Platform Confusion: A Spotlight on the Concept of Platforms and Their Classification in the Context of Business Ecosystems

A platform, sure, that’s something you can build other things on. I’m sure we all remember the “Lego plates”, which provided the basis for giving free rein to one’s imagination and indulging in architectural test projects.

In economics and science, it is unfortunately not as easy to define the platform concept as in the example described above. Various researchers have had to conclude that an overarching definition of the concept does not exist (ex. [1]–[3]). With this article, we will now try to shed some light on platform definitions. In addition, we will shed light on the core value driver – network effects – and establish the concept’s connection to business ecosystems.

Before we delve into different definitions, let’s take another look at why the topic is relevant to us and which players we can associate it with. If we look at the most valuable companies in the world, we can see that a good half of the 10 most valuable companies have built their business models around digital platforms and are significantly more valuable (in terms of market capitalization) than the linear business models [4]. In this context, it is hardly surprising that there are now also various investment products, such as ETFs, which are based on the hypothesis that platform-based business models are more successful than others [5]. When analyzing platform-based business models, companies such as Uber, Amazon or Alibaba are mentioned again and again. Even though the business models of the aforementioned companies show significant differences upon closer inspection, they have one thing in common: they enable the connection of different actors [3], [6], [7]. For example, Amazon provides the connection between demanders of products on the one hand and providers on the other. Uber enables connecting the demand for transportation with the corresponding supply. With the recognition that platforms connect different actors, we also already have a first basic definition of platforms [8].

A pragmatic division of the platform concept is further possible into internal and external platforms [9]. Here, internal platforms focus on what happens within a company, while external platforms enable interaction with actors outside the company’s boundaries [9]. However, since we basically want to focus on platforms through which various external actors interact and create value, this division can be neglected in the following.

Cusumano et al. (2020) differentiate platforms based on the way they create value and generate competitive advantage – their view is thus strategic-economic. According to them, three platform types exist: transactional platforms, innovation platforms, and hybrid companies. [10, S. 49]. While transaction platforms act as intermediaries for exchanges between different parties, innovation platforms provide the technical basis on which actors can develop complementary innovations [10]. As an example of a transaction platform, we can think of the Apple app store (following [10]). Matchmaking between application providers and demanders takes place via the platform. With Apple iOS, an innovation platform is given that provides the technical framework on which app developers can develop solutions (following [10]). Finally, Apple itself represents a hybrid company, since it operates both transaction and innovation platforms.

Finally, a more technical view of the topic of platforms is offered by Tiwana et al. (2010) with their description of a platform as “extensible codebase of a software-based system that provides core functionality shared by the modules that interoperate with it and the interfaces through which they interoperate” (p. 676). In other words, the platform is seen here as the core for creating shared value in a digital context. In the context of digital ecosystems, in which we often move today in connection with business models, this classification of a platform could provide a helpful delimitation for understanding the processes that are carried out on it.

Finally, a combination of the innovation platform with a more technically driven view is provided to us by Bonina et al. in a recent publication [13]. An illustration of their understanding of a platform is shown below:

Source: Adapted from Bonina et al. 2021, p. 876.

Bonina et al. [13] describe platforms as a base on which other actors can create value. According to them, platforms have a core architecture, which consists of modules provided by contributors. In addition, there is a peripheral domain where complementors use the provided modules to design new applications and services (platform complements). At the level of supply and demand, the platform complements then represent the supply that can be used by end customers. This view can be illustrated once again with the aid of an app store, in which app developers (complementors) use APIs and SDKs (core architecture provided by contributors) on a platform to make new offerings available to users.

So much for building platforms.

Before we attempt to situate platforms within the context of business ecosystems, let’s address the question of what determines the value of a platform.

The main value driver of platforms in the economic sense is network effects. In simple terms, network effects on platforms grow with the number of users of the platform [14]. Network effects can be divided into direct, indirect [15], and so-called data network effects [14]. Direct network effects arise when users of the same user group trigger them – for example, when the value of a platform such as the social network Facebook increases due to the number of acquaintances who use it. In the same way, the value of communication platforms, such as WhatsApp, increases for the user due to the number of users who can be reached via it. Another example is offered by the Clubhouse communication platform, which experienced hype in Germany in 2020 and whose value seems to be strongly related to the number of users there. Indirect network effects describe when the value of the platform depends on the number of users of another user group – for example, the value of Facebook for providers of advertising, which depends on the number of users of the platform. We find another example in platforms that broker tradesmen’s services – for example, “Mein Zuhause und ich[i]” by Provinzial Rheinland. The value of the platform for the end customer increases with the growing number of craftsmen offering their services there, as end customer needs can thus be met more quickly (and possibly more comprehensively). Only recently has the third class, data network effects, been theorized. Gregory et al. [14] describe that an increasing amount of data, which can be exploited with information technology, increases the value of a platform to the user. For example, the more data available to evaluate and generate recommendations accordingly, the better the recommendations on a platform become. Better recommendations now make the platform more attractive to the user, who again generates new data with each new interaction. The provision of recommendations can also be seen as the “curation” of the platform and its importance should probably not be underestimated. The more offers there are on a platform, the more difficult it is for the end customer to keep track of the various offers. Accordingly, the importance of suitable recommendations increases – if you had to invest a lot of time every time to find the product you are looking for on Amazon, you would certainly leave the platform.

However, network effects are not so simple that their analysis can be reduced to “are there” or “are not there”, and far from being an infinite growth story. Andrew Chen describes this issue very vividly in a recent article at [16]. Previously, network effects were often seen in the context of Metcalfe’s Law. In brief summary, it describes the effect that with each additional user of an app, the growth in value can be expressed by n^2. Drawing on observations from biology in the 1930s, Chen now goes on to describe that there are far more complex relationships associated with network effects than the simple exponential positive growth relationship described above. It can be observed, he says, that a certain corridor is formed in which network effects have a positive influence on the growth ratio. A growth, for example of the population of meerkats, beyond the corridor does not lead to a positive increase of the growth ratio, but has on the contrary an opposite effect [16]. This example may also be observable with reference to microeconomics in the construct of diminishing marginal utility of an additional unit. Thus, we cannot just assume an unlimited scaling potential with respect to network effects. At this point, the jump out of the animal kingdom, into business. Chen illustrates the concept using Uber as an example: there is a certain time of arrival of a driver, which should not be exceeded for the customer. On the other hand, there is also an arrival time, which it is not beneficial to fall below, because the customer himself needs a certain time to get out of the house to the vehicle. The drivers could be seen here as a corresponding variable, assuming that in principle a faster arrival at the customer’s can be made possible by an increasing number of drivers. [16]. This example is illustrated graphically below:

Source: Adapted from Andrew Chen [15].

Other questions that need to be addressed in the context of network effects include what is the minimum number of users that a platform needs to launch [16]. In this context, a crucial challenge for platform owners is often raised – the so-called “chicken-and-egg” problem [17], [18]. The problem describes which side of a platform to discriminate, i.e., to price, for example, in order to make network effects work [17]. Another question to be answered is what features the platform needs for a successful launch [16].

We have now learned about different perspectives we can take when thinking about platforms. Further, we have explored the economic potential behind platforms with network effects. Finally, we will now combine the topic of the platform with that of business ecosystems.

The basic concept of the platform forms the basis for value creation and value accumulation in business ecosystems [19], [20]. De Reuver et al. (2018) go on to describe that platforms are needed to cultivate a business ecosystem and thus include the idea that business ecosystems are an evolution of a platform. If we look at companies today that excel at exploiting business ecosystems, we quickly find the connection to platforms. We could see an example here in Apple, which exploits various platforms (e.g., App Store, iOS, watchOS, macOS) to gain the most comprehensive access to complementors and customers. App developers use the functionalities of the platform in conjunction with complementary offers from other developers and their own creativity to develop new services.

Yes, the definition of platforms is not quite as easy in economics and science as it is in connection with Lego. As of today, science in particular owes us a clear definition that can be considered universally valid. However, the lack of a definition by no means diminishes the importance of the concept – especially in connection with business ecosystems, it just seems to fit the analogy of the “Lego plate”, without which the greatest architectural masterpieces could not have been built so well.

[i] german for “My Home and I”

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[16]       A. Chen, “Beyond Metcalfe`s Law for Network Effects, and Towards a Better Model,” a16z Future, Metrics and Mindsets, 2021.

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Dennis Vetterling

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