How Digital Platforms Have Become Double-Edged Swords

It’s not difficult to see how digital technology and innovation have rapidly transformed our world over the last three decades. If the industrial revolution was built by the factory system, the changes we see today are organized around digital platforms.

Indeed, the most valuable companies in the world — Microsoft, Apple, Amazon, and Google — have harnessed platform power to achieve rapid growth and market dominance as their products and services permeate our daily life. And just as these platforms have grown in size and scale, the opportunity for abusing their power has also become very real.

How should regulators and competitors respond? What are the emerging platform battlegrounds that will shape our future? In a time when platforms have become double-edged swords — capable of being used in both positive and negative ways — how do entrepreneurs and managers pursue growth responsibly?

These are the kinds of questions authors Michael A. Cusumano, Annabelle Gawer, and David B. Yoffie contend with in their most recent book, The Business of Platforms. The book builds on their body of research drawn from studying and working with platform companies for the last 30 years, and the authors use new data to critically examine the impact platforms have on the economy and society.

MIT Sloan Management Review digital editor Ally MacDonald spoke with the authors about their research, and what follows is an edited and condensed version of their conversation.

MIT Sloan Management Review: You’ve been studying platforms for a long time. What keeps you interested?

Michael Cusumano: There is a lot of confusion and hype over exactly what platforms are, how they function as businesses, and what contributions they make. We wanted to dispel some of the platform mania using real data. How do companies create and run platforms so that they make money? How do you make sure [the platforms are] positive rather than negative for both the economy and society? Those were the kinds of questions we were thinking about as we set out to research and write the book.

What are the key distinctions between platform types that leaders need to understand today?

Michael Cusumano: We find platforms in nearly all industries. These are businesses that bring together different market actors in a way that generates network effects — positive feedback loops where the value each user experiences increases as more users adopt the platform. We divide platforms into two basic categories: innovation platforms and transaction platforms. The first type facilitates outside innovation, and the second facilitates the exchange of information, goods, or services.

It’s also important to understand how some firms create both types of platforms and use them to reinforce each other. This is what we refer to as a hybrid platform company, a business model that was not apparent when we wrote our first book in 2002 on platform leadership.

That book focused only on what we now call innovation platforms — software ecosystems like Windows and Android. In 2002, we were not thinking of a company like Amazon as a platform business — it was mainly an online store reselling books and then electronics products.

The strategy of bringing different market actors together and creating a business that can grow in a nonlinear way, based on network effects and digital technology, is a newer platform phenomenon. Still, there wasn’t much data collected on how new platform companies performed over time, so this project really started out for us as a data collection exercise.

What did that data gathering process look like?

Michael Cusumano: We were looking to determine whether platforms are more efficient than their non-platform competitors in the same industry segments. We went through the Forbes Global 2000 and identified platform companies that came about with the PC, web, and mobile markets. It turns out that platforms are more efficient. They roughly do the same amount of revenue with half of the number of people, because they’re accessing resources outside of the firm and creating a business without actually owning nearly as many assets as comparable non-platform companies.

But there’s a bit of a myth about how common these platforms are. Of the Global 2000, we identified only 43 companies that fit the category of a post-PC “digital platform.” Even among those firms, many are a mixture of product and platform businesses. The ones that survived and became very powerful public companies are relatively rare.

Of the digital platforms that do exist, the majority are transaction platforms. Part of the reason is that creating an innovation platform is more difficult. This entails platform entrepreneurs introducing a technology that other firms will adopt as core to their business and then build products and services around. Take operating systems, for instance. We have one dominant software platform for PCs and only two for smartphones because powerful network effects between users and applications make these winner-takes-all or winner-takes-most markets.

What does the data tell you about the rate of failure for platform companies?

Michael Cusumano: We tried to count how many failed attempts there were for each of the successful platforms. It turns out platform companies fail at a rate that’s at least comparable to failure in entrepreneurship more generally. The number of surviving platforms in our research was 17% — so in addition to the 43 successful digital platforms, there were 207 failed companies that we were able to identify.

It’s also important to note that you can have success on certain levels, such as market valuation, but not necessarily on a business level. Look at Uber. Uber is not yet successful as a business. It lost $1.8 billion in 2018 and $4.5 billion in 2017, which suggests that there are some serious flaws in its business model that the company needs to correct. Part of the problem is that Uber subsidizes both sides — riders and drivers — who come to its platform. Instead of just receiving commissions per ride, for example, Uber pays drivers a set fee and bonuses to join the platform. And Uber keeps the price of rides lower than taxis or other competitors. Venture capital has been necessary to fund these driver and rider subsidies.

Uber also uses the independent contract worker strategy to keep costs low. However, there is a downside. Data we found indicated that 12.5% of Uber drivers quit every month, despite the set payments. This means that, every nine months, Uber loses and replaces all of its drivers. So Uber has to spend billions of dollars each year attracting new drivers with bonuses, in addition to keeping prices low. Uber and most other platform companies that are not public lose money, which helps explain why there have been so many failures.

Annabelle Gawer: Some of these companies are willing to lose so much money because they believe that at the end of the road, there’s going to be a winner-takes-all market. This is one of the myths we bust in the book.

Traditional platform theory says that when you have a platform business you’re going to have network effects, and we do agree with that. But the widely held belief that when you have network effects you’re going to have room for only one or two winners at the end, essentially a monopoly, creates a situation where platform companies believe that they have to eliminate rivals at all costs. We demonstrate in the book why this has become such a risky strategy.

First of all, it’s not always the case there will be a winner.

Second, it’s not just about network effects. In order to dominate a market, successful platforms need to ensure that users are satisfied with their offering — and [they] find it unnecessary to use multiple competing platforms, a practice called multi-homing.

Third, they also need to reduce the impact of niche and differentiated competitors.

Finally, like any traditional business, all platform companies need to construct significant entry barriers so that new competitors don’t keep appearing.

In the book you talk about the trade-off between openness and curation for platform companies. What does this trade-off look like in practice, and how does it create a dilemma for platform governance?

Michael Cusumano: When a platform tries to control or curate activity and participation in the platform, it can directly depress network effects — [that’s] in addition to being a costly expense, like in the case of Facebook hiring 30,000 people as content moderators. Additionally, on the economic side, Facebook has admitted that the content where they sell the most ads are the flagrant fake news stories that go viral. So, in the short term, limiting participation or activity with this kind of content may result in a less vibrant and economically less successful platform, at least in short-term network effects. However, over the longer run, a business is not sustainable if it allows criminal activity, fake content, or general malfeasance on its platform.

David Yoffie: An area that has been underdeveloped is platform governance — the rules and procedures that limit and control the activity that takes place on the platform. Those rules have been vague. They need to be tightened up and they need to be enforced. Companies should create departments that can monitor activities and potentially control them directly. Facebook’s 30,000 content monitors probably aren’t enough. Companies must scale up these activities, which will raise costs. They are also using artificial intelligence and machine learning tools, but they need more people as well. We also argue that there need to be more transparent terms and conditions. Users have to understand with greater clarity exactly what behavior is acceptable on the platform and what’s not.

How do managers in platform businesses get ahead of the curve in self-regulating when digital disruption has become such a competitive advantage?

David Yoffie: One of the examples we use in the book is Amazon’s decision to start paying state taxes to all states. Legally, they were not obliged to do so. Platform businesses need to recognize the possibility that regulatory change is going to happen, and that it is going to be painful when it does. If you can find ways to preempt regulation, you’re likely in a better position to gain an advantage. That’s part of what the Amazon example provides.

Michael Cusumano: Other theoretical examples would be Airbnb agreeing to be governed by city hotel regulations, or companies like Uber, Lyft, or Deliveroo deciding to hire full-time drivers as regular employees, with full benefits and higher salaries. Right now, they’re treated as independent contractors and not paid very much. Some of the drivers live at poverty-level salaries. New York City is already stepping in to raise the minimum wage for sharing-economy workers.

At the same time, people have argued for many years that gig-economy platforms would not exist without the temporary worker strategy — that’s what has kept some of their costs 30% or so lower than conventional companies. But that doesn’t mean it’s necessarily a good long-term business strategy. As we’ve discussed, Uber loses three-quarters of its workforce with driver churn every six months. This sort of platform chaos is not a great way to build a stable company, one with engaged workers and satisfied customers around the world.

Annabelle Gawer: The irony is that companies who have succeeded in these early Wild West days, flouting regulation and achieving positions of dominance, have benefited from easier conditions than the small, newer platforms that are following them. If regulations that are coming into place will apply to all platforms — and that’s an argument that the powerful platforms have made — it’s going to place a greater burden on new entrants, who will not benefit from the freedoms their predecessors [had] to establish a foothold. Even so, I do think [regulations are] necessary.

In terms of new platform battlegrounds, how will emerging technologies such as AI play a role?

David Yoffie: We talk about two different emerging cases in the book, which are both based on AI — driverless cars and voice recognition.

If you think about where firms are going with driverless cars, there is going to be a transition away from an Uber-like platform to transportation as a service, where you’re no longer going to have independent players connecting with passengers. Instead, you’re going to have a fleet of cars that a company will own and direct. That fundamentally changes the economics and performance of the business. In that way, AI doesn’t necessarily produce a path toward a new platform battle.

On the other hand, all the things that we see going on in the world of voice recognition look like a classic platform war. The main players will continue to be Google and Amazon, and maybe Apple. We’ll probably see some platform players from Korea and China, and we expect a platform struggle to play out in a similar way to what we’ve seen with the smartphone market.

There’s a lot of AI hype, but AI has differing effects depending upon the business. In some cases, we will see industry-level platform battles, and in other cases, we will see companies battling each other on the product level.

Michael Cusumano: We also found that platform thinking can help with innovation in sectors that we had not considered before. Two examples we discuss in the book are quantum computing and gene editing.

Quantum computers are actually not digital; they’re analog devices. They rely on quantum effects to perform calculations that, at least theoretically, will be impossible with conventional digital computers. In this space, we see a few companies and universities building what are new innovation platforms. But they’re not going to get very far until there is an ecosystem to generate applications and services, such as for cryptography and secure communications or solving particular optimization and simulation problems.

Each participant is trying to replicate what happened in conventional computing to stimulate the creation of third-party applications, programming tools, and services — like quantum computing as a service. A couple of companies have already launched these services, including Microsoft and IBM. Quantum computing will become more important as a new type of innovation platform over the next two decades or so, but it’s familiar because it still involves information technology.

Gene editing, however, is different, and moving faster — applications are already being developed and used today. As with quantum computing, we see some key universities and companies, including many startups, emerging along with some larger pharmaceutical and biotech companies.

Players will need to build an ecosystem around some of the core technologies, such as CRISPR. Some of this activity is happening already. We see companies and universities creating building blocks and tool kits — some are publicly available, using open source methods, and some are controlled by patents but broadly licensed.

We see a lot of patents nowadays related to software platforms and smartphones, but the information technology field has benefitted a lot from shared knowledge and cross-licensing agreements. As a result, Google, Microsoft, and Apple have been able to mobilize thousands of companies that build applications for their platform ecosystems.

In the biotech and pharmaceutical world, it’s been more of a race to find that one patent that becomes your multibillion-dollar drug. The competition resembles a zero-sum game with closed silos of innovation, rather than the grow-the-pie-together approach that we saw with PCs, internet services, and smartphone ecosystems. New technologies like gene editing may evolve a lot more slowly in a closed innovation model where companies do not work together or widely cross-license their patents. Here, the drug and biotech industry can learn from the more open, less zero-sum philosophy we’ve seen in other platform industries.

* This article was originally published here

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