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Network Effects on Online Platforms‘ Popularity

Didi became the world’s largest ride-sharing company in 2016 after merging its domestic rival in China and pushing Uber out of the Chinese market. Its number of users has been dramatically increasing over the recent years, while there are a few new competitors developed by some other leading Internet companies in China. Under the tough competition within the China market, one reason for Didi to continue to be the largest ride-sharing company is the network effects, which also applies to many other famous Internet platforms around the world.

There are typically two types of network effects: same-side (or direct) effect and cross-side (or indirect) effect. Take Facebook for example, same-side effect means that the more Facebook friend you have, the more likely you are to attract new friends to join your Facebook network. Using Uber’s case, cross-side effect means that more drivers will attract more riders. The author points out that the value provided by a platform will continue to rise with the increasing number of participants when the network effects are strong. For example, as more and more people are attracted to use Instagram due to network effects, more interesting contents will appear on Instagram, which will attract even more users to register. As a result, it is easier for online platforms that are already in big scale to expand its consumer groups.

If we think about what we have learned about Network Effects in class, the user will only be willing to use the online platform if p <= r(x)*f(z), as p is the cost that the potential user has to pay in order to use Instagram. In the Instagram example, as more and more people are using Instagram, z (the fraction of the population using the product) will increase, which will increase the Network effects, f(z). r(x) is the reservation price that the potential user has for Instagram. If more interesting and relevant contents are shown on Instagram due to the increased number of users, then the potential user will have a higher reservation price for Instagram. Since both reservation price and network effect increases, the potential user is more likely to register for Instagram than before.

Under this situation, we may predict that r(x) and f(z) are positively related, because the increased network effect can bring more interesting contents to the platform, which will increase the reservation price for potential users. At the same time, as the reservation price increases, more users will use the platform, which will increase the network effect further.

The Network Effect on online platforms is not limited to social media websites: it is applied to almost every sector of the Internet industry. It will be a good strategy for firms to utility the Network Effect, so that the firm’s promotion and advertising will become easier. For example, Amazon is taking good use of its review system, as users are more likely to visit Amazon to read product reviews when the number of reviews increased. As more people are purchasing on Amazon, the number of reviews will also increase sharply. Network Effect on Amazon is not only same-side effect but also cross-side effect. As more users are shopping on Amazon, the number of sellers will also increase.

 

Source: https://hbr.org/2019/01/why-some-platforms-thrive-and-others-dont

 

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