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Hypercontinuous Innovation and Demand-Side Learning: Why Digital Platforms Enjoy Longer and More Expansive Market Leadership Positions

December 2024
Academy of Management Perspectives

Authors:

University College London

Strategy and innovation scholars are increasingly concerned about the market power of successful digital platforms—and so are the authorities tasked with regulating the so-called Big Tech companies. This raises the question: What has allowed these digital platforms to enjoy such sustained and expanded positions of market leadership? Building from and extending upon the increasing returns to adoption literature, this article develops a framework of digital dominance. The framework explains how firms’ adoption and use of digital technologies such as big data analytics and cloud streaming has enabled two shifts in the underlying drivers of increasing returns to adoption. First, digitization facilitates a transition away from forcing sharp generational breaks to hypercontinuous innovation, enabling uninterrupted network externalities and impeding substitution. Second, firms’ learning orientation has shifted from the supply to the demand side, which has amplified customer switching costs and enhances platforms’ ability to adapt to market change. These shifts also do not act in isolation; there are powerful feedback mechanisms that help to further entrench and expand the dominance of these firms and their platforms. The framework contributes to the strategic management literature and offers implications for policy makers.

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HYPERCONTINUOUS INNOVATION AND DEMAND-SIDE LEARNING:

WHY DIGITAL PLATFORMS ENJOY LONGER AND MORE EXPANSIVE

MARKET LEADERSHIP POSITIONS

Joost Rietveld

UCL School of Management

University College London

One Canada Square, Level 38

London E14 5AA, United Kingdom

j.rietveld@ucl.ac.uk

Melissa A. Schilling

Stern School of Business

New York University

40 West 4th Street

New York, NY 10012, USA

mschilli@stern.nyu.edu

This version: December, 2024

Accepted for publication in the Special Issue on Platform Regulation at the Academy of

Management Perspectives.

Acknowledgements: We thank Martin Kilduff, Davide Ravasi, Joe Ploog and the attendees of the UCL School

of Management Reading Group for their valuable feedback. We further benefited from feedback received at the

84th annual meeting of the Academy of Management and Charles River Associates. All mistakes are our own.

HYPERCONTINUOUS INNOVATION AND DEMAND-SIDE LEARNING:

WHY DIGITAL PLATFORMS ENJOY LONGER AND MORE EXPANSIVE

MARKET LEADERSHIP POSITIONS

ABSTRACT

Strategy and innovation scholars are increasingly concerned about the market power of

successful digital platforms—and so are the authorities tasked with regulating the so-called

Big Tech companies. This raises the question: What has allowed these digital platforms to

enjoy such sustained and expanded positions of market leadership? Building from and

extending upon the increasing returns to adoption literature, this article develops a framework

of digital dominance. The framework explains how firms’ adoption and use of digital

technologies such as big data analytics and cloud streaming has enabled two shifts in the

underlying drivers of increasing returns to adoption. First, digitization facilitates a transition

away from forcing sharp generational breaks to hypercontinuous innovation, enabling

uninterrupted network externalities and impeding substitution. Second, firms’ learning

orientation has shifted from the supply to the demand side, which has amplified customer

switching costs and enhances platforms’ ability to adapt to market change. These shifts also

do not act in isolation; there are powerful feedback mechanisms that help to further entrench

and expand the dominance of these firms and their platforms. The framework contributes to

the strategic management literature and offers implications for policy makers.

Keywords: platform competition; network externalities; organizational learning; innovation;

digital transformation.

INTRODUCTION

Successful digital platforms

tend to enjoy sustained periods of market leadership across

multiple product markets, leading to growing concern by both researchers and regulators.

Strategy scholars have started looking at how digital platforms exploit their market power

through acts of over-orchestration, self-preferencing, and thwarting innovative new entrants

(among other concerns; e.g., Cutolo and Kenney, 2021; Jacobides, 2021; Jacobides and

Lianos, 2021; Lv and Schotter, 2024; Rietveld, Ploog, and Nieborg, 2020). Regulators such as

the EU’s European Commission and the UK’s Digital Markets Unit have introduced novel ex

ante legislation aimed specifically at curbing the power of dominant digital platforms.

Frameworks such as the Digital Markets Act (DMA) and the Digital Markets, Competition

and Consumers Bill (DMCC) designate dominant platforms as “gatekeepers” and their

sponsoring firms as having “strategic market status”.

Evidently, digital platforms possess

certain features that afford them to be dominant for longer, across multiple product markets.

However, despite the scale and scope of such firms as Alphabet, Amazon, and Apple

and their respective offerings, we still know surprisingly little about what has allowed these

market leaders to sustain and expand their platforms’ dominance so dramatically. This

question cannot be fully addressed by pointing to network externalities alone (also see: Teece,

2023). After all, network externalities have been identified as an isolating mechanism since

the 1980s—well before the advent of most digital technologies (Economides, 1989; Farrell

and Saloner, 1986; Katz and Shapiro, 1985; 1986). Moreover, network externalities are

insufficient at explaining why digital platforms can leverage their dominant positions across

We define “platforms” here as the hub of a platform ecosystem, in which a stable core (such as a smartphone

operating system or a music-streaming service) orchestrates the relationship between a wide range of

complements (such as software applications or music titles) and prospective end users. This definition is broad

and includes platforms where the hub merely mediates relationships in multisided markets (e.g., Uber) and more

complex platforms in which the hub is an active producer of complements (e.g., Apple’s iOS or Netflix).

For the DMA, see: https://commission.europa.eu/strategy-and-policy/priorities-2019-2024/europe-fit-digital-

age/digital-markets-act-ensuring-fair-and-open-digital-markets_en and for the DMCC see:

https://commonslibrary.parliament.uk/research-briefings/cbp-9796/ (last accessed November, 2024).

seemingly unrelated product markets (e.g., Amazon entering web services, Netflix expanding

into original programming, Alibaba entering financial services - Adner, Puranam, and Zhu,

2019; Ozalp et al., 2022). Therefore, in this article, we ask: How do firms leverage digital

technologies to extend and expand the dominant positions of market-leading platforms?

Building on the increasing returns to adoption literature (Arthur, 1989; Katz and

Shapiro, 1985), and insights from research on “Big Data” and new product development

(Agrawal, Gans, and Goldfarb, 2020; Hagiu and Wright, 2023; Tan and Zhan, 2016), this

article develops a framework of digital dominance that aims to explain why digital platforms

enjoy longer and more expansive positions of market leadership through their firms’ adoption

and use of digital technologies. It argues that firms’ adoption and use of digital technologies

has enabled two qualitative shifts in how firms learn and adapt their offerings in the face of

exogenous external change, which can potentially erode their market-leadership positions.

First, online connectivity technologies such as automatic online updates, cloud

computing, and streaming enable firms to make near-constant seamless improvements to the

core functionality of their platforms, without any loss of compatibility with existing end users

and complementors (e.g., Ansari and Garud, 2009; Kretschmer and Claussen, 2016; Rietveld

et al., 2020). That is, technological innovation has become hypercontinuous rather than

discontinuous. Indeed, firms can now update their offerings in real-time, often without any

noticeable disruption, and sometimes without even notifying the customer. Rather than

needing to “sell” the customer the idea of buying the new version of the product, the firm can

seamlessly ensure the customer is always on the most current version, thus eliminating the

typical decision moments when a customer might consider switching to another platform. As

we will discuss, this makes it much harder for new entrants to “technologically leapfrog” a

dominant platform by offering superior standalone functionality (Schilling, 2003). A digital

platform with an initial advantage in network externalities can thus continue to grow those

network externalities without interruption, ever increasing in size and strength.

The second is a shift in firms’ learning orientation from the supply to the demand side.

Traditionally, in technology-driven industries, learning benefits tended to accrue from

accumulating experience on the supply side, including identifying better suppliers, improving

inbound logistics, lowering production unit costs and waste rates, and an overall increase in

efficiency (Lieberman, 1987). The move away from physical products manufacturing and an

exponential increase in readily available customer data, conveniently retrieved, stored, and

analyzed through automation technologies such as big data analytics and machine learning,

have enabled a shift in focus to powerful demand-side learning effects (e.g., Clough and Wu,

2020; Gregory et al., 2020; Hagiu and Wright, 2023). Demand-side learning effects can

manifest in the form of more precise identification of customer needs and preferences,

algorithmic predictions of consumer choice, and the development of new features that better

cater to customers’ preferences. Furthermore, digital platforms often engage customers to be

co-creators in personalizing their experience (e.g., creating playlists, providing reviews that

train their algorithms, etc.) which results in increased customer switching costs. Thus,

demand-side learning effects can help to create much stronger lock-in mechanisms for

customers, and help the firm identify new markets in which to expand their offerings.

The framework presented here helps to further explain the feedback mechanisms at

play that can entrench a platform’s dominant position even further. For instance, strong lock-

in effects can give market leaders more time to learn from their customers resulting in

superior product updates that can further extend a platform’s life cycle and keep rivals at bay.

The article aims to make three contributions. First, the framework enhances our

understanding of how firms’ adoption of digital technologies affects strategy and competition

in the age of digital transformation. Strategy scholars have started to investigate the various

consequences of digital transformation on such issues as competitive dynamics and industry

structure (e.g., Adner et al., 2019), organization design and organizing (e.g., Giustiziero,

Kretschmer, Somaya, and Wu, 2023; Kretschmer and Khasabi, 2020), and how firms create

and capture value (e.g., Amit and Han, 2017; Rietveld, 2018). This article contributes by

documenting how digital technologies affect the mechanisms that underpin and sustain

market leaders’ positions of dominance. It argues firms can leverage digital technologies to

increase switching costs and extend network externalities. It further illustrates how digital

technologies can help market leaders guard against potential forces of benefit erosion.

Second, the article holds important implications for research on platform competition.

The platform competition literature has long pointed to network externalities as the pre-

eminent factor in winner-take-all battles (e.g., Boudreau, 2012; Cennamo and Santalo, 2013;

Shankar and Bayus, 2003). However, the literature has mostly neglected the role of

organizational learning, which is an integral driver of increasing returns to adoption—

particularly in the presence of big data (e.g., Arthur, 1989; Farrell and Saloner, 1986; Gregory

et al., 2020). Learning effects, too, can confer first-mover advantages and are within the

firm’s control—unlike network externalities that are often heavily influenced by idiosyncratic

factors outside the firm’s control (Clough and Wu, 2020; Schilling, 1998, 2002). The

framework highlights the importance of firms’ learning orientation in the context of platform

competition. It illustrates how learning from end users and complementors can help firms

adapt to changing customer preferences and sustain a platform’s competitive advantage.

Third, the article sheds light on how demand-side strategies can contribute to a firm’s

absorptive capacity. Demand-side strategies look downstream to customer markets as an

important determinant of value creation and a prerequisite for value capture (Adner and

Zemsky, 2006; Priem, 2007). This literature surmises that “mundane” resources can be a

source of competitive advantage if they help a firm better cater to customer preferences

compared to rivals (Priem, Butler, and Li, 2013; Rietveld, 2018; Ye, Priem and Alshwer, 2012).

This article goes beyond the notion that customer heterogeneity matters by demonstrating that

firms can leverage digital technologies to generate insights about customer preferences and

use these insights to enhance their learning capabilities. The knowledge and capabilities

gleaned from customer interactions can help firms create superior value by refining their

offerings, improving how they orchestrate their ecosystems and expanding their scope.

BACKGROUND LITERATURE

Many technology products exhibit increasing returns to adoption, where the benefit a

customer derives is contingent on the product’s cumulative adoption (Arthur, 1989; Katz and

Shapiro, 1985). Increasing returns to adoption can create “tipping” effects or “winner-take-

all/most” dynamics, where one or a few competing products end up capturing a large share of

the overall market (Farrell and Saloner, 1985; 1986). When this happens, the market converges

on a dominant design (Utterback and Abernathy, 1975; Anderson and Tushman, 1990; Suarez

and Utterback, 1995). Well-known examples of markets that have coalesced around a

dominant design include PC operating systems (Microsoft’s Windows), keyboard layout

designs (QWERTY), video cassette recorders (JVC’s VHS), and video game consoles

(Nintendo’s NES). Firms sponsoring dominant technology products often enjoy market-

leadership positions that are “sticky” and reap superior performance compared to rivals.

Drivers of increasing returns to adoption are two-fold: network externalities and

learning curve benefits (Arthur, 1989; Schilling, 1998). Network externalities occur when the

value of a product to a user increases with the cumulative number of active users of the same

product (Economides, 1996; Katz and Shapiro, 1986). Network externalities can be either

direct or indirect (Clements, 2004). That is, network externalities can arise because

connectivity among a product’s end users is important and/or because the availability of

complementary goods and services is key to the product’s performance. A product’s installed

base of users can directly impact the outcome of a competitive interaction when connectivity

matters. The size of the installed base can also attract complementors (increasing the

availability of complements), and the availability of complements can attract users

(increasing the installed base); the importance of complements can thus lead to indirect

network externality effects. The availability of software applications for personal digital

assistants (Nair, Chintagunta, and Dubé, 2004), video game titles for video game consoles

(Clements and Ohashi, 2005), and even advertisements for lifestyle magazines (Kaiser and

Wright, 2006) have all been found to positively impact demand for the products they

complement. Network externalities are a significant driver of value for digital platforms and

they often constitute a large portion of a digital platform’s overall value proposition.

Learning curve benefits occur when firms enjoy advantages that stem from

accumulating experience (Lieberman, 1987). The more a product is manufactured and sold,

the more it can be improved and the more efficient its producer becomes. Learning curve

benefits are commonly associated with a firm’s supply-side improvements, such as lower

production costs, increased efficiency, fewer defects, and higher product quality: “A firm that

has sold higher volumes than its competitors in earlier periods will move farther down the

learning curve and achieve lower unit costs than its rivals” (Besanko et al., 2013; 379).

Learning effects can confer an early-mover advantage (Lieberman and Montgomery, 1988;

Suarez and Lanzolla, 2007), and are especially key to the long-term performance of firms that

operate on thin margins to quickly accumulate a large user base (Chou and Shy, 1990).

Products that exhibit increasing returns to adoption can enjoy powerful market

positions akin to natural monopolies (Bensaid and Lesne, 1996; Cabral, Salant, and Woroch,

1999). Once a product attains a dominant position customers become “locked in.” The self-

reinforcing feedback mechanisms that emanate from increasing returns to adoption can

entrench a product’s market position and confer a sustained competitive advantage on the

product and its sponsoring firm (e.g., McIntyre and Srinivasan, 2017; Sun and Tse, 2007).

A growing body of research has looked at how firm strategies and market-level

factors influence the duration of a product’s market dominance (e.g., Adner and Kapoor,

2016; Ansari, Garud, and Kumaraswamy, 2016; Giachetti and Marchi, 2017; Schilling, 2002;

2003). However, most of this work is embedded in industry settings where the focal product

has important physical asset components. As a result, many of the conclusions drawn from

this work are premised on market and cost assumptions that are intrinsically related to the

evolution of physical products. Digital products break down many of these assumptions. For

example, for physical products, the goal of improving performance and/or reducing costs are

typically met (as noted previously) by learning efforts on the supply side. Digital products

with intensive customer connectivity do not obviate these concerns, but they give rise to

hitherto unavailable data on the demand side. Big Data enables an incredibly rich

understanding of customer preferences, that in turn, enables the design of products that better

meet customers’ needs (Agrawal et al., 2020; Hagiu and Wright, 2023; Tan and Zhan, 2016).

As such, much of the opportunity to improve products now stems from learning on the

demand side, which has different implications for sustaining market leadership.

The rise of digital products also affects product life cycles in dramatic ways: the costs

of retooling manufacturing to produce upgraded physical products (on the producer side) and

the cost of purchasing upgraded physical products (on the customer side) collectively led to

an expectation that each version of a product will have a lifetime that justifies that expense.

Other things being equal, physical products with longer lifespans are often considered to be

of higher value by customers (Cooper, 2004; Wieser, Tröger, and Hübner, 2015), and product

versions with longer life cycles often generate more profits for their producers (Stark, 2022).

Digital products turn these expectations on their head. Digital products can often be updated

rapidly and at low cost. They can also be transferred seamlessly to the customer on a frequent

basis, in essence avoiding the generational breaks that characterize most physical technology

products. As we will show, this, too, has a tremendous effect on the prospects for sustaining

market leadership in the context of digital platforms with network externalities.

In sum, the rise of digital products with extensive customer connectivity has altered

the dynamics of platform competition and market dominance in ways that are not yet well

understood. Building on earlier work on generational breaks and nascent research on Big

Data, we develop a framework that explains why digital platforms might enjoy longer periods

of market dominance, and dominance over a wider range of markets, than non-digital

platforms, simultaneously revealing implications for scholars, managers, and policy makers.

DIGITAL DOMINANCE FRAMEWORK

This section develops a framework of digital dominance, explaining how market

leaders sustain and expand their digital platforms’ dominance through the use of digital

technologies such as big data analytics, machine learning, and artificial intelligence. We

contend that though digital technologies may not have fundamentally altered the drivers of

platforms’ increasing returns to adoption, they have brought upon two qualitative shifts in

how firms establish and maintain their platforms’ increasing returns to adoption: 1) a shift in

technological innovation from being discontinuous to becoming hypercontinuous, and 2) a

shift in organizational learning from the supply to the demand side. These shifts can aid firms

and their platforms in strengthening their isolating mechanisms and protect against forces of

benefit erosion. Figure 1 graphically depicts the digital dominance framework.

--- INSERT FIGURE 1 ABOUT HERE---

Enabling Hypercontinuous Innovation and Alleviating Generational Breaks

Most digital platforms can be decoupled from physical hardware products. Some

comprise software products that can be downloaded to and executed from non-dedicated

hardware devices (e.g., Microsoft Windows runs on an array of personal computing devices),

whereas others are hosted on centralized servers and offered via cloud-based computing or

streaming technologies (e.g., Netflix delivers video content directly from the cloud).

The first major change brought upon by firms’ use of digital technology therefore is a

shift in the nature of technological innovation and the platform life cycle. Prior to the

decoupling of technology products from dedicated hardware products, firms’ innovation

efforts typically adhered to the traditional new product development process, whereby the

product and process design are frozen prior to commercialization, and then the product is

static for some period while the firm sells the product, enabling it to recoup its investment

and earn profits. For physical products, which typically require fixed capital investments for

production and distribution, constant product design change would be an extreme source of

inefficiency (if not impossible). Furthermore, choosing when to freeze product design is an

important strategic decision because freezing too early may forfeit important product design

improvements that could be made, and freezing too late may forfeit revenues and market

share that could be captured from commercialization. There are also important considerations

to be made about the optimal product life cycle from the customer’s perspective. If customers

must pay for each version of the product, they may have an adverse reaction to the rapid

introduction of improved versions. Consumers will experience regret if they feel that they

could have waited for an improved version and may subsequently delay their purchases.

Collectively, this means that for physical products, the firm must carefully weigh the

advantages of introducing an improved version sooner to compete against benefits provided

by competitors, versus waiting to introduce a version with more (or bigger) improvements

later that customers may feel more compelled to purchase. The net result of these dynamics is

a punctuated evolution of the product from the customer’s perspective (i.e., discontinuous

innovation), even if firms are always working on product innovations behind the scenes.

Concerns over compatibility also can play an important role in these evolutionary

dynamics. When the overall value of a technology product such as a two-sided platform or an

ecosystem depends on the availability of complementary goods, it is crucial that end users

have access to a wide variety of compatible complements—and that complementors can sell

to a large base of end users (e.g., Adner and Kapoor, 2010). Major updates to a platform’s

core functionality can imperil the compatibility of complementary products, which can lead

to a fragmentation of the platform-plus-complements bundle (Henderson and Clark, 1990;

Schilling, 2000). Such fragmentation can be detrimental to a platform’s market position: Loss

of compatibility expunges the network externalities a platform enjoys, and can lead to

widespread confusion among customers (Simcoe and Watson, 2019). Indeed, whereas a

continuation of compatibility can sustain a platform’s life cycle, a loss of it can result in

desertion by both complementors and end users (McIntyre et al., 2021). It is for these reasons

that innovation for many platform offerings is typically punctuated: Periods of marked

technological discontinuity are followed by relatively long periods of stability.

The market for video game consoles has closely followed this pattern. Roughly every

five to eight years, console manufacturers such as Nintendo and Sony release new platforms

to the market. These “next generation” gaming consoles often offer significant improvements

over their predecessors, with major upgrades in processing power, improved graphics, and

novel controller functionality (Schilling, 2003). The introduction of a next-generation gaming

console marks a pronounced generational break and unlocks a period of intense competition.

Console manufacturers typically release their consoles within close temporal proximity of

each other, encouraging consumers and complementors alike to reconsider their adoption

decisions. It is during these inter-generational transitions that the market is prone to

disruption, as new entrants—such as Microsoft’s Xbox in 2001—are offered a window of

opportunity, and consumers and complementors consider switching to one of the available

alternatives (Schilling and Rietveld, 2016).

After a new console has been launched to the

market, any subsequent changes tend to be aesthetic and/or aimed at reducing costs.

Digital connectivity technologies such as cloud computing and streaming have

enabled continuous technological innovation whereby firms can introduce improvements to a

platform’s core functionality over the course of its life. At its extreme, firms can engage in

hypercontinuous innovation where platforms are updated in real-time, almost constantly—

without forcing customers to adopt new hardware. In some instances, the end user may not

even be required to consent to the update, or be aware of it. This, in essence, erases the

switching costs for the user to move between generations of a platform offered by the same

provider. At the same time, many complements are now similarly decoupled from physical

products (e.g., cartridges, DVDs, paper) enabling online updating that does not require

issuing a recall or forcing end users to purchase these products a second time around. Even

when end users choose not to adopt the latest updates to a platform, system development kits

and application programming interfaces can easily detect the version a consumer is on and

remain fully functional. Moreover, both the platform’s core functionality and its complements

are often fully backward compatible, allowing the overall product bundle to coevolve in

synchrony and avoid issues associated with fragmentation or loss of compatibility.

Firms have limited means for keeping their customers locked-in during a generational transition. On the end

user side, console manufacturers sometimes make their next generation consoles backward compatible with the

prior generation (Hann, Koh, and Niculescu, 2016; Kretschmer and Claussen, 2016), whereas on the

complementor side platforms sometimes subsidize video game development costs and distribute early versions

of next generation system development kits (SDKs) to their most valued and loyal developers. Such tactics can

incentivize early adopters to upgrade but have limited effects on the platform’s long-term success.

This happens, for example, when console manufacturers introduce slightly modified models of the same video

game console. Some of these product improvements are visible to consumers (such as when Sony introduced the

PlayStation 3 Slim and Super Slim models) whereas others are not visible to consumers, as they reflect changes

that have been made “under the hood”—such as when Sony made improvements to its PS3 graphics chips.

These incremental upgrades do not require consumers and complementors to upgrade to retain compatibility.

Uninterrupted Network Externality Growth. One of the main advantages of

issuing frequent updates to a platform’s core functionality is the reduced need for (and

occurrence of) generational breaks. As discussed above, marked generational breaks can give

customers pause, potentially resulting in switching to a competing platform or abandoning

the market altogether (Christensen and Bower, 1996; McIntyre et al., 2021). A potential loss in

users on either side of the platform (i.e., end users or complementors) can be extremely

detrimental when a large portion of a product’s overall value is driven by network

externalities (Choi, 1994; Farrell and Saloner, 1992, Schilling, 2022). Similar to how an

increase in users on either side of the platform can lead to an exponential increase in value

creation, so too can a sudden loss in users result in an exponential decrease in value for the

remaining users on the platform. In the absence of generational breaks, a market-leading

platform may therefore enjoy uninterrupted network externalities. That is, the platform can

continue to grow its user base on both sides of the market in spite of regular updates.

This can hold true even when a digital platform is tied to a dedicated hardware

product. Consider the case of Apple’s iOS operating system, a proprietary operating system

that runs on i-devices such as the iPhone and iPad. Apple has interleaved generational

transitions for its iPhone and iPad hardware products with major updates to its iOS platform.

Generational transitions for iOS have historically been introduced a short period before the

release of new hardware products. For instance, iOS 15, a recent installment in the operating

system, was released on June 7, 2021, with the iPhone 13 Pro smartphone being released three

months later, on September 24, 2021.

Updates to iOS can be downloaded free of charge and

by giving current generation smartphone users access to the next-generation operating

system, Apple is offering them a strong incentive to purchase the latest version of its

hardware. Notably, any major updates to iOS are backward compatible with approximately

https://www.bankmycell.com/blog/iphone-evolution-timeline-chart (November, 2024)

three prior generations of the iPhone and iPad hardware affording consumers a grace period

before upgrading their devices. A similar dynamic happens on the developer side to ensure

that apps, too, remain fully functional. Interleaving software and hardware updates this way

allows Apple to co-evolve its smartphone ecosystem without any loss of compatibility. Firms’

use of connectivity technologies can thus facilitate uninterrupted network externalities by

preventing a platform’s loss of compatibility with its end users and complementors.

Extended Technology Life Cycle. Firms’ newfound ability to issue frequent updates

to a platform’s core functionality and the subsequent potential for uninterrupted growth in

network externalities have made it much harder for new entrants to displace dominant

incumbents. The pace of substitution, or the time it takes for a new technology to replace an

existing one, can vary greatly from one industry to the next (e.g., Agarwal and Bayus, 2002;

Henderson, 1995). It depends on both the performance of the new technology and that of the

existing technology it is looking to replace (Adner and Kapoor, 2016; Christensen, 1992a). A

novel technology’s performance can only be assessed in relative terms, i.e., against that of the

existing technology. Put differently, if an existing technology has the potential to improve

continuously, this significantly raises the bar for any new technology looking to compete.

Moreover, from a customer’s perspective, the performance of a platform is

determined by a combination of factors that span beyond its standalone value. Indeed, the

availability of complements and the size of a platform’s installed base largely determine the

benefit a customer derives in the context of a platform market (e.g., Adner and Kapoor, 2016;

Christensen, 1992b; Henderson, 1995; Kapoor and Lee, 2013; Schilling, 2003; 2022). In the

absence of any substantive improvements to a platform’s core functionality, it is the

accumulation of complements and the platform’s user base that can generally sustain a firm’s

early-mover advantage (as discussed earlier). However, as technological progress continues

to be made, significant improvements offered by a new platform’s core functionality can lead

to the displacement of an existing platform—despite the new platform’s lack of users

(Christensen, 1992a; Schilling, 2003). This act of displacement by a new platform offering

superior standalone performance is commonly referred to as “technological leapfrogging.”

It is evident how allowing for continuous improvements over a platform’s life can

increase barriers to leapfrogging and entrench the dominant position of a market leader

(Adner and Kapoor, 2016). By continuously updating the iOS operating system, Apple has

created an additional barrier for rivals to overcome on top of its expansive app ecosystem and

massive user base. Potential competitors must also approximate the functionality and quality

of the latest version of iOS (instead of that of the initial iteration, with which Apple first

entered the market). Moreover, extending the life cycle of an existing platform can have a

negative impact on the upward performance trajectory of a rival platform, as complementors

and end users will hesitate to switch to a new platform so long as the incumbent platform

offers sufficient benefits. Continuous improvements thus not only extend the life cycle of a

dominant platform; they also create additional barriers to achieving benefit parity for any

rival offerings. As such, when a sponsoring firm can make continuous improvements to a

platform’s core functionality the point of substitution by rivals likely gets postponed.

In sum, digital transformation has allowed firms to decouple their platforms from

dedicated hardware products. Through the adoption and use of digital technologies, firms can

issue updates and improvements to their platforms without triggering a generational break

and jeopardizing their compatibility with existing users. As a result, innovation has become

hypercontinuous and this has allowed for uninterrupted growth in network externalities. The

combination of issuing constant updates and an increasing installed base has made it much

harder for new entrants to compete—thus extending the life cycle of dominant platforms.

Demand-Side Learning and Increasing Customer Switching Costs

The advent of digital technologies has eliminated physical production requirements

and minimized the marginal costs of production for many firms. While this may inadvertently

restrict firms from moving farther down the learning curve on the supply side—after all, there

no longer is a physical production component—the use of automation technologies such as

big data analytics and machine learning provides ample opportunity for learning on the

demand side (Clough and Wu, 2020; Greggory et al., 2020; Hagiu and Wright, 2023). The

second major change brought upon by firms’ use of digital technology therefore is a shift in

the orientation in organizational learning away from the supply side to the demand side.

Most digital platforms are now continuously connected to the Internet, where every

customer interaction can be tracked in real-time and stored on servers for subsequent retrieval

and analysis. The cost of collecting, tracking and analyzing data is now negligible in many

markets (Goldfarb and Tucker, 2019). These data can be used as input for training algorithms

and identifying opportunities for novel product features or new products altogether (Agrawal

et al., 2020). This, in turn, can facilitate organizational learning and bolster new product

development capabilities (Agrawal et al., 2020; Hagiu and Wright, 2023; Tan and Zhan, 2016).

As a firm accumulates data and insights from its end users and complementors, it can develop

new knowledge and leverage this knowledge to enhance its capabilities and respond to

external change (Nonaka and Takeuchi, 1995; Von Hippel, 1986; Roy, Lampert, and Stoyneva,

2018). Such demand-side learning can set in motion a virtuous cycle where experienced firms

are better able to retain existing customers as well as attract new ones.

Increased Customer Switching Costs. Demand-side learning effects can increase

customer switching costs in at least two key ways: 1) the firm accumulates superior customer-

specific knowledge that enables it to offer value customers would forego if they would leave,

and 2) customers develop firm-specific knowhow and co-created benefits that are not fully

transferrable to a competing platform. These two levers are interwoven and largely

inseparable. As the firm interacts with a customer and observes their preferences and

behavior, it acquires knowledge that enables it to provide benefits to the customer that the

customer could not obtain from rivals with whom they have not had such interactions. At the

same time, the interactions the customer has with the firm are a sort of investment that reaps

returns in the form of better knowledge about how to use the platform and its complements

(i.e., “learning costs” (Klemperer, 1987)), and products and services that better cater to their

needs. If the customer were to consider switching to a platform from another firm, the

customer might have to invest again in significant interactions before obtaining similar

benefits (what Burnham, Frels, and Mahajan termed “procedural switching costs” (2003)).

This is readily observable in online recommender systems and curated selections of

complementary goods on many digital platforms. Search queries on Google, browsing on

Amazon, and streaming on Spotify and Netflix all provide these platforms with valuable data

on a user’s revealed and latent preferences. These data are subsequently fed into

recommender systems that transform these data into personalized suggestions such as

Amazon’s “Customers Who Bought This Item Also Bought” products and Netflix’s “Because

You Watched…” videos. The more a customer interacts with a platform, the more effective its

recommender systems become, and the smarter the firm becomes at developing customer-

specific knowhow and offering superior value. Firms can also use customer data to promote

overlooked complements or highlight complementors with whom the firm has favorable

contractual agreements (Rietveld, Seamans, and Meggiorin, 2021). It can promote these

complementors and their offerings through curated selections, such as eBay’s “Top Rated

Sellers” (Elfenbein, Fisman, and McManus, 2015; Rietveld, Schilling, and Bellavitis, 2019).

Indeed, research suggests that online recommender systems can significantly increase a

customer’s volume and variety of consumption on a platform (Fleder and Hosanagar, 2009).

In many digital platforms, customers are also encouraged to invest in creating

personalized benefits of the platform through such features as an option to save preferences

for payment and delivery, to create playlists or wish lists, to build lists of contacts with which

an experience is shared, etc. For example, Amazon and Giftster both make it easy to save

wish lists, baby registries, wedding registries, etc. Film review platform Letterboxd makes it

easy to save watchlists and connect to friends with whom the user wants to exchange reviews

and film recommendations. These features are a form of co-creation between the customer

and the platform that increases both the value a customer derives and the investment (which

is the basis of procedural switching costs) the customer has made in the platform.

Additionally, these user-generated lists can be an input for organizational learning by helping

the platform identify complements and/or features that are of apparent value to its users.

Many of these benefits are not easily transferable to competing platforms.

Competitors cannot easily replicate the knowhow that leads to better recommendations

because they lack access to the data required to train their algorithms (Agrawal et al., 2020).

Furthermore, most platforms make it difficult to transfer saved preferences or co-created

features to competitors. This differential access to customer data reduces the likelihood that a

particular user switches from their current platform to a competing platform with an

otherwise comparable offering. The knowhow and features that are derived through

interaction between the platform and the customer thus create powerful lock-in mechanisms.

Enhanced Absorptive Capacity. At the aggregate level of learning across customers

and over time, the shift in learning orientation to the demand side can also help firms accrue

absorptive capacity that gives them an advantage in sustaining their dominance. Absorptive

capacity is an organization-level generalization of a phenomenon first observed at the level of

individual cognition: learning to learn (Ellis, 1965). As individuals learn across repeated

trials, they get better at the learning process itself. They become better able to interpret

feedback, better able to identify sources of failure, better able to choose among potential

methods to try, etc. Similarly, as organizations learn across repeated trials (e.g., products,

customers, markets, etc.) they become more efficient and effective at identifying which

product features are most important to improve, better methods of improving products,

interpreting the significance of different elements of customer feedback, and more. Thus, as a

firm accumulates experience, it gets better at reaping gains from subsequent experience, and

it can use this improved learning ability to respond better and faster to market change than

competitors with less experience. Firms that fail to invest in organizational learning, on the

other hand, are likely to exhibit considerable inertia, and will struggle to catch up to those

with absorptive capacity even if they have access to the same data (Cohen and Levinthal,

1990; Nelson and Winter, 1982; Schilling, 1998; 2002; Sosa, 2009; Tripsas and Gavetti, 2000).

First-mover advantages (such as those conferred by customer switching costs) can

dissipate over time, where the rate of erosion depends on several external factors and the

extent that a firm can counter these forces of erosion (Utterback and Abernathy, 1975;

Lieberman and Montgomery, 1988). The greater a firm’s absorptive capacity, however, the

more adept it will be at responding to external change, and the less vulnerable it will be to the

erosion of advantage from external change. One external driver of the erosion of advantage is

changing customer preferences (Carpenter and Nakamoto, 1989; Tripsas, 2008). When

preferences change, customer-specific knowhow can quickly become outdated—unless the

firm is able to assimilate and utilize knowledge about such changing customer demands. As

noted earlier, customer interactions are an important source of learning and can enable firms

to detect changes in customer preferences. Firms that use automation technologies can track

countless customer interactions that, when aggregated and analyzed, provide insight into

what product features are trending or trailing, how the firm can better orchestrate its

ecosystem, and even identify underserved market opportunities to expand its offering.

Consider again the example of Apple’s iOS. Apple proactively tracks the App Store

application ecosystem in search of new product ideas that it can integrate into its operating

system.

A well-known case in this regard is the flashlight. At one point, there were over

1,000 flashlight apps available in the App Store, some of which had glowing user reviews and

strong sales. Recognizing the value of flashlight functionality to end users, Apple introduced

its own flashlight functionality and integrated the feature into the seventh generation of the

iOS operating system. By closely tracking end users’ interactions with apps, Apple learns

about new and valuable ideas, and it can leverage these into novel product features for its

operating system (also see: Parker, Van Alstyne, and Jiang, 2017; Wen and Zhu, 2019).

Doing

so allows Apple to continuously update and improve its iOS operating system, ensuring that it

remains competitive vis-à-vis rival operating systems and potential new entrants.

Customer interactions data can also enable firms to better orchestrate their ecosystems

(Wareham, Fox, and Giner, 2014). The rewards-based crowdfunding platform Kickstarter, for

example, started operations in 2009 by manually screening all the projects creators submitted

to the platform. By keeping track of every single project, including several performance

indicators such as whether a project had reached its funding goals or any instances of

fraudulent or opportunistic behavior on the part of the project creator, Kickstarter was able to

significantly revise its project requirements and update its code of conduct for creators. In

Apple co-founder Steve Jobs has been quoted saying: “We have always been shameless about stealing great

ideas.” The list of ideas that Apple has taken from the app developer community is long and ranges from fertility

trackers to video editing software. The practice of having an idea copied by Apple has even entered industry

vernacular and is referred to as “getting Sherlocked.” The origin of this term harks back to Apple’s desktop

search tool “Sherlock,” which copied many of the features of a third-party application tool called “Watson.”

https://www.washingtonpost.com/technology/2019/09/05/how-apple-uses-its-app-store-copy-best-ideas/

(November, 2024)

The reverse is also true. The platform can pass on insights and data from customer interactions to its

complementors to help them learn and improve their offerings (Cennamo et al., 2023). For example, in 2011,

crowdfunding platform Kickstarter introduced a Creator Dashboard to give project creators insight into backers

and their funding pledges. Supplying rich information on backers can help creators become more effective in

marketing their projects. Such passing on of customer-behavior insights to complementors can be argued to not

only create switching costs for a platforms’ end users, but also create lock-in effects for complementors.

https://www.kickstarter.com/blog/the-new-creator-dashboard (November, 2024)

2014, after Kickstarter had hosted over 150,000 projects, the firm implemented “Launch

Now,” a machine-learning algorithm incorporating thousands of data points to check whether

a project is ready to launch.

Launch Now resulted in a significant reduction in the resources

required by Kickstarter to monitor the creative projects seeking entry and improve the quality

and conduct of those projects that it granted access to its crowdfunding platform.

Finally, firms can leverage demand-side learning to expand their offerings and

increase the scope of their platform ecosystems.

The Chinese e-commerce giant Alibaba, for

example, used transactions data from small businesses on its e-commerce platform to launch

Ant Financial Services, a data-driven micro-lending business (loans no larger than ¥1 million

RMB, or $160,000 USD) for small and medium-sized enterprises—a market that had been

largely overlooked by the major banks in China (Zeng, 2018). Data on prior interactions can

help predict a borrower’s creditworthiness and inform the appropriate interest rate to charge

for a loan. Furthermore, because lending decisions are made based on Alibaba’s own data on

the would-be borrower, decisions can be made in minutes (typical bank-loan decisions take a

week or longer). Another example is Netflix entering original programing by commissioning

two full seasons of House of Cards after analyzing customer interactions data that indicated

there was an appetite for a remake of the original BBC series by viewers who also enjoyed

movies starring Kevin Spacey and those directed by David Fincher (who would direct the hit-

series remake).

Taken together, these examples illustrate how firms can enhance their

absorptive capacity through demand-side learning and anticipate market changes.

In sum, the shift in organizational learning to the demand side as facilitated by firms’

adoption and use of digital technologies has increased customer switching costs by way of

https://www.kickstarter.com/blog/introducing-launch-now-and-simplified-rules-0 (November, 2024)

It is worth noting that while demand-side learning can increase the opportunity for firms to expand their

scope, it has also been observed that digitalization can enable firms to pursue positions of hyperspecialization

(Giustiziero et al., 2023). Therefore, digital transformation should be seen as factor that can increase a firm’s

options for scope as opposed to having a monotonic relationship with scope.

https://www.salon.com/2013/02/01/how_netflix_is_turning_viewers_into_puppets/?123 (November, 2024)

better serving existing individual customers through algorithmic recommendations and

curated selections, and by increasing customer investments in the platform. The shift has

further enhanced firms’ absorptive capacity at the aggregate level, which has resulted in firms

being better able to mitigate forces of benefit erosion through data-driven updates, enhanced

ecosystem orchestration, and differentiation of their offerings in adjacent domains.

Feedback Mechanisms

The various effects of digital technologies on the entrenchment of a platform’s

dominance (i.e., uninterrupted network externalities, extended platform life cycle, increased

switching costs, and enhanced absorptive capacity) should not be viewed in isolation. There

are interdependencies between each of the effects that can generate self-reinforcing feedback

mechanisms. While each of the individual effects alone has the potential to sustain or expand

a platform’s dominance, it is the interdependencies between them that can set in motion a

virtuous cycle that can further strengthen the firm’s isolating mechanisms, making it even

harder for rivals to erode a platform’s dominant market position (see Figure 2).

--- INSERT FIGURE 2 ABOUT HERE ---

First, there is a powerful connection between increased customer switching costs and

uninterrupted network externalities (also see: Lam, 2017). Firms that develop customer-

specific knowhow and create superior value will be more likely to retain their customers and

thus grow their installed base over time. The reinforcing effect also works in the opposite

direction: As a digital platform enjoys uninterrupted network externalities, this can create

additional barriers to switching for the customers who have adopted the platform.

Second, there are interdependencies between uninterrupted network externalities and

a platform’s extended life cycle (e.g., Adner and Kapoor, 2016; Schilling, 2003). Firms that

invest in extending the life of a platform will have more time to accumulate an installed base

and thus enjoy stronger network externalities. Similarly, platforms with a large installed base

create additional hurdles for rivals to overcome (beyond a platform’s standalone value),

making it harder to equal a dominant firm’s offering, which can slow the pace of substitution.

Third, firms that invest in learning to develop and utilize knowledge from customer

interactions can leverage this knowhow to innovate and evolve a platform in ways that create

additional customer value (e.g., Cohen and Levinthal, 1990; Zahra and George, 2002). Indeed,

improvements to a platform’s core functionality will only extend its life cycle when these

changes cater to customers’ needs and preferences (Henderson, 1995; Tripsas, 2008). In the

reverse direction, firms that successfully manage to prolong the platform life cycle are

granted more time to learn from customer interactions and update their knowledge bases.

Finally, there are interdependencies between customer switching costs and enhanced

absorptive capacity (e.g., Nonaka and Takeuchi, 1995; Von Hippel, 1986; Roy et al., 2018).

The longer a customer uses a platform, the more time the firm has to deepen its knowledge

about their preferences and improve its offering. Firms that interact with the same customers

for a very long time are likely to accrue a much richer understanding of the customer’s

preferences, and better anticipate how they will evolve. Firms’ ability to act on customer

interactions similarly depends on their absorptive capacity: The stronger a firm’s absorptive

capacity, the more effective its development and utilization of that deep knowhow.

Amazon, by way of its Prime subscription service, enjoys several of these feedback

mechanisms. Amazon Prime is a paid subscription service that offers members access to

several premium features including free and expedited delivery, but also music and video

streaming, in addition to various other benefits. Prime members are likely to make more

purchases on Amazon’s Marketplace platform because they want to exploit the benefits

offered by the paid subscription service. Some of these feedback mechanisms were noted by

former Amazon CEO and founder Jeff Bezos: “We get to monetize [our subscription video]

in a very unusual way. When we win a Golden Globe, it helps us sell more shoes. And it does

that in a very direct way. Because if you look at Prime members, they buy more on Amazon

than non-Prime members, and one of the reasons they do that is once they pay their annual

fee, they're looking around to see, 'How can I get more value out of the program?'”

Furthermore, by bundling user accounts across different products (e.g., Marketplace,

Prime Video, etc.), Amazon can collate diverse data points for a comprehensive perspective

on customers’ behavior and their preferences. These data can then be leveraged to predict

lucrative product categories that Amazon may wish to enter on Marketplace, or the type of

content it should contract for its Prime Video streaming service. Growth in one part of

Amazon’s business can boost Prime membership rates, which generates positive spillover

effects to other parts of its business where growth may be slower. Notably, some of these

dynamics (e.g., locking in sellers, cross-selling consumers, competing with sellers) are at the

heart of the US Federal Trade Commission’s antitrust lawsuit against Amazon.

The firms behind Asian “super-apps” such as WeChat, Meituan and Grab have

similarly leveraged the reinforcing feedback mechanisms enabled by digital technologies

(Prud’homme, Chen, and Tong, 2023). WeChat, for instance, started out as an instant

messaging service in 2011. Before long, in 2012, it added brand accounts, allowing firms to

communicate with end users. From here on, the app evolved into a social media platform not

unlike Facebook or Instagram. Then, in 2013, it added a mobile wallet, which was later

expanded to in-store payment functionality in 2014. WeChat also added taxi-booking

functionality via ride-hailing app Didi in 2014. It has since added numerous other services,

including insurance and medical. From 2015, WeChat started monetizing business customers

more aggressively by allowing for advertisements in consumers’ timelines and it added

digital games to its platform. By 2018, the app had become an integrated platform boasting

Source: https://finance.yahoo.com/news/amazon-ceo-jeff-bezos-said-144157906.html? (November, 2024)

See: https://theplatformlaw.blog/2023/09/28/amazon-ftc-federal-trade-commission/ (November, 2024)

more than 1 billion monthly active users—it is colloquially referred to as the “everything

app.” By regularly adding new services, Tencent increases customers’ switching costs for

WeChat. The overall value created by super-apps keeps increasing; not only as a function of

the added services, but also by way of adding additional users to the installed base. The

combination of added services and added users allows Tencent to prolong the life cycle of

WeChat and making it harder for rivals to successfully displace the dominant super app.

DISCUSSION

While digital transformation has had profound effects on the business landscape,

many questions remain about how firms’ adoption and use of digital technologies affects their

business strategies (cf. Adner et al, 2019; Porter, 2001). One noteworthy observation is the

prevalence of dominant digital platforms, including those by Alphabet, Amazon, Apple and

Meta, among others. The notion of market dominance predates digitalization, but the

sustained duration and expanded scope of digital platforms’ market-leadership positions

suggests that the forces behind increasing returns to adoption are now stronger than ever.

This article developed a framework that explains why digital platforms enjoy longer

and more expansive positions of market leadership compared to their non-digital

counterparts. The framework highlights two important changes enabled by firms’ adoption

and use of digital technologies: 1) a shift from a punctuated technology life cycle to

hypercontinuous innovation where generations of platform technology are more seamlessly

interconnected, and 2) a shift from supply- to demand-side learning. These changes hold

implications for the manifestation and defensibility of early-mover advantages that underpin

the competitive advantage of incumbent market leaders. The framework also explains why

isolating mechanisms persist as a function of uninterrupted growth in network externalities

and increased customer switching costs. The framework further documents how firms can

guard themselves against forces of benefit erosion by decelerating the pace of substitution

and enhancing their absorptive capacity. Finally, the framework identifies feedback

mechanisms that can help sustain digital market leaders their dominant positions.

Implications for Technology Competition and Digital Strategy

The insights conveyed by the framework provide several important implications for

technology competition and digital strategy. First, when firms use digital technologies to

improve their products it becomes even harder for new entrants and challenger firms to

dethrone a dominant incumbent. Research on challenger strategies has been incomplete, but

extant literature suggests that competing technologies need to be sufficiently differentiated

from their dominant counterparts, particularly with an eye on emerging customer preferences

(Adner and Snow, 2010; Suarez and Kirtley, 2012). Other research suggests that there are

windows of opportunity for challenger firms that open when customer preferences change or

when technology shocks occur (Eggers, Grajek, and Kretschmer, 2020; Giachetti and Marchi,

2017). Digital technologies, however, allow incumbent technology sponsors to constantly

evolve and respond to changes in the external environment more quickly than ever before.

This implies that, all else equal, new entrants will find fewer untapped market segments to

serve, with shorter windows of opportunity to capture them. That said, all dominant

technologies eventually retreat, which raises important questions about how and when

dominant digital platforms are displaced (e.g., Madsen and Walker, 2017; Roy et al., 2018),

and to what extent this can be attributed to the strategies of challenger firms.

Second, the flipside of hypercontinuous innovation is that firms cannot rest on their

laurels if they wish to maintain or improve their relative standing in the market. While

pioneers may get a head start, this does not mean new entrants cannot catch up. Indeed, the

“evolvability” of digital technologies not only affords incumbents the possibility to extend

their platforms’ life cycles, but it also grants rivals the opportunity to continuously improve

their platforms (Agarwal and Tiwana, 2015). If Apple had not integrated flashlight

functionality into iOS, it might have inadvertently created a window of opportunity for

rivals—be it competing operating systems or complementors looking to disrupt from

within—to offer superior value (e.g., Adner and Lieberman, 2021; Ansari et al., 2016). This is

consistent with the notion of “Red Queen” competition, where firms must match the learning

and new product development capabilities of their rivals simply to maintain their standing in

the market (Barnett and Hansen, 1996; Derfus et al., 2008). As such, while continuous

investments in a platform’s performance can bring substantial advancements in consumer

surplus (e.g., Cutolo and Kenney, 2021), it may come at a cost for firms when it does not

significantly move the needle in terms of producer surplus. This “running in place” implies

that the overall value captured from digital technologies could be impaired: Some firms may

attain dominant market positions, but their profit margins could remain thin in certain cases.

Third, the shift from supply-side learning to demand-side learning increases the

salience of demand-side strategies for competitive advantage (Adner and Zemsky, 2006;

Priem, 2007). Improved and continuous insight into customers’ preferences can help firms

“grow the pie” (Brandenburger and Stuart, 1996). Adding features to a product or leveraging

technology in novel ways can increase existing customers’ willingness-to-pay and help attract

diverse customer segments to a digital platform. However, customers have heterogeneous

preferences and often derive diminishing marginal utility from narrow technological

improvements (Adner and Levinthal, 2001). Ignoring this can be perilous. Firms that are in

relentless pursuit of incremental improvements driven by technological advancements, aimed

at serving only their most profitable customers, are the ones most at risk of getting disrupted

(Christensen, 1997). Firms ought to be guided by intelligence gathered from their demand

environments. This is what the business models of successful technology companies have

been designed to do, as illustrated by Netflix’s data driven content development strategy or

Apple’s methods for spotting proven app functionality to absorb into its operating system.

Implications for Policy

Competition authorities all over the world have expressed concerns about how digital

platforms behave in relation to their competitors, complementors and end users. Though there

is disagreement about whether and to what degree Big Data is imitable or constitutes a source

of market power (e.g., Lambrecht and Tucker, 2015; Santesteban and Longpre, 2020; Sivinski,

Okuliar, and Kjolbye, 2017), the scale and scope of the Big Tech firms has led competition

watchdogs to typically operate from the position these firms hold significant market power

and any further expansion of their market positions would be bad for competition and

innovation. Various efforts have recently been introduced aimed at restraining large digital

platforms from becoming even bigger and more powerful. These efforts range from ex-ante

regulations such as the DMA and the DMCC as well as market investigations such as the

CMA’s mobile ecosystems study, to ex-post interventions aimed at specific companies and

behaviors including the FTC’s recent lawsuit against Amazon’s alleged monopolistic conduct

in e-commerce and several agencies’ investigations into the effects of Microsoft’s acquisition

of video game publisher Activision Blizzard on cloud gaming. These regulatory efforts have

picked up in pace over the last few years and they are testimony to the fact that platforms’

market leadership positions can be rather sticky in the age of digital transformation.

That said, these regulatory interventions into digital markets have had mixed results.

For example, the FTC lost five out of seven recent merger challenges that it brought to

court.

On the one hand, while the drivers of market power appear to have evolved as

outlined in this article and others, agencies have mostly brought traditional theories of harm

https://www.skadden.com/insights/publications/2023/04/quarterly-insights/are-the-ftc-and-doj-losing-antitrust-

battles (November, 2024)

to make their case. These traditional antitrust theories that tend focus on acquirers foreclosing

their rivals or conglomerates leveraging their market power may be ill equipped at addressing

the complex realities of digital platforms and ecosystems. On the other hand, agencies have

struggled to successfully bring cases on the basis of so-called ecosystem theories of harm.

These novel theories of harm that address how large technology companies leverage their

strength in one market segment to expand and impose their presence in another are largely

without legal precedent and remain somewhat elusive.

It also appears that regulators and

economists are still looking for the appropriate tools to formalize such theories in ways that

can convince the courts.

It is here where strategy scholars can contribute to the debate by

drawing on such concepts as business models, platforms and ecosystems, dynamic

capabilities, and isolating mechanisms (also see: Teece, 2023). Indeed, even economists have

recently resorted to the resources and dynamic capabilities frameworks in attempts to explain

synergies arising from digital mergers (e.g., Chen, Elliott, and Koh, 2023). There is an

opportunity for technology innovation management and strategy scholars to have impact by

helping regulators better understand how platform ecosystems function, what the drivers of

market power are, and when ecosystem “plays” do and do not result in consumer harm.

In this light, we should clarify that attaining a dominant market position in and of

itself is not strictly illegal or a sufficient basis for competition authorities to intervene.

Competition law intends to ensure markets are efficient and that competition is fair, with

market participants “competing on the merits.” According to both US antitrust doctrine and

EU competition law, holding a dominant market position that approaches monopoly power

For a recent example of where an ecosystem theory of harm was brought successfully, see the European

Commission’s decision to block Booking.com’s proposed acquisition of eTraveli:

https://ec.europa.eu/commission/presscorner/detail/en/ip_23_4573 (November, 2024)

For an economic discussion on ecosystem theories of harm, see: https://cepr.org/voxeu/columns/ecosystem-

theories-harm-digital-mergers-new-insights-network-economics-part-1 and

https://cepr.org/voxeu/columns/ecosystem-theories-harm-digital-mergers-new-insights-network-economics-part-

2 (November, 2024).

“is not only not unlawful; it is an important element of the free-market system.”

Firms’

ability to raise prices above marginal costs (i.e., exercise market power) is ultimately what

induces risk taking and creates a powerful incentive to innovate. Indeed, so long as markets

are contestable—meaning firms can enter with the prospect of generating non-zero profits

post entry—we should expect competitors, dominant market leaders included, to engage in

innovation to protect and enhance their positions. The presence of competitive constraints

benefits consumers. Things become potentially more problematic when markets are no longer

contestable or when market leaders abuse their dominance by unfairly competing with (or

excluding altogether) rivals and complementors or implementing policies that can harm

consumers either directly or indirectly. In such cases, we might expect platforms to further

entrench their dominant positions without engaging in the type of innovation that ultimately

benefits consumers. Another area of potential concern is dominant platforms acquiring start-

ups that develop disruptive innovation that has the potential to displace or unseat an

incumbent market leader (Cunningham, Ederer, and Ma, 2021). Such acquisitions could lead

to short term benefits for consumers if the target’s innovation gets integrated into the

platform’s core (as seems to be the case with recent AI applications), but it may cause long

term harm if it reinforces a platform’s dominant position to the point where the market

becomes incontestable and new entrants are deterred from competing. Dominant firms have a

“special responsibility” to not allow their conduct to impair undistorted competition,

and for

digital platforms this responsibility extends to both complementors and consumers.

While the main purpose of this article was to document how digital technologies

contribute to firms’ positions of dominance, the framework can also be of help in guiding

regulators and decision makers. For instance, when it comes to devising remedies for

Verizon Communications, Inc. v. Law Offices of Curtis V. Trinko, LLP, 540 US 398 (2003).

C-322/81 Michelin I, §10 and §57 (1981).

unseating dominant platforms or establishing conditions under which ecosystem mergers can

be given regulatory approval, we can turn to the elements in the framework for guidance. In

2022, for example, the EC accepted concessions from Amazon following an investigation into

the e-commerce giant’s entry into marketplace seller segments (also see: Zhu and Liu, 2018).

Responding to the concerns, Amazon proposed to commit “not to use non-public data relating

to, or derived from, the independent sellers' activities on its marketplace, for its retail

business.”

This commitment, which EC Executive Vice-President Margarethe Vestager

coined a “data silo,”

should prevent Amazon from further expanding its operations through

analysis of customer interactions data. Put differently, this intervention was aimed at

restraining Amazon’s demand-side learning and may have interrupted some of the feedback

mechanisms discussed elsewhere in this article. Novel policies around data portability and

interoperability, for example, such as those introduced by the DMA, are aimed at making it

easier for challenger firms to compete by jumpstarting their network externalities, reducing

switching costs, and providing an early foundation for demand-side learning effects.

Suggestions for Future Research

The digital dominance framework presented in this article offers several suggestions

for future research. For one, it starts from the assumption that an incumbent already has a

dominant position and it elaborates on the forces that allow it to sustain and expand this

advantage. It alludes, however, to the forces that will determine the factors that permit

challenger firms to successfully dislodge dominant digital platforms. For example, is there

diminishing marginal utility to ever larger datasets? As more firms have access to bigger and

better data about users, the differential value of the data held by the largest firms versus

smaller firms may shrink. Furthermore, as platforms extend and expand their reach, the users

See: https://ec.europa.eu/commission/presscorner/detail/en/ip_22_7777 (November, 2024).

See: https://ec.europa.eu/commission/presscorner/detail/en/speech_22_7850 (November, 2024).

that make up their installed base will become more heterogeneous and could become less

engaged. This may negatively impact the strength of network externalities as such factors as

the network’s cohesiveness (Afuah, 2013; Suarez, 2005), the share of late adopters (Rietveld

and Eggers, 2018), and other factors relating to users’ involvement with a technology (Nair et

al., 2004) have all been found to affect the strength of network externalities.

Furthermore, digital technologies may make it easier to better target tightly connected

communities, or users that wish to interact with specific others, rather than targeting the

population in general. This could make it possible to satiate the potential for (direct) network

externalities with a much smaller installed base and open opportunities for more specialized

platforms. To what extent do changes in the composition of the user base offer challenger

firms a window of opportunity? How does increasing customer heterogeneity affect the

transferability of the firm’s learning efforts? How does the relative importance of network

externalities change as the technology’s installed base becomes more heterogeneous and the

firm’s opportunities for learning increase? What is the appropriate balance between network

externalities and learning effects for increasing returns to adoption, and does this balance

shift over the platform life cycle as firms become more dominant? These and other questions

may shed light on when and how dominant technology platforms are likely to be displaced.

It will also be important to develop a deeper understanding of what successful

capabilities look like at different stages of the technology life cycle. Prior research in this

regard has mostly dichotomized the technology life cycle into a pre-dominance stage and a

post-dominance stage (e.g., Anderson and Tushman, 1990; Dosi, 1982), wherein product

innovation is paramount before the emergence of a dominant design, while process

innovation prevails during the post-dominance stage (Utterback and Abernathy, 1975). In the

age of digital transformation, however, these stages have become blurred. To remain

dominant, firms must continuously experiment and improve the way they operate and

manage their internal processes as well as evolve the customer-facing product features that

ultimately determine the firm’s value proposition. This suggests that the capabilities for

managing the technology life cycle have changed, as also reflected in Amazon’s “Day 1”

manifesto which emphasizes a constant customer focus towards innovation.

Finally, researchers are invited to validate and refine the arguments put forward in this

article. How does the framework hold against empirical testing, and what are some of the

nuances encountered in the real world? For example, our arguments about hypercontinuous

innovation largely assume that the platform can be continuously improved while retaining

backward compatibility, but it is also likely that retaining backward compatibility constrains

the ways in which a platform can evolve. Future research should examine how this constraint

shapes a platform’s ability to retain its long-run dominance. Moreover, the framework was

conceptualized and illustrated predominantly by using consumer-facing platforms. It will thus

be interesting to see if and how the framework holds for digital platforms targeted at

customers in upstream links of the vertical chain. One example that suggests the framework

at least partially holds is the cloud infrastructure market dominated by Amazon’s Amazon

Web Services (AWS) and Microsoft’s Azure. In a recent market study by UK’s telecom

regulator Ofcom, it was concluded that “A lack of interoperability and portability can restrict

the ability of customers to switch and multi-cloud.” Moreover, “[s]ome customers have told

us they are already concerned about being ‘locked in’ to their current provider.”

In this

market, issues relating to switching costs, lock in, and sustained positions of market

leadership appear to be just as relevant. It would also be interesting to see to what extent

digital technologies enable more sustained and expansive periods of dominance in contexts

that would not be considered digital platforms. For example, many consumer product

https://www.forbes.com/sites/quora/2017/04/21/what-is-jeff-bezos-day-1-philosophy/#28ff41711052

(November, 2024)

Cloud services market study: Final report (Ofcom, 2023; pp. 3-4)

companies now collect more customer data than ever before and use this data to make better

predictions about customer preferences. While they are often not able to update their products

in real time the way that digital platforms can be updated, does their enhanced demand-side

learning change the likelihood or sustainment of market dominance? Put more generally,

future research should assess the (boundaries to the) framework’s generalizability.

CONCLUSION

Several Big Tech companies (e.g., Amazon, Alphabet, Apple, and Meta) have recently

come under scrutiny by antitrust agencies from around the world. Concerns around the effects

on innovation, competition and consumers are all directly related to the scale and scope of the

activities undertaken by these “digital giants.” Digital technologies have enabled successful

firms to sustain and expand their platforms’ dominance across a growing range of markets.

Why do technology firms enjoy such persistent and expansive competitive benefits

from their digital platforms? This article has sought to answer this question by presenting a

framework of digital dominance. First, it argued that firms’ use of digital technologies has

shifted the nature of innovation from being punctuated to being hypercontinuous. Firms’ use

of online connectivity technologies enables seamless improvements to a platform’s core

without any loss in compatibility with complementors and end users, resulting in

uninterrupted network externalities and a deceleration in the pace of technology substitution.

Second, the article argued that the adoption and use of digital technologies has shifted firms’

learning orientation from the supply to the demand side. By leveraging automation

technologies such as big data analytics and machine learning, firms can collect, store, and

analyze millions of data points on customer interactions, which can be leveraged to increase

customer switching costs and improve the firm’s absorptive capacity. Moreover, there are

reinforcing feedback mechanisms between the various benefits conferred by the adoption and

use of digital technologies. Taken together, these factors enable market leaders to better

anticipate and respond to external changes. The digital dominance framework provides

important considerations for our understanding of digital strategy and competition policy.

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FIGURES

Figure 1. A Framework of Digital Dominance

Feedback

mechanisms

Catalyst

Shifts created by

firms’ use of digital

technologies

Effects on isolating

mechanisms and

benefit erosion

Implications for

platform competition

DIGITAL TRANSFORMATION

DIGITAL DOMINANCE

• Extended period of market leadership

• Expanded scope of market leadership

Technological innovation

becomes hypercontinuous

rather than discontinuous

Learning orientation

shifts from supply side

to demand side

Uninterrupted network

externalities

Increased customer

switching costs

Extended

technology life cycle

Enhanced

absorptive capacity

Effects on benefit erosion pressures (-)

Firms’ adoption and use of

connectivity technologies (e.g.,

cloud computing or streaming)

Firms’ adoption and use of

automation technologies (e.g., big

data or machine learning)

Effects on isolating mechanisms (+)

Figure 2. Digital Dominance Feedback Mechanisms

Uninterrupted network

externalities

Increased customer

switching costs

Extended platform

life cycle

Enhanced absorptive

capacity

• A large installed base paired with strong network externalities

can deter customers from switching to a rival platform

• Increased switching costs can lock customers in, resulting in a

larger accumulated installed base

• An extended platform life cycle affords the firm more time to

learn from customer interactions

• Enhanced capacity for learning can inform the firm how to best

extend its platform’s life cycle by adding new functionality

• Enhanced capacity for

learning facilitates the

development of

customer knowhow,

which can help the

firm improve its

platform

• The longer a customer

engages with a

platform, the more

opportunity the firm

has to learn from their

interactions

• Extending the platform

life cycle prolongs the

time the firm has to

accumulate a large

installed base

• A large installed base

creates an additional

barrier for new

entrants to overcome,

which slows the pace

of substitution

ResearchGate has not been able to resolve any citations for this publication.

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Market Orchestrators: The Effects of Certification on Platforms and Their Complementors

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Full-text available

Apr 2021

We study how a multisided platform's decision to certify a subset of its complementors affects those complementors and ultimately the platform itself. Kiva, a microfinance platform, introduced a Social Performance badging program in December 2011. The badging program appears to have been beneficial to Kiva-it led to more borrowers, lenders, total funding, and amount of funding per lender. To better understand the mechanisms behind this performance increase, we study how the badging program changed the bundle of products offered by Kiva's complementors. We find that Kiva's certification leads badged microfinance institutions to reorient their loan portfolio composition to align with the certification and that the extent of portfolio reorientation varies across microfinance institutions, depending on underlying demand-and supply-side factors. We further show that microfinance institutions that do align their loan portfolios enjoy stronger demand-side benefits than certified microfinance institutions that do not align their loan portfolios. We therefore demonstrate that platforms can influence the product offerings and performance of their complementors-and subsequently the performance of the ecosystem overall-through careful enactment of governance strategies, a process we call "market orchestration."

Disruption Through Complements

Article

Full-text available

Feb 2021

We expand the perspective on disruption by going beyond substitute products to consider the ways in which complements can impact the competitiveness of incumbents. Complementors represent a different kind of disruptive threat, one that is latent within the initial structure of value creation: complementors that disrupt are not new entrants but, rather, established actors that can shift their impact from positive to negative. With this perspective, we consider how ecosystem dynamics can clarify aspects of disruptive competition, and we use the dynamics of disruption to illuminate dimensions of competition in ecosystem settings. We elaborate three processes through which disruption through complements can occur: commoditization, adjacent entry, and value inversion. For each process we discuss specific examples, and we illustrate their interaction in the context of the automotive industry, which is fast evolving in response to technological change. In so doing, the paper fills a critical gap in the literature, which is so far missing a systematic examination of how complementors can disrupt established firms.

The Dark Side of Powerful Platform Owners: Aspiration Adaptations of Digital Firms

Article

Jun 2024

The Knowledge-Creating Company: How Japanese Companies Create the Dynamics of Innovation

Article

May 1995

How has Japan become a major economic power, a world leader in the automotive and electronics industries? What is the secret of their success? The consensus has been that, though the Japanese are not particularly innovative, they are exceptionally skilful at imitation, at improving products that already exist. But now two leading Japanese business experts, Ikujiro Nonaka and Hiro Takeuchi, turn this conventional wisdom on its head: Japanese firms are successful, they contend, precisely because they are innovative, because they create new knowledge and use it to produce successful products and technologies. Examining case studies drawn from such firms as Honda, Canon, Matsushita, NEC, 3M, GE, and the U.S. Marines, this book reveals how Japanese companies translate tacit to explicit knowledge and use it to produce new processes, products, and services.

Data‐enabled learning, network effects, and competitive advantage

Article

Oct 2023

We model dynamic competition between firms which improve their products through learning from customer data, either by pooling different customers' data (across‐user learning) or by learning from repeated usage of the same customers (within‐user learning). We show how a firm's competitive advantage is affected by the shape of firms' learning functions, asymmetries between their learning functions, the extent of data accumulation, and customer beliefs. We also explore how public policies toward data sharing, user privacy, and killer data acquisitions affect competitive dynamics and efficiency. Finally, we show conditions under which a consumer coordination problem arises endogenously from data‐enabled learning.

Capability accumulation and conglomeratization in the information age

Article

Mar 2023
J ECON THEORY

Big Tech and Strategic Management: How Management Scholars Can Inform Competition Policy

Article

Dec 2022

David J. Teece

“Digital Colonization” of Highly Regulated Industries: An Analysis of Big Tech Platforms’ Entry into Health Care and Education

Article

May 2022

Digital platforms have disrupted many sectors but have not yet visibly transformed highly regulated industries. This study of Big Tech entry in healthcare and education explores how platforms have begun to enter highly regulated industries systematically and effectively. It presents a four-stage process model of platform entry, which we term as “digital colonization.” This involves provision of data infrastructure services to regulated incumbents; data capture in the highly regulated industry; provision of data-driven insights; and design and commercialization of new products and services. The article clarifies platforms’ sources of competitive advantage in highly regulated industries and concludes with managerial and policy recommendations.

Hypercontinuous Innovation and Demand-Side Learning: Why Digital Platforms Enjoy Longer and More Expansive Market Leadership Positions

Abstract

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