Platform Papers

Management without managers?

Platform Papers is a monthly blog about platform competition and Big Tech. Blogposts are written by prominent scholars based on their research. The blog is linked to platformpapers.com, an online repository that collects and organizes academic research on platform competition.

This blog is written by Ying-Ying Hsieh & JP Vergne

In 2009, a new organizational form, which we call the decentralized platform, began to diffuse without relying on managerial authority and without having to employ anyone. The most well-known decentralized platform, Bitcoin, has millions of users, thousands of contributors, and a market valuation never achieved in history by an organization without a CEO nor shareholders. 

Based on this novel blueprint, Web3 has quickly emerged as a new way of interacting with the Internet. Unlike Web 2, where coordination between digital platform participants is orchestrated centrally by a corporation, Web3 is rooted in the idea that users should own and exchange their data without relying on corporate intermediaries. To achieve direct coordination, Web3 requires decentralized platform infrastructures that can facilitate exchange activities without the intermediation of centralized platforms (e.g., Gmail, Uber.com, Instagram).

How do decentralized platforms operate?

Decentralized platforms provide an alternative to corporate platforms for coordinating the exchange of digital assets, but without the “visible hand” of corporate managers, how does such coordination happen exactly?

Our study “The future of the web? The coordination and early-stage growth of decentralized platforms” demonstrates, based on in-depth qualitative and quantitative analyses of twenty blockchain platforms, that Web3 platforms combine three components for operating in the absence of managers: 

(1)   Decentralized Algorithmic Coordination. In the absence of a manager, participants must rely on algorithms to coordinate tasks. Instead of residing in contracts drawn out by corporate managers, platform rules are encoded in blockchain algorithms that define how participants interact and share data representing value. As long as one has trust in the algorithms, then potentially, one need not have trust in any other individual or organizational collaborators. One can then transact without having to know the real-world identities of their counterparties.

Specifically, blockchain algorithms encode design rules and programs within a shared space and time, thereby regulating routine task performance (e.g., when to perform a specific task with required resources) and rewards distribution (paid out in cryptocurrency). Design rules include how information shared across the network is verified and validated, thus providing a consistent basis for trust in the algorithms.

(2)   Decentralized Social Coordination. Despite the absence of managerial orchestration, social coordination among key contributors remains indispensable to ensure ongoing maintenance of the platform. On decentralized platforms, social coordination follows a similar structure to open-source software development (OSSD)—a common format for the developer community to coordinate their contributions to software upgrades. While in centralized platforms, proprietary development typically involves corporate employees only, decentralized platforms are developed by voluntary contributors who self-select into the project, much like Linux and Wikipedia contributors. In addition to development, decentralized platforms differ in that the verification and validation of routine tasks are performed by network validators (e.g., “miners”, “stakers”), rather than by middle managers. Social coordination ensures that sophisticated checks and balances are in place to account for the respective interests of developers and validators.  

(3)   Decentralized Goal Coordination. While traditionally, organizational goals are set by central authorities (e.g., the CEO), in the absence of managerial orchestration, decentralized platforms sometimes rely on nonprofit foundations to corral everyone’s efforts. Despite having no formal authority, for instance, the Ethereum Foundation can offer a coherent vision of how various classes of platform participants might fit together to achieve long-term goals. Based on its vision, a foundation can then offer knowledge and resources (e.g., developer toolkits and grants) to platform contributors who buy into this imagined future and are willing to help build it.

Coordination Configurations: A Balancing Act

The three mechanisms depicted above do not work in isolation, nor can they be added arbitrarily to the design of decentralized platforms. Without a centralized authority to provide a stable structure, participants must negotiate decentralization by themselves, leading to a fragile equilibrium that ideally should deliver the kind of accountability, predictability, and common understanding necessary for sustainable coordination. Finding the right balance is key to making headway to early-stage growth.

Our findings suggest that, to fuel early-stage platform growth, platform contributors should either empower a small group of core developers or create a foundation to provide a guiding vision. Intriguingly, we did not observe any decentralized platform that grew successfully by decentralizing all three components of coordination at the same time. We also did not find evidence that fully decentralized algorithmic coordination was sufficient to bootstrap a platform. That said, we did find that insufficiently decentralized algorithmic coordination could cause instability and lead to early-stage platform decline.

So, what do decentralized platforms mean for businesses and organization designers?

Fundamentally, blockchain is a coordination technology that requires group adoption. Organizations can only benefit from adopting decentralization technologies and decentralized platform business models if they understand the limits of decentralization. This understanding is particularly important for organization designers, as the adoption of a decentralization technology entails fine-tuning the degree of decentralization across different platform layers to optimize coordination.

As the first iterations of decentralized platforms, cryptocurrencies have grown beyond their initial use cases to help allocate tasks, resources, rewards, and information across a range of industry sectors. As a result, blockchain-based decentralized platforms provide infrastructure for bourgeoning Web3 applications such as DeFi, NFTs, and DAOs. Those DAOs (for “decentralized autonomous organizations”) increasingly populate the Web3 space, but already experience the limits of decentralization first-hand as centralizing forces spontaneously resurface amidst their disengaged token holders, who end up relinquishing control to a few insiders—those with the resources, competence, and time to participate in every aspect of decision-making.

Having diffused beyond cryptocurrencies, decentralized platforms will warrant closer attention from industry, academia, and regulators alike as they embody a new class of competitors in the digital economy.

This blog is based on Ying-Ying and JP’s research published in the Strategic Management Journal, which is included in the Platform Papers references dashboard:

Hsieh, Y. Y., & Vergne, J. P. (2023). The future of the web? The coordination and early‐stage growth of decentralized platforms. Strategic Management Journal, 44(3), 829-857.

Platform-Paper Updates

It’s been a relatively slow month in terms of new platform papers being published, but there have been plenty of intersting developments in the ‘real world’:

• Last week, the inaugural summit of the European Digital Platform Research Network (EU-DPRN) took place at Bocconi University in Milan. EU-DPRN aims at bringing together European scholars studying platforms from across different disciplines (e.g., strategy, marketing, information systems, economics, etc.). The first summit was a success; it featured a PhD workshop, ten paper presentations, six poster presentations, a keynote and two stimulating panels. Next year, the conference will be hosted by the UCL School of Management in London!

• The competition law community is increasingly interested in so-called ecosystem theories of harm. When large platform companies increase their scope by acquiring a target in a complementary or unrelated market, this can both benefit the acquiring firm in novel ways as well as potentially disadvantage competitors. The question is, do we need new theories of harm to analyze such acquisitions? It’s not an easy question. Recently, two insightful CEPR columns (1 & 2) got published on the topic and earlier this week the British Institute of International and Comparative Law (BIICL) hosted a roundtable debate on the topic.

Platform Papers is curated and maintained by Joost Rietveld.

Inducing more reviews may not help the market and other thoughts on the difficulty of improving reputation systems

This blog is written by Andrey Fradkin, with special thanks to David Holtz.

Reviews are everywhere on digital platforms, yet it is common knowledge that review systems are flawed. Not all users contribute reviews and those who do may have unusual experiences or preferences. In the course of writing a review, the lack of an objective standard for mapping experiences to ratings leads to inconsistencies amongst reviewers. Reviewers may also leave out relevant details to appear amicable or may exaggerate negative experiences as a form of retaliation. Despite these issues being widely recognized and discussed in the media, it seems review systems have barely improved over the past two decades. In this blog post, I will delve into the challenges of measuring reputation system improvements by examining a recent paper by David Holtz and myself.

Before discussing our results, it’s useful to consider the purpose of review systems. Review systems were introduced in the earliest digital marketplaces such as eBay to solve the problem of transacting with anonymous strangers on the internet, who could misrepresent their product or fail to deliver it altogether.[1] Reputation systems incentivized sellers to act honestly while providing buyers with the information needed to avoid potentially low-quality sellers. This created a virtuous cycle where more buyers began to trust eBay, while sellers joined eBay for access to a larger market.

While the success of eBay’s reputation system demonstrates the utility of reputation systems for most marketplaces, it doesn’t provide a blueprint for designing the best system. Platform designers must choose how to solicit reviews and how to display information from reviews. Startup platforms begin with a reputation system that is chosen by early employees based on intuition. As platforms grow, they have the opportunity to use data and experiments to improve these reputation systems. Yet the seemingly unchanging design of reputation systems suggests that this opportunity is not being seized as much as it should be.[2]

One reason for the lack of large changes to reputation systems is the lack of clarity about what signals are indicative of a better reputation system. Data scientists and researchers are often tempted to focus on immediate outcomes that are easier to measure. For example, policies such as reminders, nudges, and incentives can be shown to improve the quantity of reviews.[3] The effects of these policies on reviews are easy to evaluate because reviews come shortly after interventions, and it’s easy to map treatment assignment of potential reviewers to review behavior.

It is more difficult to measure how shifts in reviews translate to improved outcomes for the market. In a forthcoming paper in Marketing Science, David Holtz and I take an initial step in mapping review policy to market outcomes using a large-scale field experiment on Airbnb. The experiment was assigned at an Airbnb listing level for listings which did not yet have reviews. For treatment listings, buyers were offered a $25 coupon post-transaction to review. For the control listings, buyers were not offered any incentive to review after a transaction. The incentives increased review rates and induced more negative reviews (as measured through star ratings and text).

Our primary insight involves examining the impact of these reviews on the quantity and quality of matches for listings. To do so, we connect a listing’s initial treatment (whether a guest to the listing is offered a coupon to review) to its subsequent outcomes. Specifically, we track the listing outcomes for a year following the treatment assignment. This enables us to assess the effects of incentived reviews on the number of transactions and match quality indicators, such as subsequent reviews and complaints. We also provide a measure of match quality based on the subsequent experiences of guests who engaged in transactions with listings from the experiment. Note that this measurement exercise involves merging data up to two years of data after the experiment concluded.

We find that while the treatment induced more reviews, these reviews failed to improve listing outcomes both in terms of nights booked or in terms of match quality. In fact, we find that measures of match quality actually fell due to the treatment. Our key theory for the fall in match quality, which we support with additional analysis, is that the induced reviews were actually less correlated with quality than non-incentivized reviews. We find these results even though the induced reviews were more negative than non-incentivized reviews, which may naively suggest that they are more accurate.

Our results show that it is insufficient to only look at the quantity and valence of reviews in order to evaluate whether a reputation system design change improves market outcomes. Interestingly, this is our second failure to find benefits from a reputation system change on Airbnb. In a recently published paper, David Holtz, Elena Grewal, and I found that making the reputation system simultaneous reduced reciprocity and increased review rates. Yet, even with large samples, we failed to detect effects of this change on market outcomes.

Our findings, while specific to certain reputation system changes and the context of Airbnb, offer valuable insights into reputation system design more broadly. Had we solely focused on the effects of these changes on reviews, we might have erroneously concluded that these alterations significantly benefited the platform. Other platforms should also adopt this long-term analysis of transaction volume and match quality when experimenting with reputation system design.[4]

Circling back to the motivation of this post, I propose that a lack of innovation in reputation systems is, at least in part, due to the absence of a robust framework for evaluating changes in reputation system design. It is hard to advocate for resources in an organization when data scientists and product managers might struggle to assess the key performance indicators of a product change. It is also hard to advocate for longer-lived experiments and long-run analyses when product development cycles are often much shorter than a year.

What can data scientists and managers do to alleviate these constraints to improving and analyzing reputation systems? An obvious suggestion is to advocate for longer and more ambitious reputation system experiments. Additionally, it can be worthwhile to reserve time to re-analyze old experiments using a longer data horizon. Such re-analysis can yield surprising insights that will have implications for future design decisions. Statistical techniques such as surrogate analysis can also be used to learn about the longer-run effects of reputation system policies.

To summarize, by incorporating a comprehensive perspective that accounts for transaction volume and match quality, platforms can make better-informed decisions and design more effective reputation systems that genuinely benefit users and improve overall market outcomes.

This blog is based on Andrey’s research published in Management Science, which is included in the Platform Papers references dashboard:

Fradkin, A., & Holtz, D. (2023). Do incentives to review help the market? Evidence from a field experiment on Airbnb. Marketing Science.

Does corralling a dominant platform help complementors?

This blog is written by Sruthi Thatchenkery and Riitta Katila.

Digital markets are increasingly dominated by a small number of technology platforms, prompting fears that platform owners may abuse that market power. Of particular concern is the threat that dominant platforms pose to complementor firms that depend on the platform for access to customers. For example, Google has been fined by the EU for privileging its own review and shopping comparison results over competitors on its search engine. Similar concerns have been expressed regarding Amazon promoting its own products over third party sellers in its online retail business or Apple leveraging excessive fees on developers through its App Store.

Traditionally, antitrust regulators focused on preventing firms from achieving a dominant market share in the first place, outlawing actions aimed at monopolizing markets and blocking mergers and acquisitions between firms with already high market share. But outright preventing platforms from growing to dominate their markets may be difficult, since platform markets are characterized by powerful network effects that create significant returns to scale and often yield winner-take-all outcomes. Breaking up platforms that become “too big” therefore may not be viable nor even beneficial to consumers.

Perhaps, then, it is more feasible to restrict a platform’s ability to exploit the market power that comes with massive scale. Regulators around the world are attempting to do so via newly introduced antitrust legislation, such as the Digital Markets Act in the European Union and the American Innovation and Choice Online Act in the United States. In a break from typical antitrust tactics, these new regulations do not seek to stop platforms from accumulating dominant market shares; instead, they enact rules that are meant to prevent large platforms from using that dominance to block fair competition and, in turn, innovation. But will constraining the behavior of an already-dominant platform actually restore competition and spur innovation in platform ecosystems?

In a recent paper, we attempt to shed light on that question by examining how a landmark antitrust intervention against a dominant tech platform impacted complementor firms. Specifically, we analyzed United States v. Microsoft, the first major antitrust case against a digital platform in the US.

The allegations against Microsoft were starkly reminiscent of what firms like Google, Apple, and Amazon stand accused of today: Microsoft purportedly used its dominance in operating systems (i.e. platforms) to block application developers’ (i.e. complementors’) visibility and access to customers, limiting innovation and customer choice. In 2001, the U.S. Department of Justice and Microsoft resolved the case via settlement, in which Microsoft agreed to stop engaging in activities that had been deemed anti-competitive, such as blocking customers from uninstalling its own complementor applications or punishing OEMs that tried to promote third-party complementor products.

While popular attention focused on “the browser wars” between Microsoft and Netscape, the allegations against Microsoft extended broadly across both consumer and enterprise software. We specifically examine the effects of the settlement in the enterprise infrastructure software industry. Infrastructure software consists of back-end applications that allow organizations to manage and maintain complex IT systems. The industry is divided into five complementor product markets – application development, application integration, database tools, network and system management, and security – which run on central platforms known as server operating systems. At the time of the intervention, Microsoft owned the dominant platform in the industry (Windows Server) and competed in all five complementor markets. However, Microsoft only achieved significant market share in two complementor markets: database tools and application development.

From a research design perspective, this variation in Microsoft’s complementor market share allows us to distinguish between markets that are likely to be sensitive to the intervention from markets that are not. In the “treated” complementor markets where Microsoft held significant share, the intervention suddenly constrained a powerful competitor, opening up strategic possibilities that may not have seemed viable before. In contrast, in the “control” markets where Microsoft held only minimal share, the intervention would not significantly change the competitive environment. Examining post-intervention differences between complementors in the treated versus control markets can thus yield insight into the impact of the intervention.

What we find is that the Microsoft antitrust intervention was a mixed bag for complementors: innovation went up, but profits went down. On the positive side, complementor innovation (measured as patents or R&D expenditures) increased in treated markets, particularly among those with the lowest market share. So, the intervention did in fact spur innovation among the complementors that would have been most vulnerable to Microsoft’s dominance. However, profitability dropped in treated markets – again, particularly among the complementors with low market shares. High market share complementors (who arguably did not require the same protection from regulators) did not significantly increase innovation nor did they suffer a profit penalty. Thus, while the intervention successfully prompted technical innovation, the firms that innovated did not actually benefit financially.

Why couldn’t complementors translate innovation into profits? Our interviews with industry experts indicate that complementors may have been more reliant on Microsoft than they had realized. Platforms like Microsoft often provide essential assets such as development tools to complementors in order to foster innovation on the platform. After being restrained by antitrust intervention, a dominant platform may not be as willing or able to freely share those assets, forcing complementors to incur the costs of developing replacements themselves. Indeed, several such assets were withdrawn by Microsoft after the settlement. Complementors – especially low-share complementors – were thus able to innovate but lacked the assets to bring those innovations to market in a profitable way.

Complementors and platform owners should therefore be mindful of complementor dependence on platforms. Complementors should not assume that antitrust action against platforms will be solely beneficial to them and should instead seek to develop resources and capabilities that allow them to profitably capitalize on innovation opportunities that might emerge. Platforms, meanwhile, should not encourage excessive dependence among complementors, as the long-term viability of a platform ecosystem depends on a fostering a variety of high-quality complements – a task that requires a healthy population of complementors innovating on the platform.

For regulators, our findings do offer some encouraging evidence that conduct remedies (i.e. interventions that restrict the behavior of dominant firms) can be effective in sparking technical innovation in platform ecosystems. But, innovation without profit is unlikely to foster healthy competition in the long term, particularly if profit penalties are disproportionately suffered by smaller, more vulnerable firms. Antitrust intervention that places shackles on dominant platform is therefore not an automatic cure for competition concerns. Instead, taking targeted steps to reduce complementor dependence on platforms for essential assets (or ensure those assets remain available after intervention) could be a potential win-win for both platforms and regulators: it could reassure regulators wary of abuse of platform market power while also benefitting the platform by invigorating profitable innovation across its ecosystem.

This blog is based on Sruthi and Riitta’s research published in the Strategic Managment Journal, which is included in the Platform Papers references dashboard:

Thatchenkery, S., & Katila, R. (2023). Innovation and profitability following antitrust intervention against a dominant platform: The wild, wild west? Strategic Management Journal, 44(4), 943-976.

Platforms can promote early adoption by shaping market expectations

This blog is written by Kevin Boudreau.

Online platforms have become an integral part of society and are used for a wide range of activities. To continue expanding and innovating in this area, successful launches of new platform ventures are crucial. However, most platform ventures fail to take off due to the chicken-and-egg problem – potential users are hesitant to adopt platforms that do not have a large installed base and network effects. This study empirically tested one way that theorists have long conjectured was a way to induce adoption: by marketing in a way that seeks to shape would-be adopters expectations of future platform success. The logic here is that if (at least some) consumers believe the platform will eventually take off, they will join, thereby catalyzing network effects. A field experiment was conducted to test the theory that the initial growth of a platform depends on the market’s subjective expectations of the size of the future installed base.

Chicken-and-Egg–and the Problem of Multiple Market Equilibria
(And, why is marketing a far more strategic challenge in platform industries?)

Before explaining the role of expectations, it is useful to precisely understand the nature of the chicken-and-egg problem that new platforms must overcome. Better understanding this question clarifies why the job of marketing and promoting a platform involves far more than just the traditional marketer’s job of creating awareness and extolling the benefits of a new offer.

Let’s use a simple example to explain this idea. Imagine a market with only two potential buyers, A and B, who are considering adopting a new platform. The idea extends to cases of larger markets, but let’s just consider two possible buyers to explain the crux of the idea.

For simplicity, consider the costs of adopting the platform is $1. This might include whatever purchase or access charges to get onto the platform, the hassle of learning how to use a new platform, and whatever other financial or non-financial costs.

If there are network effects, the benefits of adopting the platform depend on how many people also adopt it. For simplicity, let us suppose that if everyone adopts the platform, both A and B will enjoy benefits worth $2 (and the net benefit is $2, minus the cost of $1). If no one adopts the platform, there will be zero benefits (and the net benefit of adopting is zero, less the cost of $1). Thus, this simple set up illustrates a simple illustrate of payoffs from adopting a platform where value comes from network benefits (rather than “stand-alone” benefits, which do not depend on whether others join).

We can represent the possible outcomes in a table. The rows represent A’s decision (to adopt or not), and the columns represent B’s decision. The numbers in each cell represent the payoffs (or benefits) that A and B receive, respectively. This game payoff matrix shows that there are two possible stable outcomes, or equilibria in this market. In the “everybody adopts” equilibrium in the top right quadrant, both A and B adopt the platform; in the “nobody adopts” equilibrium in the bottom left quadrant, no-one adopts.

Therefore, the challenge for executives, marketers, and strategists in platform industries is that moving from non-adoption to adoption is more than just “marketing” in the traditional sense of building awareness and convincing individuals of the value of an offer. Here, successfully marketing and promoting adoption of the platform means coordinating market actors—as a group—out of the stable and self-reinforcing “nobody adopts” equilibrium to establish an altogether new equilibrium of “everybody adopts.” But, of course, no one wants to adopt the platform here and find out they are the only one.

While this abstract example is in a market of just two people, the same idea applies to real markets for platforms, with many more potential adopters or even multiple types of adopters, where some minimum critical mass must be coordinated onto a platform to successfully built a network and network value.

Coordinating the Market to Adopt – by Influencing Subjective Expectations

Coordinating a large number of adopters onto a new platform is typically extremely difficult and, in most cases, fails. One theorized strategy for promoting adoption that has been hypothesized by economists as a means of solving the chicken-and-egg adoption problem is to somehow influence consumers’ expectations that others will eventually adopt the platform.

Can this strategy work? How? The multiplicity of possible equilibria creates fundamental uncertainty about what others will do and which outcome will eventually emerge. Accordingly, adopters cannot “look forward and reason back” as in usual rational expectations, and expectations about future adoption are necessarily subjective. Therefore, conventional means of influencing rational expectations, such as economic signaling and pre-commitments, should not work as usual.

So, if expectations can be formed and influenced, how can this possibly work? If it simply isn’t possible for consumers to objectively form a rational expectations that the platform will eventually be widely adopted – can they at least form subjective expectations that a platform will be adopted? Moreover, is it even possible for executives and marketers of a platform to influence expectations themselves, given consumers should see platform executives and managers as biased storytellers about the future of their own platform?

A Field Experiment

It is exceedingly difficult to design research studies to systematically empirically study platforms. This is especially so in attempting to explicitly test how nuanced growth dynamics might be influenced by something unobservable as subjective expectations. The question of empirically testing expectations in platform industries was, in fact, an idea I had even back in graduate school at MIT, when I was thinking about the strategies I saw platform executives pursue. It took me some years to imagine a research design that would be appropriate for testing these questions, and more time to then implement the research in a live platform.

A research design was devised and implemented with the goal of investigating how statements issued from a platform about its own expectations of future size of its installed base of users affected the likelihood of individual’s adopting the platform. The experiment was conducted by randomly varying messages to 16,349 individuals identified as potential adopters of a newly launched product development platform focused on the Internet of Things (IoT). The experiment varied the content of the invitation, which included a statement regarding the number of users and companies that the platform expected would join. The invitation also included a statement about the current installed base, to disambiguate effects of statements regarding expectations from the true installed base. A central challenge of this research design was to create random variation in both statements of expected installed base and current installed base across individuals, while avoiding any deception whatsoever. (Details are explained within the paper.) The research was carried out as part of the first 60 business days following the launch.

Findings

The experiment found evidence consistent with adoption patterns being significantly affected by statements made by the platform, and overcoming the initial “chicken-and-egg” problem. Remarkably, the subjective statements regarding expectations of the future installed base had a larger influence on adoption rates than did disclosures of the true current installed base—at least this was true during early adoption. For example, statements of larger numbers of expected users caused more adoption than smaller numbers. Further, stating a small (current or expected) installed base of users led to lower demand than stating nothing at all. Statements of expectations had no effect on adoption once the installed base grew large.

Implications for Promotion, Marketing, and “Evangelism” of New Platform Ventures

The findings have important implications for the promotion, marketing, and “evangelism” of new platform ventures. The results suggest that the promotion of a new platform should focus on the potential size of the future installed base rather than the current size. Small current or expected installed bases of users should not be disclosed, as it can lead to lower demand than stating nothing at all. Instead, the focus should be on generating expectations of a large future installed base. This is particularly relevant for new platforms, which face the chicken-and-egg problem, as the expectations of the market can lead to a self-fulfilling prophecy.

The demonstration that adoption patterns can be significantly affected in a context of fundamental entrepreneurial uncertainty raises important questions. Better understanding of how and why platform entrepreneurs are able to persuade and influence market participants could bring fundamental advances to the ability to market and promote the growth of platforms. In the paper, I discuss alternative plausible explanations of why statements by the platform could have had an influence. The explanation that is most consistent with the facts is that (some) boundedly rational adopters respond to statements by the platform simply at face value, influenced by some plausible characterization or storyline of the future. In such an explanation, the adopters might even believe themselves to be prescient, as their expectations were self-fulfilling. (In this case, the eventual adoption of thousands and thousands of users in continents around the world turned out to be consistent with claimed expectations.)

If this explanation is true, adopters might not require a fully rational and credible basis for accepting statements and we might imagine some considerable role to be played by rhetoric, charisma, moral suasion, and other tools of influence. Certainly the world of platforms and tech have had persuasive storytellers and persuaders, including the likes of Steve Jobs. The last time I checked there were 2,330 “Chief Evangelists” listed on LinkedIn.

This blog is based on Kevin’s research published in Management Science, which is included in the Platform Papers references dashboard:

Boudreau, K. J. (2021). Promoting Platform Takeoff and Self-Fulfilling Expectations: Field Experimental Evidence. Management Science, 67(9), 5953-5967.

Strong platform control can encourage rather than depress complementors’ innovation activity

This blog is written by Yuchen Zhang, Jingjing Li, and Tony Tong

Digital platforms are drawing increasing attention from innovators and entrepreneurs, whether it is about creating a new platform, or providing product offerings and exchanging new ideas on an existing platform. Apple’s App Store, which hosts millions of mobile apps published by developers and used by billions of users, is a great example of a platform that harnesses the power of distributed innovation agency. Indeed, platforms like the App Store are now being seen as a “semi-regulated” marketplace or unique organization that foster innovation and entrepreneurship under the leadership of the platform owner (Apple). Yet, such leadership is surely easier said than done. After all, platform owners (Apple) do not own the product offerings (apps), nor do they have direct control over their business partners—complementors (app developers).

So what can a platform owner do then to coordinate and orchestrate the value creation activities of autonomous complementors (often in thousands or millions) that are critical to the vibrancy and success of a platform?

This question was the focus of a research paper we recently published in Strategic Management Journal. We invoke the idea of “platform governance”, or the “set of rules and policies that platform owners adopt in order to coordinate and deploy co-specialized assets and capabilities of complementors.” While platform governance manifests itself in different mechanisms, in this paper, we argue and show that platform owners can practice what we call “access control” to shape complementors’ innovative activities. Rather than looking at complementors’ economic exchange activities on a platform (e.g., listing and selling of mobile apps), we examine their exchange of knowledge (about developing apps) with each other. The “free” sharing of problem-solving techniques, code writing tips, and product development experiences, is critical to software development and has even been considered by some as underpinning the recent open source revolution.

We look at how the lack of access control in the form of “jailbreaking” shapes app developers’ knowledge sharing activities. Specifically, Apple’s iOS platform is well-known for adopting a strict gatekeeping policy that controls for what (apps) or who (app develops) have access to the platform; this policy denies platform access to imitating apps and developers in order to encourage app innovation. The jailbreak of the iOS—which was more common in the early years of iOS development—is a hacking that exploits loopholes to remove Apple’s built-in restrictions, allowing users to install apps not officially approved by Apple’s App Store. Jailbreaking would therefore introduce pirated apps that imitate or directly copy the features and functionalities of the existing apps approved by the App Store and trigger so-called business stealing and user attrition, putting a competitive pressure on those iOS developers who develop “legit” apps and seek to profit from app sales.

As illustrated in the figure below, we leverage the unexpected timing of the jailbreak of iOS 7 in 2013 as a natural experiment, to compare the Q&A activities of iOS app developers (treated group) and otherwise comparable Android app developers unaffected by the jailbreak (control group), on StackOverflow.com, an active online forum of software developers. The jailbreak significantly weakens (for a short period) Apple’s strict access control, such that imitating app developers (and apps) can now gain access to the App Store and users of iOS devices. We find that the jailbreak reduces the amount and quality of the knowledge shared by iOS app developers—their posting of Q&As decreases by 7%, and the quality of their posts by 15%. Such effects can be seen as causal given the research design.

The lapse in access control therefore dampens developers’ incentives to share knowledge (and innovate). In other words, strong access control, at least in the case of Apple’s iOS, will appear beneficial to the platform ecosystem and platform owner, because it enables more vibrant knowledge sharing among app developers, which should enhance their innovation and product development effort.

In practice, a platform owner can resort to other tools or design features to implement access control. These include charging a membership fee or implementing an involved process to weed out low-quality complementors (e.g., eHarmony charging registered users a non-trivial fee), designing certain screening processes to determine which complementors will be allowed platform access (e.g., Uber requiring drivers and their cars to pass certain criteria), and selectively restricting or opening up the use of boundary resources (e.g., APIs, SDKs, code libraries) to particular complementors. However, it’s important to note that none of these is meant to imply that stronger control is always “better.” In fact, the degree to which platform owners should grant access to complementors will depend on many factors, including the specific business model or ways in which platform owners profit from innovation. For instance, Apple makes a lot of profits from the commissions charged on app sales through its App Store, making it important that it implement strong access control to encourage the creation of high-quality apps. By contrast, for Google’s Android, most profits are coming from advertisement and related services; lesser access control does not affect it as much.

What other governance mechanisms, in addition to access control, may be at the platform owner’s disposal to coordinate and orchestrate complementors’ value creating activities? In a review article published in the Journal of Management, coauthored with Liang Chen, Shaoqin Tang, and Nianchen Han, we examine this question by reviewing existing knowledge on digital platforms written by scholars in management, MIS, marketing, operations, economics, and other related fields. This is done based on our view that although the leadership role of platform owners is well recognized, a coherent approach to understanding how they govern their relationship with complementors to create and appropriate value is still lacking. We use two core dimensions of organizational governance—incentive and control—to synthesize platform governance research into eight mechanisms, and we map them to a multitude of seemly idiosyncratic platform design tools and features, summarized in the figure below.

To conclude, platform governance matters a great deal. Our study shows that one specific form of platform governance, access control, matters to complementors’ innovation activity by encouraging cooperative behaviors in knowledge sharing. Before we herald openness as the holy grail of innovation, it is time to also celebrate the important role of platform control in motivating complementary innovation.

This blog is based on Yuchen, Jingjing, and Tony’s research published in the Strategic Management Journal, which is included in the Platform Papers references dashboard:

Zhang, Y., Li, J., & Tong, T. W. (2022). Platform governance matters: How platform gatekeeping affects knowledge sharing among complementors. Strategic Management Journal, 43(3), 599-626.

Does a meta-search platform favor its affiliated sales channels in its ranking?

This blog is written by Reinhold Kesler.

Digital platforms have become important intermediaries in many markets, be they traditional or newly created ones. Their key promise is to bring about lower search and distribution costs, better matches of market participants, and transparency about offers. However, they also have been shown to be able to steer consumers toward certain products and suppliers through their recommendations. This steering is increasingly often met with concern, especially with the presence of vertical integration of platforms along the consumer journey and possible incentives to bias the recommendation.

The corresponding policy debate is shaped by prominent cases of the European Commission that involve Google favoring its comparison shopping service over others and Amazon’s hybrid role as a marketplace and seller giving an edge over third party sellers. In this respect, the European Digital Markets Act (DMA), which entered into force in November 2022, aims to restrict the power of large online platforms that are designated as gatekeepers. These are subject to do’s and don’ts with regard to a range of business practices. One of the prohibitions comprises self-preferencing, which forbids treating own services and products more favorably than those of a third party.

In a recent study published in Quantitative Marketing and Economics, we explore self-preferencing in the context of online hotel booking. In particular, we empirically study whether an integrated meta-search platform favors its own affiliated sales channels. Meta-search platforms pool offers from different hotels – as do online travel agents like Booking.com and Expedia – but, in addition, for each hotel, they display the different sales channels available from which they predominantly retrieve payments through cost per click (CPC, see Figure 1, left). This gives a vertical and horizontal ranking, respectively (see Figure 1, right). The two rankings allow a price comparison on a more aggregate level, thereby making these platforms often the starting point of a consumer journey towards booking a hotel and economically relevant, according to a sector inquiry by the German competition authority. Interestingly, the two major online travel agents (Booking.com and Expedia) each own a meta-search platform (Kayak and Trivago), where the respective acquisitions raised concerns of search bias in favor of related sales channels.

We web-scraped search results for overnight stays in Paris from 2014 until 2017 on Kayak, the meta-search platform already belonging to the Booking Holdings at that time. In turn, we look at the determinants of both the vertical and horizontal rankings and, in particular, whether the company affiliation plays a role.

For the horizontal ranking, we find that online travel agents of the Booking Holdings are more often in the most prominent spot than they are among the cheapest sales channels. In regressional analyses accounting for differences in prices and popularity, among other factors, we indeed find Booking-affiliated online travel agents to be more likely among the visible and most prominent sales channels on Kayak than competing online travel agents. For the vertical ranking, the results suggest that hotels are ranked worse when rival online travel agents, i.e., the ones affiliated with the Expedia Group, are the cheapest sales channel. On average, such hotels are ranked eight positions worse. We do not find this pattern for the vertical ranking in an analogous empirical analysis on Google Hotels, a meta-search platform that is not vertically integrated with an online travel agent.

However, our empirical analysis is also subject to caveats. First, we are not able to observe the actual cost per click paid to Kayak by sales channels, which may vary and are presumably taken into account for the rankings. We try to address this by analysing the non-integrated meta-search platform Google Hotels, which potentially experiences a similar heterogeneity in payments and distinguishing chain and independent hotels, which may differ with respect to setting the CPC. Second, a concern may be the presence of systematic differences across sales channels unobserved to us (e.g., one channel having a breakfast option), which are potentially leading to better ranking positions. However, Kayak’s main goal is to provide comparability across offers, and Google Hotels may again serve as a benchmark, while this kind of differentiation of amenities is also not clear given similar commission rates across online travel agents.

Assuming these caveats to be less problematic, the results indicate that Kayak takes joint revenues of the integrated firm into account (i.e., both commissions and cost per click, see Figure 1) and that it favors affiliated sales channels.

Although we cannot provide a definite conclusion on a socially optimal ranking, there are potential risks of ranking optimization by a vertically integrated meta-search platform, as suggested by the results. First, such a ranking may diverge from consumer interests and, by this, lower search quality. Second, worse ranking positions that come along with lower prices elsewhere may work similarly to price parity clauses that have been abolished in some European countries and are also prohibited for gatekeepers designated under the DMA.

This brings us back to the policy debate revolving around the power of digital platforms and how to warrant a contestable and fair digital economy. Our article provides a case in point to study how vertical integration affects the recommendation of products and suppliers by a digital platform but also demonstrates the challenges of the empirical analyses which necessitate better data. More generally, such empirical studies can inform the crucial debate about the implementation and enforcement of regulations involving business practices like self-preferencing.

This blog is based on Reinhold’s research published in Quantitative Marketing and Economics, which is included in the Platform Papers references dashboard:

Cure, M., Hunold, M., Kesler, R., Laitenberger, U., & Larrieu, T. (2022). Vertical Integration of Platforms and Product Prominence. Quantitative Marketing and Economics, 20, 353–395.

It was another strong year for academic research on platform competition

In this post I take stock of the academic research on platform competition published in 2022. Using Platform Papers data, I look at the volume of research published across academic subdomains and how it compares to prior years. I further look at some of the themes covered by this research and how it maps on to developments in ‘the real world’. Finally, I briefly look ahead to 2023.

A look at the numbers

In 2022, a record number of 100 papers were added to the Platform Papers references dashboard, up from 93 papers in 2021.[1] The breakdown across academic subdomains is as follows:

• Management & Organizations: 39 papers

• Information Systems: 24 papers

• Marketing: 21 papers

• Economics: 16 papers

The share of papers published in marketing journals this year stands out. While the disciplinary subsamples are fairly small and therefore volatile, it does seem that marketing scholars are increasingly interested in platform competition research: Whereas in 2018 papers published in marketing journals accounted for less than 3% of all papers added to the database, in 2022 this was 21%.

Management Science was by far the most prevalent outlet with 22 platform papers published in 2022 (of these, 13 were published in marketing departments). Other popular journals include Information Systems Research (12 papers) and the Strategic Management Journal (11 papers).

A look at the content

Thematically, there is a pretty even split between research covering issues related to ecosystem governance and orchestration (35 papers), research studying network effects, winner-take-all dynamics, and pricing (31 papers), and research addressing heterogeneity within and between platforms (31 papers). Corporate scope (e.g., platforms vertically integrating into the complementor space) received considerably less attention this year with 17 published papers.[2]

Below I list five papers that got published in 2022 that I am particularly excited about:

1. Fending Off Critics of Platform Power with Differential Revenue Sharing: Doing Well by Doing Good? (Bhargava, Wang & Zhang; Management Science). I like this paper because it ties into the ongoing discussion about whether and how to regulate dominant platforms. The authors look at a very specific but oft-debated policy: dominant platforms changing their revenue sharing structure to be more favorable to smaller, less successful sellers. The economic model suggests that such a policy change not only benefits small sellers on the platform, but also larger sellers, and ultimately the platform itself. As the authors note: “Hence, an intervention that ostensibly offers concessions and generous treatment to producers might well be self-serving for platforms and good for the entire ecosystem.”

2. From proprietary to collective governance: How do platform participation strategies evolve? (O’Mahony & Karp; Strategic Management Journal). This paper tracks how a platform’s governance evolves over time and how it affects user participation on the platform. It’s another important contribution to illustrate that governance is highly dynamic and strategic. Platforms that open up attract higher participation rates, but participation by users declines when the platform’s framework of rules becomes unclear. (Twitter, anyone?!)

3. Positive Demand Spillover of Popular App Adoption: Implications for Platform Owners’ Management of Complements (Lee et al.; Information Systems Research). Traditional frameworks for analyzing competition do not unequivocally apply to platforms. Whereas competition from successful market participants can be detrimental in traditional markets, this paper finds strong empirical support for positive spill-over effects. Popular apps increase the adoption and usage of non-popular apps on the platform. These effects apply to apps released prior to the launch of the popular app as well as those released after.

4. Local Network Effects in the Adoption of a Digital Platform (Kim et al.; The Journal of Industrial Economics). Not all network effects are created equal and managers and academics are increasingly coming to grips with this. Studying the fantasy sports markets, this paper finds that “the size of a county’s existing user base on the platform significantly impacts the number of new adopters in that county, while the size of the user base in nearby counties does not.” In other words, participation by users that are more closely connected in the real world results in stronger network effects on the platform.

5. The Future of the Web? The Coordination and Early-Stage Growth of Decentralized Platforms (Hsieh & Vergne; Strategic Management Journal). This time last year some platform managers might have worried that decentralized platforms would soon put them out of work. The ‘Crypto Winter’ will have somewhat cooled those expectations, but decentralized governance of platforms certainly hasn’t gone away entirely either. This pioneering paper analyses 20 cryptocurrency platforms to better understand how decentralized platforms are governed. The authors document three distinct governance mechanisms: 1) algorithmic coordination, 2) social coordination, and 3) goal coordination.

I should note that these papers neatly map on to the Five Platform Competition Trends to Watch in 2022 that I identified at the beginning of the year when I started the Platform Papers Substack.

Speaking of which, I am grateful for all the scholars who have contributed their time and knowledge by translating their platform competition research papers into digestible blogposts. While I highly recommend going back and reading all of these excellent blogs (as well as subscribe to ensure you don’t miss out on any future blogs), here are the three most read blogposts from 2022:

1. Big Tech Platforms’ Entry into Healthcare and Education (by Özalp, Ozcan & Gawer). This blog describes the process of ‘digital colonization’ where firms like Apple and Google enter highly regulated markets such as health care and education by deploying various forms of data capture to generate data-driven insights, ultimately translating into new products and services, while being mindful of the highly regulated and sensitive nature of these industries.

2. Platform Envelopment and Network Effects (by Allen, Chandrasekaran & Gretz). This blog describes how envelopment—the strategy of absorbing the core functionality of another, often complementary platform—can help new platforms reduce their dependence on network effects and solve the chicken-and-egg problem. The main idea is that by absorbing an outside technology, firms can increase the standalone value of their own platform.

3. When Freemium Succeeds (by Rietveld). In this blog, I discuss how social product features such as multiplayer modes for video games, ridesharing functionality in ride-hailing apps, and virtual collaboration tools in productivity software can both help and harm a freemium product’s widespread adoption in its respective market. Social features can generate network effects, but they may fall flat if the addressable market for a product is insufficient.

Looking ahead

As regulation gets implemented and antitrust enforcement continues to target digital platforms, I am confident that 2023 will be another eventful year for platform competition research. My objective is to keep updating the references dashboard on a monthly basis and publish blogposts based on outstanding academic articles at similar intervals. I also hope to add some new functionality to the website and ensure it keeps running smoothly. Notably, I am part of the organizing team for the first summit of the European Digital Platform Research Network (EU-DPRN). This will be a two-day academic conference on digital platforms and ecosystems taking place on June 8 and 9th in Milan, Italy. The program will be accompanied by a one-day practitioner conference. More details to follow soon.

I hope you enjoy following Platform Papers and that you continue to do so in 2023. If you have any feedback or suggestions for improvements, feel free to reach out to me directly.

Happy New Year!

Successful platform ecosystem orchestration requires more than network effects alone

This blog is written by Melissa A. Schilling.

One of the key jobs of a platform ecosystem’s sponsor (or “hub”) is to find a way to overcome the classic “chicken-and-egg” problem that new platform ecosystems typically face, i.e., the ecosystem may not be valuable to one or more types of participants until it has a critical mass in other types of participants. For example, video game consoles need games to be attractive to attract buyers, but game developers prefer to develop games for consoles that already have a lot of users; drivers are not interested in joining a ride sharing platform until there are a lot of customers, and customers get little value from using a ride sharing service until there are a lot of drivers, and so on. As a result, solving the “chicken-and-egg” problem has received considerable attention by both scholars and managers (see a great blog post here), and many platform managers see this as the crux of thjob.

Gettingthe point where the platform ecosystem has enough of the right kinds of participants is crucial to survival and should be celebrated, to be sure, however this is just where it starts getting interesting. A strong platform strategy should leverage several other levers to increase the value creation and capture in the ecosystem. I’ll discuss three here: Selective promotion to unlock stars and manage customer perception of depth and breadth, leveraging the data to build and refine new products and services, and facilitating scale benefits individual complementors could not achieve on their own. 

Selective Promotion

Through selective promotions like endorsements, awards, marketing campaigns, and more, the platform hub can direct attention to high quality complements that deserve more attention than they are getting, thereby increasing the likelihood that they become “stars” in the ecosystem. This type of attention directing by the hub can be very powerful. Apple provides a great example: When Apple features particular applications on the home screens of its iOS App Store in categories like “Editor’s choice,” “App of the Day,” “Best new Games,” those applications may get up to six times as many downloads as other applications during the period they are featured.  

Furthermore, through selectively targeting different types of applications, a platform hub can manage end-users’ perceptions of the range and overall quality of the ecosystem, and can spur consumers to try a broader range of products from the ecosystem. Consistent with this, research by Rietveld, Schilling and Bellavitis (2019) on video games found that platform sponsors select games for endorsement not only based on their quality and sales performance, but also on the degree to which they can unlock unrecognized value in the game, and the game’s potential to enhance the balance of the overall portfolio. Specifically, platform sponsors were more likely to endorse games that had high quality and good initial sales but were not market leaders. Additionally, they were more likely to endorse games that were in a high-value genre.  

Leveraging the Data

The hub of a platform is often in a unique position to capture and utilize the data generated by the platform. Many platforms use the data to create better experiences for their customers, such as through providing recommendations and reviews. However, platform hub managers often do not fully leverage the opportunity to more proactively collect, use and sell data that could increase the value of existing complementors, or to catalyze the creation of complements that do not yet exist.

For example, lodging platforms like Expedia and Airbnb have access to exceptionally rich data on which lodging options customers choose and at what price. Not only do they have aggregate data on market trends in lodging, but they can also track a user’s choices over time and assemble a portfolio that tells them a lot about what that customer values and how much they’re willing to pay for it. They also have access to review scores and comments lodgers leave after their experiences. Both Expedia and Airbnb provide the review scores to other customers and utilize them (to varying degree) in search rankings, and Airbnb uses customer choice data in its dynamic pricing recommendations it makes to hosts. However, both platforms could be making considerably more use of their customer data. For example, both platforms could be providing data to their lodging providers on the degree to which customers would value additional features such as in-room dining, basic kitchen appliances, acceptance of pets, etc. They are uniquely positioned to help lodging providers in a given locale differentiate themselves to better tap underserved market segments. They can even identify geographic locations in which particular market segments are not being served at all and advise developers on opportunities that exist to develop or expand the range of hotel offerings, or they could even choose to develop these lodging options themselves!

One company that has done this exceptionally well is the popular movie streaming platform, Netflix. In 2017, Netflix started Netflix Studios, and began recruiting some of television’s most successful writers and producers to start making original content in house. By 2021, Netflix was spending over $5 billion on original content, making it one of the largest film production companies in the world. For a movie rental service to vertically integrate into developing its own content seemed a peculiar move at the time, because making films and television shows requires fundamentally different technology, equipment, personnel, and expertise than distributing films and television shows. What could a specialist in media distribution know about media production? A lot, it turns out.

Netflix’s rapidly growing datasets enabled it to know which customers liked which films, which genres were growing, which new stars were gaining followings, which new production houses were gaining traction and more. The relationships it had cultivated with small independent filmmakers and budding actors also helped ensure the firm’s access to a pipeline of new creative talent and helped build goodwill toward the company. Sean Fennessey, a writer for pop culture website The Ringer, explained how important Netflix was to frustrated filmmakers who could not raise enough support to get a major studio movie off the ground, “To the creators stifled by the rise of Hollywood’s all-or-nothing focus on franchise films, Netflix felt like salve on an open wound.”

Netflix also used its massive distribution reach and selective promotion to drive viewers to its original content, building audiences for its series and crafting its reputation as a first-tier production house. Netflix profited in multiple ways from its original content: Having popular exclusive shows helped attract and retain subscribers, and having both a large audience and a powerful library of original content gave it more bargaining power when negotiating license fees for content produced by others. Collectively, it was a powerful advantage.

In some platform ecosystems, the platform hub’s diversification into its complementor’s business could create more harm than benefits. Complementors with many platform choices, for example, might prefer opt to participate in platforms in which the hub is not a direct competitor – a dynamic known as “channel conflict.” In this situation, the platform hub can instead offer its data and advisory services to existing complementors and would-be complementors rather than entering these businesses through direct ownership.

Facilitating Scale Benefits

Another way in which a platform hub can help to unleash greater value in its ecosystem is through identifying those activities of complementors that would benefit by greater economies of scale, and either providing assets for those activities that complementors can access or providing another means by which the complementors can pool their scale. For example, if multiple complementors would benefit from the development of a powerful data analytics engine or sophisticated advertising capabilities, the platform hub can either provide those activities itself, or help to convene collaborative relationships that enable its complementors to share that effort and expense.

A great example of this is provided by Soteria Investments, a platform created to facilitate the buying and selling of distressed debt. The buyers and sellers of distressed debt are of highly variable size; a handful of large banks and investment firms make hundreds of transactions a year, while the vast majority of players make less than a dozen transactions a year. The buying and selling of distressed debt was historically a human-mediated transaction – sellers either searched for buyers directly among their contacts, or hired an investment banker to search on their behalf and then paid that investment banker a retainer fee, commission, or both. Furthermore, the deal flow in distressed debt is very segmented by geography and industry – a given seller might only have construction loans in the Midwest for sale, for example, giving them relatively little exposure to overall trends in deal flow and pricing, while also giving them inadequate incentive to invest in acquiring and analyzing a broader and deeper base of data. By providing a platform for buyers and sellers to find each other, Soteria helps a wider range of buyers be exposed to a wider range of sellers, while also collecting multinational and multi-industry data on deal flow and pricing. Access to that pooled data also gives it both means and incentive to invest in state-of-the-art data analytics capability that it can then offer to participants in its ecosystem, helping them to achieve more efficient pricing, greater control over risk, and faster transaction consummation.

Notably, facilitating the pooling of scale has another benefit to the platform hub: by enabling complementors to obtain the benefits of larger scale without actually achieving larger scale can help keep the complementors on a more level playing field, preventing one or a few from rising to a dominant position that increases their bargaining power over the hub.

Now you’re ready to orchestrate!

This process of managing an ecosystem to help participants be more successful both individually and in combination is usually termed “orchestration.” The platform manager is like a conductor that directs all the players to perform in ways that come together into a harmonious whole. It’s a complicated job – there are complex competitive dynamics and other interdependencies between the participants in an ecosystem that require careful thinking through. The platform hub manager must also take care to not be too heavy handed lest they alienate their complementors – rather than hierarchical authority, most platform hub managers rely on incentives and guidelines that are to some degree jointly negotiated with the complementors themselves. But if orchestration is well done, the ecosystem becomes much more powerful and valuable than the sum of its parts.

This blog is based on Melissa’s research published in Organization Science, which is included in the Platform Papers references dashboard:

Rietveld, J., Schilling, M. A., & Bellavitis, C. (2019). Platform strategy: Managing ecosystem value through selective promotion of complements. Organization Science, 30(6), 1232-1251.

How brands can regain access to their consumers by building their own platforms

This blog is written by Julian Wichmann, Nico Wiegand, and Werner Reinartz.

In today’s platform era, marketplaces and matchmakers such as Amazon, Alibaba, and co. have become powerful intermediaries to brand offerings: These brand aggregation platforms offer consumers virtually endless shelves, provide product recommendations and reviews, and allow them to easily compare offerings. As such, traditional brands struggle to keep direct contact with its consumers and to build and maintain customer relationships.

But brands are not standing by idly; they are building own platform offerings with the goal to reconquer the direct interface to consumers. Importantly, these platforms are not mere copies of the highly transaction-focused Amazon and its peers. Instead, they focus on increasing the number of touchpoints and establishing direct and intimate relationships with consumers. To do so, these brands build upon their core offering and provide value around it by holistically addressing consumers’ various needs associated with the core offering. Take Nike’s training platform Nike Run Club as an example. It offers an expansive array of functionalities revolving around Nike’s core offering of sports apparel such as a community of fellow runners, running competitions and challenges, and routing and tracking features. As a result, Nike can establish and leverage daily touchpoints with many consumers and extend its core offering to comprehensively address consumers’ running-related needs. We call this novel type of platform brand flagship platform, which we introduce and discuss in the paper “The Platformization of Brands” published in the Journal of Marketing as well as an accompanying article in the Harvard Business Review entitled “Building Your Own Brand Platform”. In the following, we analyse these nascent brand flagship platforms and offer guidelines for brands looking to platformize.

We conceptualize digital platforms as places where consumers and third parties crowdsource (crowdsend) products, services, and related content generated by (provided to) the platform participants. That is, rather than only the brand itself providing value, a brand flagship platform considerably expands its offering while simultaneously maintaining feasibility by incorporating consumers and third parties into the value creation process. Let´s return to the example of Nike Run Club for illustrative purposes. The platform does not only sell custom sportswear but also offers social events, fitness challenges and leaderboards created by the community. Moreover, users can find expert guidance, motivational music playlists, personal training by third-parties as well as exclusive Nike products on the platform. Many of these functionalities only thrive through the integration of consumers (e.g., challenges, leaderboards, and other community feature) and third parties (e.g., expert guidance, music playlists, coaching). Hence, consumers are benefiting from Nike´s expansion as they combine various elements of the service to find the most compatible product-service-content combination for their needs. Typically, the value created on brand flagship platforms can be classified by five distinct platform building blocks: transaction block (e.g., providing products for sale), community block (e.g., forums and profiles), benchmarking block (e.g., tracking runs, leaderboards), guidance block (e.g., AI coaching), and inspiration block (e.g., video content, blogs). Hence, brand flagship platforms do not only nor primarily serve as a sales channel for the brand. Instead their goal is to build an ecosystem surrounding the brand’s core offering by providing a mix of customized products, services, and content; and involving consumers, professionals, and third-party companies in the value creation.

The degree of crowdsourcing and -sending taking place on a flagship platform has important strategic implications for a brand because it affects how to set up and run the platform as well as which type of consumer relationship emerges from it. Hence, brands need to pursue the flagship platform style that best suits their strategic goals.

The platform as an instrument. These platforms enable low degrees of crowdsourcing and crowdsending so that value creation is strongly driven by the brand itself. As such value creation is typically focused on the brand’s core offering and the platforms’ functionalities tend to focus on enabling transactions or providing functional extensions to the brand’s core offering. As a result, relationships with consumers tend to be superficial as they use the platform only to meet immediate consumption-related needs. Example: Philips Sonicare

The platform as a guide. Platforms in this category foster high degrees of crowdsourcing but low degrees of crowdsending. Users of these platforms profit from the brand and third-party businesses but they usually do not create any significant value themselves. The goal of the platform is to expand the platform’s scope by inviting companies and professionals from related areas to join. By doing so, consumers are able to satisfy different but related needs which creates a genuine bond with the platform’s brand. Example: Whistle (by Mars)

The platform as a canva. Here, the brand encourages a high level of crowdsending while crowdsourcing is limited. As such, consumers can freely create value and interact with each other with limited oversight by the brand which requires a more laid-back platform management style. The high level of engagement with the brand leads to a strong attachment meaning that consumers strongly identify with the brand. At the same time, however, the limited oversight bears risks such as anti-social and fraudulent behaviors taking place on the platform. Example: Lego Ideas

The platform as a companion. These platforms enable both intensive crowdsourcing and -sending. As such, consumers are highly involved in the platform receiving and generating value. For example, a user that uses Nike Run Clun will benefit from the routes, challenges, and advice provided by other platform participants. But at the same time, that user will also provide value to the platform by uploading her runs, her favorite routes, or by providing own advice. Through this continuous give and take, this platform type is highly flexible and adaptable so that it is able to grow with the consumer. This is why Nike Run Club can remain relevant for consumers even as they advance in their skill and progress from total novice to avid runner. This perpetual companionship can elicit strong feelings of attachment and identification in consumers. Example: Nike Run Club; Bosch DIY & Garden

In order to do build a brand flagship platform, managers must rethink their market and start shifting the place of value creation from selling goods to satisfying consumers’ varied needs associated with the brand’s core offering. Given this substantial shift, the transition to a flagship platform should be a slow adaption process rather than an “all or nothing” approach”.

The transition consists of three important steps:

• First brands should have a clear vision of the territory they want to cover. They determine how far and how fast they want to stray from their traditional territory while consumers define the needs that they want to see satisfied.

• Second, each brand must decide which strategic goal(s) they are pursuing and adjust their platform offering accordingly. This also implies an adjustment of observed KPIs. Especially as a brand strives towards strong relationships, its KPIs evolve beyond sales towards a broader system that captures the engagement and value provided by and generated from the various platform participants.

• Third, managers need to decide which parts of the value creation they want to make, buy (from third-party firms), or earn (from consumers). Importantly, a flagship platform with varied functionalities can only be feasible if the brand relegates some parts of the value creation to third-parties and consumers. Hence, managers must confidently give up some control in the process.

This blog is based on Julian, Nico and Werner’s research published in the Journal of Marketing and the Harvard Business Review, which are included in the Platform Papers references dashboard:

Wichmann, J. R., Wiegand, N., & Reinartz, W. J. (2022). The platformization of brands. Journal of Marketing, 86(1), 109-131.

Wichmann, J. R., Wiegand, N., & Reinartz, W. J. (2022). Building your own brand platform. Harvard Business Review, 100(5).

Absorbing functionality from another platform can help platforms solve the “chicken-and-egg” problem

This blog is written by B.J. Allen, Deepa Chandrasekaran, and Richard T. Gretz

Would you purchase a smartphone if it only offers a few compatible apps? Would you purchase a video game console if only a few games were available for the console? Is your answer ‘Probably not’?

Both these examples highlight an important relationship that characterizes hardware/software platform markets: The value of the platform (e.g., smart phone, video game console) increases as a larger amount of compatible software (e.g., mobile apps, video games) becomes available, also known as indirect network effects. Indirect network effects create a critical feedback loop: A platform with a greater amount of compatible software is attractive to consumers; more software becomes available as more consumers buy the platform.

This feedback loop creates a major challenge for new platform entrants and platforms with small networks in industries where these network effects are strong. It’s a classic chicken-and-egg problem: How can a platform grow their market if they lack sufficient quantities of related software? How can they attract software developers if they do not have enough adopters? Platforms in this position are dependent on software providers and possess low negotiating power.

So, what options does a new platform have in this scenario? It can try to build a stronger network, say by investing heavily in software provision (e.g., Nintendo making their own games). Or it can try to innovate radically to leapfrog the established competing platforms, which may be risky and difficult.

We propose a third strategy to compete effectively in these networked markets. In a new paper in the Journal of Product Innovation Management, we propose the use of platform envelopment in mitigating these indirect network effects. Platform envelopment is an innovation strategy, originally proposed by Eisenmann, Parker, and Van Alstyne (2011), wherein an existing platform absorbs the core technical features from another platform such that the functionalities of multiple platforms are combined. Envelopment involves more than just adding additional features; it is characterized by absorbing the core functionality of another platform. For example, a video game console absorbing the functionality to play DVD movies. We propose that platform envelopment can increase the stand-alone value of the platform, which should encourage consumer adoption even with limited software provision. This, in turn, decreases the platform’s reliance on software provision. Interestingly, platform envelopment has, thus far, largely been considered as a strategy for moving into an adjacent market. As a result, existing research has focused on how envelopment impacts performance in the enveloped industry (e.g., how a video game console offering DVD compatibility impacts the platform’s performance in the DVD market). In our paper, we look at the impact of platform envelopment on platform success in its original industry (e.g., how a video game console offering DVD compatibility impacts the platform’s performance in the gaming industry).

Platform envelopment offers two benefits to the platform. It makes the platform more attractive to the consumer (e.g., a video game console becomes more attractive with the ability to stream Netflix, Hulu, etc.). Critically, we propose that with envelopment, the amount of software becomes less important to overall platform performance. This is because envelopment attracts different segments of consumers who derive less value from the core functionality the hardware provides. Again, consider the example of video game consoles – consoles that only play games attract core gamers – all they care about are games. Core gamers value the console based on the available games. However, there is another segment out there – let’s call them novice gamers –who enjoy gaming but are on the fence of whether to buy the console because they are not sure if their novice interest is worth the investment. However, if the platform also doubles as a Blu-ray player, this might justify the purchase by novice gamers. These consumers, compared to core gamers, are not as influenced by the quantity of games available. Then, you have non-gamers who may purchase the console simply to use the Blu-ray player functionality; game provision is completely irrelevant for non-gamers. Importantly, our theory suggests that the net importance of software for the industry as a whole will decrease once these new consumers enter the market. Further, as the focal platform (e.g., video game console) is “borrowing” the network of the enveloped industry (e.g., Blu-rays), the marginal value of any one software supply is diminished. We call this phenomenon of weakening indirect network effects decreasing indirect network sensitivity.

We test our theory by examining the impact of platform envelopment on platform success in the video game industry with two studies. The first study looks at the relationship between hardware and software demand in the video game console industry in the US over several years and spanning several console generations. In the second study, we leverage data from several countries and consider this relationship in the context of a broad set of gaming platforms including gaming consoles, handheld devices (e.g., Gameboy), mobile phone gaming, online gaming, and PC gaming. Both studies find that platform envelopment increases the demand for the platform while simultaneously decreasing the importance of software supply on platform demand. Our findings suggest that a platform envelopment strategy can provide new entrants and firms with smaller networks a way to decrease their dependence on available software while increasing the platform’s stand-alone value, allowing them to better compete with larger platforms with established networks.

Can platform envelopment be an effective strategy in other industries? Let us consider video streaming. Compare the differences in strategy between Netflix and Amazon’s entertainment arm. Netflix relies mostly on third-party video content and is currently facing increasing costs for streaming rights. As Netflix offers no other functionality beyond streaming video content, it is dependent on content providers, which in turn leads it to raise the price charged to consumers. Netflix is now investing heavily in developing its own content to mitigate its reliance on movie providers. However, our research suggests that it could be beneficial for Netflix to consider streaming other content, such as music. Amazon’s entertainment arm utilizes this strategy by streaming both shows and music, making it less dependent on only one type of content provider.

This blog is based on BJ, Deepa and Rick’s research published in the Journal of Product Innovation Management which is included in the Platform Papers references dashboard:

Allen, B. J., Chandrasekaran, D., & Gretz, R. T. (2021). How can platforms decrease their dependence on traditional indirect network effects? Innovating using platform envelopment. Journal of Product Innovation Management, 38(5), 497-521.