The influence of app monetization strategies on user review ratings.
Purpose – The purpose of this paper is to contribute to the marketing literature and practice by examining the relationship between app monetization models and their user review ratings.
Design/methodology/approach – A set of hypotheses was developed, that state which monetization models generate the most positive and variable consumer reactions expressed as review scores. Data obtained from the Google Play Store is used to test the hypothesis. Findings – App monetization models are related to online consumer review scores. Average ratings differ significantly between the four pricing models. Removing upfront or in-game payment obligations seems to positively impact the average rating of games, except when all forms of payment are removed. Therefore, the current optimal payment model with regards to user satisfaction seems to be freemium. The consistency of review scores is also related to the different monetization strategies and differs significantly between the groups.
Research limitations/implications – The research demonstrated the influence of app monetization strategies on user satisfaction empirically. The study was conducted in the context of games for the android platform and the generalizability of the findings for other app segments or marketplaces should be further tested.
Practical implications – This study explains the effects of traditional and new app
monetization strategies on customers. This information supports app developers in choosing a specific payment strategy for their apps.
Originality/value – The paper is the first to explore the impact of all four app monetization strategies on the average consumer review scores and the variance of these scores. It
contributes by identifying consequences of old and new pricing models on online consumer satisfaction.
Mark Wolkenfelt 10671269
June 29, 2016 2015/2016
Frederik Situmeang Antecedents and impacts of online reviews
Project 4: Bachelor Thesis
1. Introduction ... 3
2. Literature ... 4
2.1 Market developments ... 4
2.2 Business model effects ... 6
2.3 Product valuations ... 7 2.4 Satisfaction ... 8 2.5 Mental Accounting... 9 2.6 Fairness ... 11 2.7 Customer responses ... 12 2.8 Research Focus ... 13
3. Theory & Research questions ... 14
3.1 Monetization Strategies ... 14
3.2 Average satisfaction ... 15
3.3 Satisfaction variability ... 17
4. Methodology and research design ... 18
4.1 Data collection and data sources ... 19
4.2 The research sample ... 20
4.3 Data extraction ... 21 4.4 Operationalization of constructs ... 22 4.4.1 Payment models ... 22 4.4.2 Satisfaction ... 23 4.4.3 Variability of satisfaction ... 23 4.4.4 Pricing ... 23 4.4.5 Additional variables ... 24 5. Results ... 24
5.1 Payment models and average satisfaction ... 26
5.2 Payment models and satisfaction variance ... 27
5.3 Additional relationships between variables. ... 28
6. Discussion... 29
6.1 Summary of results ... 29
6.1.1 Monetization strategies and satisfaction ... 29
6.2 Discussion points ... 30
6.2.1 Limitations and future research ... 30
6.2.2 Positive points ... 31
7. Conclusion ... 31
1. Introduction
Since Apple created the App Store in July 2008, the number of apps available has increased from 800 to over 1.5 million in July 2015. The continuous growth of the sales of smartphones has further increased the demand of mobile applications (Chen & Nath, 2004). Within the App Store, more than 100 billion apps have been downloaded and over 30 billion dollars in revenues has been paid to app developers (Lunden, 2015). The potential value of a popular app attracted many new entrants to the app market and increased the industry rivalry, making it harder to achieve the top positions on charts and successfully generate revenues. It was estimated that at the end of 2014, 2% of the app developers, claimed about 54% of all App revenues (Vision Mobile, 2014). Due to the increased rivalry and growth of the app market, new monetization strategies emerged. Fields (2014) describes monetization as ways for the developers to get the users to pay them. The two initial models were free apps with
advertisements and premium paid apps. On October 15, 2009, Apple announced the
introduction of in-app purchases. This feature allows developers to charge money for in-app advanced features, functionalities or virtual goods (Chen, 2009; Átila et al, 2014), after an app has been downloaded by a consumer. These in-app purchases created new potential business models for developers. The new freemium is a monetization model in which an app is provided free of charge and earning are generated by in-app purchases. Since the official introduction in 2011, in-app purchases have become a major monetization method for apps stores and developers. For example, a small Finnish development studio named Supercell, creator of two popular freemium IPhone and Android games, is making 2.4 million dollars a day (Strauss, 2013). Gartner Inc. (2013) predicts that in-app purchases will account for 48 percent of app store revenue by 2017, up from 11 percent in 2012.
Due the relatively short existence and fast growth of the app industry and the recent introduction of the new in-app purchases, the effects of the different monetization models on
consumers has not yet been investigated extensively. Within this thesis, theories about the effects of traditional pricing models in relationship with buyers' perceptions of value and perceived price fairness are analyzed. Linking new app pricing models to these existing theories could possibly generate new insights on differences in effects of traditional and new app monetization strategies on customers. This information could support app developers in choosing a specific payment strategy for their future apps or could help transforming and optimizing the monetization models of their current apps.
2. Literature
2.1 Market developments
The rapid changing and continuously evolving state of modern technology has changed the way companies can do business (Amit & Zott, 2001; Mendelson, 2000; Teece, 2010). Marginal production costs for companies active in the app industry are diminishing fast, because the digital distribution of their products does not require any carriers (e.g. disks) and allows unlimited shelf space. Research indicates that drivers of competitive advantage for the companies are not primarily derived from their competence in strategic factor markets, but mainly in their ability to engage and with and entice payments from consumers (Rietveld, 2016). Different payment models and monetization strategies could therefore play an important role in the success of apps.
Firms in numerous markets, including video games, music and mobile applications, already started reacting on these changes by creating a new business model that offers basic features for free and that monetizes end-users for extended use or complementary features through micro-transactions (McGrath, 2010). The largest WordPress e-commerce solution WooCommerce for example, let’s store owners use the basic version of the plugin to set up a webstore page and integrate basic shopping functions into their current WordPress website.
The company charges additional fees for additional functionalities like dynamic pricing for store owners who could benefit from these more advanced features. This business model consisting of a combination of an initially free product bundled with paid elements is called the freemium model. A currently popular app Clash Royale, another example of a freemium good, offers users various micro transactions to reduce the waiting times or boost their abilities in the game.
Successful companies that use the premium business model do more than just
changing the pricing strategy or revenue model. They integrate distinct capabilities and apps offer more involved interactions with end-users (Hienerth et al., 2011). The in-game
purchasing options need to be integrated in the design of the application and is according to Seufert (2014) one of the most important and shaping choices made during the development of the final product. In for example Angry Birds 2, one of the most popular freemium gaming apps currently in the Google Play Store, a player gets five attempts within a certain amount of time to complete a specific level, and if all five attempts are used, the player has to wait a certain period. Players can add extra lives using in-game currency, which can be accumulated by playing the game and without paying any money. However, playing the game for free rewards relatively few currencies and this leads to large waiting times. In this case,
purchasing currencies is the only option to eliminate these waiting timers and to keep playing the game. This works the same for games as Candy Crush, in which completing a level can be difficult and if a player does not complete the level in a certain amount of tries, the person has to wait an increasing amount of time.
Besides these distinct capabilities of freemium apps, research indicates that the freemium business model allows consumers to more accurately assess their product
evaluations before paying, compared to premium payment models (Bowman & Ambrosini, 2000). Apps in for example the Google Play Store offer a width variety of in-game purchase
options with diverse pricing, giving consumers the possibility to act on their willingness to pay and therefore minimizing any unused consumer surplus from possible differences between the price and the willingness to pay. The main focus for companies adopting this freemium strategy is therefore converting nonpaying users into paying customers (Pauwels & Weiss, 2008). Research has shown that price is an important element in consumers purchases and therefore it has a large influence on consumers’ satisfaction judgments (Herrmann et al., 2007). In order to investigate this particular relationship in the context of the app store, the following question has been asked: what is the influence of the different app monetization strategies on user satisfaction?
2.2 Business model effects
Several researchers have investigated the effects of different business models on product and firm performance, but this relationship in the new digital environment and connection with consumer satisfaction has not yet been investigated intensively. Due to the relative short existence of the freemium model, literature about this model is not widely available yet. Lee, Kumar and Gupta (2013) and Voigt and Hinz (2015) were one of the first papers to
empirically examine the freemium business model and research by Rietveld (2016) builds further on their findings by indicating that the freemium business model helps consumers in establishing their personal value assessment. Traditionally, in case of a premium product, product evaluations are convoluted as value assessments and are based on expectations and perceptions of benefits (Bowman & Ambrosini, 2000; Priem, 2007). For these premium products, consumers can only assess product evaluations prior to consumption of the particular item. This means that consumers need to make assumptions about the expected benefits and overall quality of a good or service based on observable attributes (Boatwright, Kalra & Zhang, 2008).
2.3 Product valuations
Product valuations are especially hard for consumers to generate for premium goods and services in entertainment markets like app stores (Priem, 2007; Zeithaml, 1988). For these goods and services, consumers rely on external sources of valuation such as consumer and expert reviews, word-of-mouth and industry-based certifications (Wijnberg, 1995; Wijnberg & Gemser, 2000). These external indicators of quality can make the consumers’ process of creating a value assessment easier and less complicated, but can become a problem for companies because these indicators are independent of the firm. Identifying possible effects of different payment models on consumer review scores could be helpful for companies if they wish to influence the online review scores by executing a certain monetization strategy. In contrast to apps for which consumers need to pay upfront, apps that are free to download allow consumers to form an accurate and personal evaluation of the product before actually having to pay. They do not have to rely completely on external cues like for example review ratings and incomplete product information, before they make a purchase decision. This free to use version seems especially appropriate for entertainment goods and services that are labelled as “experience goods” (Shapiro and Varian, 1998). The free and freemium app model reduces the influence of external indicators such as expert reviews and awards on consumer decisions (Wijnberg, 1995; Wijnberg & Gemser, 2000). This could be beneficial for companies that have limited control over these external quality indicators and could imply that consumers of free and freemium games are able to form a more accurate estimation of the value of the product before deciding to paying compared to premium apps.
2.4 Satisfaction
Numerous studies have examined the role of a product or service price on consumer satisfaction (Herrmann et al., 2007). The price is one of the several product or service
attributes considered relevant for consumer satisfaction (Fornell et al., 1996). The definition of consumer satisfaction has been evolving since Cardozo (1965) introduced the concept within the marketing literature. Howard and Sheth (1969) were one of the first to describe consumer satisfaction as “a related psychological state to appraise the reasonableness between what a consumer actually gets and gives”. Researchers are not consentient about the description and use different definitions and measures for consumer satisfaction (Szymanski and Henard, 2001). Most researchers do however consider satisfaction as one of the most important factors within the overall field of marketing and customer research (Jamal, 2004). All literature seems to agree that the concept of satisfaction requires the presence of a goal that the consumer want to achieve.
Anderson, Fornell and Lehmann (1994) demonstrate that price is an important factor of consumer satisfaction. Their research shows that when consumers are asked to evaluate the value of an acquired service, they usually think of the price. Churchill and Surprenant (1982) also explain that consumer satisfaction resulted from comparing the rewards and actual costs of a purchase. As for the relationship of price to satisfaction, Zeithaml and Bitner (1996) indicated that the degree of satisfaction was subject to the factors of service quality, product quality, price, situation, and personal factors. The price however, has not been fully
investigated in previous empirical studies (Bei and Chiao, 2001).
Oh (2013) categorize satisfaction with the purchase process (e.g. payment options, interactions with employees) and satisfaction with the outcome of the purchase. Research has shown that these concepts are correlated but both unique (Bitner and Hubbert, 1994; Shankar et al., 2003). Bechwati and Xia (2003) demonstrated this by showing that in the context of
online decision aids, consumers’ perceptions about how much effort these systems put into providing the recommendations influence their satisfaction, independent of the type or quality of recommendations that were offered. Furthermore, Spreng et al. (1993) explain that the consumers’ satisfaction with the availability of product information in the process of evaluating the product has an important influence on the overall satisfaction judgements.
This specific factor of satisfaction can be particularly import when comparing the consequences of monetization strategies within the app industry. Freemium products offer a model closely related to a free to use trial version which can help customer to evaluate the product and collect more information before their final purchasing decision. Research from e.g. Herrmann et al., 2007 indicates that this positive satisfaction with the initial stage of information search, is likely to carry over to the satisfaction with the outcome of the purchase. The freemium try before you buy concept could therefore benefit the overall satisfaction of their users, by giving an option to establish a more accurate valuation of the app in comparison to premium products before having to pay for it.
2.5 Mental Accounting
Literature indicates that mental accounting in which consumers mentally track the costs and benefits of a transaction in order to reconcile those costs and benefits on completion of the transaction, is part of the valuation by consumers (Prelec and Loewenstein 1998; Thaler 1980, 1985). Thaler (1999) defines mental accounting as “the set of cognitive operations that individuals employ to organize, code, evaluate economic outcomes, and keep track of
the activities”. Prelec and Loewenstein (1998) describe the psychological linking of costs and benefits as “coupling”. Thaler ( 1980, 1985 ) provides an extensive framework regarding this tracking of costs and benefits associated with routine consumer transactions that could also be applied to app purchases by consumer. His research indicates that consumers open a
mental account when starting a transaction (e.g. making a payment) and closes that account when the transaction is completed (e.g. product consumption). When closing this mental account, consumers create a psychological link between the costs and benefits related to the transaction. Prelec and Loewenstein (1998) explain that this link between payment and consumption is strong when it is clear which particular payment is financing a specific consumption.
Thaler (1985) introduces the sunk-cost effect in relationship with consumer coupling. His framework indicates that consumers open a mental account during the payment for the product with the expectation of closing that account on consumption. Between those two events, consumers keep the payment in their account at full negative hedonic value,
indicating that the sunk cost impact of that payment remains consistent during the coupling process. Therefore, previous payments that are non-recoverable costs influence consumer decisions about their future behavior related to that transaction (Garland and Newport, 1991). The mental coupling process is also explained by Monroe (2003), he indicates that a “buyers' perceptions of value are mental trade-offs of what they believe they gain from a purchase with what they sacrifice by paying the price”. A negative balance after coupling the costs and benefits would according to the results, reduce the overall perceived value. Many authors consider value as the best and most complete antecedent of satisfaction (Oliver, 1996, 1997, 1999; McDougall & Levesque, 2000; Day & Crask, 2000). Perceptions of gain-loss ratios, specifically in financial terms, influence purchase valuations.
In case of traditional premium apps, which require an upfront payment, customers can couple the costs and benefits at the time of consumption as described by Prelec and
Loewenstein (1998). For freemium apps, these costs occur after the first consumption, which could indicate that a potential negative coupling balance could be prevented by the consumer through not purchasing the in-app content.
2.6 Fairness
According to Zeithaml (1988) the consumers’ cognition of a price is something that must be sacrificed in order to obtain certain kinds of products or services. Their findings suggest that the lower the perceived price, the lower the consumers’ perceived sacrifice is. Additionally, Herrmann et al. (2007) found that price has a direct strong influence on consumers’
satisfaction judgements, but also indirect via price fairness perceptions. Martins and Monroe (1994) have identified that buyers believe that a perceived unfair price is caused by a lower value than a financially comparable fair price. Fairness has been defined as a judgment by a person of whether an outcome and/or the procedure towards reaching a certain outcome is acceptable, understandable or just (Bolton, Warlop, and Alba 2003). Early research by Homans (1961) has shown that the construct fairness consists of a distributive part. He
describes that the distributive side of fairness represents the fairness of outcomes and that this part is related to the consumers’ judgement of a relationship based upon the distribution of rewards from their contributions to the relationship. This means that an unequal distribution of earnings or benefits between the participants involved in a certain exchange relationship could create a sense of unfairness.
Previous research has also indicated several factors that influence perceptions of price unfairness by consumers and potential consequences of these perceptions (Bolton et al., 2003; Campbell, 1999).Voss et al. (1998) also show in their research that satisfaction is a function of price, performance and expectations. Their findings suggest that perceived price fairness, could be a dominant determinant of satisfaction. Their data shows that when a consumer experiences an inconsistency (e.g. unfair outcome), it had a stronger negative effect on the overall consumer satisfaction. More researchers including Oliver and DeSarbo (1988) and Oliver and Swan (1989) found comparable results. The reason for these strong effects
could be that the feeling of price fairness is subjective and most studies investigate them from the buyers’ perspective. Therefore, the judgment of a customer tends to be influenced by their self-interest, which means that costumers tries to maximize their own outcome (Oliver and Swan 1989). Their findings suggest that the feelings related to advantaged and disadvantaged price inequality are different. Perceived unfairness is less intense when the imbalance is to the customers’ advantage than when it is in their disadvantage. A buyer that experiences a disadvantage in price unfairness and expresses his or her feelings in a review, cannot be compensated by one buyer that feel an advantage in price unfairness, because the latter feels his positive emotion relatively less intense.
As discussed previously, consumers which are interested in a freemium product could theoretically prevent an unfair price disadvantage more effectively by evaluating the product more accurately before having to pay for it compared to premium and premium+ (with in-game purchases) products.
2.7 Customer responses
Wirtz and Kimes (2007) demonstrate that in case a consumer views a firm’s practice unfair, it is likely that consumer responses negatively. Xia et al. (2004) show that a consumers’
perception of an unfair price or negative valuation with the corresponding negative emotions are usually directed towards the party that is perceived as the cause of the unbalanced
situation. According to their findings, in the case of price unfairness, the target of the consumers’ perception and the emotions is therefore usually the seller. Consequently, when customers experience a price as unfair in their disadvantage, this could lead to negative consequences for the seller, including customers writing negative reviews about the product or service online or they actively to damage the reputation of seller in any other form. Oliver and Swan (1989) demonstrate that unfair price perceptions lead to dissatisfaction.
Dissatisfaction is described as a negative experience that is associated with anger (Storm and Storm, 1987).
Grégoire (2007) describes that this perceived betrayal is a key motivational force that drives customers to take actions in order to restore the fairness. Together with Xia et al. (2004), he indicates that a negative perceived price fairness eventually causes actions by the consumers like retaliatory behavior, which results in negative word of mouth and vengeful complaining online. Research from Tripp (2011) also indicates that feeling betrayed causes customers to complain online. His research also shows that that betrayal is not simply a case of extreme dissatisfaction. Betrayal is associated with anger, which is a strong negative emotion that motivates customers to respond more strongly.
Grégoire & Fisher (2008) describe these efforts by consumers to punish and cause inconvenience to a firm for the damage it caused as customer retaliation. In contrast to reparation, in which customers try to improve their own situation by receiving some form of compensation, retaliation seems to be driven by the desire to “bring down” or hurt a firm in any possible way (Walster et al., 1973). They state that retaliation is disciplinary in essence and reparation is essentially a corrective response.
Concluding, these negative emotions of dissatisfaction, anger and betrayal, could in theory occur more often in the case of apps which require an upfront payment and could therefore lead to a larger amount of intense negative reviews in this group, in comparison with freemium apps. Consumers of free apps should in theory have even less extreme negative emotions compared to premium and freemium apps models.
2.8 Research Focus
The present literature in the field of marketing presents extensive research regarding the influence of pricing, and perceived price fairness on consumer satisfaction. Also
consequences of dissatisfaction are investigated and demonstrated comprehensively. While the link between pricing and consumer satisfaction has been investigated and found, the effects of the different new pricing models for apps has not yet been demonstrated. Most research has only looked at the traditional pricing model in an offline environment, which requires an upfront payment by costumers, before they can actually consume the product. Testing these theories on the traditional and new app payment models and comparing these results could therefore fill a part of the gap in the current literature. In this research the satisfaction ratings of millions of consumers on app will be analyzed for each app
monetization strategy in order to test the applicability of conventional pricing theories on these new pricing models.
3. Theory & Research questions
The focus of this thesis will be on mobile apps distributed on marketplaces like the Google Play and the Apple App Store. Research about the app stores is limited, possibly because these marketplaces have a relatively shortly existence. Jansen and Bloemendal (2013) define an app store as “An online curated marketplace that allows developers to sell and distribute their products to actors within one or more multi-sided software ecosystems”.
3.1 Monetization Strategies
As introduced previously in the literature review, because of the introduction of in-app purchases in 2009 within the major app stores, new monetization models emerged. Currently four main models are present in gaming genre, all with distinct features. Although a fifth pricing strategy which involves a subscription model is also present in the app store, it is currently not used by any of the top 1080 games yet. This subscription model is only
available for a very limited amount of special apps and is planned to be introduced later in 2016 for all developers, therefore this model is not included in our research.
The first monetization strategy within the app industry which requires an upfront payment is the premium pricing model. It requires consumers to pay before downloading and using an app, but offers no additional purchasing options. The closely related Premium+ monetization method also requires an upfront payment, but offers in-app purchases in addition and is capable of generating a continuous revenue stream that is associated with sales through the purchases inside the app.
The first free to play monetization method in this paper is named freemium and apps using this pricing model do not require an upfront payment, but generate their earning solely through in-app purchases. The last monetization method is called simply named free and is based on mainly on advertising. The purpose is to generate traffic or clicks to sponsored brands, products or services by linking to the advertiser. The main goals of these games is increasing the number of users, which will increase the number of ad impressions and clicks, which eventually generates higher revenues.
The first group of in-app purchases consist of virtual goods or currencies to help a user proceed and the second group consists of purchases related to unlocking extra features or content. The potential value of this method is promising and the most successful free games with in-app purchases, Clash of Clans and Hay Day, generate more than 2.4 million dollars in a daily basis (Strauss, 2013).
3.2 Average satisfaction
Although freemium games are initially free, developers are capable of generating significant revenues using the in-app purchases system. The psychological endowment effect described by Kahneman (1990) could help to explain the effects of in-app purchases for mobile games
and form a basis for the relationship with consumer satisfaction. He describes this effect as “the measures of willingness to accept greatly exceed measures to pay.” This indicates that consumers value a certain good higher if it becomes part of the person’s endowment, which means that individuals value goods they possess higher than if they do not own them. For example, in practice this suggests that sellers value their items higher than the potential buyers. In the context of games offering in-app purchases, people would value the app higher before they are able to make a purchase decision, because they already played the game and therefore created a sense of ownership. Harris (2013) supports this theory by indicating that a game with in-app purchases do not ask a consumer to spend their money until they feel they already own it. Creating this increased valuation before having to pay for a product could therefore theoretically increase the overall satisfaction for the particular product due to its pricing structure. In both premium and premium+ app, consumers are not able to increase their sense of value before they encounter the first payment moment and they should therefore theoretically value the product less when paying for the game in comparison with freemium apps, resulting in potentially less consumer surplus and a reduced feeling of overall satisfaction by the customer.
As introduced in the literature review, Spreng et al. (1993) explain that the consumers’ satisfaction with the availability of product information in the process of evaluating the product has an important influence on the overall satisfaction judgements. Freemium products offer customers a free version which can help them to evaluate the product and collect more information before their final purchasing decision. Herrmann et al. (2007) indicate that this positive satisfaction with the initial stage of information search, is likely to carry over to the satisfaction with the outcome of the purchase. The freemium try before you buy concept could therefore benefit the overall satisfaction of their users, by giving an option to establish a more accurate valuation of the app in comparison to premium
products. In case of premium and premium+ apps, this extended information seeking process does not exist and could therefore not influence the overall satisfaction positively. Both the endowment effect and the improved evaluation process, could therefore theoretically lead to higher ratings.
The predicted effect of this freemium pricing model on the satisfaction of customers is stated in the following hypotheses:
H1 (a): Freemium apps have a higher average satisfaction rating than premium apps. H1 (b): Freemium apps have a higher average satisfaction rating than premium+ apps. H1 (c): Premium apps have a higher satisfaction average rating than premium+ apps.
3.3 Satisfaction variability
Besides an increase in valuation and therefore possibly satisfaction, the number of cases of dissatisfaction could also influenced by the pricing model of apps. In case of traditional premium apps, with a purchasing price, customers can couple the costs and benefits at the time of consumption as described by Prelec and Loewenstein (1998). For freemium apps, these costs occur after the first consumption, which could indicate that a potential negative coupling balance could be prevented by the consumer through not purchasing the in-app content. A negative financial coupling balance would therefore theoretically be less likely in the freemium app model, when compared with premium apps.
In case of free apps, a negative coupling balance as a result of monetary sacrifice is not present due to the non-existing payment option. Grégoire (2007) describes that this perceived unfairness could generate a feeling of betrayal, which is a key motivational force that drives customers to take actions in order to restore the fairness. Together with Xia et al. (2004), he indicates that a negative perceived price fairness eventually causes actions by the
consumers like retaliatory behavior, which results in negative word of mouth and vengeful complaining online. Research from Tripp (2011) also indicates that feeling betrayed causes customers to complain online.
Concluding, these negative emotions of dissatisfaction, anger and betrayal, could in theory occur more often in the case of apps which require an upfront payment and could therefore lead to a larger amount of intense negative reviews in this group, in comparison with freemium apps. Consumers of free apps should in theory have even less extreme
negative emotions compared to premium and freemium apps models, because financial costs never occur.
The predicted effect of the different pricing model on the variability of customers’ review scores is stated in the following hypotheses:
H2 (a): Premium+ apps have a higher variability in satisfaction than premium apps. H2 (b): Premium apps have a higher variability in satisfaction than freemium apps. H2 (c): Premium apps have a higher variability in satisfaction than free apps.
H2 (d): Freemium game apps have a higher variability in satisfaction than free apps.
4. Methodology and research design
Due to the digital nature of the app market and the increasing possibilities of highly
structured data collection techniques with online crawler software, it is possible to conduct quantitative research on a relative large scale. Quantitative data can be used for both
inductive and deductive research and in this study the generated data is used for a deductive approach (Saunders et al., 2012). In the literature section, a basis has been formed for the hypothesis in the previous paragraph, which can be tested with the dataset. Furthermore, a
cross-sectional design is used, because this study focuses on a particular phenomenon at a particular time.
The data collected for this study is eventually relevant for all companies active within the app game industry and will potentially reveal something about the relationship between different payment models and the variability of user reviews. Game developers can use these insights to optimize the experience for consumers, by taking into account the effects of monetization models on the satisfaction of consumers. The dataset is available online for future research and it gives to opportunity for companies and researchers to verify and replicate the study.
4.1 Data collection and data sources
To test the relationship between the variability of user reviews and the payment models of mobile gaming apps, we collected the public available information about the most popular gaming apps with different monetization models from the Google Play Store website; play.google.com/store/apps. A large amount of data about apps is online accessible on this website and therefore available for collection by using custom software. This form of “Big Data” collection offers the possibility to collect user satisfaction ratings for numerous games in the Google Play Store. Wu et al. (2014) describe that Big Data starts with large-volume, heterogeneous, autonomous sources with distributed and decentralized control, and seeks to explore complex and evolving relationships among data. Every day, 2.5 quintillion bytes of data are created and 90% of the data in de world today were produced in the last two years (IBM, 2012). The possibilities with regards to collecting Big Data has grown fast and different tools are available to capture, manage and process the data. A benefit from using these collection methods is the possibility to create a relative large dataset with fewer resources compared to for example a survey. It offers an economical and time efficient
solution, because once the data collection software is configured, the data extraction is automatic process and the amount of data collected is mainly constraint by computer power.
The Google Play Store is a fitting research site to test the different hypothesis. All monetization models are present on the platform and this gives the opportunity to identify the effects of choosing a specific payment model on for example download statistics, review scores, and ranking, while controlling for other relevant factors including genre and PEGI rating. The platform offers all four different payment models discussed in this thesis; premium, premium+ (with in-game purchases), freemium and free. Furthermore, the marketplace was launched on October 22, 2008 and currently has more than 1.43 million apps published and over 50 billion downloads, making one of the biggest marketplaces, offering a large variety of apps.
4.2 The research sample
Information was collected from the 540 top gaming apps with an upfront payment
requirement and the 540 top free apps listed in the Google Play Store including all genres and was acquired on 27 May 2016. The ranking and data of the apps in the top list changes daily (Google, 2016) and therefore the data for both groups was collected simultaneously. For each app, 18 data objects were selected and extracted to the dataset. The elements collected for each game consist of the following objects: ranking, title, genre, developer, PEGI category, number of reviews, average review score, number of review scores per star category (from 1 to 5), monetization model, price, date of latest update and the number of current active installations.
The distribution of apps with and without in app purchases differs between both groups. Of the 540 free gaming apps, a majority of 409 games (75.74%) were freemium with in-game purchases and 131 apps (24.26%) had no payment options in the game. In the group
of 540 paid game apps, 190 games (35.19%) offered in-app purchases and the majority of 350 games (64.81%) offered no in-app purchases. The games in the top 540 premium sample had an average price of 3.25 euro and counted an estimate of 152.349.219 active installs with a total of 8.569.300 reviews. Within the free and freemium sample of 540 games, the
estimated number of installations was much higher at 21.568.139.253 installs with a total of 432.611.359 reviews.
With this dataset, the relationship between the variables “monetization model” and “variability of reviews” can be investigated using correlation analysis. The additional
elements collected for the dataset would help identifying confounders and eliminate potential biases within the analysis.
4.3 Data extraction
In this study, software and extraction services from Kimono Labs, Import.IO and Apifier are used to collect the data from the Google Play Store. Since this Store has protective
mechanisms against non-human (software) visitors in order to prevent abuse of their website and resource intensive datamining, each crawler program had various difficulties reading the full data set and were therefore unable to collect all the data objects individually. For
example, a challenging data object to collect was the pricing for premium and premium+ apps, because this information was integrated in the purchase button of apps and the visibility of these buttons was protected against non-human users like crawlers. Presumably to prevent computer programs to artificially generate downloads for certain apps in order to increase their popularity rankings in the store. Furthermore, the most popular app list within a certain category initially only shows 60 items and this list increases when a person manually scrolls down the page. After 300 items, the list could not be expanded anymore with only scrolling down, but it also needed a click on a special read more button, possibly also in order to
prevent crawling and data scraping. Due to these and several other challenges, a combination of several data collection programs was used, which allowed the collection of all the data objects and the control for the imported elements by comparing data for the same objects. The data was collected in two stages, first both the top 540 lists were generated with for each app their corresponding link to the details page, the game title, the purchasing price and the name of the developer. These 1080 links in total were used as a source for another crawler to analyze and extract additional elements from the corresponding app details pages. This fixed ranking made it possible to use multiple extractors independently of each other to generate all data without being interrupted by the constant changing ranks of the Apps in the store.
4.4 Operationalization of constructs
The different content variables of the app games used in this research need to be operationalized. All the different variable will be operationalized by using the different construct discussed in the literature review and theoretical framework.
4.4.1 Payment models
The top charts for both free and payed games allowed the collection of 540 apps in each category, creating a list of 1080 apps. In both categories, free and payed, two more categories were created. Games which required an upfront payment, were divided in the “premium” and “premium+” apps. Premium games required an upfront payment but had no additional in game purchase options in contrast to the premium+ apps, which offered in-game purchases. In the free category, another separation was made between the app with and without in-game purchasing options, creating the freemium (with in-game purchases) and free games.
4.4.2 Satisfaction
In this research, publicly available review scores are used to measure customer satisfaction. Consumer review scores are presented on a one to five scale and are obtained from the app details page inside the Google Play Store. For each rating score, the amount of reviews is collected, making it possible to calculate the exact average review score for each app.
4.4.3 Variability of satisfaction
The amount of extreme emotional reactions can be measured by the consistency of the review score. Apps with more extreme reviews (outliers) have a higher variance within these scores. Therefore, this study will investigate the relationship between the four monetization methods (premium+, premium, freemium and free) and the variability of their review scores. Variance will be used to describe the variability of the reviews, because this is the most commonly used measure of variability in the marketing literature (e.g., Dacin & Smith, 1994).
Differences in variability supports the theory that indicates that a payment strategy influences the amount of intense negative emotions of e.g. anger and betrayal as discussed in the
literature review. The variance of the review scores was calculated by the following formula:
In which f represents the frequency of a certain score expressed as x. N is the total numbers of reviews.
4.4.4 Pricing
For each game, multiple pricing elements have been collected. Firstly, the purchasing price of the game was identified. Free and freemium apps do not have a price listed in the Google Play Store, therefore the price for these games was set to $0 (free). Besides the purchasing
price, the pricing range of in-game purchases was also identified. With this data, a new variable was created, called “in-game price mean”. This variable indicates the average price of the in-game purchases available in-game.
4.4.5 Additional variables
A few control variables were collected while analyzing the details pages of each app. Age restriction was created by extracting the PEGI rating for each app. The PEGI rating consist of four groups: Everyone (All ages), Everyone10+ (age 10+), Teen (age 12+) and Mature (18+). The amount of installations also collected and was given as an estimation (e.g.
10.000-50.000). The mean of both numbers was used to calculate and create the variable “Mean Installs. Furthermore, the genre and ranking in the Google Play Store based on popularity was collected and inserted to the dataset. Lastly, the date on which the corresponding app was update most recently was added to the dataset.
5. Results
The influence and relationship of multiple collected variables was investigated within and between the monetization models. First, the collected data for all four groups of apps; free, freemium, premium and premium+ was compared with a one-way ANOVA analysis. Free and freemium apps are related to the free to play category. Premium and premium+ are related to the payed app category. Both categories contained of 540 apps, making 1080 apps in total.
Within the free to play category, developers use in-app purchases in 75.4% of the gaming apps listed, which makes the freemium model the current most popular choice. This is in contrast with the payed category, in which only 35% of the apps use in-game purchases. This indicates that the implementation of the in-game purchase option for the payed apps is
less expeditious for the payed app category in comparison with the free to play apps. Free and freemium games were the most downloaded apps with an average number of active
installations of 24.3 million for each free app and 45.03 million for each freemium app. For the payed apps category, the average number of installs was 140.918 for premium apps and 544.375 for premium+ apps. These numbers show that in both the free to play category and payed category, apps with in-game purchases had the highest amount of users. Also, the apps offering these in-game purchases, were ranking the highest, both in the free to play and payed list, indicating a potential preference by consumers for both models using in-app purchases.
Figure 1: Variance & rating means
Table 1: Descriptives
5.1 Payment models and average satisfaction
The first hypotheses from the theoretical framework are about the satisfaction expressed as average rating of apps in relationship with the four different monetization models. H1(a) stated that the freemium apps have a higher average satisfaction rating than premium apps. H1(b) predicted that freemium apps have a higher average satisfaction rating than premium+ apps. H1(c) stated that premium apps have a higher satisfaction average rating than
premium+ apps. Using a One-Way ANOVA (Table 1) average ratings of the four groups were compared. The average rating between all groups was significantly different (p = 0.000). As predicted, freemium apps have a significant (p = 0.000) higher average rating than premium apps, confirming H1(a). Also, freemium apps have significant (p = 0.000) higher average ratings compared to premium+ apps, confirming H1(b). Finally, premium apps have significant (p = 0.000) higher average ratings than premium+ apps, supporting H1(c).
Table 2: One-Way ANOVA (F-test)
5.2 Payment models and satisfaction variance
The second hypotheses from the theoretical framework are about the variance of the rating of apps in relationship with the four different monetization models. H2 (a) stated that Premium+ apps a higher variability in satisfaction than premium apps. H2 (b) stated that premium apps have a higher variability in satisfaction than to freemium apps. H2 (c) Premium apps have a higher variability in satisfaction than to free apps. H2 (d) stated that freemium game apps have a higher variability in satisfaction than to free apps. Using a One-Way ANOVA (F-test), the average variances between the four groups were compared. Levene’s test, a common assessment to test for homogeneity of variance is used in this study. The test statistic for this method is calculated by diverging the data for each group from the average group value and comparing these absolute values. The average variance between all groups was significantly different (p = 0.000). As predicted, premium+ apps a higher variability in satisfaction than premium apps H2 (a). However, the results showed that premium apps do not have a higher
variability in satisfaction than freemium apps, rejecting H2 (b). Furthermore, premium apps do not have a higher variability in satisfaction than free apps, rejecting H2 (c). H2 (d) is also not significant, indicating that freemium game apps do not have a higher variability in satisfaction than free apps and therefore also the final hypothesis is rejected.
Table 3: Test homogeneity of variances
5.3 Additional relationships between variables.
After establishing a link between the payment models and satisfaction, it is also possible to examine possible relationships between average satisfaction scores and the number of active users in an app. For the entire sample of 1080 apps, average rating is a predictor of the amount of installs (Pearson Correlation = 0.074, p = 0.015). For the group consisting of free and freemium apps only, the average rating is also a valid predictor of the amount of installs (Pearson Correlation = 0.163, p = 0.000). For the freemium model group, consisting of 407 Apps, the average rating is also a valid predictor of the amount of installs (Pearson
Correlation = 0.158, p = 0.001). However, for the group consisting of both premium and premium+ apps, average rating is not a significant predictor for the amount of installations (Pearson Correlation = 0.108, p = 0.216). For the premium group only, this relationship with the amount of installs is not significant (Pearson Correlation = 0.051, p = 0.119) and this also applies for the premium+ group (Pearson Correlation = 0.099, p = 0.087)
The variance of review scores and the in-app prices were also correlated. In the freemium group, a strong negative correlation between in-app pricing and the variance of the reviews score was found. The higher the prices, the lower the variance (Pearson Correlation = -0.132, p = 0.004). However, in premium+ group, this relationship was not significant
(Pearson Correlation = 0.066, p = 0.182).
6. Discussion
6.1 Summary of results
The main goal of this paper was to examine the influence of the different app monetization strategies on user satisfaction. Particularly the satisfaction expressed in review scores and the variance of these ratings. Therefore, several hypotheses were created to answer the main research question of this study. The research question was: in what degree is the consumer satisfaction affected by the monetization model of an app?
6.1.1 Monetization strategies and satisfaction
Based on psychological theories from Kahneman (1990) and customer satisfaction models from Spreng et al. (1993), we assumed in our hypotheses that freemium apps would have a higher average satisfaction rating than premium apps and premium+ apps. Indeed, the results of our study show confirmation of these differences. The last hypothesis regarding average satisfaction assumed that premium apps have a higher average satisfaction rating than premium+ apps. As predicted, a significant difference has been found in our study and this confirms that the positive effect of the freemium model on satisfaction disappears when an upfront payment is required.
Based on previous studies from Prelec and Loewenstein (1998), Grégoire (2007) and Tripp (2011), we also hypothesized that adding an upfront payment requirement, could increase the variability of consumer satisfaction. The results of our research only confirmed
the first hypothesis that premium+ apps have a higher variability in satisfaction than premium apps and did not support the predicted relationships between the other payment models. These conflicting results could indicate that other variables perhaps play a stronger influence on the variability and additional research would be needed investigate this subject and create a better understanding of which factors influence the variance of ratings.
6.2 Discussion points
6.2.1 Limitations and future research
Inevitably, this study has several limitations. First, the empirical relationships found in this study comes from the most popular game apps listed in the Google Play Store. More research is needed to validate the findings in other app niches, such as communication, music and audio and social apps. Also, increasing the sample size beyond the top 1080 list could further increase the representativeness of the sample. The Google App Store consist of millions of apps and the top performing games may not be fully representative for the rest of the games. One could also look at other app marketplaces such as the Apple App Store and try to find similarities or differences in outcome. Second, more control variables could be added to eliminate con-founders which influence the rating of apps. For example, the development budgets of developers. The type of data collection used in the study could is quantitative in nature and could therefore lack a form depth required to study consumer behavior. Analyzing the content of reviews next to the rating, could help identify more qualitative factors
influencing their decision.
The results from our study indicate if there is a correlation between certain variables, but they cannot conclude anything definitive about causality yet. Additional research is required in order to form a basis for a more comprehensive understanding of the causality. Future research could also investigate other relationships with the monetization strategies of
gaming apps, developing a more extensive theory about the effect and implications of these pricing strategies.
6.2.2 Positive points
The particular relation investigated in this paper is a quite new subject within the business studies. It contributes by identifying consequences of old and new pricing models of mobile gaming apps on online consumer satisfaction. The study is the first to explore the impact of all four app monetization strategies on the average consumer review scores and the variance of these scores. This information could support app developers in choosing a specific payment strategy for their apps.
Furthermore, a large dataset is generated consisting of 1080 apps creating a sample with a large variety of developers, pricing structures, genres and active users. This possibly increased the generalizability of our results and could make the findings useful for a larger group gaming apps. The dataset is available on request in order to allow researchers to replicate or extend the study.
7. Conclusion
To answer the research question and possibly predict the effects of different monetization strategies in practice, the top 1080 gaming apps in the Google Play Store were analyzed. The four monetization models used in this research include; premium, premium+ (with in-game purchases), freemium and free. A set of hypotheses was developed, that stated which monetization models would generate the most positive and variable consumer reactions expressed as review scores. The first set of hypothesis were based on both the endowment effect and the improved evaluation process related to the freemium model and hypothesized that these effects would lead to higher average ratings. The second set of hypothesis were
based on the theory that a negative perceived price fairness eventually causes actions by the consumers like retaliatory behavior, which results in negative word of mouth and vengeful complaining online. These negative emotions were hypothesized to occur more often in the case of apps which require an upfront payment than in comparison with freemium apps, resulting in more consistent review scores for the latter group. The results and new insights with regards to these pricing models could support app developers in choosing a specific payment strategy for their apps.
The research data indicates that these four app monetization models are related to online consumer review scores. As hypothesized, average ratings differ significantly between the four pricing models. Removing upfront or in-game payment obligations seems to
positively impact the average rating of gaming apps, except when all forms of payment are removed. Therefore, the current optimal payment model with regards to user satisfaction appears to be freemium. The consistency of review scores is also related to the different monetization strategies and differs significantly between the groups. The expected relationship between the consistency of these reviews and the different pricing groups is however not confirmed by this study. Addition research could further explore potential explanations for this.
The study was conducted in the context of games for the android platform named Google Play Store and the generalizability of the findings for other app segments or
marketplaces should be further tested. This study is the first to explore the impact of all four app monetization strategies on the average consumer review scores and the variance of these scores.
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