upload applications to third party portals. Nokia and Microsoft are examples of companies that used to adopt this approach. With decentralized portals, multiple portal providers can compete with each other for customers and available apps. The disadvantage to this approach is that it becomes difficult for the consumer to obtain a clear picture of the supply of applications as apps are dispersed across different application portals.

Centralized portals are becoming the standard. Here there is only one application portal that functions as the publication platform for apps. This approach is adopted by Apple and Google. There are several advantages for developers. For example, all potential customers are reached through one portal. Furthermore, purchasing and billing happens with one simple touch on the screen. However, the centralized portal gives the portal owner a competitive advantage; all apps have to go through Apple and they receive 30% of the app‟s revenue. In addition, developers are confined by restrictions of the portal owner. As a result, Apple‟s portal, which is more restricted then Google‟s open source approach, led to “black” portals like Cydia which can be accessed by “jailbreaking” the iPhone, allowing “black” apps to be installed. In Figure 1, the basic process of the development and purchasing of apps through centralized portals can be seen:

FIGURE 1

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12 1. App developers create an app and publish it in an Application Marketplace. These application portals (i.e. application marketplace) are needed so that apps can be passed on to consumers. They take on the role of an intermediary between developers and consumers. Holzer and Ondrus (2009) state that the mobile portals are essential because of their intermediary role; they are a crucial element in the distribution process.

2. The consumer visits the application marketplace, chooses an app to his or her liking and downloads it.

3. Payment for the app flows through the application market and, 4. The payment is being passed on to the developers.

Concluding, where decentralized portals offered developers a choice in platforms and distributors for their apps, Apple‟s centralized portal App Store put Apple in a unique position as sole publisher and distributor.

Table 1 shows four different types of integration in which platforms control the distribution system of apps:

Full integration. The way Apple and Nokia approach the distribution of apps. Full integration gives the platform owner strict control over every step of the distribution process. They manufacture the smartphone (e.g. iPhone), its platform or operating system (OS) (e.g. iOS), control the application portal and its content (e.g. App Store) and provide the development tools and support to the app developers.

Portal integration. Google‟s approach to apps; the OS and application market, in Google‟s case Android and Android Market respectively, are integrated and in management of one firm. Smartphones that use the OS (e.g. Android) are being manufactured by other firms (e.g. HTC, Samsung).

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13 type of integration has the OS and smartphone produced by one firm that does not have its own application portal.

No integration. This approach has a platform which focuses solely on the core business that is the platform, or OS. Microsoft was an example of this approach; they did not manufacture their own smartphones or offered their own application portal but only developed an OS for others.

TABLE 1: Platform integration (adapted from Holzer & Ondrus, 2009) Platform owner… …controls

Application Portal

…manufactures Smartphone

Full integration Yes Yes

Portal integration Yes No

Device integration No Yes

No integration No No

A shift has been taking place where all players move to Apple‟s or Google‟s approach of integration. There is no major platform left that utilizes device integration or no integration; Microsoft headed towards portal integration with Windows Phone Marketplace integrated in its OS and RIM and Nokia fully integrated with the introduction of App World.

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2.3 Network externalities

Network externalities arise when the utility that a user derives from consumption of a good increases with the number of other agents consuming the good (Katz and Shapiro, 1986). There are three possible sources of these positive consumption externalities:

1. A direct physical effect of the number of purchases on the quality of the product (e.g. the use of telephones and faxes becomes more useful when many people use them).

2. Indirect effects (e.g. a consumer who purchased a Blu-ray player at the point of its introduction will hope that other consumers will buy a Blu-ray player as well because the variety and amount of software (i.e. Blu-ray discs) available will be an increasing function of the number of hardware units (i.e. Blu-ray players) that have been sold.

3. For a durable good when the quality and availability of post-purchase service for the good depend on the experience and size of the service network, which may in turn vary with the number of units of the good that have been sold (e.g. sales of new, foreign car brands were initially slow because consumers‟ awareness of the less experienced and thinner service networks that existed for new or less popular brands).

In general, the indirect effects are applicable to apps; it is because of the high adoption of

Apple‟s iPhone that app developers want to develop apps for it and therefore the App Store has an enormous amount and variety of apps. This is largely due to the large installed base of iPhone users Apple already had before launching the App Store. Critical mass is herein an important issue: the point at which a certain minimum number of users have adopted an innovation so that the rate of adoption of the new communication technology suddenly takes off (Rogers & Allbritton, 1995). In short, network effects have led to demand side economies of scale (Shapiro & Varian, 1998).

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15 Nokia, Android) to chat with each other. It is this compatibility between different operating systems that increases this direct network externality and therefore causes one consumer‟s value for an app like Whatsapp to increase even more.

2.4 Costs and pricing

Apps fall under the category of digital information goods and this has some important implications for their costs and the setting of their prices. Information goods are essentially all goods that can be digitized (e.g. books, databases, magazines, movies, music, computer software) (Shapiro & Varian, 1998). Information goods the fixed costs of production are large while the variable costs for reproduction are small or non-existent. Furthermore, production is not held back by capacity constraints (Varian, 1995, 2000). This also holds true for apps. The biggest chunk of the costs is the development of the app. Once the app‟s development is completed and becomes available in the App Store for download, no additional costs occur.

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16 price for their app from this matrix ranging from €0.79 up to €799.99. Aside from selling an app for a price there are other business models possible:

- Trial/ lite versions. Gives the user access to the app but with certain restrictions; apps can have in-app advertisements (for which the developer receives a fee), may be available for a limited amount of time or are a „lite‟ version that lacks certain features. Users can try the app out and when they are tired of the advertisements, the time period has ended or they want the additional features, the app has to be bought.

- Freemium versions. Freemium (free and premium) apps allow the consumer to download the app for free to get acquainted with its basic features (often paired with embedded advertisements), however to fully make use of all its functions the consumer must pay to get the premium version.

- Variations are also possible. For example, a game can be bought for €0.79 and additional items (e.g. levels, power-ups) can be purchased in-app.

With regard to Internet companies, the creation of revenue streams is often most perplexing because of customer expectations that basic services should be free (Teece, 2010). To a certain extent, this also applies to apps; with the launch of the App Store, plenty of apps where for free. In the case of the Android Market, all apps were free for the first couple of months. This way you will get more customers throwing themselves at the app store (it is new and it is free!) but finding the sweet spot of what customers are willing to pay after a period of free apps is difficult to determine. The fact that revenue for the App Store is $1 billion against the 10 billion apps that have been downloaded suggests that most of the downloaded apps are still free ones.

This research will address these issues and try to obtain a good indication of what users are willing to pay for an app and which business model should be the most successful.

2.5 User adoption

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17 part thanks to network externalities. However, it is also important to look at existing literature regarding the acceptance of technology and the adoption of new technological products.

2.5.1 Smash hits

As Gourville (2006) states, the value captured of innovations will be most effective when the innovation makes significant product changes while the change consumers have to make in their behavior remains limited. When an innovation manages to establish this, Gourville calls them „smash hits‟ (see Figure 2). Apps greatly increased the functionality of a product, the smartphone, but consumers did not have to alter their behavior all that much. Sure, their phone was suddenly equipped with an onboard App Store where they could shop for apps but the way Apple implemented it was very intuitive, as is their focus with all their products. Downloading an app was as easy as one press on the iPhone‟s touchscreen and felt instantly familiar for iPhone users. This is one of the reasons that apps were widely adopted by users and became these „smash hits‟.

FIGURE 2

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18 2.5.2 Adoption of hardware-software

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19 2.5.3 Technology Acceptance Model

To further gain insight in consumer acceptance of apps of Davis‟ (1989) Technology Acceptance Model (TAM) proves to be useful (see Figure 3).According to the TAM there are two variables that are particularly important in explaining the acceptance or rejection of information technology: the perceived usefulness and the perceived usefulness of use one experiences in using an information technology (this can be both hardware as software).

Perceived usefulness can be defined as “the degree to which a person believes that using a particular system would enhance his or her job performance”. When the perceived usefulness is high for a system, the user believes in the existence of a positive use-performance relationship.

Perceived ease of use, in its turn, refers to “the degree to which a person believes that using a particular system would be free of effort”. If an application is perceived to be easier in its use than another, it is more likely to be accepted by users.

FIGURE 3

Davis’ original Technology Acceptance Model (Legris et al., 2003).

Theoretical foundations for the importance of these two variables are found in several theories namely; expectancy-theory, self-efficacy theory, behavioral decision theory, diffusion of innovations, marketing and human-computer interaction.

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20 for other information technologies, adapted and updated (Adams, Nelson & Todd, 1992; Agarwal & Prasad, 1997; Bruner and Kumar, 2003; Karahanna, Straub & Chervany, 1999; Klopping & McKinney, 2004; Lee, Yan & Joshi, 2010; Legris, Ingham & Collerette, 2003; Magni, Taylor & Venkatesh, 2010; Pagani, 2003; Venkatesh, 2000; Sun & Zhang, 2006).

Venkatesh (2000) researched the determinants of “perceived ease of use” and adds them to the TAM: “Anchors” (computer self-efficacy, perceptions of external control, computer anxiety, computer playfulness) and “adjustments” (perceived enjoyment and objective usability). Examining more recent literature that researched the model is useful to relate it to apps: Legris et al. (2003) state that while TAM is a useful model, it has to be integrated in a broader model that incorporates human and social change processes and variables related to the adoption of the innovation model.

Pagani (2003) adopts the TAM model (see Figure 4), with the inclusion of the “enjoyment” (i.e. “fun”) attribute and identifies the factors that predict consumers‟ adoption of third generation mobile multimedia services, which essentially are the precursors of today‟s apps. In her research, “usefulness” and “ease of use” are respectively the first and second most important adoption factors, followed by “price” and “speed of use”.

Lee et al. (2010) researched the dynamics of user‟s belief in software applications and discovered that self-efficacy, usefulness and intention to use are likely to be dynamic and increase with time.

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FIGURE 4

Adapted TAM model of adoption of multimedia mobile services (Pagani, 2004)

Bruner and Kumar (2005) explain consumer acceptance of handheld Internet devices and create a consumer oriented TAM; c-TAM. They add a hedonic aspect to the TAM model; again, the attribute “fun”. This is an important aspect of apps as the category “games” is the most popular but other apps should also provide fun in using them (Purcell et al., 2010).

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FIGURE 5

TAM for mobile commerce (Wu & Wang, 2005)

Users‟ intentions were affected significantly by perceived risk, cost, compatibility and perceived usefulness. During this research, as the authors themselves say, application development and mobile commerce were in their infancy. The mobile phone environment looked very different then than it does this day and consumers were not familiar with mobile commerce as they are today. However, with the aforementioned kept in mind, their research is useful and will be incorporated in the methodology section.

Having analyzed the different iterations of TAM and their related studies, specifically the models that focus on the mobile phone environment (Figure 4 and Figure 5), the following factors are expected to be the most important for the success of an app and will be incorporated into the questionnaire:

- Perceived usefulness. It is expected that users of apps find them useful. A high perceived usefulness should contribute to a user‟s decision to download an app.

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23 - Enjoyment. The hedonic aspects enjoyment and fun are expected to contribute to the success of an app. If apps are fun to use, the expectation is that they will be downloaded more quickly.

- Price/cost. The issue of price as an important factor has already been addressed in section 2.4.

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3. METHODOLOGY

3.1 Looking at the demand side

In the previous chapter an exploratory literature review has been performed to gain insight in the history of apps and relevant concepts were discussed that relate to the success of apps. In this section the research method will be explained. The goal of this study is to look at the demand side of apps; smartphone users. The goal is to determine what they find important in apps, and to gain insight in their app usage behavior.

3.2 Sampling

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3.3 Data collection

The questionnaire was constructed based on prior studies and checked by the author‟s supervisors. After their feedback was implemented, the questionnaire was approved. Data for the mains study was collected over a period of roughly one week, starting mid-September 2011 via an online survey, designed with the website thesistools.nl (see Appendix C). The results came in rather quick; after two days the minimum sample size of 60 was achieved. Response rate then diminished and after a week, the current sample size was achieved, ready to be analyzed. No incentives for participation were provided to the respondents.

3.4 Measures

Here, the measurements will be discussed. The discussion of the measures follows, as much as possible, the sequence of question that respondents had to answer because certain measurements were mixed to be sure that respondents stayed focused on their answers and not filled them in blindly. Furthermore, types of questions asked were also mixed to prevent respondent fatigue.

Firstly, respondents were asked what kind of smartphone they possessed and if they ever downloaded apps (multiple-choice). If the answer was no, the respondents were taken to the „Thank You‟ page. This to be certain that respondents had knowledge about the working of apps.

3.4.1 App usage behavior

When it was verified that respondents had basic knowledge of apps, the questionnaire continued. Questions were asked about respondents app usage behavior. Frequency questions were asked to see how often respondents download apps and for how much of them were paid (multiple-choice). Then, respondents were asked to divide 100% among app categories that they used most and downloaded most. This, to ascertain which kind of apps were most popular. The questions were adapted from the aforementioned Nielsen report (2009) and Agarwal and Prasad (1997). 3.4.2 Important factors for purchase

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26 Respondents were presented statements about important App Store factors and the importance of External Reviews. The App Store factors were rating, number of ratings, description and position in the “Top-download” charts (price has a separate section and will be discussed more in depth in section 3.4.3).

To further determine which factors users find important, two apps were presented that shared similar features but differed in other factors: rating, number of ratings, price and position in the “Top-download” charts. Respondents had to pick the app of their preference and provide the reason why via a multiple-choice question.

3.4.3 Attitude towards price

„Attitudes towards price‟ related questions were about the availability of a trial/ lite version (Likert-scale) and how often and why respondents convert an app (multiple-choice). A 5-point Likert scale (ranging from „never‟ to „often‟) was presented about in-app purchases.

Furthermore, an app, „Game X‟, was presented as it would appear in Apple‟s App Store. Rating, number of ratings, a description and position in the “Top-download” chart were given. Respondents were asked what they were willing to pay for this app with use of the Price Sensitivity Meter (PSM) (Van Westendorp, 1976). Four questions were asked:

- At what price would you begin to think the app is TOO EXPENSIVE to consider?

- At what price would you begin to think the app is TOO CHEAP that you would question the quality and not consider it?

- At what price would you begin to think the app is EXPENSIVE, but you still might consider it?

- At what price would you think the app is CHEAP – a great buy for the money?

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27 1. The “Point of Marginal Cheapness” (PMC) where the lines „Too cheap‟ and „Expensive‟

intersect. This is the lower limit of the acceptable price range.

2. The “Point of Marginal Expensiveness” (PME) where the lines „Too expensive‟ and „Cheap‟ intersect, which represents the upper limit of the acceptable price range.

3. The “Indifference price point” (IPP) where the lines „Cheap‟ and „Expensive‟ intersect. This is the point at which the same percentage of respondents think the product is either cheap or expensive.

4. The “Optimal price point” (OPP) where the lines „Too cheap‟ and „Too expensive‟ intersect. OPP is optimal in the sense that the price sensitivity to the product for being cheap is equal to that of being too expensive, and is often the recommended price.

3.4.4 Perceived ease of use

On a Likert-scale, respondents were given statements about the Perceived ease of use (PEOU). This concepts stems from the TAM-model and questions have been adapted from prior studies (Davis, 1989; Agarwal and Prasad, 1997; Klopping & McKinney, 2004; Venkatesh, 2000; Karahanna et al.,1999). Statements asked about the ease of finding the right app, ease of use, and the learning curve for using apps.

3.4.5 Enjoyment

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28 3.4.6 Perceived usefulness

More Likert-scale based statements addressed users‟ perceived usefulness (PU). There were statements about convenience of apps and their usefulness in daily routine. For the use of good statements, again questions from prior studies were adapted (Karson, 2000; Davis, 1989; Karahanna et al., 1999; Klopping & McKinney, 2004; Venkatesh, 2000).

3.4.7 Demographics

Finally, the questionnaire ended with some demographic questions. The respondents were asked about their gender, age and their daily work.

3.4 Analysis

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4. RESULTS

4.1 Descriptive statistics

Table 2 lists the sample attributes. One hundred and forty respondents started with the questionnaire. Out of those 140 respondents, fifteen left the majority of the questions unanswered and, consequently, were deleted from the results. Nineteen respondents did not have to continue with the questionnaire because either they never had downloaded an app (seventeen people) or did not know if they ever had downloaded an app (2). An additional seven respondents skipped or overlooked some questions however they were left in as the majority of their answers were still valid and useful for the results.

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TABLE 2: Attributes of the respondents

Frequency Percent Valid Percent

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4.2 App usage behavior

4.2.1 Most downloaded and used apps

Figure 6 shows which categories of apps are downloaded and used most by all users. Figure 7 and Figure 8 show the most downloaded and used categories per different smartphone respectively. Respondents were asked to divide 100% among the different app categories to determine users‟ download behavior (e.g. 20% for a category means of all apps downloaded, 20% falls in this category) and app usage behavior (e.g. 20% for a category means of all the time spent on using apps is spent on using an app from this category). The most popular apps in general are 1. Social apps (25% downloaded, 34.21% used) 2. News/ Weather apps (21.07% downloaded, 21.7% used) and 3. Games (17.54% downloaded, 12.55% used). Social and News/ Weather apps are more used than they are downloaded as opposed to games which are being more downloaded than they are used. With social apps it is likely that users only have a couple of social network apps (e.g. Facebook and Twitter) but use it to frequently update their status. The other way around is also possible, as it is can be with games: users download several games but only one or two games can keep their interests. The use and download behavior across the different platform are comparable. The „Other‟ category stands out somewhat but this is can be explained by the small sample of 8 respondents (and is kept in the charts for the sake of completeness).

4.2.2 Frequency of downloads and use

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32 The number of apps people have paid for is significantly different between iPhone users versus Android (p < 0.000) and BlackBerry users (p < 0.000). iPhone users download the most paid apps; 72.5% have a few paid apps while 19.6% have paid for about half of their apps while the remaining 7.8% have never paid for an app. This is in stark contrast to the Android and Blackberry users which show similar behavior to each other (p = 0.948); 80.8% and 80% respectively have never paid for an app while the remaining 19.2% and 20% respectively have a few paid apps. A probable reason for this is that roughly two-thirds of Apple‟s apps are paid ones where with the apps in the Android market it is the other way around (Distimo, 2011). This could suggest that iPhone users are more willingly to pay for an app than Android and BlackBerry users. In the case of iPhone and Android users a reason for this could be that iPhone users are more used to the idea of paid apps, whereas Android users feel that apps should be free. In short, the respondents‟ free app/ paid app download ratio roughly reflect the free app / paid app available in the app stores.

There was a significant difference (p = 0.008) between students and (self-) employed respondents and the amount of paid apps they have. Sixty percent of the students do not have paid apps, 28.9% have a few paid apps while the remaining 11.1% have roughly paid for half their apps. For (self-) employees the statistics are inverted; 32.5% have no paid apps, 59.3% have a few paid apps and the remaining 9.3% have paid for half their apps. This would suggest that students, who in general have less to spend, are less willing to pay for apps than (self-) employees, who in general have more to spend.

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FIGURE 6

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FIGURE 8

4.3 Attitude towards price

4.3.1 Attitude towards price related factors

The majority of the respondents (77.45%) agreed (30.39%) or strongly agreed (47.06%) with the statement that price is an important factor when buying an app. There were no significant differences between iPhone and Blackberry users (p = 0.251) and Android and Blackberry users (p = 0.347). There was a significant difference between iPhone and Android users (p = 0.043) however, the differences were not contrasting. Rather, Android users were more extreme in their answers (53.8% strongly agreed) than iPhone users (34% strongly agreed).

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35 between the different smartphone users (iPhone and Android: p = 0.408, iPhone and BlackBerry: p = 0.728, Android and BlackBerry: p = 0.433). However, regarding converting free/ trial apps into fully paid apps there were significant differences between iPhone users versus Android (p < 0.000) and BlackBerry users (p < 0.000). Differences between Android and BlackBerry users were not significant (p = 0.246). No BlackBerry user has ever converted an app. Of the Android users, 88.9% have never converted an app. With iPhone users it is more equally divided; 47.1% never have converted a free/ trial app to a fully paid one. Of the 53.9% who had ever converted an app, obtaining extra features was the most important reason, followed by the reason to remove in-app advertisement. The frequency of converting an app with iPhone users is low; 12.3% convert apps occasionally while the majority (77.7%) who ever converted an app have done this only one or two times.

The results show that in-app purchases are not popular. Most BlackBerry (90%) and Android (92.3%) users never buy in-app purchases (similar results; p = 0.675). iPhone users are less reluctant; 60.8% never buy in-app purchases. The remaining iPhone users purchase in-app seldom or once in a while. This differs significantly from Android users (p = 0.06) and BlackBerry users (p = 0.071).

4.3.2 Van Westendorp’s Price Sensitivity Meter

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36 The PSM outcomes of the individual smartphone categories show that with each subsequent category, the numbers of respondents are roughly being halved. This should be taken into account as each subsequent PSM analysis loses validity. Nevertheless, it is interesting to look at the differences; notable differences between iPhone and Android users are the IPP (€0.16) and to a lesser extent, the PME (€0.06). Most striking with BlackBerry users is that their willingness to pay for apps is significantly lower. However, it should again be noted that only 12 useful PSM outcomes from BlackBerry users were available.

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37

FIGURE 10

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38

FIGURE 12

TABLE 3: Overview of the PSM findings

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39

4.4 Important factors for purchase

4.4.1 App store factors: rating, number of ratings, description and position in “Top-download charts”

The rating (i.e. number of stars) an app receives was considered an important factor when considering the purchase of an app; once again, iPhone users seemed to feel this the most; 60.8% found this factor to be important, 23.5% deemed it very important. For Android users, distribution was: 42.3% important and 34.6% very important. The differences between iPhone and Android users were not found to be significant (p = 0.216). BlackBerry users were more divided about the importance of the rating; 30% thought the rating was important, 10% thought very important while 15% thought it was not important and 20% not important at all (p = 0.004). BlackBerry users‟ opinions were significantly different from both iPhone users (p = 0.017) and Android users (p = 0.058).

The opinions about the importance of the number of ratings/ reviews were widely dispersed and not significantly different between iPhone and Android users (p = 0.395) and iPhone and BlackBerry users (p = 0.232). Of all users 37.2% did not find the number of ratings important while 38.3% did find it important. The remaining respondents found neither (24.5%). Differences between Android and BlackBerry were found to be significant (p = 0.091). An explanation can be found in the fact that 22.2% of the BlackBerry users found the importance of the number of reviews not important at all as opposed to only 3.8% of the Android users. Additionally, none of the BlackBerry respondents found the number of reviews very important as opposed to 19.2% of the Android users.

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40 Opinions about the importance of the position of an app in “Top-download charts” are not significantly different between the different smartphone users (for iPhone and Android users, p = 0.238; for iPhone and Blackberry users, p = 0.356; for Android and BlackBerry users, p = 0.252). The majority of the respondents believe that the position of an app in “Top-download charts” is important (44.1% thinks it is important, 23.7% thinks it is very important). Of all respondents, 14% did not think it was important and 18.3% thought neither.

4.4.2 External reviews

External reviews of apps on the internet were not considered important by most respondents. Opinions of iPhone and Android users were very much alike (p = 0.985); 22.7% did find online reviews important and 6.7% found them very important. However, 29.3% considered online reviews not important while 20% did not find it important at all. The remaining respondents (21.3%) were indifferent towards online reviews. BlackBerry users seemed to find online reviews less important than iPhone and Android users as none of the BlackBerry users found online reviews very important, 5.3% found them important, 26.7% found online reviews not important and 42.1% found them not important at all. The remaining 26.7% of the BlackBerry users were indifferent towards online reviews. The differences, however, were not found to be significant compared to iPhone users (p = 0.123) and Android users (p = 0.219).

4.4.3 App X and App Y

Two fictional apps „App X‟ and „App Y‟, both similar in nature, were presented to the respondents as they would appear in Apple‟s App Store. Factors that the respondents could assess were; price, rating, number of ratings and the app‟s position in the “Top-download” chart (see Appendix C, question 16 and 17). The majority of the respondents chose „App X‟ (79.1%). No significant differences were found between different smartphones users (for iPhone and Android users p = 0.558; for iPhone and BlackBerry users, p = 0.743; for Android and BlackBerry users, p = 0.934).

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“Top-41 download” chart. The remaining 5.9% of the respondents that chose „App Y‟ based their decision mainly on the rating (94.4%). The remaining 5.6% chose „App Y‟ because of the price, which is striking because „App Y‟ is more expensive than „App X‟. The above findings were found to be highly significant (p < 0.000). The reasons for choosing „App X‟ or „App Y‟ were highly similar

for iPhone and Android users (p = 0.909). BlackBerry users differed from iPhone users (p = 0.09) and Android users (p = 0.081) mostly in that they found the number of ratings less

important (5.9% for BlackBerry users, 20.3% for iPhone and Android users). Furthermore, the position in the “Top-download” chart was for none of the BlackBerry users a decisive factor as opposed to 18.8% for iPhone and Android users. This could suggest that BlackBerry users are less influenced by peers than iPhone and Android users as both the number of ratings and the position of the app in the “Top-download” chart give an indication about the popularity of the app.

Concluding, price was considered the most important factor and the main reason „App X‟, the cheaper app of the two, was chosen in favor of „App Y‟. The rating was considered to be the second most important factor and was for most respondents the reason to choose the more expensive „App Y‟. Here, the perceived quality of peers proved to be more important than price.

4.5 PEOU, PU and enjoyment.

4.5.1 Perceived Ease of Use

Questions related to PEOU asked if respondents thought apps were easy to use, easy to find, and if it took them long to understand how to use.

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42 Additionally, none of the iPhone users found apps hard to use and 6% remained neutral while 5.3% of the BlackBerry users did find apps hard to use and 15.8% remained neutral.

Additionally, most iPhone and Android users agree that the apps they seek are easy to find (46.7% agree, 44.0% strongly agree) (p = 0.474). BlackBerry users are less agreeable to this statement; while the majority (52.0%) agrees, only 5.0% strongly agree that apps are easy to find. Furthermore, 21.1% neither agree nor disagree, 15.8% disagree and the remaining 5.3% strongly disagrees. These differences are significant compared to iPhone users (p = 0.001) and Android users (p = 0.08). A possible explanation for these findings could be that certain type of apps cannot be found because they simply do not exist due to the fact BlackBerry‟s app library is less extensive than that of Apple‟s App Store and the Android Market.

Finally, the learning curve for the use of apps appears to be short as 87.4% disagrees with the statement “It took a long time before I understood how apps work” (43.2% disagree, 44.2% strongly disagree). The remaining responses consisted of 6.3% “neither disagree nor agree” as well 6.3% who agreed with the statement. No significant differences were found between iPhone and Android users (p = 0.395) and Android and BlackBerry users (p = 0.428). The differences between iPhone and BlackBerry users were close to being significant (p = 0.141). When comparing the answers of iPhone and BlackBerry, 73.3% of the BlackBerry users and 92% of the iPhone users disagreed or strongly disagreed with the statement. Furthermore, 15.8% of the BlackBerry users and 2% of the iPhone users neither agreed nor disagreed and 10.5% of the Blackberry users and 6% of the iPhone users agreed with the statement. It would seem that more BlackBerry users need a longer time to understand apps than iPhone users however, as mentioned above, the differences were not found to be significant.

4.5.2 Perceived Usefulness

Questions related to PU asked if the respondents thought apps were useful in their daily routine and if they thought that apps made their life more convenient.

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43 appeared to be more dispersed. This difference was significant when comparing iPhone to BlackBerry users (p = 0.089) however, it proved not to be significant in relation to Android users (p = 0.172).

Secondly, the majority of the respondents also believed that apps made their lives more convenient albeit slightly less positive then about the usefulness in the daily routine (49.5% convenient, 18.9% very convenient). Again, here no one thought that apps did not made their lives more convenient at all, 5.3% thought that apps did not made their lives more convenient while 26.8% were neutral. Here, differences between the different smartphone categories were not apparent (for iPhone and Android users, p = 0.328; for iPhone and BlackBerry users, p = 0.196; for Android and BlackBerry users, p = 0.167).

4.5.3 Enjoyment

To measure the enjoyment, statements were proposed about; the fun of using, the enjoyment of using and the effect of in-app advertisement.

Firstly, the fun of using: the greater part of the respondents agreed that apps are fun to use (58.9% agree, 25.3% strongly agree) while 5.3% disagreed. There were none who strongly disagreed and 10.5% of the respondents remained indifferent. No significant difference was found between iPhone and BlackBerry users (p = 0.385) and Android and BlackBerry users (p = 0.287). However, a significant difference was found between iPhone and Android users (p = 0.09). Android users seem to be more explicit when asked if they have fun using apps; 42.3% agreed and also 43% strongly agreed, whereas 68% of the iPhone users merely agreed and 20% strongly agreed.

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44 agreed. This difference was found to be just outside the significance barrier (p = 0.012). Although 57.9% of the BlackBerry users agreed with the statement, which is more or less the same percentage as iPhone and Android users have in that category, 26.3% do not get enjoyment by using apps. This opposed to iPhone and Android users, of which only 4% and 7.7% disagree with the statement respectively. These differences were found to be significant (for iPhone and BlackBerry users, p = 0.005; for Android and BlackBerry users, p = 0.077).

Finally, most respondents agreed that in-app advertisement is seen as a nuisance (30.9% agreed, 40.4% strongly agreed). There was a relatively large part that was indifferent toward in-app advertisement (20.2%). The remaining respondents disagreed with the statement (6.4% disagreed or strongly disagreed (2.1%). Android users seemed to be more indifferent than the other smartphone users (30.8%) however this was not proven to be significant (for iPhone and Android users p = 0.262; for Android and BlackBerry users, p = 0.453). Responses from iPhone and BlackBerry users were very similar (p = 0.922).

4.6 Network related factors

The last part of the results looks at network related factors. These statements were aimed to see if peers/ friends who already have a certain app have any influence on the user‟s decision to adopt that app and to gain insight in users‟ perceptions of apps that are depended on interaction with other users (such as „WhatsApp‟). A majority takes into consideration if peers/ friends already have the app prior to purchase (38.5% agreed, 22.9% strongly agreed). The remaining respondents disagreed with the statement (13.5% disagreed, 8.3% strongly disagreed) while 16.7% were indifferent. It would seem that BlackBerry users take their peers/ friends more into account, as 55% agreed and 22.9% strongly agreed, however this proved not to be significant (for iPhone and Android users p = 0.436; for iPhone and BlackBerry users, p = 0.414;for Android and BlackBerry users, p = 0.473).

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46

5 DISCUSSION & CONCLUSIONS

This final chapter will first discuss the results and provide managerial implications. Secondly, the conclusion section will summarize this thesis and explain the success story of apps and the factors that determine their success. Finally, the limitations of this research will be dealt with and suggestions for further research will be given.

5.1 Discussion

5.1.1 Differences: iPhone, Android and BlackBerry users

The app „revolution‟ started with Apple and their App Store still offers the most complete app library to users compared to its competitors. While iPhone and Android now offer similar app experiences and the gap closes in terms of sheer number of available apps, Google‟s Android Market only has one-third the number of paid applications as Apple's App Store (eWeek.com). Moreover, most Android developers are not commercially successful (Distimo, 2011). BlackBerry lags behind altogether. All this is being reflected in the results of the survey: it is clear that iPhone users are more familiar and app oriented than Android and BlackBerry users, the other main respondent categories. BlackBerry users are more “app-averse” (e.g. they want to pay less for apps, download apps less frequently) and also Android users are more cautious than iPhone users. Implication for managers of development studios is that the iPhone is the logical platform of choice to develop for considering its users are the most app-minded.

5.1.2 Most downloaded and used apps

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47 question was misinterpreted by the respondents. However Nielsen‟s 2009 app report also showed that social networking apps were frequently downloaded. Although social and news are among the most popular categories, earning money through traditional user downloads will prove to be difficult for new developers as both categories are already saturated and come forth of existing social networking or news companies. Moreover, these apps are often downloadable for free. Developing in these categories of apps should use a different approach by working on commission and develop apps for other firms (such as news companies or social sites). Games, then, are the most successful app category when the top-grossing chart, possibilities and user preference are taken into account. A managerial implication then, is for developers that are aiming for a commercially successful app, a game app will most likely yield the best results.

5.1.3 Price related and App Store factors

A striking finding in general was that the amount of paid apps users download is relatively low as about half survey respondents never paid for apps and the other half only paying for a few apps. Furthermore, most users were reluctant when it came to buying in-app purchases. This is remarkable as Apple App Store Top 200 „most grossing apps‟ consists of many apps that are free to download. These apps earn their money because users by additionally in-app (i.e. through the freemium business model) (appshopper.com). Therefore it was to be expected that the respondents would make use of in-app purchases. Clearly, this is not the case. It seems that Dutch app users differ from the global tendency in that they are less willing to pay for apps, which is something to consider when app developers tailor an app to the Dutch market. This implicates for managers that when developing an app tailored to the Dutch market, a freemium approach will most likely not yield positive results rather the app should be sold for one fixed price.

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48 lie between €0.49 (PMC) and €0.99 (PME). This is in congruence with the most popular, successful App Store apps which are priced at €0.79 or (use the freemium approach). The indifference price point (€0.90) and optimal price point (€0.87) are higher than the minimum app price of €0.79 (with the exception for BlackBerry users whose price points are considerably lower). This would suggest that developers could charge more for an app. Apps that fall into higher tiers are less likely to appear high in “Top-download” charts. It should however be noted that the PSM has to be used as an indication and not as a definitive price determinant. Furthermore, there could be a „chicken and egg‟ situation; did the respondents price the app objectively on its features or are they used to the price level of €0.79? The difficulty for a developer is that once competitive apps are all priced at the minimum (and optimal) price of €0.79, the developer cannot compete by underpricing his app. Therefore, the second most important aspect for the user is perhaps the most important aspect for the developer: rating.

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49 5.1.4 Perceived ease of use, Perceived usefulness and enjoyment

Next to price, perceived ease of use, perceived usefulness and the hedonic aspect of enjoyment were all found relevant. The success and wide adoption of apps therefore can also be attributed to these factors. Prior to the introduction of the App Store the ease of use was much lower as users had to manually search and install apps through third party developers. With the introduction of one central store where all apps can be found, downloaded and installed greatly increased the ease of acquiring an app. This is being confirmed by the results of the survey. Furthermore, apps are all designed in such a way that they are easy to use. This also is backed up by the results, were the respondents agreed that apps are easy and fun to use. Managers should take note of this as "quick cash-in" apps that lack these factors will be noticed by users and undermine an app's success.

5.2 Conclusion

This thesis set out to explain the success of apps and gain insight in factors that determine the success of an app. The app economy has grown rapidly in its short existence however; little to no research has been performed about this subject. Therefore, an exploratory study was conducted about this topic by first examining the inception of apps and exploring their relatively short history. Secondly, existing literature was reviewed that covered relevant concepts and looked at similar industries as current literature about apps is virtually non-existent: subjects that were discussed were network effects, (hardware and software) adoption literature, the technology acceptance model, business models and information goods. With the knowledge gained from the literature review, a survey was constructed and held under smartphone users to look at their app habits and factors that they found important when using apps.

The combination of a central store where apps can be downloaded and an already large installed base of smartphone users paved the way for the success of apps. Entry barriers for developers were low and apps could be easily developed. The App Store functioned as a mediator: developers gained easy access to all potential customers at once and customers found all available apps in one place.

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50 of the TAM model that are important in the acceptance and adoption of technology were also found important for the adoption of apps: perceived usefulness, perceived ease of use and enjoyment were all found relevant to users‟ perception of apps.

There were found significant differences between the three largest categories of smartphone users: iPhone users are the most comfortable with the use of apps followed by Android users. BlackBerry users the least app centered. While social, news and game apps are the most popular apps, games are the most commercially viable apps. When looking at the most grossing apps; the freemium business model or offering the app for €0.79 brings forth the most successful apps. Developers should take note, that (Dutch) respondents of the survey were reluctant to buy in-app, suggesting that the freemium model would not be successful in the Dutch app market.

5.3 Limitations and suggestions for future research

This thesis has several limitations that should be mentioned. In general, being one of the first exploratory studies about smartphone apps, there is still much that could be addressed in future research as every aspect of this study could be researched more in depth. That being said, what follows are some limitations and suggestions that can help future research on their way.

Firstly, the sample size consisted of mainly students or respondents who were just started working, resulting in a young respondent panel (mainly between 20-30 years old). While these respondents most likely are the people who use apps the most, it is an imperfect representation of the entire population. Furthermore, the respondents were mostly Dutch and their habits do not reflect the habits of other nations or cultures. A good example where this becomes apparent is the fact that the respondents were reluctant to buy in-app purchases while data on high grossing apps suggest that these apps perform very well. Therefore, future research should focus on finding a more differentiated, larger sample. Furthermore, sampling frame error may occur; being an online questionnaire, respondents may have answered more than once.

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51 respondents do not care for a game app and therefore are willing to pay less for it. Another possibility is to design surveys tailored for each type of smartphone. It became apparent that there are significant differences between the different smartphone users.

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52

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“THERE’S AN APP FOR THAT!”

“THERE’S AN APP FOR THAT!”

Explaining the success of apps

“THERE’S AN APP FOR THAT!”

Explaining the success of apps

ABSTRACT

PREFACE

Table of contents

1.

INTRODUCTION

2.

LITERATURE REVIEW

3.

METHODOLOGY

4.

RESULTS

6.

REFERENCES