Platform Papers

Adding social features may help freemium apps become a superstar, but it could also backfire…

This blog is written by Joost Rietveld

Back in 2012, Sarah Needleman published an article in The Wall Street Journal titled “When Freemium Fails”. The article remarked that, despite the many attractive features of the popular digital business model, many freemium products fail to attract sufficient users, let alone generate revenues. Indeed, it is not uncommon for freemium products to have conversion rates (the share of paying users among a product’s overall user base) between two and five percent. Academic research, including by myself, later substantiated these observations: Freemium may offer users an attractive entry point into a new product, average use rates and customer spending on these products, however, tend to be well below that of traditional paid products.

Still, some of the most popular digital products nowadays are brought to market with a freemium business model. Examples include the hit video game Fortnite, the online dating application Tinder, and the file-hosting service Dropbox. Base versions of these products are offered at no cost to consumers, who can pay for optional content and features (such as character upgrades, exposure to romantic partners, or extra storage capacity) via in-app purchases. These examples represent a select group of freemium superstars—freemium products that are among the most-downloaded products in a market, which also generate substantial revenues for their commercializing firms.

This raises the following questions: Which freemium products are more likely to become a superstar? How do these dynamics differ from paid products?

The double-edged sword of social product features

In a recent study published in the Strategic Management Journal (open access), co-authored with Joe Ploog, we address these questions by focusing on a common design strategy for freemium products: the inclusion of social product features, such as multiplayer modes in video games (e.g., Fortnite’s Battle Royale), ride-sharing functionality in ride-hailing apps (e.g., Uber’s Pool), and virtual collaboration tools in productivity software (e.g., Google Sheets’ Share). Such social features enable interactions among a product’s user base, which, when present, enhance the product’s value in use.

Analyzing a large dataset of 9,700 digital PC games released on the Steam platform, we find that freemium products that incorporate many social features (such as online competitive play or local cooperative play) are 49 percentage points more likely than freemium products without any social features to become a superstar when they are released when the platform itself has a larger rather than smaller installed user base. Conversely, when the platform’s installed user base is small, freemium products are 26 percentage points less likely to become a superstar if they incorporate many social features. Notably, we find that these mixed effects do not apply to paid products.

Social product features can help freemium products attain widespread diffusion. By their very nature, freemium products enjoy strong social referral. We (as consumers) tend to be more likely to recommend products when they exhibit low risks to adoption (i.e., when they are free). We are also likely to share freemium products because we want to reciprocate to the products’ commercializing firm the benefits we receive for free. Social product features can amplify these tendencies. When the value of a product depends on the number of users, we might be even more likely to recommend a product, in the hopes of growing its user base and expanding the social engagement.

That said, a product with many social features likely won’t generate much value if it lacks a large user base. Indeed, a game like Fortnite isn’t much fun if there is no one to play with. Google Sheets doesn’t differentiate itself much from Excel if it wasn’t for the ability to collaborate in an online environment. Uber’s Pool functionality won’t help you save (on the environment) if there’s no one to share a ride with. Even worse, absent a large user base, consumers may feel they are missing out on key features if the product strongly relies on social functionality for its value proposition.

This is where the size of the platform’s installed base comes in. The platform’s installed base is a strong indicator of a product’s demand potential. Products released on a digital platform can only be adopted and used by those users who have first adopted the platform itself. When a product’s demand potential is constrained—because it is released on a platform with a small installed base—social engagement is less likely to occur. Users may be reluctant to adopt—let alone recommend—these products. To be sure, a freemium product without social features faces an equally constrained demand potential, however, it doesn’t require a large user base to confer its core benefits.

Finally, why, then, did we find that these mixed effects of incorporating social features do not apply to paid products? First, paying users are more likely to make an independent value assessment that is less reliant on social referral. After all, the decision to adopt a paid product is more consequential given the upfront payment that’s required. Second, users will, on average, spend significantly more time on paid products because they want to “get their money’s worth”. Put differently, users may dip in and out of freemium products because they do not incur any costs to adopt these products in the first place. Combined, these differences suggest that, even when paid products incorporate social features, they will be less reliant on widespread diffusion to create value for their users.

In conclusion

Freemium is a difficult business model to get right. Despite its many appealing features, most freemium products fail. That said, when freemium products do succeed, they tend to succeed to the point where they become superstars. These superstars enjoy disproportionately more users, more usage, and more in-app purchases. Our research on digital PC games shows that freemium products are more likely to succeed by incorporating social features when they are launched on a platform with a large installed base, whereas freemium products without any social features are more likely to succeed when they are launched on a platform with a small installed base. These findings hold important implications for firms’ product design choices and for their product-market strategies.

This blog is based on Joost’s research published in the Strategic Management Journal, which is included in the Platform Papers reference dashboard:

Rietveld, J., & Ploog, J. N. (2022). On top of the game? The double‐edged sword of incorporating social features into freemium products. Strategic Management Journal, 43(6), 1182-1207.

How building trust in online platforms can help users but may harm intermediaries

This blog is written by Grace Gu and Feng Zhu

Intermediaries are everywhere in our economy: brokers in the finance and insurance industries, headhunters in the labor market, distributors in retail, housing agents in real estate, and online platforms in the information technology industry, just to name a few. In 2010, intermediaries contributed an estimated 34% of the US gross domestic product.[1] However, all intermediaries face the risk of disintermediation, in which two sides circumvent the intermediary to transact directly and avoid the intermediary’s fees.

Disintermediation, wherein network members bypass a hub and connect directly, can be a big problem for any platform that captures value directly from matching or by facilitating transactions. Imagine that you hire a plumber from a platform like Homejoy and are satisfied with the service. Would you really go back to Homejoy to hire the same plumber again? After a consumer is matched with a service provider, there’s little incentive to return to the platform. Additionally, after obtaining enough clients from a platform to fill his or her schedule, the service provider won’t need that platform anymore. This was exactly the problem that doomed Homejoy, which shut down in 2015, five years after it was founded.

Examples like this are not hard to find. For instance, the traditional role of book publishers as intermediaries was weakened when Amazon enabled authors to sell directly to readers through its self-publishing services. Li & Fung, a supply-chain management company that connects global retail brands with Chinese manufacturers, suffered ongoing decline in revenue as retailers disintermediated to work with manufacturers directly. A survey by ZBJ.com, the largest online freelance marketplace in China, indicates that approximately 90% of transactions are conducted outside the platform after clients and freelancers have been matched on its platform. Hotels and airlines offer incentives to lure customers to book directly with them, thereby shrinking the revenue for online travel agencies.

How to address the issue of disintermediation? Because of the importance of disintermediation with regard to firms’ strategies and survival, platforms have used various mechanisms to deter disintermediation. Some platforms try to avoid disintermediation by enhancing the value of conducting business on them. But those services become less valuable once trust develops among platform users—and these strategies can backfire as the need for the platform decreases. This is what our study investigates.

In our recent study published in Management Science, we are curious to learn how user trust affects disintermediation, leveraging a randomized control trial (RCT) in a large online outsourcing platform. This platform enables clients to find freelancers who satisfy the clients’ job requirements to initiate and complete job contracts, and the platform charges a per-transaction service fee that is approximately 10% of each transaction’s value. In an RCT in 2015 which lasted for four weeks, the platform provider shows freelancers’ satisfaction scores (SSs) to a random sample of clients, illustrated in the Figure below. SSs are a newly developed measure of a freelancer’s business reputation based on his or her complete work history on the platform.

Our analysis of the above RCT generates interesting findings: As the platform improved its reputation-rating system, trust between clients and freelancers grew stronger, and disintermediation became more frequent, offsetting the revenue gains from better matching. We also identify other contributing factors to disintermediation tendencies, like being geographically near one another, having easily divisible jobs, and clients themselves having high ratings.

To platform managers, our findings highlight that disintermediation can sometimes render less effective an intermediary’s strategy to improve its profitability through enhancing trust. It is important to note that the main objective of our research is not to conduct a net benefit analysis to determine whether it is optimal for platforms to implement better reputation systems. Rather, because trust building is important for platform growth, it is of vital importance for a platform to build as much trust as possible. At the same time, our research suggests that as a platform builds more trust to facilitate transactions in its marketplace, it needs to adopt appropriate strategies to counter increased disintermediation.

Platforms could use a variety of strategies to reduce disintermediation as they enhance trust. Airbnb, for example, enhances trust and safety through host ID verification and background checks. At the same time, Airbnb reduces disintermediation by withholding host data, such as listing address or phone number, until the payment is made. Thumbtack, a marketplace that connects consumers with local service providers such as house cleaners, captures value pre-transaction: when customers post job requests on Thumbtack, service providers can send quotes to the customers; service providers pay fees to Thumbtack only if customers respond. Disintermediation affects Thumbtack less strongly because its model captures value before two parties agree to work together.

Other platforms recognize that the motivation to disintermediate comes from the service fees they charge and adopt different value-capture strategies to prevent disintermediation while still enhancing trust. For example, Chinese outsourcing marketplace ZBJ, which launched in 2006 with a 20% commission model, began pursuing other revenue sources after calculating that it could lose as much as 90% of its business to disintermediation. In 2014, ZBJ leveraged big data analytics to find that new business owners often used ZBJ to outsource logo design. However, after logo design, many of these clients would also need business and trademark registration. Thus, ZBJ began offering this service and has now become the largest provider of trademark registration in China. Replicating this experience, ZBJ began providing several other services to its marketplace participants. With these revenue streams, the company decided to significantly reduce its commission to 2% and shifted its resources from fighting disintermediation to growing its user base and building trust (for example, by encouraging clients and freelancers to communicate). Because of these changes, the company obtained a valuation over $1.5 billion in 2018.

Platform managers may use our study as a starting point to come up with more creative strategies to mitigate the tension between trust building and disintermediation. We hope that our study inspires a growing stream of academic research as well as managerial best practices to prevent disintermediation and enhance platform value capture.

This blog is based on Grace and Feng’s research published in Management Science, which is included in the platformpapers references dashboard:

Gu, G., & Zhu, F. (2021). Trust and disintermediation: Evidence from an online freelance marketplace. Management Science, 67(2), 794-807.

[1] Spulber, Daniel F. 2011. “Should Business Method Inventions be Patentable?” Journal of Legal Analysis, 3(1): 265–340.

How Google and Apple use “digital colonization” to enter highly regulated industries

This blog is written by Hakan Özalp, Pinar Ozcan, and Annabelle Gawer

Big Tech platforms (GAFAM in the Western World: Google/Alphabet, Amazon, Facebook/Meta, Apple, and Microsoft), have risen to become the most influential and largest firms in the world. Their rise through their data-centric approach has been related to increased efficiency and innovations in many areas. However, there are also many concerns related to market power, legacy incumbents, and privacy which are now shaping specific regulations for these firms, as seen in the recent adoption of the Digital Markets Act (DMA) and Digital Services Act (DSA) by the European Parliament, and similar regulations that are upcoming in US, UK, as well as many other countries.

Although these regulations aim to broadly regulate the market power of platforms, there are some industries that are just now experiencing increasingly more activity by these Big Tech platforms and are witnessing initial signs of disruption: These are highly regulated industries such as healthcare and education.

These highly regulated industries are represented by their higher entry barriers, slower-moving nature, considerable state-involvement, and many incumbents protected from competition for a long time. As such, these industries are also associated with large potential for increased efficiency and innovation. This in turn makes the benefits and (potential) harms of Big Tech platforms’ activities critical in these industries since value creation is extremely important (e.g., patient lives saved by new technologies), yet concerns around data privacy, fair competition, and public services that are based on (human) rights are even more salient (e.g., medical or learning records already used by Google and others or “right to education”).

In a recent paper in the California Management Review, we explore how Big Tech platforms enter and compete in highly regulated industries. Our findings highlight that the crux of platform entry into highly regulated industries is access to sensitive data. Big Tech firms rarely aim to directly offer the “primary service” (e.g., providing school education or becoming primary healthcare providers) in these industries, but they rather focus on capturing data as a pathway to other value-adding activities. They do so by engaging two modes of data capture simultaneously: using their own hardware or software to build their own datasets (“direct data capture”, such as the continuous health data collected through an Apple Watch); and forming partnerships with state or private actors (particularly primary service providers) in the industry for access to existing data (“indirect data capture”, such as the UK’s National Health System’s (NHS) partnership with Google’s Deepmind where they provided patient data to Google).

As Big Tech firms combine the data they captured directly and indirectly, they can provide superior data-driven insights, which can add significant value to incumbent service providers (e.g., through saved lives, better learning outcomes, and lower costs). Ultimately, they may design and commercialize new products and services for the highly regulated industry, through which Big Tech platforms can capture value in multiple modes, including leveraging captured data in their other activities (e.g., advertising).

Overall, in highly regulated industries where access to primary service data (e.g., individual-level clinical data or educational records) is a bottleneck due to the sensitivity of this data, those firms that overcome this bottleneck, generally through indirect data capture combined with data-driven insights, gain a unique competitive advantage in the design of new products and services where incumbents (e.g., medical device manufacturers, textbook publishers) have historically operated without such data capture or analysis capabilities. This allows Big Tech platforms to change power dynamics in these highly regulated industries by providing unique added value through data-driven products and services that other industry players increasingly rely on, which in turn commoditizes these players over time.

We name this process (summarized in the figure below) of entry by Big Tech platforms in highly regulated industries “digital colonization”, which we specify as composed of four stages: (1) Provision of data infrastructure services to incumbents; (2) direct and indirect data capture in industry; (3) provision of data-driven insights; and (4) design and commercialization of new products and services.

For platform strategy, we suggest that superior data analytics capabilities that enable the generation of data-driven insights matter even more in highly regulated industries, as they offer a pathway to break the high entry barriers in these industries and solve the bottleneck of data access. We also find that, rather than replacing existing actors and competing head-on to offer primary services, successful platforms add value to the existing value chain in highly regulated industries through data infrastructure services, data-driven insights, and finally new products and services through a multi-stage strategy. Initially, platform managers need to negotiate access to primary service data. At the same time, they need to make efforts to capture novel data through proprietary hardware and software user interfaces. In this context, platform firms may find that subsidizing hardware and access to services can work effectively to “buy their way” into data access. After this initial stage, they can focus on capturing value through a variety of data-related industry activities, such as selling data-driven insights or designing new products and services.

Our findings also highlight the paramount importance of a platform’s policies and special procedures in dealing with the usually sensitive data within highly regulated industries. While taking precautions in the use of sensitive data may seem limiting for value creation (and capture) for a platform, this is necessary, as eventually, a platform’s value for its users is also driven by how well it balances its diverse stakeholders’ interests, especially in terms of data privacy and security, but also in terms of the explainability and fairness of its AI-driven activities.

Another important set of implications is for incumbent firms, who need to respond to Big Tech firms’ uniquely powerful form of competition. We suggest that incumbent firm managers need to formulate their own data capture and analysis strategies and decide quickly whether they will compete or partner with entering platforms. Eventually, incumbent firms need to consider the long-term implications of initially value-creating actions of Big Tech firms, such as providing data driven insights, and whether these will, over time, lead to the commoditization of incumbents’ own activities.

From a regulation and policy point of view, our study highlights that special consideration should be given to regulating access to data and the usage of technology providers in highly regulated industries. This is in line with recent work that suggests platform regulation should consider issues like “sharing (in situ) platform data” and “data mobility/portability” above and beyond a purely market power-based approach that is utilized in the utilities sector. A particular balance needs to be established between giving enough space for platforms to bring their data-driven innovations to various industries and setting clear guidelines as to what data they can access, use, and combine, and what additional responsibilities they must carry while operating in highly regulated industries.

Finally, considering the balance between value creation and capture, we highlight an important dilemma for policymakers and regulators: platforms with more advanced data capture and analysis capabilities can provide more significant added value through better prediction but also these are the very platforms that pose additional privacy and security concerns in a highly regulated industry with sensitive personal data.

To close off, given the dire need of innovation and increasing efficiency of highly regulated industries such as healthcare and education, finding ways to combine the benefits brought by digital platforms with rights, ethics, fairness, and respectful consideration of personal data will become an increasingly important problem to solve in the years to come.

This blog is based on Hakan, Pinar, and Annabelle’s research published in the California Management Review, which is included in the platformpapers references dashboard:

Ozalp, H., Ozcan, P., Dinckol, D., Zachariadis, M., & Gawer, A. (2022). “Digital Colonization” of Highly Regulated Industries: An Analysis of Big Tech Platforms’ Entry into Health Care and Education. California Management Review, OnlineFirst.

Rural entrepreneurs exhibit worse responses to algorithmic changes, negatively impacting the efficacy of platform governance.

This blog is written by Wesley Koo and Chuck Eesley.

In many countries, rural areas have long been experiencing a troubling pattern of brain drain, leading to a dearth of business and employment opportunities. Meanwhile, in some parts of the world, the rise of digital platforms has created a path for rural entrepreneurs to earn a living through online selling.

It is one thing for entrepreneurs, whether urban or rural, to create and operate an online store, as some platforms have made it relatively easy to manage an online store – even by using just a smartphone. One troublesome aspect of online selling is that platforms frequently tweak their algorithms. This requires entrepreneurs to keep up with these changes and constantly adjust their strategy. If not, their products may not surface in customer searches.

The next problem is that platforms do not always communicate the changes in the most straightforward manner, leading to confusion among entrepreneurs. As we show in a recent paper, compared to rural entrepreneurs, urban entrepreneurs have access to offline sources of information that helps them “see through the veil”. This advantage can lead to lasting gaps in performance, especially after a major algorithmic change.

The Offline Interface for Online Platforms

For online business and digital platforms, our research highlights the importance to understand the offline interface – a term we use to describe the local, offline factors (be it economic, social, cultural or political) that affect participants’ ability to navigate a digital environment. This concept can be illustrated using the example from our paper, which explores how entrepreneurs respond to a major algorithmic change on a prominent e-commerce platform in China.

On e-commerce platforms, entrepreneurs usually start out as scavengers, either making their own products or pulling excess supplies from their environment (e.g., local factories). You see this phenomenon on platforms in both developing and developed countries. Entrepreneurs sourcing from nearby sources might sell socks, car radios and baby products, whatever assortment of products they manage to line up – or figure out what might sell based on a recent fad.

In May 2013, the platform examined in our paper was trying to increase the professionalism of its sellers (entrepreneurs) by encouraging them to specialize in a single product category. The goal was simple from the platform’s perspective: Over time, entrepreneurs would become category experts able to provide top-level customer service. To achieve this, the platform modified its algorithm to prioritize entrepreneurs who sold many products within one or two product categories (a characteristic termed “category focus”).

However, the platform had difficulty conveying this change to entrepreneurs. A somewhat cryptic announcement said that Big Data would be used to construct buyer profiles and that a new algorithm would “help entrepreneurs lock onto potential buyers and implement targeted marketing”.

Vague communication such as this is common. While hiring better communicators might help, the reality is that transferring complex information to others in a digital environment is more difficult than it seems. First, people do not always check their inboxes for platform-initiated communication. Email announcements are the poor cousins of the communications family, as they tend to be text-only. They may be crafted in a language that is poorly understood by much of the intended audience. If recipients see questions that need to be clarified, they can neither request nor receive immediate feedback.

As media richness theory has shown since the 1980s, communications that are richer in cues and more personalised, such as in-person discussions, are significantly more effective at helping decode potentially confusing messages. If we were to tell you about our research findings, we would probably do a better job sitting down and talking to you face-to-face than via this article alone.

Another reason for unclear communication is that a platform may want to hide the details of an algorithmic change – from its competitors, from public scrutiny and from participants themselves (to prevent some from “gaming the rules”).

These communication issues lead to equivocality in entrepreneurs’ interpretation of platform algorithms, and that equivocality is unevenly distributed across different segments of the entrepreneur population.

Rural entrepreneurs are left to figure things out by themselves

Let’s go back to the Chinese e-commerce platform. Based on our study of 2,395 randomly selected e-commerce entrepreneurs, the sales of rural and urban entrepreneurs were on par before the algorithm change. In fact, rural entrepreneurs, which comprised nearly 40 percent of the sample, sold a little bit more. But after the tweak, rural entrepreneurs actually went in the wrong direction by decreasing their category focus. This sank their stores’ rankings and resulted in sales that were 24 percent lower than those of their urban counterparts.

After four months, rural entrepreneurs had caught up in terms of category focus, but their sales remained inferior until several months later. This suggests that algorithms rewarded the entrepreneurs who “solved the puzzle” early, and that advantage was sustained over time.

Among urban entrepreneurs, guess who were the best performers? Those nearest to the platform’s headquarters. Their category focus doubled after the algorithm change. Moreover, it is telling that the biggest predictor of an entrepreneur’s behavior was not what their same-category competitors did, but what other entrepreneurs in their physical vicinity did.

In an online environment filled with irrelevant, vague and even false information, entrepreneurs can often be led astray. In such situations, the offline interface is all the more relevant: Knowledgeable sources of information in the local environment can help entrepreneurs address points of confusion and devise the appropriate strategy. Rural entrepreneurs are not less capable than their urban counterparts, as shown by their performance before the algorithmic change. Rather, their local, offline networks just don’t give them access to the same level of help to navigate the ever-changing platform environment.

What are examples of high-quality information sources? Our field research in China showed that discussions with experts – often former platform employees or individuals connected to platform designers – were important for entrepreneurs’ success. Self-organized learning groups are also more prevalent in cities. Yet, these opportunities to acquire valuable information were less available in rural areas.

We also met rural entrepreneurs who would take a long-distance bus to cities in order to learn more about the latest platform direction. As a rural entrepreneur told us: “Oftentimes, many rule changes on the platform are quite subtle and hard to interpret. But there are these platform gurus in Beijing, who can see through the veil. You must go on a ‘pilgrimage’ to obtain the ‘holy scriptures’ from [them].”

Lessons for platform governance

The first order of inequality in the digital economy is internet access. A number of initiatives are trying to fix this inequality, for instance the Next Billion Users projects by Google. But if we look at third-party entrepreneurs on an e-commerce platform, they theoretically have the same ability to reach all customers. Geography is not supposed to matter. Except that it does.

In practice, information asymmetry due to offline differences is common. This poses a major issue for platform governance, which is not unlike what governments face when implementing new policies or regulations.

Our research suggests that, before implementing major algorithmic changes, a platform needs to study its audiences carefully and assess their diverse information needs. Participants in remote geographic areas need special attention. This could mean creating learning groups with experts from nearby cities or sending clarifying messages or videos in a targeted manner, in which case the platform needs to implement well-designed pre-testing. The platform could also help information-disadvantaged participants by creating ranking algorithms that do not rush to punish those who fall behind on day one. In other words, algorithms that are less dependent on prior performance could better enable certain disadvantaged participants to adjust to a new platform order.

Is our research only applicable to China? Far from it. It is relevant for the planet’s 3.4 billion rural dwellers, who represent a vast pool of potential entrepreneurial talent for the digital economy. You may first think of developing countries, but most first-world economies also have rural areas that need to be revitalized. In U.S. states, such as Ohio and Kentucky, young talent leaves and hardly ever comes back. Online entrepreneurship could help lure talent, but it is important to promote a rich local information environment in these communities to nurture it. These lessons are valuable for a post-pandemic world in which an increasing number of people switch between online and offline in their work and daily routines.

This blog is based on Wesley and Chuck’s research published in the Strategic Management Journal, which is included in the platformpapers references dashboard:

Koo, W. W., & Eesley, C. E. (2021). Platform governance and the rural–urban divide: Sellers’ responses to design change. Strategic Management Journal, 42(5), 941-967.

What do we know so far, and how can we learn from international business research?

Platform papers is a monthly blog covering the latest academic research on platform competition. Blogposts are written by prominent scholars based on their research included in the platformpapers reference dashboard. This month’s blog is written by Liang Chen.

Pundits often rave about platforms dominating the digital economy. But have platforms lived up to the hype in global markets? Not quite, except perhaps for a handful of big names. Even for a posterchild like Alibaba, it was struggling right at the doorstep in Southeast Asia—the acquisition of Lazada stumbled into cultural clash and saw heavy CEO turnover in only a few years. Shopee, a start-up in Singapore, ended up becoming the region’s retail powerhouse and outgrew Alibaba’s entire international e-commerce. What went wrong? Is winner-take-all a mirage on the global stage?

International network effects, or not?

The hype about platforms is based on network effects. If foreign users and complementors are drawn to a fast-growing platform, international success should be a natural extension of domestic dominance. Conversely, when network effects decline across the border, we cannot expect platforms to dominate international markets. For some industries like ride-sharing and digital coupons, users mostly consume products and services offered by local complementors. On social media, users may be only attracted to local complements due to common preferences. Despite the frenzy about network effects, it is not rare that platforms must establish the ecosystem of users and complementors in a new country from scratch.

Multinationals vs platforms

International business (IB) scholars have been studying firms’ foreign expansion for decades. The focus has always been on multinationals—why they venture abroad, where they expand, how they enter each market, and how well they perform. A consensus arises that establishing a physical operation overseas can circumvent the transaction costs associated with cross-border dealings. Equity entry, i.e., setting up a foreign subsidiary, is considered the default mode—otherwise we would know no multinationals. But platforms are a different beast. A platform has a loosely coupled relationship with its complementors without which it is simply of little use.

While multinationals mandate what a foreign subsidiary should be doing, platforms seek to get complementors on board and prompt them to create value. Managing a subsidiary across borders is hard; coordinating a loosely coupled network of complementors surely is even harder. On the one hand, platforms rely on complementors’ local knowledge and initiative in exploring new use cases and offering locally appealing products and services. To achieve this, complementors must have substantive autonomy. On the other hand, platforms need to push complementors to make platform-specific investments. Such investments help create more value (e.g., by differentiating the platform), stronger synergies with other complements in attracting users, and they can increase switching cost for complementors. Yet only when the platform exerts a certain degree of control will complementors be willing to make such platform-specific investments. The balance between autonomy and control is a tricky one and poses an ongoing challenge in an unfamiliar market.

Ecosystem-specific advantages

IB scholars traditionally depict multinationals using the notion of firm-specific advantages (FSAs). FSAs mostly derive from intangible assets, and they determine internationalization strategies and outcomes. But we all know platforms are called platforms precisely because they are not defined by proprietary assets alone. In competing with rivals abroad, platforms need ecosystem-specific advantages (ESAs) instead. ESAs arise when a platform has a great number or range of complements, when the activities within the ecosystem are highly complementary as shown by Amazon’s flywheel, and when the platform firm can govern the complementors such that their interests are aligned with the platform’s goals.

However, the transfer of ESAs across borders appears challenging. The platform will likely face a chicken-and-egg problem every time it expands into a new country. When a multinational platform cannot piggyback on its existing advantages to gain a head start, the risk of being outsmarted by a local rival becomes a real concern. After all, a local competitor will know the market better and it is not constrained by a globally consistent interface or design. Moreover, platforms may find it frustrating that even one technical bottleneck in the local market could paralyze the flywheel and deprive the platform of its ESAs. Alibaba is known for its commitment to an asset-light platform model; yet it had no choice but to invest extensively in logistics across Southeast Asia, and it also deployed the domestic logistic arm Cainiao to help enhance Lazada’s delivery efficiency. Still, the sluggish adoption of digital payment across the region poses another roadblock to leveraging Alibaba’s ESAs.

Charting new courses

As platformpapers keeps adding new publications, it is also getting clearer that our understanding of platform internationalization is still in its infancy. A closer look at IB scholarship may unveil several avenues worthy of future research endeavors.

1. Would we benefit from new theoretical lenses for studying multinational platforms? IB has heavily utilized transaction cost thinking in characterizing multinationals. But platforms “externalize” rather than “internalize” operations. In my research, I argue that property rights theory may be the most appropriate tool. You might agree or disagree, but the time is ripe for alternative perspectives.   

2. In IB, local adaptation is key to success. But adapting what? On a platform, complementors will mostly take care of the adaptation of product offerings. What seems intriguing is whether and how platforms effectively adapt their governance strategies to the conditions of a new market. Failures in certain foreign markets may be attributed to an inability to adapt platform governance and design, rather than simply cultural differences.

3. Scholars exhibit a growing appetite for linking corporate strategy with platform research. As an example, Uber continually reconfigures its portfolio in different countries; it doubled down on ridesharing in some markets while prioritizing Uber Eats (especially grocery delivery) in others. But we don’t have a clear idea why. In Hong Kong, Uber just shut down food delivery and turned its focus to Uber Taxi. How is this different from resource redeployment? Why do platform firms diversify into other businesses (often through acquisition) in a foreign market?

4. Regulators always play a salient role in IB, but perhaps more so for platforms. Changes in regulation can force changes in the business model and even illegalize a platform business to the extent that it must divest (e.g., Airbnb quitting Berlin in 2016). On the contrary, Grab worked closely with Thai authorities for years—despite ridesharing being illegal—and sought support from all major parties in the 2019 general election. IB research has a lot to say about non-market strategy and may be particularly informative on how platforms can establish legitimacy in a foreign territory. We know that corporate social responsibility has been an effective means, and perhaps “ecosystem social responsibility” is the next big area for researchers to explore.

5. Research on emerging market firms suggests that they venture abroad to escape from underdeveloped institutions at home. Platform firms, too, may be escaping from home regulations that are either too restrictive or misaligned with other countries. It is no coincidence that Chinese household names like ByteDance, Tencent, and Alibaba all embarked on hiring sprees in Singapore lately. This also raises the overlooked question of what kind of places will be favored by platform firms for establishing physical operations.

To be sure, platform scholars are paying growing attention to country differences (e.g., regulations), and it has proved exciting to work out the various implications for platform strategy. The question is: can our research keep up with the way platform businesses expand globally? I believe that the dialogue between IB and platform researchers is no longer an option but an imperative.

This blog is based on Liang’s research published in the Journal of International Business Studies and is included in the platformpapers reference dashboard.

Chen, L., Li, S., Wei, J., & Yang, Y. (2022). Externalization in the platform economy: Social platforms and institutions. Journal of International Business Studies, 1-12.

Li, J., Chen, L., Yi, J., Mao, J., & Liao, J. (2019). Ecosystem-specific advantage in international digital commerce. Journal of International Business Studies, 50(9), 1448-1463.

Complementors must recognize (and mitigate) the power asymmetries and the uncertainty endemic to platform-based markets

This blog is written by Donato Cutolo and Martin Kenney.

Within less than three decades, platform ecosystems have become dominant structures shaping a range of markets and industries. This is visible in the rapid growth in the number and size of online platforms offering value propositions to users and firms. The sheer size of these markets and the millions of businesses that have moved their operations to, or were created on, platforms means that we have entered what can be characterized as the Platform Economy. With this shift, online platforms increasingly mediate economic transactions and social relationships. This appears to be irreversible.

There have been tremendous benefits for both entrepreneurs and existing businesses. Platforms connect businesses to customers they could not otherwise reach and provide various tools and resources, enabling them to easily and quickly grow their business. And yet, these remarkable business opportunities create a strong dependence upon a profit-maximizing platform owner that is only responsible to its shareholders. In our article, published in the Academy of Management Perspectives, we delve into the challenges of platform-based entrepreneurship, recognizing how the power imbalances generate new risks for every complementor dependent on a platform (or considering becoming so).

Of course, the fundamental source of power for online digital platforms is their attractiveness, particularly in situations where they control access to users with a monopoly or monopsony position due to the winner-take-most aspects in many of these markets. Participation has become necessary for many companies’ survival and growth, as potential buyers and competing firms are enticed by the opportunities platform-organized markets offer. Given the winner-takes-most features of network-based industries, platform dependence increases as the availability of viable alternatives outside the platform decreases. 

Platform power is ultimately embedded and expressed in its software architecture and terms and conditions. This is where the rules are embodied and where subtle changes in the software’s algorithms or terms of participation, such as fees, the earning structure, or product acceptability can nullify any business strategy. This power can be used to direct attention and orient behaviors, and, more importantly, it provides incalculable advantages to the platform. For instance, direct control of the technological infrastructure upon which all activities are conducted allows the owner to identify particularly innovations or business models that can be encouraged, emulated, or destabilized depending upon the interests of the platform. 

Consider the plight of a new startup that develops an exciting, innovative app to sell through an app store. This venture is entirely at the mercy of the app store owner. To illustrate, repeatedly, Apple has embedded formerly externally provided “killer” apps into its installed software, thereby destroying that innovators “rents.” Similarly, previously approved apps have been removed unilaterally without warning, and this can occur even as the platform introduces its competitive app. The point, of course, is that in its private marketplace, the platform owner is a potential competitor that controls the rules and can rewrite the rulebook at any second. 

In the case of any platform decision, appeals are routed to an anonymous, all-powerful Kafkaesque bureaucracy. The rules for generating decisions are largely opaque. In other words, all the players, many of whom are forced onto the platform due to necessity, are in a state of continuous fear, precarity, and doubt. They can appear both capricious and draconian, and all efforts to get the platform owner’s attention may be futile.  

This reality means that platform-based entrepreneurs must be aware of these risks and dangers and, from the beginning, understand their options. While uncertainty has always been a defining characteristic of entrepreneurship, platforms come with new forms of uncertainty surrounding both market structure and demand that are endogenous, self-interested, and opaque: competing on platforms means competing with other complementors and potentially the platform itself. In another article, written with Andrew Hargadon and published in the MIT Sloan Management Review, we discuss some strategies that platform-based firms can experiment with to leverage the platform’s resources without becoming subservient.

One of the most potent ways a platform-based entrepreneur can address the risks associated with platform dependence is to multi-home. There are three general types of multihoming. The first is the classical case, where a firm operates through multiple platforms. The second type is channel multihoming, where a platform-based firm uses different channels, e.g., sells on a platform, operates its own website, and may even establish a physical store. Generating off-platform income streams is an important strategy to protect a platform-based business, and if extremely successful, it may allow disintermediation of the platform. The final one is platform multiplexing, where platform-based entrepreneurs adopt the different tools available from various platforms to develop new value propositions, reach new customer segments, or build new organizational capabilities that would not be possible on any single platform.

Sometimes platform-dependent firms can even join forces to collectively maximize their positions’ effectiveness and defensibility. For example, in 2018, after AbeBooks, an Amazon subsidiary, abruptly banned booksellers from several countries due to what it said was the increasing cost and complexities of operating in those countries, antiquarian book dealers from 27 countries pulled more than 3,700,000 books from the platform. After two days of protest, Amazon apologized and retreated.

Finally, and most interesting, is the fact that platform-dependent ventures are becoming more proactive and engaged with governments and the legal system. This can occur at the individual level through actions such as the lawsuit between Epic Games and Apple. But, more importantly, at the systemic level, such as, supporting the development of novel and innovative regulatory frameworks to mitigate platforms’ power. At a more systemic level, the question is whether there are solutions that maintain the benefits of platforms and sustain the incentives to generate them while protecting the community of those who buy or run a business on the platforms. Of course, building upon Karl Polanyi’s observations regarding the rise of capitalism, in another venue we argued that it is normal for the rest of society to react to a transformational change in the organization of capitalist economies with regulations to redress massive imbalances of power. While colleagues, Michael Cusumano, David Yoffie, and Annabelle Gawer recognize this and have recommended that the online platforms self-police themselves, state intervention may be necessary because, as Lord Acton, perhaps, hyperbolically, cautioned us, “power corrupts but absolute power corrupts absolutely.” Policymakers in the US, EU, China, and other jurisdictions are expanding their focus to consider the subordination of ever-greater parts of the economy to a few powerful platforms and their owners.  

In sum, despite, and because of, the great opportunities offered, new and established firms are increasingly attracted and locked into platform markets and become entirely dependent upon them. As these platforms continue to intermediate ever-greater parts of the economy, entrepreneurs who depend on platforms must develop strategies to mitigate the uncertainty endemic to platform-based business environments. 

This blog is based on Donato and Martin’s research published in the Academy of Management Perspectives, which is included in the platformpapers reference dashboard:

Cutolo, D., & Kenney, M. (2021). Platform-dependent entrepreneurs: Power asymmetries, risks, and strategies in the platform economy. Academy of Management Perspectives, 35(4), 584-605.

Do Spotify curated playlists influence our listening decisions? And, are these playlists biased?

This blog is written by Luis Aguiar.

Many online markets have come to be dominated by large digital platforms in recent years, prompting concerns about potential abuse of market power and stirring important public policy debates. In 2020, the European Commission announced its Digital Markets Act, a new proposal about the regulation of large digital platforms that potentially act as gatekeepers in their respective industries.1 With the ability to determine what – and how – information is provided to their consumers and the amount of exposure given to their suppliers, digital platforms are indeed well-positioned to influence which products ultimately succeed.2

Like numerous other sectors, the music industry and its structure have been drastically affected by digitization. Prior to the Internet, decisions about which songs to distribute and promote were made by record stores and radio stations in rather fragmented markets. With the advent of music streaming, a few platforms are now performing both the promotional function of radio stations as well as the revenue generating function of record stores. Because digital technologies have allowed to drastically reduce the costs of producing music, another effect of digitization has been an astounding growth in the number of new songs made available to consumers. As of 2022, Spotify’s catalogue included a total of 70 million tracks.

Against this backdrop, an important value-creating function of music streaming platforms is to help consumers find products that they like. They do so in large part by relying on playlists – which are both informative lists of songs and utilities for listening to music – to promote new as well as established songs to their users. Any user is free to create playlists on Spotify, but the most followed lists on the platform are controlled and operated by Spotify itself. Of the top 1000 most followed playlists on Spotify, 866 are controlled by Spotify, and their cumulative followers account for 90% of the followers of these 1000 lists. If these playlists affect individuals’ consumption decisions, then Spotify can play an important role in determining song and artist success, including the determination of which songs and artists are discovered in the first place. In this context, it is therefore natural to ask the following questions. First, does Spotify have power to influence users’ listening decisions, via its playlists? Second, does Spotify exercise that power in a biased fashion?

Is Spotify powerful?

In a recent paper, we explore whether Spotify has the ability to influence users’ listening decisions through its playlists. More specifically, we ask whether playlist inclusion affects the number of streams that songs receive and whether it affects consumers’ discovery of new songs and artists.

Playlists and promotion

We focus on distinct types of playlists, all operated by Spotify. First, we assess the impact of the four largest global playlists – which offer the same content to any user around the globe – on song performance. The playlists we focus on are Today’s Top Hits, RapCaviar, Baila Reggeaton, and ¡Viva Latino!, which are generally used to promote already widely known artists and songs. Because they have a large base of followers, any song that is added to one of these playlists will witness a sharp increase in the number of users exposed to it. These discontinuous jumps in followers allow us to identify a causal effect by looking at how streams change right at the time of playlist inclusion.

We estimate that the average effect of appearing in Spotify’s most-followed playlist, Today’s Top Hits, is of around 260,000 worldwide daily streams. Because songs tend to stay on this playlist for about 74.4 days, the overall effect of appearing on Today’s Top Hits is of about 19.4 million streams. It follows that about a quarter of the average streams for songs that make the playlist is caused by having made the list. And given Spotify’s recent reported payments of roughly $4 per thousand streams, this translates to about $77,000 in payments from Spotify alone.

Playlists and product discovery

Second, we consider playlists that specialize in new music, and potentially new artists, as opposed to already known songs. Every week, Spotify constructs a rank-ordered list of 50 new songs – called the New Music Friday lists – for each country in which it operates. As their name indicates, these playlists are updated every Friday and generally include songs that are literally new to Spotify, providing consumers with new information and potentially promoting the discovery of new songs and artists.

Does appearing on the New Music Friday lists increase the probability of a song’s ultimate success? Figure 1 presents the share of songs at each New Music Friday rank that ultimately appear in the corresponding country’s Top 200 streaming charts. It shows that songs with better ranks are more likely to appear in the top of the daily charts. For instance, close to 85 percent of the songs ranked #1 in a country’s New Music Friday list appear in the country’s top 200 daily streaming charts. While this suggests that high recommendation ranks matter for performance, the relationship depicted in the figure is also driven by the ability of curators to predict which songs are going to be successful. But the New Music Friday lists are also characterized by the fact that songs tend to appear in different ranks across countries. For instance, a given song may be ranked 5th on the New Music Friday list in the US, but only 16th in the New Music Friday list in Canada. Would this song be more likely to succeed in the US, where it got a better rank, relative than in Canada?

Assuming that countries have similar tastes but are treated with different rankings, we can measure the effects of the New Music Friday rankings by comparing the streaming performance of the same songs in different countries where they have received different rankings. We find that songs that obtain a #1 rank on the New Music Friday lists are about 50 percent more likely to appear in the streaming charts (relative to a song ranked 50th) and this effect falls sharply with the rank, to about 18 percentage points at rank 10 and to about 4 percentage points at rank 20. Focusing on new artists only provides similar results, indicating that the New Music Friday lists indeed help in the discovery of new artists.

So Spotify is powerful, but is it biased?

The above analysis shows that Spotify – through its largest playlists – has the power to influence artists and their products’ commercial success. But does Spotify exert this power in a biased fashion? Of particular interest is the question of whether major and independent music labels are treated differently by Spotify. While the platform does not own any of the music in its catalogue, major labels do have ownership stakes in Spotify, which could give the platform incentives to provide advantageous promotion of their products.

In a recent paper, we ask whether the rankings of the New Music Friday lists are biased against music from independent labels. We assume that the curators of the New Music Friday lists rank songs in order to maximize the total streams of the listed songs, which amounts to positioning songs that are expected to perform better higher up the ranks. And indeed, Figure 1 above shows that this is a reasonable assumption, and ranks assigned on the lists can therefore be interpreted as a measure of the curators’ ex-ante assessment of a song’s future success.

To test for bias, we can then compare the streaming performance of indie and major songs that were assessed to have equal promise (i.e. that received the same rank). If, say, major songs outperform indie songs in terms of streams, then the ranking is biased against majors since they should have been awarded a better rank. And indeed, Figure 2 shows that major-label songs stream more, conditional on the rank they are assigned.

Given the important concerns about the treatment of women in the music industry, one may also consider the possibility of bias against music from female artists. What we find, perhaps surprisingly, is that songs my male artists tend to stream more than songs by women, conditional on rank. See Figure 3.

Spotify’s New Music Friday ranks therefore appear to favor music by indie artists and, to a lesser extent, music by women. Additional calculations show that curators appear to rank songs as if they valued indie streams about 40 percent more than major label streams, and music by women about 10 percent more than music by men.

It is hard to say why Spotify would behave in such a biased way, but one can speculate. It might be that promoting music from independent labels could create a more favorable environment for future streaming rate negotiation, by deconcentrating market power from the major labels. Spotify may also be responding to criticism  from the industry, leading the platform to actively promote work from the groups voicing concerns.

Our analysis is naturally not without caveats. First, even if the New Music Friday lists favor independent-label music and music by women, we cannot rule out that other playlists, and other promotional activity at Spotify, favor different sorts of music. One should also be careful not to interpret our results as evidence that these groups face a generally welcoming environment in the recorded music industry.

Beyond its interest for music industry participants, our results and approach should be of relevance for observers of platforms more generally. In particular, our approach to measuring platform bias can be applied in any context  where a platform ranks products, and where one can observe the ex-post success of each listing, such a click through rate.

This blog is based on Luis’ research published in the International Journal of Industrial Organization and The Journal of Industrial Economics, which is included in the platformpapers reference dashboard:

Aguiar, L., & Waldfogel, J. (2021). Platforms, power, and promotion: Evidence from spotify playlists. The Journal of Industrial Economics, 69(3), 653-691.

Aguiar, L., Waldfogel, J., & Waldfogel, S. (2021). Playlisting favorites: Measuring platform bias in the music industry. International Journal of Industrial Organization, 78, 102765.

How hackatons can boost the diffusion of platform technologies.

This month’s blog is co-written by David Clough, Tommy Pan Fang, and Andy Wu.

In 2017, an entrepreneur named Billy McFarland created a successful viral marketing campaign for a bold, new music festival called Fyre Festival. Unfortunately, McFarland’s logistical ability did not live up to his aptitude for spin. The festival itself was a disaster, documented in real time through Instagram posts that morphed into icons of superficial influencer culture. Lost in the legend of the Fyre Festival fiasco is the lesser-known detail about its actual intended purpose: McFarland organized the event to draw attention to Fyre Media, a new technology platform for event organizers to connect with musicians—an “Uber for the performing arts.”

Contrast this with the experience of Twitter, who in 2007 installed a pair of giant flat-panel screens at Austin’s South-by-Southwest (SXSW) festival, an eclectic annual gathering of media, film, and technology professionals. Users could find their tweets broadcast on the giant screens at SXSW by texting a short message to Twitter. As Parker, Van Alstyne, and Choudary (2016:97) describe, this served as a pivotal moment in the launch of Twitter: “seeing the feedback on large screens in real time and watching as thousands of new users jumped into the fray created enormous excitement around Twitter…by the end of SXSW, Twitter usage had tripled, from 20,000 tweets per day to 60,000.”

In their efforts to launch a new platform, Fyre Media and Twitter both sought to exploit what we refer to as a temporary gathering, albeit with radically different outcomes. We set out to investigate when, how, and why new and growing platforms might use temporary gatherings to attract new users and new third-parties—such as App developers—who act as complements to the platform. We chose to study software development hackathons, where technology platforms promote themselves to software developers on a regular basis. Hackathons attract thousands of software developers who share ideas, exchange technical know-how, and focus intensively on creating software. By the end of a hackathon, many attending developers will have built and executed an early version of a new piece of software, and some will have received prizes that recognize their innovations. A software platform business can sponsor these hackathons, providing financial, in-kind, and in-person logistical support to attendees. This sponsorship provides a way for technology companies to educate and encourage developers to adopt their platform.

Our research, which is published in the Strategic Management Journal, supports the idea that sponsoring temporary gatherings helps platform owners seed the spread of a new platform. We undertook a large-scale quantitative study using three years of data from DevPost, a public clearinghouse for information about hackathons around the world. We measured platform adoptions by hackathon participants over time using a novel approach that analyzes the code that software developers post publicly in GitHub, an online repository in which developers upload code for ongoing projects.

We found that hackathons are particularly well suited to spreading platform technologies. Developers often do not have good information about the costs and benefits of joining a particular platform. The social nature of a hackathon can solve a number of problems for developers: helping them overcome the initial obstacle of learning to use a new platform, helping them understand the platform’s use cases, and—perhaps most important—allowing developers to gauge the popularity of a new platform.

The collaborative nature of hackathons allows developers to gather information about whether and how to use a platform by observing and teaching one other. We observed that when developers at the hackathon had used the platform in the past, other developers were more likely to adopt that same platform. At these temporary gatherings, platforms are spreading by word of mouth through informal, face to face interactions between developers, a process sometimes referred to as social learning. Furthermore, we find that software developers pay close attention to prize-winning projects — so if there’s a platform that underlies a project that wins a competition, the prize helps draw attention towards that platform.

But over and above the peer-to-peer learning, we find that temporary gatherings also help align the expectations of attendees over which platforms are becoming fashionable. We refer to this as social coordination. Software developers prefer to adopt widely-used platforms because of network effects, and attendees at a temporary gathering can recognize when a bandwagon is emerging around a new technology and then choose to join it. These events also allow developer sentiment towards a platform to converge. Thus, when executed correctly, sponsorship of temporary gatherings can be a catalyst for technology platform companies to generate positive buzz about a platform’s prospects and accelerate a platform’s growth. 

For a firm or an entrepreneur who is launching a new platform, a central challenge—referred to as the chicken-or-egg dilemma—is how to get an initial set of users or complementors to join the platform in the first place. The Twitter anecdote suggests that perhaps one way to do this could be to sponsor a temporary gathering and use the concentrated mass of attention to get on peoples’ radars. But the Fyre Festival teaches us an important caveat: the sentiment that emerges at a temporary gathering is not always positive! Fortunately, we think that most managers can do better than the Fyre Festival. We anticipate that going forward more savvy entrepreneurs will adeptly use temporary gatherings to help new platforms achieve critical mass, perhaps helping challenge some of the entrenched players that currently dominate the digital space.

This blog is based on David, Tommy, and Andy’s research published in the Strategic Management Journal and is included in the platformpapers reference dashboard.

Fang, T. P., Wu, A., & Clough, D. R. (2021). Platform diffusion at temporary gatherings: Social coordination and ecosystem emergence. Strategic Management Journal, 42(2), 233-272.

What the academic research can tell us about platforms in 2022

Many platform companies (e.g., Amazon, Meta, Apple, and Google) are thriving and so is the academic research on platform competition. According to platformpapers, there are now 460 research articles published on platform competition across the fields of economics, marketing, management, and information systems. Notably, 60 of these articles were published in 2021. These research studies aim to better understand how firms such as Amazon, Kickstarter, Apple, Uber, Taobao, and Nintendo, as well as many others, compete and manage their ecosystems.

With recent developments in many platform-related areas—regulatory pressures to curb platforms’ dominance, the metaverse, blockchain technologies, platforms making record-breaking acquisitions, just to name a few—2022 is shaping up to be an interesting year for platform competition … and for the researchers trying to make sense of it all. Drawing on some of the latest—and greatest—academic research on platform competition, below I identify five major trends to watch in 2022.

1. The ongoing push for dominance … and regulation

We are living in a ‘platform economy’ and the modus operandi of firms operating in it can be best described as ‘platform capitalism.’ Many of the world’s most valuable firms measured by market capitalization operate platform business models. And from the looks of it, these platforms will get bigger before they get smaller. Whether it’s Amazon expanding its logistics network, Microsoft buying games publisher Activision Blizzard, or Facebook rebranding into Meta and making an aggressive push for early leadership in the metaverse, dominance is the name of the game. From the firm’s perspective there are clear benefits to market dominance (also see 4. Network effects, but not like you know them), but dominance also attracts scrutiny from antitrust agencies. For these platforms, it’s not so much a question of if, but rather when (and how) they will get regulated.

Recent economic theory has argued that acquisitions on one side of a platform (e.g., Facebook buying WhatsApp and Instagram) can create value for users on the other side of a platform (e.g., Facebook’s end users). Empirical research on platforms providing pet-sitting services adds nuance by arguing that value creation is attenuated when merging platforms are sufficiently differentiated and when users have heterogeneous preferences. One alternative to government intervention through regulation is self-regulation. Research on the sharing economy finds that a reduction in Airbnb listings following platform self-regulations led to a reduction in crime (i.e., assault, robbery, and burglary) in affected areas. Platform self-regulation either at the firm level or through the formation of industry-wide coalitions (e.g., the video game industry’s Entertainment Software Rating Board) may yield strong results when paired with credible threats of government regulation.

2. Platforms’ increasingly visible hand of curation

The days of platforms plainly facilitating a marketplace where buyers and sellers can transact are long gone. Not only is the supply of content, apps, and other products on successful platforms ever-increasing (there are now more than 2.2 million apps available on the iOS App Store), so is the importance of these markets to platform companies’ bottom line. For example, Apple’s earnings from apps in 2019 has been estimated at $15-18.3 billion. The share of revenues from apps relative to Apple’s overall earnings is also growing, given that hardware sales are slowing down due to saturation. More so than ever, platforms are trying to steer consumers in their selection of products, as the number of options can be overwhelming and platforms often have skin in the game.

Selective promotions of complements including apps, songs, and other products, can be a powerful tool for platforms to shape the overall value of their ecosystems. Selective promotions can take on many different forms, including Spotify’s curated playlists, Google’s Play Awards, and Kiva’s Social Performance Badges. Through such selective promotions, platforms can exercise their power by highlighting products or product categories that showcase the platform’s distinctive features or where the platform benefits disproportionately, for example, because it has favorable contractual terms in place or because the platform is itself a seller of products. The research has shown that platforms are indeed biased in their recommendation of products, and that selective promotions can influence the type and number of products launching onto the platform in subsequent periods.

3. Complementors strike back

In response to platforms’ tightening grip, complementors—the firms and individuals that rely on platforms for selling their products—are fighting back. Power dynamics between platforms and complementors are typically lopsided. Markets dominated by large platforms offer few alternatives for complementors, and complementors often are small entrepreneurs that depend on platforms for their businesses. Recently, however, complementors have started to band together to increase their bargaining power. One example is the Coalition for App Fairness, which has various well-known companies as members, including Epic Games, Match Group, and Spotify. Each of these companies individually have brought lawsuits against Apple, demanding better terms on the iOS App Store. Both the European Union and the Federal Trade Commission have started investigations into platforms’ conduct towards complementors, alleging that platforms are abusing their power.

Two common strategies complementors use to reduce their dependence on platforms include disintermediation and competing at the platform level. In settings where complementors repeatedly interact with the same customers, such as freelance marketplaces and sharing economy platforms, complementors can avoid paying the platform fees by conducting their business off the platform. Especially complementors that have a strong reputation may decide to disintermediate. Another strategy to reduce an existing platform’s power is for complementors to build their own platforms. Epic Games is pursuing this strategy with its Epic Games Store and so is Nike with SNKRS. While such a strategy requires significant resources and upfront investments, it may pay off in the long run.

4. Network effects, but not like you know them

Without buyers and sellers, many platforms are just an empty shell. Early-stage platforms face a ‘chicken-and-egg’ problem, or what has recently been coined the ‘cold start’ problem. Once a platform succeeds in attracting users at scale, however, this often triggers a self-reinforcing dynamic with even more users joining. A large part of any platform’s value proposition is captured by its user base, which can make it extremely difficult for new platforms to compete with a dominant platform in the presence of network effects. That said, network effects are no magic bullet. Some platforms with large user bases are struggling to defend their market positions and to make profits because network effects are local rather than global (Uber, anyone?), others are finding that network effects do not carry over from one period to the next, and the role of AI seems ambiguous yet important.

Research in the video games industry found that freemium games can generate strong network effects by incorporating social features such as online multiplayer modes. Other research found that gaming platforms can reduce their reliance on network effects by including standalone features such as PlayStation’s DVD player. The success of these strategies depends on external factors such as the size of the overall market. Research in the bike-sharing industry surprisingly found that the entry of a second platform competitor leads to an expansion of the overall market, therefore benefitting the incumbent platform. Even other research has argued that a firm’s accumulation of user-generated data can result in strong ‘data network effects’ through machine learning and artificial intelligence.

5. Decentralized platforms stake their claim

Blockchain technologies and the associated cryptocurrencies are increasingly entering the domain of traditional platform markets. Axie Infinity introduced the ‘play-to-earn’ business model to the video games industry and it showcases how publishers can use blockchain technology to facilitate transferability of virtual items across games and into the metaverse. Online sneaker and streetwear resell platform StockX will soon allow consumers to invest in Vault NFTs, non-fungible tokens that are tied to physical products traded on, and held by, the platform. In both cases, the blockchain is making these platforms more decentralized and, arguably, more democratic. Given the scrutiny platforms are facing, allegations about anticompetitive behavior, and complementors’ discontent over these issues, could this spell the end of the centralized governance model as we know it?

Academic research on blockchain-based platform business models is still scant. On average, the central role of the platform company does seem to diminish in the context of blockchain platforms. Whether a more centralized or decentralized governance mode is best for a platform’s success likely will depend on various factors, including the benefits conferred by reduced user opportunism and uncertainty, versus costs related to coordination and complexity. Conceptually, the pros and cons of a decentralized governance mode facilitated by the blockchain appear to map on to the concept of platform openness: Platforms that are more open tend to grow more quickly and garner the support of different types of complementors, but they also struggle to provide direction and capture value. Unsurprisingly, perhaps, the optimal level of (de-)centralization is somewhere in the middle.

In the coming months, I will invite authors from these and other studies to contribute to the blog by reflecting on their research and placing it into a broader economic and societal context.

Stay tuned!