“Incumbency advantage” among Big Tech platforms recognizes that network effects prevent users from leaving established platforms for emerging competitors. Gary Biglaiser, Jacques Crémer, and André Veiga discuss some of the reasons users delay leaving and how to reduce incumbency advantage.

If you told students that the same class was on offer in the classroom next door, but with free pizza and beer, they would move without being asked twice. So why don’t users show the same mobility when it comes to social networks? Even if there might be a better experience on offer, people tend to stay put. 

Suppose you had an idea for a social network with the same basic services as Facebook, but with a better interface, better algorithms, and an overall more pleasant user experience. You might nonetheless hesitate to launch it. Why? Everyone wants to participate in the network that most other users are also a part of. For each of your potential new users, moving to your network is useless unless the other users also migrate. This is known as the “network effect.” The upshot is that nobody wants to be the person who starts the migration process. Some argue that this coordination problem explains why Google+, the tech giant’s attempt at a social network to rival Facebook, was unsuccessful.

Economists call this advantage wherein an established network has a lock on the lion’s share of users the “incumbency advantage.” The incumbency advantage of tech giants unsettles regulators because it could make for a poorer experience for users. If incumbents know they are unlikely to lose users, they have little incentive to charge low prices or provide high quality products. This is one of the reasons why the European Union is proposing tougher regulation for established incumbents or “gatekeepers.”

Although a large proportion of economists who research Internet networks accept the existence of incumbency advantage, we know very little about what determines its importance.  Therefore, it is difficult to advise incumbents, entrants, or regulators. Indeed, there are cases where incumbency advantage seems to be limited.  

In 2018, the European Commission approved Microsoft’s acquisition of GitHub, a platform for collaborative software development. At the time, one regulatory concern was that Microsoft would have the incentives to leverage GitHub’s incumbency advantage to favor Microsoft’s own development tools. Eventually, the European Commission decided this would unlikely be the case because the sophistication of GitHub’s users, as well as their capacity to coordinate their actions, meant they would migrate quickly to a new platform if quality degraded. Therefore, the European Commission determined incumbency advantage did not create lock-in in this case. There has been very little criticism of the Commission’s  decision, and we find it reasonable, but we also know of no theoretical or empirical basis for the belief that incumbency advantage was small in this case.

To provide a clearer understanding of how incumbency advantage works, we developed a model in which users are provided multiple opportunities to migrate from an incumbent platform to a new (entrant) platform with higher quality services and better user experience. The model stresses the fact that, even if each user is convinced that a new network would be better if all users migrated, network effects give each user of an incumbent network an incentive to wait until many fellow users have migrated. Of course, if every user waits for the others, there will be no migration. Ultimately, we wanted to uncover exactly how much better the entrant network needs to be to overcome the incumbency advantage created by this waiting game between users.

“We find that, for many reasonable scenarios, the entrant network would need to be so much better than the incumbent that incumbency advantage effectively precludes entry even with a more efficient entrant”

We find that, for many reasonable scenarios, the entrant network would need to be so much better than the incumbent that incumbency advantage effectively precludes entry even with a more efficient entrant. Offering only slight improvements in some areas just isn’t enough to win people over.

To be more concrete, imagine a user debating whether she should switch from Facebook to Google+. She is convinced that Google+ is better in terms of speed and user interface, but – as one unkind commentator described – the experience is like a tumbleweed blowing through the desert. This means she will delay moving to Google+ and continue using Facebook, knowing she can switch at any time, if indeed Google+ “picks up.” If all users make the same decision, no one ever switches to Google+.

To put it yet another way, users behave like a large group of pedestrians trying to cross a busy road without a walk sign. One at a time, they are unlikely to risk it. They are far more likely to be successful if they’re inspired to cross simultaneously. However, nobody wants to be the first to step onto the busy road. In the end, it is likely that everyone ends up staying on the same side of the road, waiting for someone else to take the first step. According to our model, the same holds true for moving between social networks. This reluctance to switch is rational from each individual user’s viewpoint but harms the group overall.

Perhaps paradoxically, our model predicts that the more opportunities there are to switch networks, the less likely it becomes that users will migrate. Why? Suppose a user only remembers to check the new network once a year. If she doesn’t switch at that time, and the new network becomes extremely popular during the next year, she might miss out on the advantages of the new network, as well as the interactions with those users who have switched, for an entire year. In this case, not switching can have a high cost. Suppose instead that the user checks every hour. She’ll then be extremely comfortable with delaying a decision since she can change her mind in the next hour. If this is the case for every user, more frequent chances to migrate can increase incumbency advantage.

Another important insight of our model is that if users can participate in several networks simultaneously (commonly known as “multi-homing”), then they are also more likely to migrate across networks. The possibility of multi-homing makes it less costly to take that initial step, because you do not have to lose contact with the participants in the incumbent network when you join the entrant. This has important policy implications: regulators and competition authorities should be suspicious of attempts to make multihoming more difficult and should take positive steps to make multi-homing easier. For instance, regulators can favor data portability among networks or make it easier to conduct business across different networks.

Our theory also shows that if networks can commit in advance to limit the number of users who will be able to join, incumbency advantage can decrease. In this case, individuals who reject early opportunities to migrate might never be able to migrate in the future, since the entrant platform might reach its capacity. This “fear of missing out” may be a strong motivation for individuals to migrate early on, which facilitates the entry of new networks. 

Lacking guidance from economists, authorities have had to act mostly on intuition to assess the impact of proposed mergers and the circumstances that competitors require to thrive. Our model is a first step in providing regulators with the tools to assess and quantify the advantages held by established social networks.

Big tech companies may throw up their arms at the prospect of regulation and argue competition is “only one click away,” but such arguments don’t account for how network effects are a source of incumbency advantage for established networks. Much more research, both theoretical and empirical, is needed to explore when and how this affects competition, consumer welfare, and the policies we can recommend to regulators and competition authorities.