In a negative network effect as the network grows in usage or scale, the value of the platform might shrink. In platform business models network effects help the platform become more valuable for the next user joining. Negative network effects (congestion or pollution) reduce the value of the platform for the next user joining.
As highlighted in the interview with Sangeet Paul Choudary, author of Platform Scale and Platform Revolution:
The more people using the highway system, the more traffic jams you end up in. Or the more people in a room, the less likely it is to have good decent conversation just because it gets crowded, but also because everybody is talking too loudly and so you can’t hear and you can’t meet the right person within that room.
So we understand congestion in traditional terms because in the traditional world we have networks that were limited by scale.
When it comes to the digital world, instead, where there are fewer scale limitations. Or at least those can be initially overcome.
Congestion, therefore, is primarily about the usage of the network.
In the bits world, it is possible not only to overcome congestion but also to build a more solid engineering infrastructure as usage increases. Of course, network congestions can also be bad for platforms (poor network design, over-subscription, security attach due to overused network parts).
Another form, of negative network effect, can take place for platform business models.
Where network congestion is primarily about the size of the platform.
Yet, also for digital platforms scale might create situations of diseconomies.
What happens is the more users come on board, the more difficult it becomes to manage quality of the interactions.
If we think of social media, or publishing platforms at scale, among the most difficult task, that requires dozens if not hundreds of engineers and humans (Google might have thousands of human quality raters) to fix spam and low-quality user-generated content.
Examples of negative network effects
Google case study
In a platform that leverages direct side network effects after a certain number of users, it might also result in increased spam on the platform which can’t be easily managed through automated processes, or human curation, thus diluting the value of the platform.
Take the case of how Google, back in the days, was offering an index of the web with its core algorithm called PageRank. At a certain point had to figure out also how to manage the spam on its index.
Practitioners understood how to trick Google’s core algorithm into showing up spammy results on top of that. This would have jeopardized the value of the overall platform, thus resulting in a diluted value of that.
Thus Google had to start to build up a solid team dedicated to spam and update its algorithms to avoid spam in search results in order to keep its platform valuable.
Google has thousands of engineers and thousands of human quality raters focused on both preventing spam and on low-quality content.
Airbnb case study
Take Airbnb where, for instance, more hosts improve value for users on the platform. On the two-sided, more value is created by more hosts (travelers have more selection)? On the other hand, the value of the platform is diluted on the hosts’ side of the platform.
They will find themselves competing for the same users.
Thus it becomes crucial to understand what’s the proper ratio between travelers and hosts on the platform to make sure it keeps being valuable on both sides.
Tinder case study
Take the case of a dating app that draws value from having people encounter locally. If more users join but from locations spread across the world, not much local network effects are picking up.
Quite the opposite. If a critical mass is not reached at each local hub, the platform might lose value quickly. Imagine the case of a woman looking for a date. The faster she will be able to meet the best match.
The more the platform will be valuable. However, to be very valuable, the service has to make sure those people can meet in a place nearby. And it there are no matches available locally, the dating platform would lose value quickly.
Platform business models can leverage network effects to enable the core platform to become more valuable over time. However, they need to factor in negative network effects, which if picked up might not only prevent the success of the business but also destroy it.
- Social Media Oversaturation: As social media platforms grow in popularity, they can become oversaturated with content. This leads to users feeling overwhelmed by excessive posts, notifications, and ads, diminishing the overall user experience.
- Ride-Sharing Surge Pricing: Ride-sharing services like Uber and Lyft may experience negative network effects during peak hours. As more users request rides, surge pricing kicks in, resulting in higher fares. This can discourage users from using the service during busy times.
- E-commerce Delivery Delays: Online marketplaces like Amazon can face delivery delays during peak shopping seasons (e.g., Black Friday). Increased demand can strain logistics, causing slower deliveries and frustrating customers.
- Online Marketplace Counterfeits: Marketplaces like Alibaba have struggled with counterfeit products being sold by third-party sellers. As the platform scales, it becomes challenging to police the authenticity of all listings, potentially eroding trust among users.
- Video Streaming Quality: Video streaming platforms may experience negative network effects during high-demand periods. This can lead to buffering and lower video quality, frustrating users who expect seamless streaming.
- Messaging App Spam: Messaging apps can suffer from spam and unsolicited messages as their user base grows. This can result in a poorer user experience, as users receive unwanted content and may miss important messages.
- Online Review Manipulation: User-generated review platforms such as Yelp or TripAdvisor can face issues with fake reviews. As the platform attracts more businesses and users, the incentive for fraudulent reviews also increases, potentially reducing the trustworthiness of reviews.
- Online Learning Platform Overload: During peak educational seasons, online learning platforms can experience server overloads and slower response times due to increased user activity. This can hinder the learning experience for students.
- Crowdfunding Campaign Saturation: Crowdfunding platforms like Kickstarter or Indiegogo may experience saturation, where numerous projects are seeking funding simultaneously. This can make it harder for individual campaigns to stand out and attract backers.
- Online Auction Bidding Wars: Auction websites like eBay can see negative network effects when too many users bid on the same item. This can drive up prices to levels that some users find unreasonable, deterring participation.
Key Highlights about Negative Network Effects in Platform Business Models:
- Introduction: Negative network effects occur when the growth or scale of a network reduces the value of the platform for new users. While positive network effects make platforms more valuable with increased usage, negative network effects can lead to congestion and pollution, diminishing the platform’s value.
- Network Congestion:
- Analogous to physical congestion in highways or crowded rooms.
- Increased usage leads to reduced quality of interactions.
- Relevant in traditional networks with scale limitations.
- In the digital realm, scale limitations are fewer due to technological advances.
- Solutions involve solid engineering infrastructure and avoiding overuse.
- Network Pollution:
- As platforms scale, managing quality interactions becomes challenging.
- Diseconomies can arise as more users join.
- Maintaining quality requires mechanisms to manage interactions.
- Examples of Negative Network Effects:
- Google Case Study: Google’s search algorithm faced spam issues that could dilute the platform’s value. Google invested in engineers and human raters to combat spam and maintain value.
- Airbnb Case Study: In two-sided platforms, imbalance between user types (e.g., hosts and travelers) can dilute value. Proper balance between sides is crucial for sustained value.
- Tinder Case Study: Local network effects are important in platforms like dating apps. Matching users in the same location enhances value, while widespread user locations can diminish it.
- Key Takeaways:
- Negative network effects can counteract the benefits of positive network effects in platform business models.
- Network congestion and pollution reduce the platform’s value for new users.
- Proper balance, quality control mechanisms, and local network effects are crucial to managing negative network effects.
Connected Economic Concepts
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