How do inferior standards prevail in two-sided markets?
A simulation study on technological path dependence
with an application to platform competition in the smartphone industry
Preliminary draft, dated 15. November 2010
Please do not cite or circulate without permission.
Author:
Tobias Georg Meyer
Contact information:
Freie Universität Berlin, School of Business and Economics
Path Dependence Research Center
Garystr. 21
14195 Berlin, Germany
Tel.:
+49 30 838-52121
Fax:
+49 30 838-57186
Email: tobias.meyer @ fu-berlin.de
Abstract
This paper proposes a generic agent-based simulation model to investigate the role of
indirect network effects for the evolution of technological path dependence. It sheds
light on how inferior standards prevail in two-sided markets. By doing so, it contributes
to the theoretical literature on path dependence by complementing existing empirical
case studies with a formal simulation model. This controlled research design facilitates
analysis of the long-lasting impact of small events in the early phase of industry
evolution reinforced by indirect network effects. The model identifies favorable
conditions to the emergence of path-dependent winner-take-all market structures and
exemplifies the competitive dynamics on the grounds of empirical data for the global
smartphone industry.
How do inferior standards prevail in two-sided markets?
1
Introduction
What do dating communities, credit cards and operating systems have in common? All
of them are examples of markets in which two or more groups of actors interact via
intermediaries or “platforms”. Platform providers must get all sides of the market on
board. As such, heterosexual dating platforms need to attract business from both men
and women in order to flourish. Consumers prefer credit cards which are widely
accepted by merchants, while merchants are only likely to accept credit cards carried by
many consumers. Lastly, operating systems are more valuable to consumers given a
large supply of compatible third party applications. On the other hand, application
developers are attracted by platforms with a large user base.
The introduction phase of a platform may suffer from a “chicken-and-egg” problem.
However, once a platform is successfully established positive feedback loops often lead
to path-dependent winner-take-all market structures. This is especially the case with
software platforms which are strongly affected by the presence of indirect network
effects. For instance, Microsoft Windows’ long-lasting dominance in the PC market is
known as “everybody’s favourite example” for path dependence (Shapiro & Varian
1999; Dobusch 2008).
Recently, a new “standards battle” evolved in the emerging smartphone industry.
Smartphones developed as the result of converging media, data and communication
networks and they are widely expected to become the essential personal communication
device of the future. Their operating system is at the heart of an ecosystem composed of
handset manufacturers, network operators, software application providers and
consumers. Despite the significance of the industry and growing media attention on the
platform competition, so far little scientific research has been conducted on this topic
(Lin & Ye 2009). Following to the case of Microsoft Windows: Will the mobile world
become another example of path dependence in the area of software standards?
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How do inferior standards prevail in two-sided markets?
Or, more generally: What are the conditions for two-sided markets to become locked-in
to an inferior platform?
1.1
Technological path dependence
Competing technologies are at the very origin of path dependence theory (David 1985,
Arthur 1989). Despite a growing stream of literature on organizational path dependence
(for an overview see Sydow et al. 2009), the existence of market dominance of an
inferior technology still is the most obvious and intriguing argument for path
dependence and market failure. The case of QWERTY constitutes the omnipresent
example. Path dependence theory stresses the importance of “small events” which are
magnified in the course of time by positive feedback mechanisms resulting in a,
potentially inefficient, “lock-in” state. These feedback loops can be generated either by
supply-side economies of scale, adaptive expectations and/or network effects (Arthur
1994).
On the grounds of Arthur’s (1989) early works on increasing returns and lock-in, a rich
stream of literature focuses on network effects as the driving force behind the concept of
increasing returns (for an overview see McIntyre & Subramaniam 2009). Network
effects, or network externalities, are also called demand-side economies of scale. They
refer to the increase in consumer’s utility from a product when the number of other
people consuming that product (“network participants”) increases.
Network effects can be divided into “direct effects” and “indirect effects”. Direct
network effects occur when the number of network participants directly influences a
product’s utility (Rohlfs 1974). The classic example is the telephone network: being the
only telephone user on earth does not yield much benefit. But with other people
adopting the technology, the installed base grows and the utility for each individual
consumer increases. Some recent examples for the presence of direct network effects are
online communities such as Facebook.
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How do inferior standards prevail in two-sided markets?
Indirect network effects occur when the utility of a product is influenced by its
complementary products. Consumers indirectly benefit from an increase in network
size, as it improves the availability of complementary products – which in turn enhances
the value of the original product. A prominent example are DVD-players: With more
and more people buying compatible DVD-players, they exhibit a rather small benefit
from the ability to exchange DVDs with peers using the same technology (“direct
network effect”). What is more important is the increased supply of complementary
products due to a larger “installed base”: consumers benefit from being able to rent or
purchase a wider variety of pre-recorded DVDs (“indirect network effect”). This in turn
makes their DVD video system more valuable.
“Classic markets” are based upon the assumption of diminishing returns: successful
firms eventually run into natural limitations (Arthur 1994). In contrast, the evolution of
markets characterized by network effects is characterized by increasing returns. Here,
success breeds success. Today’s TIME-industries (Telecommunications, Information
Technology, Media, and Entertainment) are especially prone to, mostly indirect,
network effects which may lead to path-dependent market structures.
1.2
Two-sided markets
The concept of indirect network effects is refined by the theory of “two-sided markets”
(Rochet & Tirole 2003; Parker & Van Alstyne 2005; Rochet & Tirole 2006; Armstrong
2006; Eisenmann et al. 2006) which was initially conceptualized to explain behavior in
credit card markets. Two-sided, or more generally multi-sided, markets are served by
products, services, or technologies (referred to as “platforms”) that connect different
types of economic actors which provide each other with network benefits (Hagiu &
Yoffie 2009). The term “two-sided market” may seem redundant at first glance as
markets always consist of at least two economic actors. However, the notion of “twosidedness” stresses the interdependence between the strategic actions of the different
agents involved. In traditional one-sided markets, firms serve different types of
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How do inferior standards prevail in two-sided markets?
customers, but they lack the interdependency: For instance, hair salons can choose to
serve either men or women or both. By contrast, dating clubs must serve both men and
women.
Examples for MSPs can be found in very diverse industries and Evans & Schmalensee
(2008) distinguish between four different types:

Exchanges and matchmaking activities (e.g. dating services, stock exchanges,
employment agencies)

Advertising-supported media (e.g. web portals, magazines)

Transaction devices (e.g. payment systems like credit cards or travellers’ checks)

Software platforms (e.g. PC operating systems, video game consoles)
Indirect network externalities give rise to a "chicken-and-egg” problem for the provider
of a MSP (Caillaud & Jullien 2003). Consider the example of a video game console: To
attract potential buyers, the platform should bring along a large supply of compatible
games, but game producers will be willing to develop only if they expect a viable user
base. Gupta et al. (1999) refer to this as “two-way demand contingencies”.
Two-sided markets and competing platform intermediaries are not an entirely new
phenomenon. In many cases, one may think of two-sided markets as a holistic
perspective on indirect network effects (Sundararajan 2006). Theoretical and empirical
evidence reveals that two-sided markets at a mature stage are often dominated by a
handful of large platforms (e.g. video game consoles: Microsoft X-Box, Nintendo Wii and
Sony PlayStation) or by a single player, such as eBay as the leading online auction
platform. Indeed, most prominent examples for technological path dependence can be
regarded as a two-sided market, such as the battle for market dominance between VHS
and Betamax as video standards, or the case of Microsoft vs. Apple in the PC market.
Hence, two-sided markets are a highly interesting field of study for enriching path
dependence theory.
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How do inferior standards prevail in two-sided markets?
To address this issue, this paper tries to shed more light on the constitutive conditions
for path dependence in two-sided markets.
1.3
Research question
Network markets are said to act “tippy” (Besen & Farrell 1994) with a single winning
standard dominating the market. Thus, the evolution of markets with network effects
possesses interesting characteristics that distinguish network industries from more
conventional markets and have severe consequences for the firms’ competitive
strategies. Following Arthur (1996), I argue that network externalities may lead to pathdependent market evolution. If a competing technology “gets ahead by chance or clever
strategy, increasing returns can magnify this advantage, and the […] technology can go
on to lock in the market” (Arthur 1996, p. 100). However, Arthur’s formulation is rather
cautious and increasing returns do not imply “lock-in determinism”. Despite strong
network effects, one finds many markets in which several incompatible technologies
survive in the battle for market dominance. Hence, the conditions by which an industry
will either be “locked-in” to a particular technology, or will allow for different platforms
to coexist, are not yet fully understood. In line with this argument, I raise the following
research question:
Research question:
“What are the conditions for two-sided markets to become locked-in to an inferior
platform / technology?”
One must sharply distinguish between high market concentration, not necessarily
inefficient, and path dependence. I consider a path-dependent industry structure as the
persistent market dominance of one or more platforms although superior alternatives
are, or have been, available. In order to understand the conditions which may lead to
path-dependent market evolution, I analyze actor specific and environmental factors.
Both will be described below.
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How do inferior standards prevail in two-sided markets?
1.3.1 Imperfect information and bounded rationality
Simon’s influential paper (1955) on a “behavioral model of rational choice” was an early
attempt to substitute the rational economic actor with its boundedly rational
counterpart. Even neoclassical economists acknowledge that a fully rational actor is not
plausible – the assumption, however, is still an integral part of most economic models.
Network markets are highly sensitive to small events in the early phase of their
emergence. Elements of bounded rationality and imperfect information, especially on
the consumer side, may have unexpected long-run consequences for the evolution of an
industry. Therefore, I raise the following propositions in line with these arguments:
Proposition 1a:
Ceteris paribus, the higher the level of imperfect information,
the more likely is a path-dependent market outcome.
Proposition 1b:
Ceteris paribus, the higher the level of bounded rationality,
the more likely is a path-dependent market outcome.
1.3.2 Single vs. multi-homing
When market participants connect to several competing platforms they are “multihoming” (Rochet & Tirole 2003; Armstrong 2006). For example, most merchants accept
both Mastercard and Visa as payment method, whereas only a minority of consumers
have both Mastercard and Visa cards in their pockets. This indicates that in many cases
the ability to multi-home differs between the various sides of the platform. Returning to
the operating system example: while software providers commonly develop an
application for several software systems, consumers typically only have one operating
system installed on their computer (Gupta et al. 1999).
Participation on multiple platforms is usually costly and the cost of multi-homing
depends on the degree of compatibility between the platforms.
Proposition 2:
Ceteris paribus, the lower the level of multi-homing of platform
participants, the more likely is a path-dependent market outcome.
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How do inferior standards prevail in two-sided markets?
1.3.3 Entry-timing und tipping points
Conventional wisdom suggests that in the presence of strong network effects a firstmover platform reaps the benefits of its early installed user-base and dominates the
market (Suarez 2004). However, quite often, indirect network effects only “kick in” after
reaching a critical mass of consumers and complementary products.
Especially in the early phase of technology diffusion there may be a window of
opportunity for late entrants to challenge the market leader. When the new technology
has not yet diffused through the entire population, competing platforms can try to
attract new users, rather than get the installed base to switch over. This finding suggests
the existence of tipping points from where late entrants have poor chance to successfully
enter the market, even when providing a superior product.
Figure 1-1:
Proposition 3:
Technology diffusion and market entry timing
Ceteris paribus, the smaller the window of opportunity for market
entry, determined by the strength of network effects and
differences in platform quality, the more likely is a pathdependent market outcome.
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How do inferior standards prevail in two-sided markets?
2
Methodology
Vergne & Durand (2010) suggest “moving away from historical case studies of
supposedly path-dependent processes” and argue towards the use of more controlled
research designs. Computer-based simulation is ideally suited to trace the complex
mechanisms of unfolding lock-in processes of path dependence (Garcia 2005).
In simple terms, scientific progress generally relies on two methodologies: (1)
theoretical analysis or deduction and (2) empirical analysis or induction. Simulation
models can contribute to both inductive and deductive research methodologies and are
recognized as a “third way of doing science” (Axelrod 1997). Simulations, like
deduction, start with a set of assumptions but do not prove theorems. Instead, they
generate “virtual” empirical data which can be analyzed inductively to uncover
unknown relationships among the variables. For most economic processes there is
usually no possibility of manipulating a real world system to answer “what if” questions.
In other settings, empirical data may be unavailable or not yet available. In each case,
simulation techniques can help to explore the effect of changing system parameters over
time.
Many of the theoretical results on multi-sided platforms to date, predominantly in the
industrial organization literature, rely on quite abstract analytical models of how
industries operate and different MSPs compete (e.g. Caillaud & Jullien 2003; Armstrong
2006; Economides & Katsamakas 2006). These models are limited in nature as they have
difficulties in capturing multilevel phenomena that are often mathematically intractable
(Harrison 2007; Galán et al. 2009). However, market structures and competitive
dynamics are the result of an interaction of multiple interdependent processes with
stochastic elements. Computer simulation techniques can help to overcome the
dichotomy between real world complexity and the oversimplified abstraction level of
analytical models.
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How do inferior standards prevail in two-sided markets?
Analytical models are constrained by the need to make them “mathematically simple
enough to be used as building blocks for higher-level aggregations (e.g. market
outcomes)” (Ho et al. 2006). It is believed that this limitation has constrained
researchers to incorporate elements of bounded rationality which usually requires a
more sophisticated modeling of the underlying cognitive processes. In a simulation
model, the macro-level structure emerges all by itself without further intervention by the
researcher.
To summarize, compared with analytical models, computer simulation allows for more
realistic assumptions “rather than to compromise with analytically convenient ones, as
is common in deductive theory” (Harrison 2007), e.g. perfect rationality, homogeneity
of economic actors, etc. Compared with qualitative research methods, simulation
models benefit from the theoretical rigor enforced by formal modeling. As Harrison
(2007) states: “A process may appear to be well understood, but an attempt to specify an
equation for the operation of the process over time often exposes gaps in this
understanding.”
Two fundamental simulation methodologies for the social sciences can be distinguished.
Agent-based simulation (ABS) describes the behaviors of adaptive agents at the micro
level. Macro structures then emerge as a result of the actions of the constituent agents
and their interactions in the simulation environment. In contrast, system dynamics (SD)
models try to capture the behavior of a complex system as a whole. For several reasons,
ABS has become the method of choice for simulations in management studies (Harrison
2007). First, macro level phenomena are usually highly complex. This makes it difficult
to specify the internal feedback loops between the system’s entities which are crucial for
SD modeling. Second, most social processes do not follow a deterministic pattern, but
include some form of contingency. ABS models can easily incorporate stochastic
decision processes at the micro level. Third, agent-based modeling follows the natural
way of thinking: Entities in the real-world are represented as actors in the model.
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How do inferior standards prevail in two-sided markets?
In conclusion, ABS modeling is the method of choice to investigate emergent
phenomena, when there is communication between agents and the complexity of agents
is high (Gilbert & Troitzsch 2005). As all the mentioned criteria are satisfied for the
research question at hand, an ABS model seems best suited for the simulation of
technological path dependence, emerging as the macro level result of the actions and
interactions of individual market participants.
Evans & Schmalensee (2008) emphasize that both theoretical and empirical works
provide evidence that the dynamics of multi-sided markets are highly dependent on the
specific institutions and technologies of the particular industry. The global smartphone
industry was selected to provide empirical guidance for model building. In order to
incorporate empirical data for the consumer part of the simulation model, a conjoint
study and a computer-based survey with N=240 participants were conducted. The
information is used as a calibration instrument for all the model’s independent variables
related to consumer behavior. Semi-structured interviews with industry experts, such as
device manufacturers, network operators and software developers, were used to
calibrate other model parameters. Thus, the external validity of the simulation results
for a specific setting, in this case the smartphone industry, is enhanced.
The simulation model was developed in JAVA using AnyLogic 6.5 as a simulation
framework.
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How do inferior standards prevail in two-sided markets?
3
The empirical case: platform competition in the global
smartphone industry
Smartphones are expected to become the essential personal computing device of the
future (Barton et al. 2006). For some time now, “classic” mobile phones are gradually
being replaced by smartphones which provide additional features such as mobile
internet access and email, music and video playback, camera and navigation assistance.
Smartphones are a result of the trend of converging media, data and communication
networks, and they have become comparable with desktop computers in terms of
performance and functionality.
There still is no industry standard of what constitutes a smartphone. Gartner, a major IT
research and consulting firm, defines a smartphone as “a large-screen, voice-centric
handheld device designed to offer complete phone functions while simultaneously
functioning as a personal digital assistant (PDA)” (Gartner 2004). The first smartphone
was produced by IBM in 1994, however the first commercially successful device was
Nokia’s “Communicator” phone introduced in 1996 (Evans et al. 2006). In 2007, Apple’s
launch of the first iPhone generation attracted high media attention and pushed the
smartphone technology forward in terms of functionality and user-friendly design. This
innovation helped the industry to take-off. Smartphones are now heading for the mass
market. In 2009, worldwide smartphone sales totaled 172.4 million units, a plus of 24%
compared to 2008 (Gartner 2010b). Most industry experts forecast continuing doubledigit growth rates for the coming years (Gartner 2010b). Moreover, it is expected that
smartphones will overtake PCs as the most common internet access device by 2013
(Gartner 2010a). For many consumers, especially in developing countries, a smartphone
will be the only way to access the internet. This further highlights their significance.
Until recently, mobile phone makers programmed proprietary operating systems (“OS”)
for their handsets, and most consumers were unaware of the underlying software system
of their device. Operating systems were “invisible engines” (Evans et al. 2006) which had
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How do inferior standards prevail in two-sided markets?
no impact on consumers’ purchasing decisions. This has changed recently. Similar to
the choice between Mac OS or Windows for a PC system, the smartphone’s operating
system now plays a central role for consumers. In addition to providing the phone’s user
interface, the OS establishes the basis for the ability to extend the system’s functionality
with third party software applications, commonly referred to as “apps”, by making
services available through application programming interfaces (APIs). As a result,
software applications are developed for a specific operating system and are incompatible
with other systems. The availability of third party software applications has recently
become increasingly popular with smartphone users. For instance, Apple announced
that it had reached one billion downloads just nine months after opening its application
store for the iPhone (Macworld.com 2009). As of August 2010, iPhone users can choose
from more than 250,000 apps on the Apple App Store (CNN 2010).
For device makers, a “go-it-alone” strategy with proprietary software operating systems
for their devices is more and more impractical. As smartphones become more
sophisticated in terms of hardware complexity and feature scope, the demand for more
powerful operating systems increases, and handset makers are incentivized to share
development costs. Both the need for compatibility and rising cost arguments, impacted
the process of standardization and created competition between smartphone operating
systems.
It is important to think of a smartphone operating system as the core of a related
ecosystem, or “platform”. Four elements constitute a smartphone platform:
1. An operating system developed by the platform provider (or: “sponsor”), either
alone or in cooperation with other companies.
2. A strategic alliance of industry partners1 which support and foster the
development of the platform. Usually, an alliance consists of the platform
1
The alliance component of a smartphone platform is not applicable to vendor specific
operating systems (Apple, RIM, Palm).
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How do inferior standards prevail in two-sided markets?
provider, handset manufacturers, network operators, semiconductor companies
and prominent software partners.
3. A base of loosely affiliated application developers who produce a portfolio of
compatible software for a platform.
4. An official application store (“app store”) controlled and operated by the
platform provider.2 An app store is a digital distribution platform which allows
consumers to browse and download apps directly to their mobile devices. Apps
are available either free of charge, or at a cost. The revenues are shared between
the developer of the app and the operator of the app store (usually in a 70:30
ratio).
Software platforms give rise to positive indirect network effects between applications
and consumers (Evans et al. 2006). Consumers are attracted by operating systems which
are supported by a large number of third party applications. In turn, software developers
favor operating systems with high market shares. As the third influential actor, handset
makers equip their devices with operating systems which provide the highest benefit to
their customers. Thus, a larger number of devices for a platform attract more
consumers.
2
For the purpose of this definition, “unofficial” app stores run by network operators, handset
manufacturers or independent software distributers are excluded. See wip (2010) for a detailed
market overview of 98 available app stores.
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How do inferior standards prevail in two-sided markets?
Figure 3-1:
Smartphone platform as a three-sided platform
Accordingly, a smartphone’s software platform is placed at the heart of a complex
relationship between handset manufacturers, software application providers and
consumers. The sponsor of a technology needs to nurture all sides to establish a
successful platform.
Considering the debate on path dependence in the PC software industry and their social
and economic consequences, competition in the market for smartphone operating
systems is a highly interesting and relevant case. However, to date, there is little research
on this topic in the scientific literature (Lin & Ye 2009). Moreover, it still is
unpredictable if the market is going to “tip” in favor of one operating system and which
platform will prevail (Hagiu & Yoffie 2009). The proposed simulation model can shed
some light on this topic by providing the theoretical foundations for a better
understanding of technological path dependence in two-sided markets.
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How do inferior standards prevail in two-sided markets?
4
Model structure
The design of an ABS model involves two steps. First, the discovery of the relevant
agents, and second, the discovery of the agents’ behaviors. There is no consensus in the
literature on the constitutive properties of an “agent”. Broadly speaking, the decisionmakers and their behaviors need to be identified (North & Macal 2007). Modeling was
conducted on the basis of a thorough industry analysis using secondary data (market
reports, media coverage, press releases) as well as interviews with representatives of the
agents (device manufacturers, network operators, software developers, consumers) and
other industry experts. Data sources include a press review covering the time-frame
from the emergence of the platforms until October 2010, as well market studies by IT
consultancies. Furthermore, my colleague and I attended two industry conferences.
Most of the model assumptions have been validated with quantitative data or semistructured follow-up interviews.
Apart from the empirical guidance through the analysis of the smartphone industry, the
simulation model draws on existing models by Gupta et al. (1999), Buxmann (2001),
Said et al. (2002), Weitzel (2004), Armstrong (2006) and Roedenbeck & Nothnagel
(2008).
The model description follows the ODD (Overview, Design concepts, Details) protocol
for describing individual- and agent-based models (Grimm et al. 2006, 2010). It is an
attempt to provide a common format in order to enhance transferability of knowledge
between models. The design concepts and details section of the ODD protocol have
been omitted due to brevity in this paper.
4.1
Purpose
The aim of the agent-based model is to identify conditions for two-sided markets to
become locked-in to an inferior platform or technology. The model analyzes the
competitive dynamics between different multi-sided platforms, potentially leading to
path-dependent market evolution, due to indirect network effects. Focus of the study is
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How do inferior standards prevail in two-sided markets?
on the role of bounded rationality, multi-homing strategies and entry timing of
competing platforms.
4.2
Entities, state variables and scales
The model incorporates three main entities: (1) competing platforms, (2) consumers
and (3) software developers. Consumers turn from potential adopters into smartphone
users and thereby choose a platform. They act boundedly rational and prefer platforms
of good quality and with a high number of apps. Consumers are either opinion leaders
or opinion seekers, with the latter being influenced in their choice by recommendations
from their interpersonal network. Software developers provide apps for either one or
multiple platform(s), depending on the market shares and the level of development
synergies between the platforms. Software developers are forward-looking and aim to
maximize reach as proxy for sales potential.
Compared to system dynamics (SD) models which try to capture the behavior of a
complex system as a whole, agent-based simulation is a bottom-up approach. It focuses
on the micro (i.e. agent) level for model design and is ideally suited for the research
question of this study. Nevertheless, a system dynamics style notation can be helpful to
visualize the agent-based model as a whole. Subsequent diagram, in system dynamics
style notation, shows a simplified structure of the agent-based model. It provides a
bird’s-eye view on the model, and highlights the system’s positive feedback loops due to
the interaction of the agents. These positive feedback loops are the crucial mechanism
for the existence of path dependence.
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How do inferior standards prevail in two-sided markets?
Figure 4-1:
Simplified model overview in system dynamics style notation
As discussed in section 3, a smartphone platform is at the heart of a complex
relationship between device manufacturers, software application providers and
consumers. For modeling purposes, I decided to disregard the role of device
manufacturers in order to focus on the primary indirect network effect. First, a device
manufacturer’s decision which platform alliance(s) to join is heavily influenced by
strategic considerations. These are difficult to incorporate into a formal model and lay
outside of the scope of my research question. Second, the inclusion of device
manufacturers into the model would mean to further differentiate between vendorspecific (Apple, RIM, Palm) and open platforms (Google, Symbian). Third, for the
purpose of my research question, it is believed that the benefit of a more accurate
representation of the industry does not justify the higher level of model complexity. The
abstraction to a two-sided market increases generalizability of the model beyond the
smartphone industry. As such, the model could be applied with minor adjustments to
the historical battle between VHS & Betamax or Bluray and HD-DVD.
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How do inferior standards prevail in two-sided markets?
Figure 4-2:
Smartphone platform as a two-sided platform
Most formal models on two-sided markets from the industrial organization literature
focus on pricing decisions, i.e. on the optimal pricing structure for the various sides
from the viewpoint of the platform provider. Following empirical indications that
differences in pricing strategies are rather insignificant for the current platform
competition under review, this model abstracts from any pricing issues. The open
platforms (Google, Symbian) provide their operating systems free of charge to both
device manufacturers and software developers, and licensing costs for proprietary
operating systems (e.g. Windows Mobile, former Symbian versions) can be considered
insignificant in relation to the total bill of materials (BoM) of a modern smartphone.
Furthermore, the pricing strategies of the platforms’ official app stores are quite similar
in terms of revenue share agreements, joining fees and submission fees. Assuming that
pricing is either insignificant or that all platforms charge the same price to the platform
participants, the model leaves out any cost considerations. Both consumers and software
developers aim to choose the platform that benefits them most, independently of cost
arguments. After the high-level description of the model, I will now go into details of the
modeled entities.
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4.2.1 Platforms
Each platform provider develops a smartphone operating system before entering the
market. The necessary resources for the development are considered as sunk cost,
outside the scope of this model. Furthermore, costs associated with the continued
presence in the market (e.g. costs for the further development and support of its
operating system, administration or marketing) are not considered.
The number of competing platforms is fixed and further market entrants are not taken
into account. Platform providers can exit the market at no cost in which case consumers
and software developers can still use the existing platform. However, it will not be
considered by new technology adopters and no additional software developers will
support the platform.
The competing operating systems vary in quality, determined by their functionality,
performance and usability. These properties are also elaborated on in the following
section on consumer utility. The platform quality can be numerically expressed by an
objective quality index (Tellis et al. 2009).
4.2.2 Consumers
Smartphones are an innovative product. Consumers first need to adopt the new
technology in general before choosing a certain smartphone device with a particular
software platform. In order to describe how agents turn from potential adopters to
adopters, the simulation model draws on the Bass diffusion model. Furthermore, the
model relies on insights from consumer behavior theory on how consumers search and
process information, and subsequently make decisions. The following figure illustrates
the two-step process of technology adoption and platform choice.
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How do inferior standards prevail in two-sided markets?
Figure 4-3:
Two-step process: technology adoption and platform choice
Potential adopters have not yet adopted the innovation: they simply are not interested in
buying a smartphone. Adopters have already been convinced of the benefits of the
innovation, either through advertising or word-of-mouth: they want to buy a
smartphone. They now search for information and decide on a platform – thereby
becoming a platform user. More detailed descriptions, including reference to the
theoretical foundations of the consumer agent model design, are laid out in the
following paragraphs.
4.2.2.1 Diffusion / adoption of innovation
Innovation is defined as the “process of bringing new products and services to market”
(Hauser et al. 2006). The success of an innovation depends on its acceptance by
consumers. Cumulative sales of an innovative durable product, such as a smartphone,
generally follow an S-shaped curve, a concept introduced in the 1960s and backed with
empirically evidence first provided by Bass (1969). The Bass diffusion model remains
one of the most common methods to predict and explain the adaptation of an
innovation at an aggregate level. The model assumes a population consisting of
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How do inferior standards prevail in two-sided markets?
innovators and imitators. When a new product or technology becomes available, the
innovators purchase it because of external influences such as advertising. These early
adopters influence others by word-of-mouth to adopt the technology as well. The
combined effect is an S-shaped diffusion pattern with a slow diffusion start, followed by
rapid growth and a final saturation phase.
Figure 4-4:
Bass model: probability of technology adoption over time
(Source: Lilien & Rangaswamy 2004)
Here, the Bass diffusion process is modeled explicitly at the individual (agent) level,
replicating the system dynamics model by Sterman (2004) with an agent-based
approach. Potential adopters come into contact with adopters through social
interactions, mapped by an interpersonal network. Consequently, the network topology
is crucial for diffusion (Rahmandad & Sterman 2008). For a long time, there was little
empirical work on the structure of real world influence networks due to lack of data
(Watts & Dodds 2007). However, a recent paper by Goldenberg et al. (2009) provides
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How do inferior standards prevail in two-sided markets?
compelling empirical evidence that the scale-free structure (Barabási & Albert 1999) is
the most realistic assumption. The distribution of people’s number of links (“degrees”)
follows a power law: some nodes are hyper-connected, whereas the majority has only
few connections (Goldenberg et al. 2009). For the model, a scale-free network topology
was chosen instead of a random ER model (Erdos & Renyi 1960) or a small-world
network (Watts & Strogatz 1998).
In the model, the external advertising effect causes a constant fraction of the potential
adopter population to adopt each time period. Social interaction takes place on the
network and a fraction of these contacts results in the adoption of the new technology.
4.2.2.2 Information search: two-step flow of communication
Consumer behavior research divides the decision-making process into three stages
(Hoyer & MacInnis 2004). The first step involves problem recognition when the
consumer identifies a perceived difference between an ideal and an actual state. This
transition is incorporated in the simulation model by applying the Bass diffusion model
sketched out in the previous section.
Once the consumer is convinced by a new technology (e.g. “I need a smartphone”), the
second step is information search, either internally from memory or externally from
outside sources, to form a consideration set of platform alternatives. As smartphones are
a novel technology, it is assumed here that first-time buyers have no preferred brands,
attributes, evaluations or experiences in memory that they can recall on. Thus, they
completely focus on external search such as advice by trusted friends or relatives,
recommendations by dealers, information from magazines, the internet or
advertisements. For modeling purposes, the various sources for external information are
reduced to two channels: (a) interpersonal sources and (b) other sources.
Research on innovation diffusion has long recognized the importance of interpersonal
communication. Lazarsfeld et al.’s (1944) two-step flow of communication model has
been widely tested in diffusion of innovation studies (Rogers 2003) and, despite its age,
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How do inferior standards prevail in two-sided markets?
is still one of the most widely used communication flow theories in diffusion research
(Watts & Dodds 2007). Opinion leadership and opinion seeking occur when consumer
either verbally influence or seek verbal influence from others. Opinion leaders spread
information and give directions for search, purchase and use. Opinion seekers seek
advice from others who have greater knowledge and experience and imitate their
purchase behavior.
In the model, agents are either opinion leaders or opinion seekers. Opinion leaders form
their platform choice by an evaluation of the available platforms in the marketplace.
However, there is imperfect information and agents may be unable to evaluate all
platforms. Thus, their consideration set may contain only a subset of the platforms
available. When asked for help, the opinion leaders will recommend the platform they
prefer or have already chosen. There is no negative word-of-mouth incorporated in the
model. Opinion seekers fully rely on interpersonal sources for information search. They
utilize the competence of linked agents and trust recommendations of opinion leaders
in their network of interpersonal relations.
4.2.2.3 Decision-making
After problem recognition and information search, consumers engage in decisionmaking. For this purpose, agents evaluate the platforms in their consideration set. As
smartphones are considered as high involvement goods, the decision is reached in a
rational, systematic manner. There are different types of cognitive choice models for
high-effort thought based decisions (Hoyer & MacInnis 2004). In this case, a
compensatory brand-processing model, also called multi-attribute model, is used.
Accordingly, a negative evaluation of one attribute can be compensated for by positive
evaluations of others. Consumers value platforms on two key dimensions: (1) the
standalone utility of the platform, and (2) the network-derived utility from the
availability of compatible apps.
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How do inferior standards prevail in two-sided markets?
The stand-alone utility (“basic utility”) of a platform is affected by the usability and
functionality of the operating system, represented by its quality index, as well as other
factors such as brand perception, design and form-factor of the handset, quality of the
hardware (processor speed, memory capacity, picture quality of the build-in camera
etc.). However, consumers cannot perfectly assess the objective quality of the platform
and their evaluation is biased by a stochastic quality perception variance. For modeling
purposes, all factors apart from the quality of the operating system are treated as
random influences, justified by the fact that design, form-factor and hardware quality
are completely OS-independent and that technological advances usually quickly spread
to all new devices independent of their software platform. Differences in brand
perception, which certainly had an impact for the success of Apple’s iPhone, are
currently not incorporated in the model.
The second dimension, the variety and quality of compatible software, gives rise to
indirect network effects. For the sake of simplicity, quality differences are not treated in
the model as these differences should balance in the software portfolio of a platform.
Hence, ceteris paribus, platforms with a larger number of available applications are
preferred. The utility due to the availability of complementary products is expressed by a
concave bounded utility function (Lilien et al. 1992). The marginal utility of an increase
in the software portfolio is diminishing, e.g. the rise from 0 to 1000 available apps is
more valued than the rise from 10,000 to 11,000. The compensatory brand-processing
model combines the “basic utility” and the “network effect utility”.
In conclusion, consumers maximize utility by choosing the platform with the highest
aggregate benefit, however, under the constraints of imperfect information and
bounded rationality.
4.2.3 Software developers
The model assumes a fixed number of software developers who develop applications for
different incompatible platforms. Application developers can program platform-specific
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How do inferior standards prevail in two-sided markets?
applications without any licensing fees, costs for software development kits (SDK) etc.
Developer agents gradually enter the emerging market because software development
for smartphones gets more attractive the more users have adopted the new technology.
On average, developers enter the market earlier than consumer agents as they can better
assess the importance of the new technology. Thus, developers “lead the market” and
consumers follow. To account for this, the model assumes an adoption rate of developer
agents that is twice as high as the adoption rate of consumers: when 50% of the
consumer agents have adopted the technology, already a 100% of the developer agents
have entered the market.
Software developers devote their development resources, measured in model time steps,
to supply complementary products for one or multiple platforms: they develop
platform-specific apps. Developer agents aim to maximize reach for their apps.
Reach = potential userbase * (total number of apps in a given time period)
Reach is a measure that is affected by the number of apps developed in a given time
period and the total number of consumers who are able to buy and use these apps. Thus,
reach serves as a proxy for sales potential. Under the assumption that sales
opportunities, app pricing and development cost (all outside the scope of this model)
are independent of the platform, maximizing reach is the profit maximizing strategy. In
that sense, the model incorporates an adapted version of the standard profit
maximization assumption as objective for developer agents.
4.2.3.1 Single- vs. multi-homing strategies: the effect of synergies
Software developers can allocate their development resources (DR) either to one
platform or multiple platforms. For example, they can spend 100% of their resources to
specialize on one platform (“single-homing”), or they can split their resources to
support multiple platforms (“multi-homing”). On the one hand, supporting multiple
platforms is beneficial for firms because it increases the potential customer base for an
app, resulting in higher reach. On the other hand, multi-homing is associated with
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How do inferior standards prevail in two-sided markets?
higher development efforts for the additional software platforms. Obviously, developer
agents must engage in a trade-off between lower market coverage when single-homing
and additional development efforts when multi-homing.
However, synergies exist when the same application is developed for multiple platforms.
The development of an identical application for two platforms takes less than twice as
much resources as the development for only one platform. For example, certain parts of
the programming code and graphics can be reused; the business model stays the same;
etc. This synergy level DS [0..1] is measured as the percentage of development resources
which produce output that can be simultaneously used for multi-platform development.
For example, a synergy level of DS=0.5 implies that 50% of the development resources
are beneficial for the focal platform and at the same time for all other supported
platforms. In practice, the synergy level depends on the degree of compatibility between
platforms. A high synergy level (DS=1) makes it rather effortless to support multiple
platforms, whereas a low synergy level (e.g. DS=0) renders it inefficient.
The development cycle DC is defined as the number of time steps needed to finish the
development of an app. In the case of single-homing, DC equals DR. In the case of
multi-homing, DC depends on the number of supported platforms and the synergy level
DS.
The following graphic depicts the synergy effects under different platform-support
strategies and the impact on the length of the development cycle and the number of
apps per time period.
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How do inferior standards prevail in two-sided markets?
Figure 4-5:
Implications of different platform-support strategies
(DR=10, DS=0.5)
In the case of single-homing, a developer fully devotes her resources to the app
development for a single platform A. She completes the development cycle after 10 time
steps. This results in a total output of 100 apps for platform A after 1000 time steps.
In the case of double-homing, the agent decides to support two platforms, A and B. The
developer splits her resources 50:50 between the two platforms. However, in each case
50% (DS=0.5) of the development resources are beneficial for the other platform,
creating “virtual resources” from synergies between the two platforms. The developer
finishes the development cycle after 13.3 time steps which results in a total output of 75
apps for platforms A and B after 1000 time steps.
For the remaining strategies the logic is the same. In the case of 5x homing, all five
platforms are supported. The development cycle takes 16.7 time steps. After 1000 time
- 27 -
How do inferior standards prevail in two-sided markets?
steps, the developer was able to develop a total of 59 apps which run on all five
platforms.
4.2.3.2 Maximizing reach
When deciding on their platform-strategy, developer agents want to know if it pays off
to provide content to a certain platform. Their decision on how many platforms to
support is influenced by the expected market shares of the respective platforms at the
end of the development cycle. The table below shows the effect on reach of different
platform-support strategies under various market share scenarios for DR=10 and
DS=0.5:
Table 4-1:
Platform-support strategies under different market share scenarios
(DR=10, DS=0.5)
Market share scenarios
Reach
Platform
Singlehoming
2x
homing
3x
homing
4x
homing
5x
homing
A
B
C
D
E
(1)
80%
5%
5%
5%
5%
4.17
3.32
3.13
3.10
3.13
(2)
60%
25%
5%
5%
5%
3.13
3.32
3.13
3.10
3.13
(3)
50%
25%
20%
2.5% 2.5%
2.61
2.93
3.30
3.18
3.13
(4)
40%
30%
20%
7.5% 2.5%
2.09
2.74
3.13
3.18
3.13
(5)
20%
20%
20%
20%
1.04
1.56
2.09
2.61
3.13
20%
Note: Reach maximizing strategies are given in bold.
Scenario (1) shows that in a very concentrated market with one dominant platform,
single-homing is the best strategy for developer agents. Additional development efforts
for a second platform are substantial even with synergies of DR=0.5, but this would
increase market coverage only slightly by 5%. Under this scenario, it is efficient to
concentrate on the largest platform and increase the number of apps per time period
instead of choosing a multi-homing strategy.
Scenarios (2)–(4) describe a situation with some major and some minor platforms.
Here, developers have to carefully evaluate the trade-off between higher market
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How do inferior standards prevail in two-sided markets?
coverage and longer development cycles. The best strategy varies from 2x-homing to 4xhoming depending on the particular market share scenario.
Scenario (5) is composed of five platforms with equal market shares. In this case,
developers can fully leverage development synergies by supporting all five platforms –
which is the dominant strategy in this setting.
Note that with no synergies between the platforms [DS=0], single-homing for the largest
platform is always the dominant strategy, independent of the market shares of the other
platforms.
4.2.3.3 Decision basis
As argued above, the platform decision depends on market shares of the respective
platforms and the synergy level for multi-platform development. However, the term
market share is ambiguous: what exactly is a platform’s share in the market and how can
it be determined? Market share commonly refers to a platform’s percentage of total sales
over a specific period of time. For smartphones, several data sources with access to
channel sales numbers provide quarterly and annual sales statistics in various
geographic splits. Thus, market share data is publicly available as decision basis for app
developers. However, a more accurate way for developers to assess a platform’s market
share is to look at its installed base, measuring a platform’s network size. The installed
base is the cumulated number of devices that were sold and are still in use. For
developers, it does not really matter how many smartphones with a specific platform
have been sold in the recent year, but how many smartphone users are potential buyers
for their apps. Installed base is a metric not commonly reported and it rests on rather
infirm grounds: what is the lifespan of a device once it was sold?
In the present model, developer agents form their opinion either on a platform’s market
share in the last couple of time periods or alternatively on installed base metrics. Actors
tend to act speculatively in a network externalities context (Sillanpää & Laamanen
2009). In the face of competing MSPs, platform participants form expectations about the
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How do inferior standards prevail in two-sided markets?
future industry structure which influence their platform-strategy. Forward looking
software developers have full access to market share data and use this data to form
expectations on future market developments which they incorporate in their platform
choice.
4.2.3.4 Decision horizon
Developers are able to switch to a different platform after entering with the “wrong”
one. They are not necessarily doomed from the start if market evolution shows that they
bet on the wrong horse. Thus, depending on recent changes in platforms’ market shares,
developer agents revise their platform strategy if necessary.
However, the strategic realignment is not an instant process. The platform choice is one
of the most important decisions for any software developer: In which market do we
want to compete in? It is therefore part of the firm’s corporate strategy (Schreyögg &
Koch 2010). Although strategic monitoring is an ongoing process, a developer’s
platform choice is not re-evaluated on a permanent basis. In practice, frictions and
delays prevent ongoing and instant switch of platforms. Platform-specific resources,
such as human knowledge or physical equipment, hinder permanent platform hopping.
Hence, developer agents can only revise their platform strategy in regular intervals.
In the model, the interval between possible strategic realignments is termed “decision
horizon”. Developer agents form a platform choice based on their current market
assessment. They are bound to this decision for a given decision horizon period and
develop apps for the chosen platform(s) during this time. Afterwards, they may revise
their platform strategy if necessary before they start the next development cycle.
4.3
Process overview and scheduling
The model comprises a time horizon of 1000 discrete model time units (“steps”)
t=0..1000. This parameter will be verified by sensitivity analyses. One step is equivalent
to a week in real-time resulting in coverage of approximately 20 years. This relation was
chosen on the basis of two considerations. First, it was aimed to cover a time horizon
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How do inferior standards prevail in two-sided markets?
long enough to allow the technology diffusion process to complete. Second, the relation
was constrained by computational requirements because a finer relation, e.g. one step
equaling a day in real-time, would increase the required computational runtime of the
simulation model by a multiple.
- 31 -
How do inferior standards prevail in two-sided markets?
5
Conclusion and further research challenge
Many prominent examples for technological path dependence fall into the realm of twosided markets, for instance, the competition between VHS and Betamax as video
standard, or the case of Microsoft Windows’ dominance in the PC market. In both cases,
indirect network effects were the major positive feedback mechanism behind the
emergence of path dependence. However, apart from empirical case studies, there is
little research on how inferior standards prevail in two-sided markets.
This paper proposes a generic agent-based simulation model to investigate the role of
indirect network effects for the evolution of technological path dependence. It aims to
contribute to the theoretical literature on path dependence by complementing existing
empirical case studies with a formal simulation model. One of the huge advantages of
simulation research is its ability to analyze complex and long-lasting processes in any
level of detail and at an accelerated pace. The embodied controlled research design
facilitates analysis of the long-lasting impact of small events in the early phase of
industry evolution reinforced by indirect network effects.
The model highlights the interplay between the diffusion/adoption of innovation and
firm strategies in an emerging industry. It aims to identify favorable conditions to the
emergence of path-dependent winner-take-all market structures and exemplifies the
competitive dynamics on the grounds of empirical data for the global smartphone
industry.
Preliminary simulation runs are currently undertaken. Model results and analyses will
be available in winter 2010.
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How do inferior standards prevail in two-sided markets?
6
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