Strategic signals in the app economy: an empirical study of Google Play Store

Shao, Jianhua (2016) Strategic signals in the app economy: an empirical study of Google Play Store. PhD thesis, University of Nottingham.

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The dominance of Android and iOS have created a duopoly in the smartphone market. In this context, both Android and iOS need to select high quality apps from its massive number of app developers to recommend to app consumers. Generally speaking, when the market is small, the platform like Android and iOS can allocate resources to manual test the app quality for selection; but as the market size increased, the required resource for selection is also increased and would challenge the platform’s capability, thus the platform faces the selection difficulty. Unlike iOS, Android adopts an open strategy to compete against iOS for innovative developers. This strategy has led to inadvertent consequences including: "fragmentation" issues, weakened governance power, entry of non-competent developers and, notably, vetting of the quality of apps and developers.

An app market is a two-sided platform based market. Research in platforms (Gawer and Cusumano, 2013) have primarily focused on what the technology platform owners should do. They assume that developers will equally respond to their platform strategy, for example, leaving the market when the platform owner sets higher barriers or enters when the platforms are more friendly. A few recent platform literatures (Boudreau, 2012; Eisenmann et al., 2011) start to look at developers’ unequal behaviour, such as developers with different competence would have different switching behaviours between different markets. However, in a duopoly setting, developers have limited alternatives but continue to work with both markets. The duopoly setting is interesting as the communication of app quality is critical for competition, and this thesis aims to examine the competent developers’ strategic behaviours from the lens of signal theory.

I argue that competent developers will engage in costly actions to compete against non- competent counterparts in such a duopoly context. For example, competent developers will spend time to make their apps to be unique and innovative in the Android app market, or enhance the technology used in their apps to protect app consumers’ privacy, and other more behaviours discussed in the thesis. It is inspired to understand these unique behaviour by competent developers as strategic signals to communicate their innovative behaviour in the app economy. Information asymmetry (Stiglitz, 2002) is believed to cause the difficulty for platform selection based on the developer’s innovativeness in such a crowded space. Based on the signalling theory (Spence, 1973), the thesis develops a signalling selection model to understand the motivation and implication of these strategic signals.

iOS is not open and is, therefore, difficult to collect research data. To study strategic signals, I collected a panel data set composed of 93% of all apps and their developers in the Google Play Store which is the official Android app market. The thesis firstly filtered out all developers who are featured as "Top Developer" by Google in the Google Play Store. They are treated as innovative developers. The thesis then filtered out all non-featured developers who are relevant to these innovative developers and treated them as non-innovative developers. The thesis qualitatively and quantitatively analyses innovative developers’ strategic behaviour in the Google Play Store.

It is found that these strategic signals are unlikely to be generated by non-innovative developers because they have a higher cost. The cost could be due to, for example, the R&D on technology advancement, the challenge of creative business model design, etc. It provides the platform owner with an opportunity to observe these signals only uniquely by innovative developers, and feedback with featuring awards. These awards would bring huge customer access and generate large long-term benefits on business performance. The benefits motivate the innovative developers to continue on these strategic signals generation, which increases the availability of innovative apps. The signals can also be observed by other developers and motivate non-featured developers to learn from featured innovative developers to increase their featuring opportunity, which is studied as the peer effect in the thesis. This peer effect in learning enables the opportunity for the platform owner to develop and maintain the app market into an ecosystem. Rather than managing all developers, as emphasised by existing platform strategies, this thesis argues the platform owner can focus on selecting a small number of innovative developers to influence the large developers community with the signalling selection model.

The contribution of this thesis is to shift the focus of platform strategy from platform centric to developer centric. I argue that innovative developers would behave differently from others. Their strategic behaviour serves as a signal for the platform owner to vet the quality of innovativeness. The thesis studied a duopoly context of app economy where switching is limited to two dominant platforms. It develops a signalling selection model to solve the selection difficulty of innovativeness in such a unique context. According to my knowledge, it is the first time the signalling theory has been applied to the app economy. The selection of innovativeness is friendly to newly entranced developers which is less focused by existing platform centric strategies. The research on app economy is still rare but the importance of the app economy is significantly increased in daily life. The large-scale data set, methodology and results from this research should be valuable for future research in the app economy.

Item Type: Thesis (University of Nottingham only) (PhD)
Supervisors: Kuk, George
Skovoroda, Rodion
Keywords: Digital Economy, App Economy, Digital Technological Platform, Signalling Theory
Subjects: Q Science > QA Mathematics > QA 75 Electronic computers. Computer science
Faculties/Schools: UK Campuses > Faculty of Science > School of Computer Science
Item ID: 32742
Depositing User: Shao, Jianhua
Date Deposited: 02 Aug 2016 08:48
Last Modified: 15 Dec 2017 18:16

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