Facebook, Online Books, and Online Dating: Network Effects in Tech
The meteoric rise of relatively young companies in this Information Age has seen many companies achieve user bases of unprecedented scale. Notably, many such companies—Facebook/Meta, Netflix, TikTok/ByteDance, and Tesla come to mind—are based in the tech sector, and these technology companies have earned household-name status in just a few years, far faster than most other brands.
To explain this growth phenomenon, Ju and Iansiti (2019) cite network effects, or direct-benefit effects, as one of a few key factors in company expansion, arguing that the strength of these network effects can help shape the trajectory of companies in the modern world. Importantly, the authors first acknowledge the conceptual underpinnings of network effects covered in class, namely that the increased adoption of a good or service can improve its value to users, and they attribute the dominance of Facebook to these network effects. This connection between the positive externalities of network effects and Facebook’s size have also been covered elsewhere, as Zimmerman (2017) writes in his article that the existence of network effects make the company a “natural near-monopoly” and that the “scope of network effects will only grow with time.” Because Facebook’s initial business model almost exclusively depended on interaction between its users, the platform would have been far less useful if only a few people used it. After all, a small user base intuitively could not provide the breadth and quantity of content that larger user bases muster, thus diminishing the website’s utility. However, with more videos, status updates, and users, Facebook is better able to connect friends—its main purpose—thus establishing positive externalities that increase the site’s value and attract other users. This, in turn, allowed Facebook to grow more rapidly than in sectors where network effects are less pronounced. As such, by applying the concept of network effects covered in class, we can help to explain Facebook’s growth over the past two decades. Additionally, while Ju and Iansiti explicitly mention Facebook, we can also apply this framework of network effects to other companies. For example, dating apps like Bumble and Tinder have seen their usage grow substantially in recent years for many of the same reasons as Facebook. Centrally, dating apps’ purpose is to help users meet other users who interest them, and these require a large pool of romantically interested people to do so. After all, these apps are more valuable when there are more users to provide profiles and chat with each other—this large user base offers a better selection for users and, thus, a better chance for ideal couples to form. This, in turn, can help to attract new users, demonstrating that the influence of network effects extends through various industries within the tech sector.
The focus of Ju and Iansiti’s (2019) article extends beyond merely noting the salience of network effects in technology—they also mention that network effects carry a variety of strengths for different goods, services, and technologies. In class, we represented network effects as f (z) and noted that changes in this value could drastically affect the scope of a product’s adoption. In their article, Ju and Iansiti (2019) apply this concept of differing network effects and distinguish between companies/products with strong network effects, including Facebook, and those with weak network effects, like video game companies. Whereas higher content creation correlates with higher user bases (and vice versa), the authors note that this effect is substantially less pronounced in sectors where sufficient quality content can be found without widespread participation. In other words, the degree of usage at which companies with weak network effects become valuable is often lower than that of peers whose models involve strong network effects. For example, a dating app—as established above—likely demonstrates strong network effects, as it becomes considerably more valuable when the pool of potential suitors increases. However, these network effects are substantially weaker in the cases of e-book lending services like OverDrive and Hoopla. If more library members were to use OverDrive or Hoopla to check out books, the quality of these services would not necessarily increase, as increased usage might leave fewer copies of books and audiobooks for each user. As such, the link between network effects and rapid growth demonstrated by Facebook may not apply to all technology companies—especially those with weak network effects. Importantly, Ju and Iansiti (2019) also assert that the strength of these network effects are not constant, expanding the discussion of network effects beyond the scope of class lectures. In particular, they note that while Windows dominated the operating system market before the turn of the century because of its high user base (and correspondingly large pool of Windows-centric software), the emerging web-app market allowed users to access software across operating systems and allowed for Windows competitors to seize market share. As such, products demonstrate varying strengths of network effects, and these network effects change over time, allowing for the unpredictable and unprecedented growth of modern technology companies.
Websites
- https://hbr.org/2019/01/why-some-platforms-thrive-and-others-dont
- https://www.dw.com/en/network-effects-helped-facebook-win/a-40418818
Bibliography
Zhu, Feng, and Marco Iansiti. 2019. “Why Some Platforms Thrive and Others Don’t.” Harvard Business Review. 2019. https://hbr.org/2019/01/why-some-platforms-thrive-and-others-dont.
Zimmermann, Nils. 2017. “Network Effects Helped Facebook Win.” Deutsche Welle. August 9, 2017. https://www.dw.com/en/network-effects-helped-facebook-win/a-40418818.