Читать книгу Platforms and Cultural Production - Thomas Poell - Страница 18

To better understand the political economy of multisided markets, it is necessary to identify the core economic principles constituting these markets and the subsequent strategic decisions faced by platform companies. These principles and decisions, together, shape the economic horizon of cultural producers. Two are particularly relevant: network effects and pricing.

Like all digital and physical networks, platforms leverage economies of scale because they benefit from internet connectivity, which, in turn, allows for network effects. So-called direct network effects dictate that the more users who join a network, the more valuable that network becomes (Katz & Shapiro, 1985). These effects, or what economists call “network externalities,” are especially pronounced in digital markets, where marginal costs – the costs of adding additional units or users – are low. Network effects help explain the potential for rapid growth: if platform markets expand, which is never assured, its growth can be sudden and swift. Apps such as Snapchat, TikTok, Instagram, WeChat, and WhatsApp are all prime examples of rapid diffusion when direct network effects are positive.

We should emphasize that direct network effects are not unique to platforms and apps: any company active in digital markets may benefit from adding additional users. What sets multisided (i.e., platform) markets apart from traditional market structures – both physical and digital – is that they also engender “indirect” or “crossside” network effects (Evans & Schmalensee, 2016; Rochet & Tirole, 2003). This means that, when positive, the more users joining a platform market on one side, the more valuable the platform becomes for users on the other side. To return to the two-sided market example of game consoles: the more players who have a PlayStation, the more valuable the game console becomes for game publishers. And vice versa, a broader catalogue of games (i.e., more complementors) offers a better value proposition to players who consider buying a new game platform. This example goes to show that network effects significantly impact complementors: since they are part of the same market (or network) as a platform company, they can benefit from a platform’s growing pool of end-users. In our opening example, game developer Zynga harnessed Facebook’s then-swift popularity to great effect.

The design of a platform’s business model shapes the economic environment in which platform-dependent cultural producers operate. When creating a platform market, platform operators are faced with a series of fundamental strategic challenges, such as: which side to attract first? Complementors or end-users? Supply or demand? That is, operators must address a “chicken-and-egg problem” and be careful to “get both sides on board” (Rochet & Tirole, 2003: 990). Likewise, there is the challenge of pricing. A key decision is whether, when, and how much to either charge or subsidize which side: end-users, cultural producers, or other types of complementors. Two-sided markets can use the income they generate from one side to provide free access to the other side. For example, in the case of Facebook – as with most social networks – access to the platform is free for end-users, their access is subsidized by advertisers. In Google’s and Apple’s app stores, end-users can download a variety of apps for free, but developers are charged a 30 percent fee over any monetary transaction.

A platform company’s options in designing a business model are dependent on a number of economic variables that differ from platform to platform and from market to market. These variables can include a company’s primary sources of revenue and profit and/or industry norms.

We already briefly touched upon the first variable, a platform’s primary business model. How a platform generates revenue determines its strategic orientation and its ability – or inability – to grow. As we have seen, the constant maneuvering on the part of the platform directly impacts complementors. For instance, it matters greatly which side – end-users or complementors – a platform favors at which time. Consider Google: in both its YouTube and Search businesses, it has shown a consistent inclination to appease advertisers over end-users or content creators (Caplan & Gillespie, 2020; Rieder & Sire, 2014).

A second variable impacting a platform’s pricing decisions are the actions of competitors and industry norms. Given how the digital media economy has evolved with its provisions of the proverbial “free lunch,” a platform has to have a compelling reason to charge end-users for access. In 2008, when Apple’s app store opened its virtual doors, premium priced games were the norm; more than a decade later, the great majority of game apps adopted the freemium business model. At times, it is surprising how quickly new business models are adopted. The 30 percent fee structure Google and Apple charge app developers for in-app transactions has been in place since 2010; only recently has this pricing standard started to shift.⁸ These examples, though, are specific to Google and Apple. In China, for example, there is a much greater variety of app store operators, hardware manufacturers, and business models (Zhao, 2019).

While we have discussed network effects and pricing discretely, we did so for analytical purposes. In reality, they are interrelated, giving way to a dizzying number of economic issues and questions faced by all actors in platform ecosystems. Managing end-users and complementors is an inherently dynamic process (Gawer, 2014; Rietveld & Schilling, 2020). In turn, this balancing act on the part of platforms – between getting users on board and improving pricing structures – introduces uncertainty and risk for cultural producers. Next, we discuss these uncertainties and risks through the lens of platform evolution. This allows us to provide a more nuanced perspective on the highly contingent relationship between platforms and complementors – which is subject not only to strategizing and economic decision-making, but also to complex temporal shifts.