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Monetization

when the time is

limited

MASTER THESIS WITHIN: Business Administration NUMBER OF CREDITS: 30

PROGRAMME OF STUDY: Digital Business MSc. AUTHOR: Jeroen Gubbels & Sophie Langer JÖNKÖPING May 2020

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Acknowledgement

For the creation of this thesis we would like to thank several people who made this an exciting and insightful journey. First of all, we would like to express our gratitude towards Ryan Rumble, for his continuous efforts into guiding us throughout this experience. On multiple occasions, he provided us with strong reflections on the purpose of our thesis, which significantly improved the quality. Also, we would like to thank Maximilian Drubba and Philip Svenningsson and the other participants in our seminar for their regular feedback from an outsider perspective.

Regarding the content of this thesis, we would like to thank all of our expert and case study interviewees for their expertise and availability to let us learn about their industry. Next, extra attention goes out to the people around us, who have supported us through their enthusiasm, network and time. Without them, this thesis would have been less fun.

Lastly, we would like to thank each other for the dedication towards this thesis. We have complemented, challenged and supported each other. We made sure that we kept our lightness even when deadlines came too close too fast and we are proud of the progress we made because of that.

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Master Thesis in Business Administration

Title: Monetization when the time is limited – a multiple case study on temporary mobile apps

Authors: Jeroen Gubbels & Sophie Langer Tutor: Ryan Michael Rumble

Date: 2020-05-18

Key terms: Mobile App Monetization, Temporary Apps, Monetization Strategies, Data Monetization, Retention, App Usage Data Analysis

Abstract

A successful mobile app monetization strategy is the foundation of any sustainable future business. App developers, in this regard, face the demanding challenge of building, maintaining and monetizing this strategy respectively. Factors, such as users' increasing unwillingness to pay for an app, impacts monetization methods negatively which makes current monetization strategies ever more challenging. Particularly for temporary apps, this phenomenon is ever influential. This research therefore addresses how companies can maximize the monetization of users if the usage of the app is limited by time. The researchers examined existing literature on app monetization and discovered that no research has been conducted on temporary apps yet, which highlights a specific research gap in a changing business environment. By conducting expert interviews on app monetization in combination with a multiple case study, investigating four temporary apps, this research found out that temporary apps do not monetize differently than non-temporary apps. This paper uncovered that there is a trend happening within the mobile app monetization industry that shifts from user-based monetization, where the user pays for the app, towards a partner-based monetization strategy. In this regard, external companies provide the revenue for the app. Particularly interesting is the potential of mobile data monetization, which is invisible for the user, thus providing a valuable strategy for the company. Comprising all executed research and insights gathered, the paper built the Mobile App Monetization Model. It examines the challenges and opportunities companies face during their monetization and which success and goal metrics are influential in their decision-making. It summarizes the current most important topics in the mobile app monetization field.

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Table of Contents

List of Figures ... VI List of Tables ... VI 1. Introduction ... 1 1.1 Background ... 1 1.2 Problem Discussion ... 2 1.3 Purpose ... 3 1.4 Operational Definitions ... 4 2. Literature Review ... 5 2.1 Mobile Apps ... 5

2.2 Monetizing Content Online ... 6

2.3 App Monetization ... 7

2.3.1 Paid Apps ... 8

2.3.2 Consideration of Paid versus Free Apps ... 9

2.3.3 Freemium Models... 10 2.3.4 Partnerships ... 14 2.4 Purchase Stages ... 17 2.4.1 Pre-Purchase Stage ... 18 2.4.2 Purchase Stage ... 18 2.4.3 Post-Purchase Stage ... 19 2.5 Research Model ... 20 3. Research Method ... 22 3.1 Methodology ... 22 3.1.1 Research Philosophy ... 22 3.1.2 Research Approach... 23 3.1.3 Research Strategy ... 24

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IV 3.2 Methods ... 24 3.2.1 Sampling Strategy ... 25 3.2.2 Interview Design ... 28 3.2.3 Data Collection ... 28 3.3 Data Analysis ... 32 3.4 Research Quality ... 34 3.5 Research Ethics ... 36 4. Analysis... 39

4.1 Tree Diagram Visualization of the Content Analysis ... 39

4.2 Monetization choice ... 42 4.2.1 User-Based Monetization ... 42 4.2.2 Partner-Based Monetization ... 43 4.2.3 Multiple Methods ... 44 4.3 Success ... 45 4.3.1 Goals ... 45 4.3.2 Success Factors... 46 4.3.3 Success Evaluation ... 52 4.4 Challenges ... 54 4.4.1 General Challenges... 54 4.4.2 Industry-specific Challenges ... 56 4.5 Opportunities ... 58 4.5.1 Current Opportunities ... 58 4.5.2 Future Opportunities... 59 4.6 Organizational Structure... 61 4.6.1 Start-up vs Corporate... 61

4.6.2 Monetization within Teams ... 62

5. Discussion ... 64

6. Conclusion ... 68

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6.2 Managerial Implications ... 69

6.3 Limitations... 71

6.4 Future Research Directions ... 73

7. Reference List ... 75

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VI

List of Figures

Figure 1: Temporary App Monetization Model ... 21 Figure 2: Tree Diagram Visualization of our Categories ... 40 Figure 3: Mobile App Monetization Model ... 67

List of Tables

Table 1: Expert Interviewees ... 29 Table 2: Case Interviewees... 30

Appendix

Appendix 1 - Interview Guidelines – Case Interviews... 89 Appendix 2 - Interview Guidelines – Expert Interviews ... 89 Appendix 3: Complete Coding Tree ... 90

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1. Introduction

______________________________________________________________________ The introductory chapter serves as the presentation of the business and research context this thesis will operate in. It starts with the challenge for mobile apps to monetize and the identification that this challenge is more urgent for temporary apps. The chapter concludes with the research purpose and our research question.

______________________________________________________________________

1.1 Background

Mobile phones play a fundamental role in our lives. In 2019, there were 6.8 billion users worldwide and the forecast for 2023 even predicts 7.33 billion users globally (Statista, 2019a). Over the years, the time and frequency that people spend on these devices is increasing (Rakestraw et al., 2013). The main reason for this are apps (Lin et al., 2015). They are opening up easy access to information in many areas, such as business, lifestyle and many more (Hsu & Lin, 2015). For a few years, the mobile market has been accounting for more turnover than the web. Within a short time, the mobile market has become mainstream and the future growth perspective for the app industry still holds a lot of potential (Berthene, 2018).

In the past few years, there has been a tendency of users to download apps for free as people are often not willing to pay for an app. This is mainly due to the fact that users cannot estimate the quality and usefulness of the app because they do not have the chance to try it out before making the purchase (Hong & Pavlou, 2014; Liu et al., 2014). In fact, 90% of the Apple App Store and 95% of the Google Play Store apps can be downloaded free of charge (Iqbal, 2019, 2019; Hsu & Lin, 2015). Generating revenue is thus more challenging for developers of all categories, as selling their app for money is not a popular method anymore (Arora et al., 2017).

This creates the need for business model innovation. Direct monetization models, meaning the revenue generation from selling an app to mobile phone users, are gradually becoming less common. It is challenging for developers to earn money just by selling the app to users of mobile devices and thus to get money in return for their efforts. Depending

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on the individual characteristics and the purpose of the app, different key metrics are important to assess its monetization potential, especially if the app is free to use. The first is the number of downloads. A higher number of downloads is the basis for the ability to create revenue within the app (Picoto et al., 2019). For some monetization strategies, app engagement is needed. In this case, if a consumer is actively engaged with the app, monetization is possible for the time of the engagement. This could be the case if the developers facilitate the engagement between the user and an external partner, for instance. Developers aim for a high usage time per session, and for a long usage period of the app itself, which is called retention. The opposite of retention is churn, which marks the point in time when the user stops using the app. These factors play an important role regarding the sustainability and profitability of an app. This means that a longer retention will presumably also have a higher monetization value per user (Datta et al., 2015; Goldstein et al., 2014).

1.2 Problem Discussion

Retention is considered one key success factor for app monetization (Datta et al., 2015), especially when the user does not have to pay for the app upfront. The longer users interact with the app, the higher the possibility to generate revenue. Retention in particular is a challenge if the expected usage of the app is temporary. Temporary apps are designed to provide a solution for specific purposes, which often last a temporary time. Examples of this could be dating apps where the app is being deleted after a partner has been found, language learning apps where the usage of the app could end after leaving a foreign country, pregnancy apps where the purpose of the app is fulfilled with giving birth, (30-day) challenges apps where the time period or goal is achieved and university apps losing their purpose after graduation. Especially the temporary element encompassed in these apps make the monetization challenge more important since the possibility to monetize ends after a certain time, whether this time span is predefined or not. For challenge apps or pregnancy apps, this time span is predefined due to the nature of the app. But for dating apps, for instance, the time span of the usage varies with the search of finding a romantic partner.

Temporary apps have in common that they are not able to create long-term engagement, since with the fulfillment of their purpose, the apps are likely to be deleted. Consequently,

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retention is not possible beyond the time of the purpose, which means that temporary apps are facing individual challenges due to their temporary nature when trying to monetize. If most apps rely on maximizing engagement and retention in order to maximize monetization, what other possibilities do temporary apps have to monetize?

1.3 Purpose

As multiple other researchers have highlighted, there is a lack of research on mobile apps and the monetization of their business models (Datta et al., 2015; Newman et al., 2018; Tang, 2016; Winnie et al., 2019), which we will build upon. While there has been some academic research on the methods of how apps can monetize, the research and structure in the field are still fragmented. Appel et al. (2019) and Tang (2016) describe methodologies for monetization, without focusing on multiple types of monetization, and Datta et al. (2015) and Eisingerich et al. (2019) focus on retention in relation to advertising as ways of monetization. Whilst research has been touching upon the alternative types of monetization and which considerations should be made, there is a lack of studies on apps with a temporary usage.

The purpose of this study lies in identifying the possibilities, challenges and considerations that need to be taken into account for app developers who created an app with limited usage time. Our explicit research question which we are aiming to answer therefore is:

“How can companies maximize the monetization of users if the usage of the app is limited by time?“

Our approach will be the following. In a holistic literature review, we will present the current state of research on app monetization and develop a theoretical model that will serve as the foundation of our research. Furthermore, we will run a two-stage research, beginning with conducting interviews with industry experts that contribute knowledge in the field of app monetization, followed by a multiple case study in which we analyze four cases of temporary apps and their monetization strategies. The expert interviews will be used to strengthen existing knowledge from research with practical knowledge for the case studies. We will then deduce the specific considerations and challenges of the

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companies behind our cases to derive implications for managers, product owners and app developers who are strategizing their app business model.

1.4 Operational Definitions

To assure a clear understanding of the terminology used in this thesis, we introduce several important operational definitions used in our context. These definitions are also explained when first mentioned, but due to their importance in our paper, they are elaborated upon here:

When discussing apps, this refers to the software applications that can be downloaded on mobile phones or tablets. They can be downloaded from an app store, such as Google Play or the iOS App Store. An example could be a calculation application or other apps like Spotify or Uber.

When discussing developers, this paper means the complete team behind an app. This ranges from developers themselves, to designers, marketeers, content creators and app strategists, as examples.

Lastly, when discussing a temporary app, it is an app where the user, at the time of the download, expects to delete the app after the occurrence of a certain event. Such events could range from physical (e.g. birth or weight loss) to mental (e.g. falling in love) and external events (e.g. moving countries or achieving a certain goal in a game). After this specified life event, the app loses its purpose.

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2. Literature Review

______________________________________________________________________ The literature review forms the foundation of our research. It will start with an introduction to mobile apps to then present current possibilities of mobile app monetization. Purchase stages will be portrayed and transferred to the app environment. The chapter concludes with our proprietary theoretical model used for the data collection and analysis.

______________________________________________________________________

2.1 Mobile Apps

Apps that are installed on a smartphone provide an extension to the possibilities of the device itself. Whereas the original purpose of a mobile phone was to make calls, now a wide variety of applications have been built on top of it. Since the release of app stores in 2008, the mobile app industry has experienced substantial growth and is not expected to slow down. In 2019, there were over 2 million apps available on the Google Play store and 1.83 million on the Apple App Store (Statista, 2019b).

With this growth, there has also been an increase in the numbers of these applications, and they have developed into specific app categories. The categories that have emerged are currently touching upon a wide variety of applications. They range from health and fitness to business, education, news, productivity, games, lifestyle onto social networking. A few example applications are, for instance, Uber, Snapchat, TikTok, and Tinder (Goldstein, 2018). In the app stores, gaming is the largest category in turnover and diversity of apps. It accounts for 25% of all active applications and an industry value of around $50 billion in 2018 (Rutz et al., 2019). Within academic research on mobile apps, gaming is also the most common field (Baghbaniyazdi & Ferdosara, 2017; Lambrecht & Misra, 2017; Lescop & Lescop, 2014).

When searching for academic literature on temporary apps, using keywords such as: temporary apps, apps with temporary duration, time limited apps, monetization of apps, monetization, temporary app monetization, and similar others, we have not found any paper highlighting a related challenge to ours. We tried searching in American English and British English. That is the reason why, to the best of our knowledge, no research has

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been conducted on the existence and monetization of temporary apps yet. These apps differ from non-temporary apps in the time-limited usefulness of their services. It is inevitable that some users of apps stop using it at a certain moment, when they lose interest for example. But with temporary apps, they lose their specific purpose due to a particular life event of the user. These events could range from physical, to mental or external events for the user. Examples are pregnancy trackers, as well as dating or university apps. Whereas the developers want users to use these apps as long as possible, they lose their purpose when users have given birth, found a romantic partner or graduated from university. As this topic is not discussed in academic literature, this paper is the first to define a temporary app and enables future researchers to build on this.

2.2 Monetizing Content Online

With the emergence of the online sphere, analog, traditional businesses with a physical presence have attempted to exploit the potential of this new marketplace by transferring their offline business into the online world. Retail shops added an online presence next to their physical presence, e-commerce began to grow rapidly (Brynjolfsson et al., 2013; Pozzi, 2013). New business models started to evolve, such as online marketplaces like Amazon and Alibaba. They generate revenue by selling third-party goods and receiving fees for successful sales (Choi & Mela, 2019). Content providers did not struggle with uploading their content, but with monetizing it afterwards. Many newspapers, as one example, started to publish their news content online and today, they mainly have two monetization options: a paywall, so only paying users can see all the content, or micropayments, such as paying for individual articles that the reader was interested in (Geidner & D’arcy, 2015; Graybeal & Hayes, 2011; Sjøvaag, 2016). But since the majority of news content can be found online through a free google search, the news content of a subscription news provider is substitutable with the content from a free provider. One tactic was to use a revenue stream that also worked as an established practice in the offline sphere: advertising. However, relying solely on advertising generated neither steady nor sufficient revenues, so many newspapers failed with their online businesses and ultimately went out of business (Pattabhiramaiah et al., 2019). These new monetization options and business models that have emerged are also applicable to the app monetization challenges.

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2.3 App Monetization

Generating revenue is crucial for the future existence of for-profit organizations. When building a commercial app, it is designed to earn revenue and typically, this can occur in two ways: directly or indirectly. A direct monetization strategy includes the actual sale of the app, so users make a payment in the app store to download the app, hereafter referred to as a paid app. Moreover, there are indirect monetization strategies which entail offering an app free of charge for the download. The revenue generation can occur by partnering up with advertisers or by introducing a version where revenue is generated by e.g. in-app purchases. The latter is referred to as a freemium strategy (Dinsmore et al., 2017; Lambrecht et al., 2014; Tang, 2016). While the monetization strategy is important for commercial apps, Arora et al. (2017) detect in their paper that a lot of developers choose to offer their app for free to adhere to common practice, without thinking much about the consequences and the monetization strategy’s potential success.

Research has identified two groups of monetization strategies, dependent on which party will provide the revenue. In essence, developers need to decide if an app will be paid for by consumers or advertisers (Rakestraw et al., 2013; Tang, 2016). Examples discussed in research of consumers having to pay for the app are through paid downloads, subscription models and in-app purchases (e.g. Kumar, 2014; Liu et al., 2014; Roma et al., 2016). On the other side, there are also companies who spend money on advertisements to make the app free for consumers. Companies might advertise in the app or provide incentivized features, and in return the consumer can use the app for free. Such features could be that the user downloads an advertised other app, to acquire benefits in the first app (Appel et al., 2019; Baghbaniyazdi & Ferdosara, 2017).

A crucial challenge to overcome is the tendency that the interaction of users with the app declines rapidly after the initial download. It is easy for users to delete an app when they do not like it and the incentive to hold on to an app is even lower when they were able to download it for free (Datta et al., 2015). While 77% of users give a mobile app a second try (Hoch, 2017), after 30 days, only around 3% of the downloaded apps are still active (Hopwood, 2017).

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Additionally, there is a continuous challenge for companies to find the right fit between app content and monetization strategies, which, similar to other business models, can also be a combination. The existing research so far has focused on the possibilities to monetize free and paid apps in general, without distinguishing between the different types of apps. Furthermore, most of the research looks at leveraging one single type of monetization (e.g. Liu et al., 2014; Yang et al., 2013), while in the business world currently there is an increasing trend of leveraging multiple sources of income (Appel et al., 2019; Arora et al., 2017).

2.3.1 Paid Apps

Due to the widespread existence of free apps, customers have, for the most part, developed an expectation that apps should be free which makes it more difficult to convince users to buy a paid version right away (Liu et al., 2014). When a developer offers a paid app, users have to be convinced to pay the requested price before seeing the actual product. If it only shows limited value, there is a chance that they will not be willing to pay (Appel et al., 2019; Wu et al., 2015).

The majority of paid apps have a price lower than 5 USD (Finkelstein et al., 2017). An interesting viewpoint is to look at user behaviour within the two biggest app stores Apple App Store and Google Play. Although customers of the Apple App Store are assumed to be wealthier and more willing to spend money on apps, the average prices of apps in Google Play are higher. At the same time, customers of Google Play are said to be more price sensitive and having lower willingness to pay for apps (Ghose & Han, 2014). Thus, they are more adaptive to free trials of the full versions of an app (Liu et al., 2014; Roma et al., 2016). This implies that the chance of successful monetization is influenced by the device of the users because they fall into different user segments with individual willingness to pay.

Another, although less popular and hardly researched topic is the paymium approach. Within this approach, the user pays for an app and is encouraged to make additional micro transactions to further increase the usage of the app (Lescop & Lescop, 2014). In that case, it is more common for app developers to lower the price for the app itself because

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users are generally less inclined to make additional purchases within the app (Ghose & Han, 2014).

Choosing a paid app is the closest option derived by a product purchase where the consumer pays upfront to receive the product. It has been found out that consumers prefer to pay upfront for products that they use for a shorter amount of time, whereas they prefer to make continuous payments for products or services that they use for a longer time, such as gym memberships as an example (Tatavarthy & Mukherjee, 2019). Referring to temporality within apps, offering paid apps might be a useful strategy for developers since they would already receive their revenue in the beginning of the usage. This is beneficial for them because they already have less time to monetize their users due to that limited timeframe. On the other side, it might be more difficult to convince users to pay for an app of limited usage time because they do not see the value compared to an app that they use for a longer time.

2.3.2 Consideration of Paid versus Free Apps

There are multiple reasons for developers to choose a certain type of monetization. Whilst in a lot of cases it is dependent on the type of app, industry, goal and purpose, there has not yet been sufficient research on the choice and implications of certain business models, specifically for apps.

In their research, Hsu and Lin (2015) look at the determinants of users downloading free and paid apps. They found out that there are multiple aspects that play a role between the user purchase intent and user usage intent. They identified that value-for-money, app rating and availability of free alternatives are important influencers for user intention to purchase. Looking at potential users of an app, their study determines that value-for-money, social value, app ratings and free alternatives are significant influencers for the users’ adoption of an app. They also identify that the presence of a free alternative app limits the willingness of a user to pay. This observation has also been confirmed by Finkelstein et al. (2017) and Jung et al. (2012). Dinsmore et al. (2016) researched this topic more extensively and concluded that the use of banner advertisement for a free app is preferred over a paid app, suggesting a user preference of not paying for an app versus paying for it. Nevertheless, looking at adoption speed, they found something else.

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Adoption speed looks at how quickly a user adopts the app, thus becoming an active user. In the paper published a year later, Dinsmore et al. (2017), researched the adoption speed of paid versus free apps. They identified that there is a higher frequency of reaching adoption levels and that the adoption speed is faster when an user purchases a paid app, than when the app is free. In their final conclusion, they make the assumption that when an app is offered for free, this could lead to harming the adoption and potential monetization of the app in the long run. It has been concluded that paid apps have a higher user adoption versus users who downloaded the app for free (Arora et al., 2017; Dinsmore et al., 2017). Combining the findings, this would infer that users prefer not to pay for an app (Dinsmore et al., 2016), but will have higher adoption rates, when they paid for the app (Dinsmore et al., 2017).

2.3.3 Freemium Models

Freemium in General

Because of the widespread existence of free apps, customers have been developing a certain expectation that apps should be free and therefore difficult to convince of a paid version. That is why developers need to monetize early within the time of the app usage (Müller et al., 2011). Users should download the free version, engage with the app and convert into paying users - e.g. by them making a payment to eliminate advertisement, to upgrade to a full version or by paying for some other specific enrichment (Dinsmore et al., 2017; Hsu & Lin, 2015; Liu et al., 2014; Rutz et al., 2019). This so-called freemium strategy - the combination of free and premium - is a widely adopted indirect monetization strategy of app developers that leads to higher numbers of downloads than just offering a paid app. Because users can try out the free version, enjoy its usage and then upgrade to access all features the app has to offer, the amount of paying customers increases. It proves to be a successful and increasingly used monetization strategy (Gu et al., 2018; Hsiao & Chen, 2016; Kumar, 2014; Liu et al., 2014). The freemium strategy is based on constant engagement with the users which can be stimulated, for instance, by in-app purchases (Rutz et al., 2019). As a corollary of that, app developers are facing the difficulty of actually transforming free users to paying users since there is a risk that some users will decide not to pay for the full version. These users are hereafter called free users. The developers thus might face a monetary loss by offering a free version to users that will not upgrade and therefore not become paying users. Liu et al. (2014) conducted a

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quantitative study to test the effects of a freemium strategy in the mobile app market. Their recommendation is to provide a trial version of the app. They suggest that the benefits of converting users with the free version, a trial, to paying users are higher than the potential loss of free users staying free users. That means that by offering a trial, the percentage of free users converting to be paid users might decrease, but this will still create the possibility to let more users try the app. In addition, by making users pay before the download, more users would be lost that would otherwise switch to competitors' apps.

Subscriptions

One possibility to implement a freemium monetization strategy is to offer the app for free and then try to convert non-paying users to paying users through a subscription fee. This could be done by integrating a subscription fee that can be paid monthly or yearly. It is part of the marketing strategy, where companies give away free features or free products and after a trial, a subscription fee will be needed to pay to continue. Regarding a longer scale, monthly subscription earnings can be a more preferred source of revenue, since it tends to be more sustainable income than advertising or income from in-app purchases which can be fluctuant (Kumar, 2014; Seidl et al., 2018). If the purpose and thus the usage time of the app is limited, more pressure is put onto the early transformation of free users to paying users, so that developers are able to exploit monetization of the temporary users. Temporality could have a positive effect on subscription pricing though, because since the duration on the user’s phone can either be determined exactly or at least estimated, developers could set the price of the periodical subscription easier.

There are several challenges that can be endured while trying to transform non-paying users into subscribing customers. First of all, introducing features that formerly non-paying users now have to pay for can have the effect that the attractiveness of the app will decrease and that users will stop using the app (Seidl et al., 2018). The users’ willingness to pay depends highly on, for instance, their price sensitivity, perceived value of the subscription benefits and the price. Additionally, users are increasingly unwilling to commit to a subscription for a longer period of time and prefer flexible models (Pauwels & Weiss, 2008). That is the reason why purchase commitment for a longer period of time is often rewarded with a lower fee. For example, yearly subscriptions are being offered at a lower price, compared to monthly payments. This strategy entails paying less per month,

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when a user commits to a yearly subscription (Rußell et al., 2020). Thereof, finding the right balance between paid and free features can be difficult, because there is a trade-off between giving away too much functionality and at the same time not acquiring enough new customers for the free features (Punj, 2015). Moreover, it is crucial to communicate the benefits of upgrading and subscribing successfully, so customers see actual value in doing so. It is a misconception to think that once users have subscribed, they will keep using the app for a long time. Thus, there is a high importance to maintain innovativeness and to offer new features frequently, so subscriptions will be ongoing and users are not going to cancel (Kumar, 2014).

In-App Purchases

In-app purchases make up a large portion of the App Store revenues. In 2014, their contribution to the total revenue was close to 80% (Hsiao & Chen, 2016). They are defined as “microtransactions within the app“ (Ghose & Han, 2014, p.1471), with different factors that drive their purchases.

The most important factor is app loyalty of the user, but also significant engagement and perceived added value in purchasing additional features. Another factor is price which also plays a substantial role (Hsiao & Chen, 2016). Furthermore, the number of apps the developer has developed influences the demand. The more apps of high quality someone developed, the higher the trust in the new app and thus the higher demand for it. A similar effect can be observed with a large quantity of reviews and a large fraction of positive reviews. Cross-chart listing - having the same app in the charts for paid and for free apps - as well as cross-platform listing - having the same app in Google Play and the Apple App Store - can additionally positively impact the demand due to an increase in the app’s presence in both stores (Ghose & Han, 2014).

Other interesting factors to consider are gender, age and income. In a study conducted by Hsiao and Chen (2016), findings suggest that gender, age and income make a difference when looking at non-paying and already paying users. For non-paying users, the factors did not have a direct impact on the willingness to pay of non-paying users, but they did have an impact on the willingness to pay of users who already paid before.

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Especially in the gaming app environment, in-app purchases are a popular way to earn additional revenue (Baghbaniyazdi & Ferdosara, 2017). Usually, users are able to purchase additional features which can be either items that help in the game or additional content that other users do not have access to (Seidl et al., 2018). The way to convince a user to make an in-app purchase can be an individual process. Ravoniarison and Benedito (2019) identify three main stages before a user makes a purchase in a gaming app. These can be divided into (1) personal traits towards a purchase, (2) the moment of exposure to an in-app purchase and (3) how these factors play a role towards the user’s acceptance of in-app purchases and their general resistance. The overarching factors that play a role are the user’s knowledge, perceived risk, belief in benefits and value of in-app purchases and their emotional responses to the game (Ravoniarison & Benedito, 2019). Next to these user decisions, research has also observed seasonal effects. During holidays, for instance, game developers can offer holiday packages or limited editions based on themes such as Christmas, Valentine’s Day or Halloween spiking revenues at a specific time of the year (Dew & Ansari, 2018).

Reoccurring In-App Purchases

Different from other monetization methods, in-app purchases benefit a lot from reoccurring purchases. Looking at a paid app or a subscription-based strategy, users either spend money once, or the revenue is spread out over the subscription period. App developers should also consider the reasonability of price within their in-app purchasing strategy. While it can be a choice to make in-app purchases more expensive, it is recommended to focus on more user-centric pricing strategies. This would be rather focusing on a long-term, multiple purchase strategy instead of a short-term one time purchase goal (Jang et al., 2019; Ravoniarison & Benedito, 2019). The experience with paid items, play frequency and social interaction increases the likelihood of buying again in the same app as well as in other apps. The most dominant influence to make another payment in a single app, is their previous purchase experience (Jang et al. 2019).

There is another aspect that is also important in mobile games. While game developers often want to make their game more engaging, researchers highlight the risk of addiction and perceived overspending (Beltagui et al., 2019; Dreier et al., 2017; Ravoniarison & Benedito, 2019). This implies that there can be a trade-off between revenue generation

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and mental instability in the form of addiction for users, and app developers need to acknowledge their responsibility.

In-app purchases have the potential to let the user make more than one purchase, and potentially increase the value per user incrementally with each purchase (Jang et al., 2019). Within the web gaming industry, there is a common perception that there is a small group of players who make significant purchases to provide the revenue for players who are less or not willing to pay (Dreier et al., 2017). Although the web gaming and app gaming sectors have similarities, there has not been conducted sufficient research to claim a similar behaviour within apps. Nevertheless, research in web gaming has shown that communities have an important influence on the users playing the game, meaning that the feeling of a community increases their engagement and retention. It has to be remembered, though, that the majority of these players are not performing purchases (Beltagui et al., 2019; Harviainen et al., 2018).

For temporary apps, it is assumed that in-app purchases pose a challenge as a monetization strategy. First, the app has to convey the benefits of the in-app purchases and evoke the need of the user to make these purchases. Second, the temporality characteristic makes it more difficult to convince the user to make follow-up purchases due to the time limited end goal and potential imbalance in perceived usefulness of spending money. Repeat purchases are desirable, but due to their difficulty, it can be challenging to get enough in-app purchase revenue to achieve desired profits.

2.3.4 Partnerships

Partnerships in General

According to Tang (2016), there are three main methods of monetization: paid apps, in-app purchases and in-in-app advertising. With these methods also comes the decision who needs to bear the monetization cost, the user or another party. So far, this study has explored the different options of monetization by leveraging the user. To be able to do this, revenue comes from another source, such as companies paying for the monetization of the app. Here, other parties provide money to the app with the intention to get something in return. In the past years, the most common method was in-app advertising (Altuna & Konuk, 2009; Lambrecht & Misra, 2017; Tang, 2016). Advertising has been a

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common practice on the web, where companies show ads in return for offering content for free. It has also been early adopted by gaming apps (Lambrecht & Misra, 2017). Another possibility that is slowly developing is the monetization of data (Bonneau, 2015). Whilst this topic is becoming popular in other businesses, it is not yet actively researched for the mobile industry, to the best of our knowledge. That means that information about data monetization can only be deployed from other industries.

In-App Advertising

With in-app advertising, the app developer receives a certain amount of revenue for each time a user gets shown or clicks on an advertisement, hereafter referred to as an ad. With this type of monetization, the user does not have to make any purchase and the revenue comes from another company that is advertising (Tang, 2016).

For mobile advertising, there are generally two ways in which an ad can be shown for in-app advertisement. The most common practice is dynamically, where a user sees an advertisement from a third-party publisher. The app developer makes the space available and the publisher provides the ad to the user. Often, the app developers do not know which specific ad this is. Another method is through curated ads. These are checked beforehand by the app developers and only these selected ads get shown. This curated exposure maintains a higher level of control on which ad gets shown, and which one does not. The level of authority in the curated method is higher (Rutz et al., 2019).

Next to the possibilities of how an ad can be served, there are also multiple types of in-app advertisement placements. These are specific locations within the in-app where the ad can be shown. Within the gaming domain, there are two specific types of ads that are most common, interstitial ads and expandable ads. Interstitial ads are generally those ads that cover the whole phone screen, and shown during the user’s play time. It forces the user to view the ad before he or she can continue to play. Sometimes, these ads can also be skipped after a certain time (Chou & Wang, 2016). Expandable ads, as the name indicates, expand upon clicking on them. The idea behind these ads is that a user gets served an informative short text, which then can be clicked on to expand into a bigger part of the screen (Bhave et al., 2013).

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In-app advertising is a popular method, although the caveats lie in the user perception, user relevance and the frequency of ads shown as important considerations (Shuba et al., 2018). The moment that there has been a negative association, it is difficult to keep engagement and later on also retention at a competitive rate. That means that as soon as users get annoyed by the number of or content shown in the ad, they tend to ignore it, but there is also a higher chance that they might stop using the app by deleting it (Altuna & Konuk, 2009; Goldstein et al., 2014; Merisavo et al., 2007).

Comparing in-app purchases to in-app advertising, in-app advertising tends to decrease the demand for an app, whereas the option to conduct in-app purchases increases the demand. The reason for that tendency is that users expect better functionality or additional features when making purchases within the app, but advertisements signify a higher chance of annoying them (Shuba et al., 2018). This phenomenon was also described by Ji et al., (2019) and underlines the importance of weighing the level of in-app advertising against the potential increase or decrease of the user base. On the other hand, whereas in-app purchasing has the advantage of not annoying users with ads, it could decrease the user experience of users who are not willing to pay for better features and functionality.

Particularly with in-app advertising, app developers have the possibility to try out different configurations of advertising. But when the usage time of the app is already predetermined by a temporary purpose, the threat of annoying users and losing them is more troublesome than for apps that could compensate losing users with other paying users over a longer time period. That puts increased pressure on deciding on the best in-app advertising strategy.

Data Monetization

Another less researched topic is the potential of monetization on customer behavioural data. Research conducted by Chen et al. (2017) describes the possibilities and potential benefits for companies to execute deep analyses on the behaviour of users. It can untangle their patterns and preferences, and thereby improve the products and services. While they only look at the internal use of data, the information can also be used for external companies. The information itself can be of value for other companies, thus making data available to sell (Bonneau, 2015).

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It is described that selling this information directly does not promise to generate strong revenues, but possibilities arise when it can be made useful for other parties. By generalizing the information or being able to acquire specific insights that can be repurposed outside the specific app, it can function towards a bigger purpose (Bonneau, 2015). An example of this generalization could be if specific personality traits can be inferred from an app, this information can be sold to other apps or companies for their targeting. The considerations of selling and providing insights into user data has been looked at by Bataineh et al. (2016). Their paper researched the feasibility of a two-sided market model, aiming to provide user data to other companies. Their market model intends to build a fundament where companies can buy and sell this specific information, thus increasing the profitability of the monetization of data (Bataineh et al., 2016; Spiekermann, 2019).

The researchers Bataineh et al. (2016), Bonneau (2015) and Chen et al. (2017) acknowledge that this system around the monetization of user data is still early in its development stages. They describe it as a potential model for the future, either for the organization owning the data or for the user providing the data.

Data monetization as a monetization strategy is slowly developing. Its benefits, not needing to bother the user and providing additional income for the company, holds potential for the future. It is a strategy that might be deemed successful for temporary apps, depending on the usefulness of the data and finding the right partner that has a purpose and the willingness to pay for the data. If the usage data is valuable and relevant already after a short period of time, a longer usage time of the app is not necessary to generate sufficient revenues for the developer.

2.4 Purchase Stages

According to Frambach et al. (2007), there are three stages of purchasing: pre-purchase, purchase, and post-purchase. This process originally comes from a traditional, non-digital background, but it can be argued that it can be applied to the app environment as well. That is the reason why first, we will present research around these stages to then develop our own theoretical model, adapted to our field, that we will base our further research on.

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The pre-purchase stage serves as a stage in which the consumer identifies the need for a product or service. He or she wants to gather information about the different possibilities that are available and the product or service characteristics of different sellers or providers (Frambach et al., 2007). Customers tend to use different channels for gathering information that are available and most convenient for them. The phenomenon of the “research shopper” emerged, which describes the trend of consumers conducting extensive online research before carrying out the purchase (Pauwels et al., 2011; Pauwels & Neslin, 2015; Verhoef et al., 2007). With the emergence of the online channel, consumers often start with an online research about their desired product and compare different products according to criteria that are important to them (Verhoef et al., 2007). They are able to find extensive information online about product characteristics, product assortment and their availability (Dzyabura & Jagabathula, 2017).

One key challenge in the pre-purchase stage is to create trust in a brand and in the respective products. In the online sphere, it is not possible to see or feel the product before purchasing it. Therefore, consumers are searching for information that is related to the quality. One tool that they are using to assess the quality of a product or service are online reviews. For online consumers, this can serve as word of mouth and has an influence on the purchasing decision (Brynjolfsson et al., 2013; Phang et al., 2014).

2.4.2 Purchase Stage

During the search for a product, it can be observed that a consumer shifts from the information gathering of product features to comparing alternatives of different sellers (Frambach et al., 2007). Furthermore, new factors become important. Consumers put more emphasis on the different channels to receive the product and how the product will get to them in the most convenient way. Channel preferences can be different for every consumer. Some prefer to order online and get it delivered, whereas other consumers prefer to conduct research online but to carry out the actual purchase in a physical store (Li et al., 2015; Verhoef et al., 2007). Especially retailers that are offering both online and offline channels, thus accompanying many potential channel preferences of consumers, gain competitive advantage over pure online or pure offline retailers (Agnihotri, 2015; Brynjolfsson et al., 2013; Pozzi, 2013).

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As Shim et al. (2001) highlight, each purchase has a certain shopping process consisting of several phases leading to the actual purchase. When the shift from purchase intent to purchase occurs, a transaction takes place. This could be a transaction of monetary value, but also of functional or social value. Each stage in its own way is aimed at buying an item successfully, without regrets afterwards (Tversky & Kahnemann, 1981). Finalizing the purchase stage, the benefits of the product have a more dominant position (Frambach et al., 2007) and its post-purchase experience get relevant.

2.4.3 Post-Purchase Stage

In the final purchase stage, the post-purchase stage, the evaluation of the consumer and considerations for future use come into play. This is the period of reflection in relation to the pre-purchase and purchase stage, potentially even the re-purchase stage (Frambach et al., 2007). This reflection looks at the previously executed search for information, executed online or offline. It evaluates whether the expectation has been in line with the result, or whether there is a presence of cognitive dissonance, where the user is dissatisfied with the product afterwards (Chang & Tseng, 2014). One of the considerations for a positive assessment or future re-purchases is the evaluation of the initial purchase (Chang & Tseng, 2014; Frambach et al., 2007). This can be accomplished in multiple ways, but Kumar & Anjaly (2017) identified six dimensions on measuring the post-purchase customer experience. These are delivery, product-in-hand, return and exchange, customer support, benefits and feel-good factors which are enablers for the identified positive progress and potential to re-purchase later on. Furthermore, the type of user on its own and in a social setting have a significant role in the post-purchase evaluation (Chang & Tseng, 2014; Joung, 2013; Liao, 2017).

The processes and stages of the purchase model look at and evaluate what the considerations are that consumers have in these stages and how each of them affects the consumer’s decision-making within the purchase journey. This is dependent on marketing activities and personal characteristic traits, pre-, during- and post-purchase. These choices can also be linked to downloading a temporary app, where a smartphone user considers to download an app, actually downloads the app and eventually stops using the app. This last decision can be perceived from two ways: the user stops using the app as it does not

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satisfy the user’s need or the app’s purpose was fulfilled, so it loses its added value. Especially the last stage is of interest for app developers since they can already estimate at the time of the download when the app will not be used anymore. Therefore, we took this process as the foundation to build our own model.

2.5 Research Model

The overview of potential monetization strategies identified in multiple other papers has been summarized in Tang’s (2016) paper on App Business Models in the Digital Era. While the research can be seen as limited, there has, to the best of our knowledge, not been any research conducted in relation to temporary apps. In temporary apps, the need for revenue generation can be perceived as more time constrained due to their limited usage time by users. Thus, there is an urgent need to identify the most successful monetization strategies tailored to the limited time the user is active in the app. Based on our literature research, we have developed a model that combines the above described literature review in relation to the temporary limitation of our research. While it is possible to combine multiple business models, this is not always recommended, as highlighted by Lambrecht et al. (2014).

Our proprietary model below (Figure 1), which is based on our literature review and which is developed for the creation of the interview guidelines, summarizes the currently available research on the monetization of apps. We identified three stages of monetization from a user’s lifetime point of view. The first stage is before the user has downloaded the app. In this pre-download stage, the app developer needs to work on providing extensive information to convince users of downloading the app. This information looks at the app before the download, so at its organic ranking, app store images, description and reviews, which often drive the user’s intent to download (Appel et al., 2019; Wu et al., 2015).

Next, there are three main directions in academic literature that can aid the monetization of an app: paid, freemium and partnerships. Within freemium, monetization can be leveraged by in-app purchases or subscriptions. The main difference between the methods in this model is the monetization touchpoint, at which stage the actual monetization takes place. While time for temporary apps is limited, this time can potentially be longer for non-temporary apps.

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In the post-usage stage, the user has already stopped using or deleted the app from their phone. Within temporary apps, this point is reached as soon as the purpose of the app has been fulfilled. For app developers, this is an opportunity to evaluate the individual usage of the user on the basis of different evaluation criteria that the developer considers useful.

Whereas this overview gives a schematic idea of the monetization methods, it does not entail which option is best or the most profitable solution. Research has not yet uncovered the potential of the methods themselves or suitability per app.

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3. Research Method

______________________________________________________________________ In this chapter, we will explain the conducted research from a methodological point of view. In light of these considerations, we put our research into a philosophical and strategic context and indicate our chosen methods and techniques. Such as the mix of expert interviews and case study interviews, which we analyzed performing a content analysis. We base our methodological approach mainly on the work of Easterby-Smith et al. (2018) and Yin (2018). Additionally, we are going to point out the measures we took to ensure the quality of our research and present the ethical implications.

______________________________________________________________________

3.1 Methodology

3.1.1 Research Philosophy

We defined the purpose of this thesis as identifying the possibilities, challenges and considerations that need to be taken into account for apps whose effective use of app time is limited. The contextual nature of our research is an indication for qualitative research, which means that we are analyzing non-numeric data (Staller, 2010). It also entails a more flexible approach to generate knowledge instead of following a predefined plan to statistically test a hypothesis, which is inherently the nature of a quantitative study (Hammersley, 2013). In this thesis, we want to get in-depth insights into the different characteristics of apps that open up or restrain different monetization possibilities. Our results thus depend on how our research participants experience the individual circumstances of different apps and what they consider as relevant and irrelevant concerning external forces that affect the apps. This is a sign for a relativist ontology since it acknowledges that there is not one reality, but there are several ones according to how people experience their reality (Easterby-Smith et al., 2018; Smith, 2008). The researcher himself pertains to the role of the element between the research and the participants. All knowledge that is gained and observations made are filtered through the researcher. It is important to accept that there are certain interpretations and assumptions made by the researcher that influence the outcome of the research (Staller, 2010) and that we as researchers are part of the reality that we are researching (Smith, 2008).

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The research epistemology is concerned with the approach of what knowledge is and how it is acquired (Johnson & Duberley, 2000). Corresponding with our ontology, our research falls into the social constructionist research, which means that our focus is on what our research participants are thinking, how they understand reality and what experiences they have made that led them to their opinions. The importance lies in letting the participants construct their reality with all assumptions and individual experiences that they have (Curtis & Curtis, 2011; Easterby-Smith et al., 2018).

3.1.2 Research Approach

Social constructionism is often connected with an inductive approach. It pertains to an exploratory nature that aims to develop knowledge and build a theory (Curtis & Curtis, 2011; Staller, 2010). For the purpose of this research, we are interested in the in-depth analysis of certain temporary apps in their real-world context. That is why we chose a multiple case study design. A case study, in its essence, tries to find out the reasons behind specific decisions. For example why something was implemented, why it was changed and how the results have been (Schramm, 1971). One major benefit of conducting a multiple case study is the possibility of finding cross-case patterns that can help shape a better theory (Eisenhardt, 1989). In order to extract the most valuable insights, our research process will follow two steps. The first step will be gaining industry knowledge through interviews with industry experts in the app monetization environment. This industry knowledge, combined with the theoretical knowledge from our literature review, will serve as the basis for our multiple case study approach.

For a successful, insightful case study, it is important to articulate precisely what should be found out, what is the overarching question, what is the unit of analysis and what procedures will take place to collect and analyze the data (Yin, 2018). The purpose and research question of our study have been defined in the first chapter of this thesis. The unit of analysis can be an individual that is studied, for instance, or a specific event (Easterby-Smith et al., 2018). In our case study, the unit of analysis is the respective monetization strategy that was initially introduced for the app. It has potentially been modified throughout the existence of the app according to how successful the initial

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monetization strategy was. The procedures of how data is collected and analyzed will be described in detail in the methods section.

3.1.3 Research Strategy

Underlying to this thesis is a theory-generating research strategy that takes a social constructionist ontology by deploying a multiple case study. This inductive approach allows us to generate and analyze new knowledge and interpret the findings and implications by acknowledging that this reality is socially constructed by the participants and the researchers. The small sample of a multiple case study allows generalizations beyond said sample but lacks valid coverage. Additionally, subjectivity of the participants is certainly recognized, but this can also be seen as a disadvantage of the social constructionist ontology (Easterby-Smith et al., 2018). That is the reason why triangulation, the usage of various measures and data sources, is important to achieve higher confidence in the results (Yin, 2018). We therefore chose the two-fold research design as one measure to accomplish a broader knowledge basis in addition to our literature review and thus to get more targeted data within our case studies for better insights to answer our research question.

3.2 Methods

As the topic of our thesis is an underdeveloped research area, there is a demand to get a deeper understanding in this field. To acquire this understanding, primary data collection of this thesis is exploratory. Hence, this thesis will be studying the identified problem and try to find relationships and specific differences on the topic (Saunders et al., 2009).

To go into more depth on our research question and temporary app cases, we acquired in-depth knowledge in the app monetization business field through expert interviews. With this approach, we built a stronger foundation to be able to find detailed information for our multiple case study (Morse, 2014). The approach of doing expert interviews strengthened our own knowledge, made us able to achieve more synergy with respondents, develop upon our initial model and reflect better on the shared experiences of the cases and experts (Aaltio & Heilmann, 2010).

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While we initially built our proprietary theoretical model based on the literature review, we used it solely for the creation of the interview guidelines. After the data collection, we acknowledged that the responses were not fitting the model as anticipated. Therefore, we used our categories that we developed within the content analysis of the interview data in order to derive the analytical insights that better fit the purpose of this thesis. For the discussion, we re-evaluated our theoretical model and combined it with the findings of the analysis. We thus conclude this thesis with our final Mobile App Monetization Model.

The process we followed is the execution of in-depth interviews with two specific parties. One group consists of individuals within the app monetization field, described as expert interviews, and the other group contains individuals within a company who are actively engaged with the monetization decision of a temporary app. This method helped us for triangulation of the data and with the chosen exploratory method to be of complementary nature to the literature review. Furthermore, it also helps in the generalizability, as far as qualitative research goes, of the information and therefore the facilitation of one research strategy to the other (Koerber & McMichael, 2008; Saunders et al., 2009).

3.2.1 Sampling Strategy

For the research area to unfold, there is a need to connect with professionals in the field and further explore paradigms and the environment. There are multiple methods available within qualitative research, and this thesis takes a non-probabilistic purposive sampling approach. This approach benefits the method as the goal is to find certain knowledgeable individuals suited for this study (Emmel, 2013). Whilst other methods could also have been assessed, this approach suited best, as respondents of both in-depth interview groups have to fit pre-set specific criteria defined below. They needed to have a track record within the app business, which would not have been guaranteed with other sampling strategies, for example with random sampling (Coyne, 1997; Easterby-Smith et al., 2018; Koerber & McMichael, 2008).

While the monetization expert interviewees have their main focus on providing insights into the overarching app monetization field, the temporary app case study respondents have been selected for their in-depth knowledge and experience into their specific domain (Robson, 2002). This broad set-up gives us a diverse overview in the understanding of

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the paradigms. Further, the maximum variation sampling of the cases helps in broadening and developing the topic for future researchers. This is due to the fact that this variety tries to find specific similarities or differences between the inherently different cases. The maximum variation in our case study stems from the different app categories (Yin, 2003).

Monetization Expert

As the first part of our research, we conducted in-depth interviews with experts in the field. For the participants, the pre-set criteria consisted of the need to have more than three years of experience within mobile applications. Not all of this experience had to be in monetization strategy, however, they needed to be included in either one of the pre-download, active usage or post-usage stages as defined in our temporary app monetization model. Furthermore, the participants needed to hold a managerial role and be included in strategic or financial decision-making. The aim of the interviews is to be able to acquire more depth, but also aiming to find out links between temporary and non-temporary apps (Tobin, 2010). Summarizing the criteria, they needed to have experience with the considerations of monetization choices. These criteria guaranteed a certain knowledge base, which builds upon the available research and creates the possibility to go into more depth for our case study (Patton, 1990).

Case Study Interviewees

Building upon the previously executed work and expert interviews, new criteria and specifications for the cases needed to be set (Koerber & McMichael, 2008). During this process, the purpose, research question, literature review and expert knowledge were re-examined, to successfully identify the appropriate respondents to fulfill relevance and credibility concerns (Staller, 2010). In line with the nature of our research question, the selection of these participants was mainly focused on the temporality of apps.

For the case study, a case was defined as a temporary app under our definition. To recall, for this study we defined a temporary app as a type of app where the user, at the time of the download, expects to delete the app after the occurrence of a certain event. As interview partners, we selected individuals who (1) had been active in a decision-making role within our definition of a temporary app and (2) are knowledgeable about a user’s purchasing journey. Next to this, the monetization experts already provide a clear

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overview of the current state business environment. For the case respondents, the user journey experience was of importance as we identified the determinant role our created model played within the app environment (Aaltio & Heilmann, 2010; Koerber & McMichael, 2008). After the demarcation and suitability of the set criteria, we found four cases that were satisfactory for our selection.

The first case, hereafter referred to as Case 1 or ‘the fitness app’, is an app that provides workout challenges for users throughout a specific time span. The most common period is 12 weeks, but other durations are also available. Since it is goal-driven, the app then loses its purpose upon completion and thus qualifies as the first temporary app case.

The second case (Case 2), a women’s health app, is an app that guides a woman through a specific time period in her life, which has a finite end. Due to its finite end, it qualifies as a temporary app. For this app we were able to get in contact with two executives, therefore performed two case interviews, namely Case 2-1 and Case 2-2. This was beneficial because we had the chance to talk about the historical development of the app, its user journey and monetization methods.

The third case (Case 3), a sports game app, was created for the temporary time period of the World Cup in 2018, by a large retailer. It was an augmented reality game with the purpose to create awareness for the online shop of that retailer. The users could collect footballs that could be redeemed for vouchers in their online shop. This app was available in a few countries in Europe, and due to the fixed time limit, it suited our requirements.

The last case in this thesis, Case 4, is an entertainment app that provides the service of using a smartphone to create light shows at parties and festivals. Originally, it was created for a business-to-business (B2B) target audience for festivals, but to be able to serve other audiences, it is now also available for business-to-consumer (B2C). Since the B2C side of the app is not temporary, we focused on the B2B side for the purpose of this thesis.

Whilst the possibility to go for only one case study could have been plausible in our study, it has been decided to not pursue this, as the current literature on this field is still fragile

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and providing a rigorous in-depth study was not expected to provide similar overarching insights in the field (Emmel, 2013).

3.2.2 Interview Design

For the ability to expand and explore the executed research on temporary mobile apps, we have leveraged the use of semi-structured interviews. In this research, we are building upon the limited available foundation within this niche of app monetization. Semi-structured interviews give the opportunity to expand on user responses, especially within an area where the knowledge is still fragmented. Determining pre-set themes based on the literature review and using open-ended questions, rich and detailed user data can be acquired. Furthermore, this helped to build on the identified topics that respondents discussed and led towards additional insights, potentially undiscussed if other means, such as closed questions, would have been used (Saunders et al., 2009). In addition, it provides respondents and interviewee with the flexibility to elaborate on given answers (Roulston, 2012).

To be able to get in contact with the experts and cases, the individual networks of the researchers and digital search methods have been leveraged. The individual network stems from the work and social background of the researchers, whereas the rest of the participants originate from outreach on digital media. Due to the international location of all participants, the interviews have been executed through the means of video telecommunication. Whilst face-to-face interviews had been the preferred choice, limitations related to location hindered us to do so. In the communication with the participants prior to the interviews, all participants allowed for the recording of the interview and possibility of transcribing in exchange for the guarantee of anonymization. 3.2.3 Data Collection

In total, 10 interviews have been executed, mixed between 5 expert interviews (Table 1) and 5 case study interviews (Table 2). The respondents are for the majority located in Europe, with one respondent originating from Mexico. The interviews have all been conducted with the means of (video)-conferencing between March and April 2020.

Figure

Figure 1: Temporary App Monetization Model
Figure 2: Tree Diagram Visualization of our Categories

References

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