PDPRIETI Policy Discussion Paper Series 21-P-001

Progress of Digital Platforms and their Impact on Japan's
Industrial Competitiveness

MOTOHASHI, Kazuyuki
RIETI

The Research Institute of Economy, Trade and Industry

https://www.rieti.go.jp/en/

RIETI Policy Discussion Paper Series 21-P-001
January 2021

Progress of Digital Platforms and their Impact on Japan's Industrial Competitiveness1
Kazuyuki Motohashi (RIETI)
Abstract
Digitalization has a transformative impact on innovation in firms and markets, and new
business models based on digital platforms are disrupting traditional industries. However,understanding the impact of digital platforms on the supply side of manufacturing industries,
where Japan's industrial competitiveness is based, is insufficient. This paper conducts anddiscusses a review of existing studies on digital platforms and the relationship between
digitalization and Japan's industrial competitiveness. A platform business can be categorized
into three groups, type 1 (internet platformer type), type 2 (producer ecosystem type) andtype 3 (IoT data-use type), depending on the existence of direct and/or indirect network
effects on the producer and consumer sides of the platform. We have compared these three
types of platforms together with "pipeline businesses" (with a traditional supply chain model)
regarding the impact of digitalization on each business model. Our analysis found that
digitalization does not directly affect the existing pipeline model, as is shown in theautomotive industry, for example. However, the convergence of virtual and physicalenvironments (CPS: Cyber-Physical System) redefines the boundaries of existing markets,
which introduces a chance of existing pipeline models being displaced by new integrated
services, based on platform models.
Keywords: Digital platform; Monozukuri; Network Effect; Industrial Competitiveness; JapanJEL classification: L14, O36
The RIETI Policy Discussion Paper Series was created as part of RIETI research and aims tocontribute to policy discussions in a timely fashion. The views expressed in these papers aresolely those of the author(s), and neither represent those of the organization(s) to which theauthor(s) belong(s) nor the Research Institute of Economy, Trade and Industry.

1

This study is conducted as a part of the Project ”Digitalization and Innovation Ecosystem:

A Holistic approach” undertaken at the Research Institute of Economy, Trade and Industry(RIETI)

１． Introduction
The information gathered by IoT devices and sensors are further enlarging the volume of data ona daily basis. According to a July 2019 IDC survey, there are more than 20 billion IoT devices now in
operation. It is predicted that by 2025 this number will have risen to more than 40 billion devices witha data production volume of 80 trillion gigabytes. Data will be produced by IoT devices that number
more than 10 times the global population and are forecasted to greatly expand the possible uses of big
data. This includes using artificial intelligence (AI) technology that increasingly serves as a “brain” infields such as internet advertising, e-commerce, manufacturing, production, autonomous vehicles,
home electronics, financial transactions, and personnel systems. Furthermore, it is widening the scopeof information technology as a general-purpose technology. Thus innovation (IoT application) is
rapidly expanding thanks to extensive data collection from people and things, and advancements intechnology (AI) that uses this data (Motohashi, 2020).
As the possibilities for innovation expand from the foundation of big data, internet platformers arerising to the fore and growing their businesses through high-volume internet information and customer
data. The “GAFA” American companies (Google, Apple, Facebook, Amazon) have a market
capitalization rising to more than one trillion US$, placing them in the top 10 global market
capitalization rankings. This is remarkable not only in size, but also in the speed of growth. The market
capitalization of Google and Apple, which hold the top positions, have more than quintupled in thelast decade. In addition, China is seeing rapid growth in the “BAT” companies (Baidu, Alibaba,
Tencent), stemming from their massive domestic mobile network data.
GAFA and BAT are growing their internet-related businesses based on a huge amount of personal
information and historical customer purchase information drawn from search engines and social
networking services (SNS). Also, the increasing introduction of IoT sensors and applications areallowing platform business to spread to diverse industries using big data. Big data activity is risingthroughout all stages of manufacturing, from pre-production design and development, through mass
production, to post-production maintenance (Motohashi, 2016). For example, Komatsu has beencollecting data on the operation of its construction machinery on a global scale and then using it forsemi-automated driving functions (among other value-added services). The automobile industry has
demonstrated a new wave of interest in using digital technology in the CASE (Connect, Autonomous,
Share, Electric) trend. In particular, internet platforms like Google and Baidu have gotten involved in
the self-driving (autonomous) field, signaling a change in the traditional structure of automotive
industry.
So, how is Japan’s industrial competitiveness fairing amid these data-driven innovations in thebusiness environment? Just in terms of market capitalization, the Toyota Motor Corporation, which isJapan’s top performer, has less than half the market capitalization of Google or Apple. However,1

GAFA and BAT revenues mostly depend upon very specific areas of business, namely internet
advertisement and e-commerce. Platform strategies based upon personal data, such as SNS websites
and purchasing history, have proven to be highly scalable (i.e., increasing in value as data volume
increases). On one hand, the IoT applications spreading to various industries are largely dependent
upon the characteristics of individual industries, making horizontal cross-industrial development
difficult. For example, Komatsu’s high value-added services are specifically for the constructionindustry. On the other hand, the U.S. General Electric (GE) company has applied its Predix concept tothe horizontal development of its superior aircraft and implemented power facility data analysis
technology in other manufacturing fields. However, GE’s data business has remained unprofitable. Its
CEO, H. Lawrence Culp, assumed his position on October 1, 2018 and has set forth a policy ofnarrowing its focus to fields in which GE is performing well (aircraft and power generation). 1 In thebusiness-to-business (B2B) world, platform strategies like GAFA’s have proven difficult.

In this paper, we analyze the advancing digitization of the economy, rise in platform business,
and Japan’s industrial competitiveness. First, in Section 2, we discuss the categories of platformbusiness and their characteristics. Platforms are defined as shared management resources andgovernance rules tying together producers and customers (van Alstyne et al., 2016). Although theirpositive feedback is characterized by respective network effects on the producer and consumer, weshall categorize platform models according to economic externality and organize their individual
characteristics. In Section 3, we use an economic model of platforms with the two-sided market of
producers and consumers to describe the features of platformer monopoly prices and their advantages
over conventional supply chain models. In Section 4, we show that, depending on the characteristicsof the goods and services handled by the platform (product architecture) and inter-network competition,
platforms are not superior in all cases. In Section 5, we shall use these theoretical studies as a basis for
examining the validity of the logic behind economic digitization leading to the rise of the platform
model and the decrease in manufacturing competitiveness. In conclusion, we will summarize anddescribe potential future research agenda.
２． Platform functions and their characteristics
The “platform” indicates a shared functional space in which multiple producers provide goods orservices to multiple consumers (Gawer and Cusumano, 2013; van Alstyne et al., 2016). For example,
iOS and Android smartphones serve as platforms mediating between general users (consumers), andthe providers (producers) of various applications for e-commerce, financial management, SNS, games,
and other functions. Similar platforms brought together producers and consumers prior to the adventof the digital economy. For example, newspapers and magazines are platforms connecting advertiserswith consumers. However, the rise of the internet has allowed the construction of digital platforms that1

https://xtech.nikkei.com/atcl/nxt/column/18/00065/00128/
2

have dramatically improved business scalability.
As shown in Fig. 1, producers and consumers both appear in a platform-based business model(van Alstyne et al., 2016). Explained with the example of iOS and Android smartphones, the firstplatform owners were Apple (iOS) and Google (Android). However, the customer value of asmartphone is greatly enhanced by producers who develop applications that run on either operating
systems (OSs). Some applications, such as Amazon, Facebook, and Uber, create their own separate
platforms. However, smartphones are immensely effective as an aggregate platform. Smartphones
also have a large swath of consumers (users). In the case of Apple and Google, this large size comesfrom smartphone use. They are able to obtain a wide variety of data (for example, location
information from mapping software, and audio information from speech recognition software), andthen apply it to their services.

Figure 1. Platform conceptual diagram
The key characteristic of platforms is that they share functions between producers or customers.
The aforementioned smartphone OS provide a shared space for producers (application developers) toopen an API (Application Programming Interface) and provide various applications. Additionally,smartphone users not only use these services, but provide information to OS owners (Apple and
Google) or producers (application owners). This includes deliberately provided information (i.e.,
posts to SNS websites) and unconsciously provided data (i.e., location information from smartphone
GPS functions and information portal search keywords); information sharing can be limited byprivacy functions. This information (data) plays a critical role in the development of services by OS
owners. This is typical of smartphone OS platforms that provide two- sided producer and consumer
side functions.
3Platform types include those that are exclusively for producers and those exclusively for
consumers. An example of a producer-only platform is the partnership program in which SAP brings
together software companies that develop applications on its Enterprise Resource Planning (ERP)system (Ceccagnoli et al., 2002). ERP is a core software that can integrate various businessadministration functions, such as production, procurement, financial affairs, accounting, and humanresources. However, multiple options are available depending on company conditions and size. SAP
does not develop each application on its own, but provides ones developed by third parties (on an
integrated company system). SAP provides a System Development Kit (SDK) to third parties inorder to create this producer-side ecosystem. Similar programs are also available from Microsoft(Azure Technology Partner) and IBM (IBM Cloud Business Partners). Here, the foundation is toprovide common resources to SDK and cloud environment partners; the aim is to increase thecustomer value of entire systems by incorporating various applications of partner companies into
platforms. However, consumers (customers) of producer-side platforms developed by SAP andMicrosoft are business customers using ERP and cloud services. No direct network effects parallel
smartphone platforms, which generate new users (consumers).
On the other hand, KOMTRAX by the construction machinery manufacturer Komatsu may beconsidered a consumer-side platform. KOMTRAX has installed a semi-automatic driving function
for energy-saving in construction machinery. Specifically, it has a P (power) mode and an E
(economy) mode when using construction machinery. It displays suggestions on which mode is best
to use depending on user circumstances (Kinukawa, et al., 2015). As the number of users increases
and the amount of data related to usage increases, it becomes possible to provide a variety of highlyprecise value-added services. However, this system-building is entirely within the control ofKomatsu, and no platform functions are found on the producer side. Consequently, it may beconsidered a consumer-only platform model. Nissan has a similar operation that collects driving datafrom its Leaf electric vehicles and relays it to insurance services according to the driving distance.
The value of a platform comes from multiple producers (application developers and smartphone
manufactures) and having a large customer base; the drawing power of a specific platform forproducers and customers represents its network effects (externality). Utilizing platform functions
gives rise to two-sided (producer side and customer side) networks. This results in two types ofindirect effects between the sides (indirect network effects), which is different from the direct effects
within the same side (direct network effects) (Gawer and Cusumano, 2013). Typical examples of
direct effects are the classic network effects seen on SNS websites, like Facebook, and incommunication services, which increase convenience for individual users as the user base grows.
This represents a positive feedback loop that results in attracting more users 2. Network effects
An economist joke goes, who is the most capable salesman in the world? The answer
is a person who first sold a telephone.

2

4

between similar customers can also be observed in Komatsu’s KOMTRAX. The more users there
are, the more data can be collected, which makes it more precise prediction and more value added
services prediction possible.
Separate from these direct effects, there are also indirect network effects from the interaction of
the producer side and customer side mediated by platforms. It is possible to use diverse applications
on smartphone platforms, but from the producer (application developer) side, it is more appealing if
platforms themselves carry many applications. In cases such as this, a feedback loop is at work.
Many application developers gather together, thus raising the efficacy of smartphone platforms andattracting more customers. An example of a producer-side platform is the SAP partnership program,which is a model that mainly utilizes indirect network effects, since the motivation of SAP partners
to join this program is the size of potential consumers. However, direct network effect (on producerside) may not be observed in this case. When the number of producers increases, there is a greater
possibility of increased competition between companies on platforms. 3 Rather, the purpose of
producer platformization is to widen the diversity of applications in a company’s ERP package andraise its benefits to customers. The result is that if the number of customers increases, then theplatform becomes more appealing to producers (partners). On the other hand, in the case of
Komatsu, there is a controlled situation (similar to the relationship between automobile and parts
manufacturers) in system creation (producer side), and thus such indirect effects are not seen.
Foregoing discussion leads us to the Fig. 2, describing the typology of platforms, depending on
the existence of direct and/or indirect network effects. First is their use of both direct and indirect
effects, as seen with smartphone OS (type 1). When such platforms are formed, in addition to thepositive feedback of consumers attracting other consumers, there are also interactions between
producers and consumers, which may lead to rapid network growth. The rapid growth in corporate
value of the GAFA and BAT internet platformers is due to the successful launching of this positive
cycle.

3Therefore, it is important that producer-side platform owners (keystones of theecosystem) maintain the complementary diversity of producers (niche players of theecosystem) for platform model success (ecosystem growth) (Iansiti and Levien, 2004).

5

Figure 2. Platform typology
Second, typical producer-side platforms mainly use indirect network effects, but not so much forthe direct network effects on producer side (type 2). Finally, there are those classified as type 3,
which are seen in customer data-using business models (those mainly using consumer-side direct
network effects). Furthermore, in regular supply chains (for example, the relationship between
automobile and parts manufacturers), parts development and specification by producers (parts
manufacturers) are completely controlled by integrated companies (automobile manufacturers). Thisis different from the way producers are allowed to freely act on shared interfaces on SAP and otherproducer-developed platforms. Consequently, there are no producer-to-consumer effects in which
product functions improved by an increase in producers also increases consumer utility. Indirect
network effects show positive feedback involving both directional positive impact between producer
and consumer. However, in case of automotive industry, there is little observed the positive effectfrom producer to consumer side, while a large consumer base attracts producer’s involvement insuch platform. Off course, current trend of digital transformation may lead to the platform model
weeding out certain pipelines (van Alstyne et al., 2016). As yet, there are no examples of theplatform model being applied to automobile parts procurement. Rather, platform business successstories have been limited to software and certain digital content, such as publications and music.
Thus, discussions may lack generality. We come back to this issue in section 4.

6

３． Economic analysis of network external effects
In the previous section, we classified three platform types, depending upon whether they were(mainly consumer side) direct networks or two-sided producer-consumer indirect networks. In direct
networks, the utility of a service increases the more users there are. Thus, a “Winner Take All” or“Tipping” phenomenon arises in competitions between services of the same quality. In pursuit ofeconomies of scale, platformers strategize to increase the number of users. Or, if this is difficult(when there are already dominant players), they attempt to diversify services and target nichemarkets (specific users).
On the other hand, if indirect network effects are produced as in type 1 and type 2, they will makecorporate competition strategies more difficult. The implications of such cases for corporate pricing
strategies and competitiveness policies have been subject to various two-sided market economic
analyses (Rysman, 2009). Here, we organize the characteristics of price action in a two-sided market
based upon Parker and Van Alstyne (2005).
A two-sided (or multi-sided) market refers to those that provide products and services to markets
that are in a dual (or more) complementary relationship (e.g., between platform producers andconsumers). For example, credit card companies connect two sides: merchants (producer side) andcard users (consumer side). Demand functions on the consumer side (c) and producer side (j) may berespectively formulated (as shown below), since there are indirect network effects in which increasing
the number of card users attracts merchant demand and further enhances utility for card users.
𝑞𝑞𝑐𝑐 = 𝐷𝐷𝑐𝑐 (𝑝𝑝𝑐𝑐 ) + 𝐸𝐸𝑗𝑗𝑗𝑗 ∗ 𝐷𝐷𝑗𝑗 �𝑝𝑝𝑗𝑗 � (1)𝑞𝑞𝑗𝑗 = 𝐷𝐷𝑗𝑗 �𝑝𝑝𝑗𝑗 � + 𝐸𝐸𝑐𝑐𝑐𝑐 ∗ 𝐷𝐷𝑐𝑐 (𝑝𝑝𝑐𝑐 )Here, Dx (p) is the demand function for a single consumer side (x=c) or producer side (x=j) market.
According to indirect network effects, demand is calculated by multiplying either demand function by
Exy. 4 All credit card companies collect fees from merchants (Pj>0), but while some collectmembership fees from card users, others do not. This means that, in terms of card operation costs, thelatter heavily subsidize the customer side. Thus, a negative price could be set on either side (Pc <0here). In a two-sided market, price action may be formulated to maximize (assuming cost 0) the total
profits from both sides (𝜋𝜋 = 𝑃𝑃𝑐𝑐 ∗ 𝑞𝑞𝑐𝑐 + 𝑃𝑃𝑗𝑗 ∗ 𝑞𝑞𝑗𝑗 ). However, depending on the indirect network effects

on production from the consumer side, even if consumer-side profits (𝑃𝑃𝑐𝑐 ∗ 𝑞𝑞𝑐𝑐 ) are negative, greater

profits (𝑃𝑃𝑗𝑗 ∗ 𝑞𝑞𝑗𝑗 ) could be obtained from the producer side.

Parker and Van Alstyne (2005) have modeled the pricing strategy of companies in a monopolistic

Although indirect network effects are treated additively here, variations (i.e., adding
or multiplying E * qj (Pj) instead of multiplication type or recursive type (E * Dj (Pj)))
are conceivable functional types. Parker and Van Alstyne (2005) show that similar
price actions can be seen with these functional types.

47

position in both markets.

The indirect network effect (𝜕𝜕𝑞𝑞𝑦𝑦 ⁄𝜕𝜕𝑝𝑝𝑥𝑥 ) in Expression (1) on market y frommarket x is referred as a spillover effect, and the ratio r of the spillover effects of C->P to P->C (relative
spillover effects), as follows.

𝑟𝑟 ≡𝐸𝐸𝑐𝑐𝑐𝑐 ∗ 𝐷𝐷𝑐𝑐′ (𝑃𝑃𝑐𝑐 )(𝜕𝜕𝜕𝜕𝑗𝑗 ⁄𝜕𝜕𝑝𝑝𝑐𝑐 )=(2)(𝜕𝜕𝜕𝜕𝑐𝑐 ⁄𝜕𝜕𝜕𝜕𝑗𝑗 )𝐸𝐸𝑗𝑗𝑗𝑗 ∗ 𝐷𝐷𝑗𝑗′ �𝑃𝑃𝑗𝑗 �

When we compare monopoly prices (p*) in two-sided markets and monopoly prices (p^) in single
markets, if r=1 (relative spillover effect is equal), both prices match (Px*=Px^). If r>1 (spillover effectfrom consumer markets onto producer markets is relatively large), the optimum two-sided market
price in consumer markets is lower than single-market prices (Pc*<Pc^), and greater than single
market monopoly prices in producer markets (Pj*>Pj^) (the reverse if r<1).

Figure 3. Pricing in a two-sided market (in case of r>1)
In each respective market, the dotted line shows a single-market demand curve (Dx (Px), indirect
network effects are 0), and the solid line shows the (inverted) demand curve for two-sided markets,
which have the addition of Exy*Dx (Px) indirect network effects. Single market monopoly prices areconferred by Px^ (＝-Dx/D’x), maximizing profits (Px＊Qx＝Px*Dx (Px), assuming costs are 0)
along the dotted line section. On the other hand, two-sided market monopolistic enterprises take priceaction maximizing as the following (which is the total of profits in both markets):
𝜋𝜋＝𝑝𝑝𝑐𝑐 ∗ �𝐷𝐷𝑐𝑐 (𝑝𝑝𝑐𝑐 ) + 𝐸𝐸𝑗𝑗𝑗𝑗 ∗ 𝐷𝐷𝑗𝑗 �𝑝𝑝𝑗𝑗 �� + 𝑝𝑝𝑗𝑗 ∗ �𝐷𝐷𝑗𝑗 �𝑝𝑝𝑗𝑗 � + 𝐸𝐸𝑐𝑐𝑐𝑐 ∗ 𝐷𝐷𝑐𝑐 (𝑝𝑝𝑐𝑐 )� (3)When we arrange this from first-order conditions (𝜕𝜕𝜕𝜕/𝜕𝜕𝑝𝑝𝑐𝑐 =0) for consumer prices (Pc) to maximize

profits, it yields the following:8

𝑝𝑝𝑐𝑐∗ =− (𝐷𝐷𝑐𝑐 + 𝐸𝐸𝑗𝑗𝑗𝑗 ∗ 𝐷𝐷𝑗𝑗 )� ′ − 𝐸𝐸𝑐𝑐𝑐𝑐 ∗ 𝑝𝑝𝑗𝑗∗ (4)𝐷𝐷𝑐𝑐

Pc* and Pj* are derived together with the expression for Pj* derived from first-order conditions for
producer prices (Pj).
Here, if r>1, then Ecj＊ (-D’c) >Ejc* (-D’j) (note that D’x is negative). As said in Fig. 3, thecoefficient of external effects (Ecj) on producer markets from consumer markets is relatively large.
Therefore, consumer market price elasticity (-D’c) becomes relatively large (the dotted line has aflatter slope). In which case, lowering consumer market prices results in larger consumer demand (Dc).
Furthermore, depending on how large the coefficient of external effects (Ecj), there may be a large
demand even if prices increase in producer markets. That is, by lowering consumer prices, even if
profits (πc) from consumer markets are sacrificed, a higher rise in profits (πj) from producer markets
may be expected. Consequently, if r is sufficiently large, then Pc* becomes to be negative. This
corresponds to the case of credit card companies targeting price sensitive consumers (with large priceelasticity), where they offer credit card services without any card holding fees. In contrast, companies(e.g., American Express) that collect card fees from users target higher-income groups that are notsensitive to card-holding costs.
When we compare one-sided and two-sided market monopoly profits, the profits of two-sided
market players are sometimes smaller in either market (and could be negative), but the sum total of
profits from two markets is higher for monopoly players in two-sided markets (which have indirect
network effects). Obviously, profits are increased from indirect network effects under monopoly prices
Pc ^, Pj ^ in individual independent markets. Consequently, if other conditions are constant, then type2 platformers are in a more advantageous position than regular supply chain players.
When direct network effects on the consumers side are added as in case 1, the spillover effects fromthe consumer side to the producer side will work even more. When we assume the direct network
effects (Ye) are added on the equation (1) (Katz and Shapiro, 1985), then the consumer market demand
function becomes as follows:
𝑞𝑞𝑐𝑐 = 𝐷𝐷𝑐𝑐 (𝑝𝑝𝑐𝑐 ) + 𝑣𝑣(𝑦𝑦 𝑒𝑒 ) + 𝐸𝐸𝑗𝑗𝑗𝑗 ∗ 𝐷𝐷𝑗𝑗 �𝑝𝑝𝑗𝑗 � (5)In case that Ye is unaffected by Pc, then (𝜕𝜕𝑦𝑦 𝑒𝑒 ⁄𝜕𝜕𝑝𝑝𝑐𝑐 = 0 ), and consumer-side monopoly prices(Expression (6)) are as follows:

𝑝𝑝𝑐𝑐∗∗ =− (𝐷𝐷𝑐𝑐 + 𝑣𝑣 (𝑦𝑦 𝑒𝑒 ) + 𝐸𝐸𝑗𝑗𝑗𝑗 ∗ 𝐷𝐷𝑗𝑗 )� ′ − 𝐸𝐸𝑐𝑐𝑐𝑐 ∗ 𝑝𝑝𝑗𝑗∗∗ (6)𝐷𝐷𝑐𝑐

The following is a comparison of monopoly prices with direct network effects and monopoly prices
without direct network effects (together with first-order conditions for Pj):
9𝑝𝑝𝑐𝑐∗∗ − 𝑝𝑝𝑐𝑐∗ = −𝑀𝑀 ∗Here 𝑀𝑀 =1

1−𝐸𝐸𝑐𝑐𝑐𝑐 ∗𝐸𝐸𝑗𝑗𝑗𝑗

𝑣𝑣 (𝑦𝑦 𝑒𝑒 ) �𝐷𝐷𝑐𝑐 ′

𝑝𝑝𝑗𝑗∗∗ − 𝑝𝑝𝑗𝑗∗ = 𝑀𝑀 ∗ 𝐸𝐸𝑐𝑐𝑐𝑐 ∗𝑣𝑣 (𝑦𝑦 𝑒𝑒 ) �𝐷𝐷𝑐𝑐 ′, and when this is positive, then (Exy<1), direct network effects and Pc rise, andPj falls. Furthermore, when direct network effects are entered, their share of total profits increases
even if there is no change in Pc or Pj (optimum pricing for type 2). Therefore, it is possible for type 1monopolistic enterprises to earn greater profits than type 2. Consequently, the monopolistic enterpriseprofits (producer surplus) of type 1 platforms (direct + indirect network effects) are greater than thoseof type 2 platforms (indirect network effects only), which are greater than those of normal supply
chains (pipelines).
４． Conditions for platform model formation
While the economic model in the previous chapter suggests superiority of platform over pipeline,
those that have adopted the platform model make up a rather minority in real world. Producer-side
ecosystems use shared platform interfaces platforms (API, System Development Kit, SDK) to freely
develop applications (permission-less innovation (Cerf, 2012)). In contrast, regular supply chain(pipeline), supplier (producer) products are supplied via customer firm’s approvals (permission). For
example, in the automobile industry, original equipment manufacturers (OEM) control the productspecifications of parts manufacturers, release only the interfaces for interconnecting parts, and limit
parts manufacturers from freely designing products.
Focusing on the supply side of type 1 and type 2 platforms, they are structured so producers are
attracted to shared management resources (iOS provided by Apple, SDK provided by SAP) provided
by platform owners. Consequently, platformers need to modularize product and service structures, andshare component interfaces with (potential) producers. However, the product architecture (designconcept) of automobiles is integrally structured with a high degree of interdependence between
individual parts. This is because the overall product performance of automobiles cannot be realized
unless all parts function normally. When developing new parts, automobile companies must control
overall product design concepts and ensure that no problems arise in any individual part. If a problem
arises due to permission-less innovation in just one part, an overall product loses value (for example,
economic loss and decreased brand value from part recalls). Consequently, it makes sense that they
stick with the pipeline model, and do not aim for platforms governed by parts manufacturer ecosystems.
In contrast to such integrated products, modular products like personal computers have overall
standardized interfaces for hard disks and memory chip parts, and thus have structures that allow forinterchangeability (Fujimoto, 2012). In addition, parts manufacturers can make independent10

improvements to functions (i.e., in the case of personal computers (PCs), there are independent makers
of display and memory devices). Since there are no difficulties replacing parts and competitive parts
offer new functions, overall technological innovation for products is accelerated. Thus, it is possible
for products to incorporate parts from companies that have successfully made risky technologicaldevelopments. As a result, the overall value of a product is expressed as the sum total of their various
parts options (Baldwin and Clark, 2001).
However, it should be noted that platforms (ecosystems) are in an intermediate position between
the supply chains (pipelines), where a large degree of control is seen for integrated products, such as
automobiles and the market where interfaces that are openly available and have parts markets, such as
PC (Jacobides et al., 2018). With PCs, there is little incentive for parts manufacturers (producers) toutilize specific PC manufacturers. This is because there is little value-added during assembly of the
final product. End users can individually procure parts from product markets and freely assemble them.
On the other hand, in the case of SAP, its ERP (core software) system is required to install partnercompany applications. Although SAP provides an SDK (software development kit) to partnercompanies, it is primarily for SAP’s ERP system. Although free application development(permissionless innovation) is allowed on the platform, the platform is still largely controlled by SAP.
That is, platform (ecosystem) models are not simply a matter of being able to assemble modules (parts)
into products, but they may exist in a situation of complementarity. The platform model requires
modularity between producers and platforms, but they are not completely modular; they are
conditioned on two-way complementarity (Distributed Super Modular Complementarity) (Baldwin,2019).
In addition, a certain number of producers are required for the network effects that underlie theplatform model. When the number of network participants has exceeded a certain threshold, positive
feedback kicks into action. A tipping phenomenon is seen in which numbers attract more numbers. In
the economic model in the previous section, we analyzed the price behavior of monopolistic
enterprises that have network effects. However, businesses are forced to decide whether the cost ofdeveloping a platform business (design change to modularization, anticipatory investment to trigger
tipping, etc.) outweighs the benefits. The condition for platform model becomes more complicatedwhere there is a (potential) competition among multiple platforms.
The decision of whether to develop a platform business requires exploration of diverse variants.
However, here, we would like to consider the differences between B2B (consumers are corporations,such as SAP or Komatsu) and B2C (consumers are individuals, such as in the case of smartphone OS),as platform business types. The major differences between the two are the number of (potential)
consumers (B2B: small, B2C: large) and differences in price elasticity. The number of potential
consumers (target market size) differs depending on the type of goods and services provided, butbusiness customers tend to be segmented into multiple markets and heterogeneous in terms of industry
11

types and company size. Related to this, individual B2B markets are small, and transactions are more
likely to emphasize function over cost (small price elasticity). Therefore, in B2B transactions, potential
indirect network effects (𝐸𝐸𝑐𝑐𝑐𝑐 ∗ 𝐷𝐷𝑐𝑐 (𝑝𝑝𝑐𝑐 )) are smaller in the producer markets in Expression (1), which
are small consumer markets. Furthermore, as price elasticity is small, the relative spillover effects (thenumerator in Expression (2) r) from consumer markets onto producer markets is also small. Producer

prices for maximizing profits are low compared to B2C markets. That is, the large profits fromproducer markets seen in Fig. 3 (indirect network effects) are not foreseen; the tipping required for aplatform is unlikely to occur.
The SAP ERP system aims at the overall optimization of various internal company activities. It is afundamental system integral to a wide variety of industries. Consequently, even B2B businesses canengage in platform businesses using indirect network effects with the anticipation of relatively large
markets. However, since the consumer (corporate user) price elasticity is small, the situation is thereverse of that in Fig. 3, which envisions a B2C market supplementing the producer market side
(distributes SDK to developers) and drawing revenue from the consumer side. Komatsu’s KOMTRAX
is a system targeting the construction industry. Therefore, the size of its user base cannot be expanded
as is the case in SAP. Consequently, there is little benefit in transitioning from a type 3 platform using
user data to the type 1 utilized in producer-based ecosystems as well.
If there is competition between producers and networks, the tipping phenomenon is even moreunlikely. In the case of inter-network competition, it is difficult to establish a model like SAP thatlowers producer prices and brings in revenue on the consumer side. SAP is quasi dominating the world
ERP market, so it is an exceptional case with this regard. When producer-side competition tightens,
there is less incentive to participate in platform ecosystems from the producer point of view. Platform
owners need to provide major incentives (reduce and open platform control) to producers tocompensate for this (Parker et al., 2018). Or platform owner is required to ensure greater diversity ofplatform services, in order to avoid too much competition among producer firms. Consequently, it isimportant for platform owners (keystone companies) to maintain producer (niche company) diversity
so that platform ecosystems can function effectively (Iansiti and Levine, 2004).
The following is a summary necessary conditions of platform models in foregoing discussion.
·

Product architecture of goods and services provided to consumers is of an intermediate nature
between an integrated (vertical integration, pipeline) and modular (parts market trading) type(super modular type splits between parts and platforms that have mutual complementarity).

·

The presence of a sufficient consumer market to demonstrate indirect network effects.
Consequently, in general B2B businesses are not suited to the platform model (generally pipelinemodel).

·

When there is tighter completion between platforms or between producers, the establishment ofa platform based on indirect network effects (type 2) is less likely.
12·To alleviate producer competition, it is important to construct platforms as ecosystems consistingof diverse producers. In addition, the construction of platforms in which wide-ranging services
are provided on a flexible basis in response to consumer needs will also lead to wider consumer
markets and positive feedback from indirect network effects.

５． Impacts of digitalization on platform dynamics
According to Moore’s Law of semiconductors, computer capabilities have explosively improved.
Information and data can now be transmitted instantaneously through the internet. Especially in recent
years, the collection of device sensor information via the internet and the spread of the internet ofthings (IoT) means that the use of digitization and big data induces transformation of manufacturing
processes (Motohashi, 2016). Furthermore, the machine learning (artificial intelligence (AI)) modelhas developed and spread. AI can now out-perform humans in various applications, such as image
recognition and speech and language processing. This information technology can now be used invarious realms of economic activity, loosening the conditions restricting the establishment of theplatform model. Consequently, the platform model is increasingly pushing the conventional pipelineto the edge (Van Alstyne et al., 2016).
On one hand, the size of Japan’s economy in terms of nominal GDP has not changed since before
the burst of the bubble economy in 1990. On the other hand, developing countries, such as China andIndia, saw rapid growth from the year 2000. In contrast, Japan’s share of the global economy shrankfrom 15% in 2000 to 9% in 2010. As Japan’s global economic standing dropped, valuation of its
industrial competitiveness also tightened. According to the IMD, Japan was first place in global
competitiveness in the WCY (World Competitiveness Yearbook) until the mid-90s but started to
decline in the latter half of the decade. Recently, Japan has hovered between 20th and 30th place(Motohashi, 2014). Early 90s Japanese corporate competitiveness definitely lay in the highproductivity of its manufacturing industry, which was able to deliver high-quality, low-cost products
to international markets. Is the retreat in the international competitiveness of Japan’s manufacturingindustry, which had been supported by strong local manufacturing, related to advancements ineconomic digitization and the platform business model? Here, we would like to focus on the supply
of goods and services (competitiveness, productivity), and discuss digitization, the relative advantageof the platform model over the pipeline model.
First, among digital products (the switch from newspapers to online advertising, video rental tovideo on demand, etc.) and O2O (Online to Online) platforms (the switch from physical stores to ecommerce, taxis to ride hailing services, hotels to private lodging businesses, etc.), there are manycases of digitization accelerating platforms and conventional business disruption (McAffee andBrynjolfsson, 2017). Most of these are B2C businesses that may be considered examples of thoroughly
exploiting indirect network effects (spillover effects) from the potential demand of a massive
13

consumer side. In addition, consumer-side direct network effects are also seen when these services
increase users. Thereafter, service level is improved by word-of-mouth. Consequently, there is a
succession of direct and indirect two-sided network effects. If a certain size can be secured on the
consumer side, then the tipping phenomenon can rapidly expand usage (type 1 platform).
There is a need to study supply-side characteristics (including the producer side) in order tounderstand the effects of economic digitization on the industrial competitiveness of manufacturing andother fields. In the previous section, we identified that the costs of platformization (risks to producers
from permission-less innovation) outweighed the benefits (speed and diversity of permission-lessinnovation) for products with integrated product architectures, like automobiles. Nevertheless,movement towards modularization is occurring even with automobiles, such as providing functions,like car navigation or car stereos, to connect with smartphones. However, there has been no movement
towards incorporating the type 2 platform model for basic functions, such as driving and navigating
functions of a car.
Vehicle technology is becoming increasingly digitized. Cruise control and steering functions arealso being managed digitally. There is definitely a high ratio of software to hardware in electronic
control units (ECU) (i.e., embedded software and onboard communication systems). Yet, as long as
overall vehicle functions are achieved with integrated hardware and software, the costs of type 2
platformization will outweigh the benefits. Some think this might change with electric vehicles.
However, even if there is some degree of overall product modularization (combining main parts, suchas motors, batteries, and inverters), ecosystems merging platform functions and permission-lessinnovation are difficult to be foreseen due to the importance of overall product safety and functionality.
In general terms, we shall clarify fundamental differences between virtual digital systems andphysical mechanical systems. The features of digital content and digital data are described in ones andzeroes, are completely duplicable, and can be moved instantly at no cost (Economics of free, perfect,
and instant) (McAfee and Brynjolfsson, 2017). In addition, software that can covert digital data into
information of economic value is described by unambiguous logical processes (Ikeda, 2002).
Therefore, it is easy to cut out commonly usable algorithms and to reuse such function as a module.
On one hand, in physical machine systems, a final product functions are derived by balancing
constituent parts, and the functions sought are multidimensional. For example, in terms of driving,
there is running stability, energy saving, downsizing, quieter operation, etc. There are no unambiguous
logical processes for improving all of them. Therefore, it is difficult to improve system functions
without coordinating individual parts (modules) (Whitney, 1996). Machine systems are being digitized(becoming software-based), but the digital portions are still subordinate to the function of physicalcomponents. Thus, there is no real change in the characteristics of the physical machine systems listed
above.
The acquisition and accumulation of digital information on things (the possibility of using big data14

on things) has major effect on expansion to service models that use things (servitization of
manufacturing, Vendrell-Herrero et al., 2017). For example, with KOMTRAX, Komatsu collects dataon the usage of a capital good (construction machinery) from the consumer side to provide valueadded service. A similar approach is taken with durable consumer goods, such as automobiles. For
example, Nissan partnered with casualty insurance companies for a service that calculates insurance
premiums according to driving distance. The producer side ensures the quality of products(construction machinery or car) within its normal pipeline, but digitalization of economy changes theway that producer’s value proposition of its product by using the big data collected from consumer
side (type 3 platform).
The movement towards digital service by the manufacturing industry (servitization) rather induces
its producer to competition with new entrants in such service market. In the case of automobile
manufacturers, ride hailing services become to be a rival. In addition, MaaS (Mobility As A Service)services combining public transportation and ride hailing services are spreading in some countries in
Europe. So, there is a need to rework strategies that only consider the product competitiveness ofthings. In addition, internet platformers, such as Google Group (Waymo), which focus on self-driving,
may represent a future threat. Companies with a massive customer base on smartphone platforms canweaponize potent network effects and leverage heavy bargaining power. However, self-driving
services are subject to regulatory systems in each country, and they find themselves in a chaotic stateof platform competition with diverse companies, including automobile and semiconductor
manufacturers. In addition, multi-tiered connectivity (e.g., communication network) is required. As
with smartphones, the formation of flat platforms consisting of two-company oligopolies is not likely.
However, with automobile manufacturers, there is growing importance for competitive strategies
based upon platform models that completely differ from the old, centralized pipeline model.

15

Figure 4. Relationship of digitization and platform dynamics
As a summary of foregoing discussion, the figure 4 is presented to understand the dynamics ofplatform models with advancement of digital economy. First, we showed that changing from a pipelinemodel like automobile supply chains to a type 2 platform (producer ecosystem type) is not likely, givethat there are no changes to customers’ expectation toward hardware characteristics (such as integrityof whole product to ensure safely requirement). Producer ecosystems like the SAP partnershipprogram may be considered models peculiar to software. The pipeline model producer rather move totype 3 platform, by using big data analytics of its user base (movement to the left of the pipeline
indicated by ①). This IoT-using platform model is often seen with durable consumer goods (i.e.,
automobiles) and capital goods (i.e., jet engines); Komatsu is just one example (Motohashi, 2016).
Next, as a development of the type 3 platform (movement upward from Type 3), we described theexample of personal mobility service through self-driving technology. This suggests the possibility of
upward move from type 3 to type 1. This is referred to as integrated servitization in which individual
products are used as an input to integrated services with superior costumer provision (indicated by②). An interesting case of such advancement is the Predix developed by GE. Predix is their attempt
to horizontally apply the data usage model they used for specific industrial equipment (such as wind
power machine and jet engine) (model 3) to wider industrial applications ((Keidanren, 2017). To that
end, they established GE-Data, a department for handling manufacturing industry data, and took thelead on standardization within the Industrial Internet Consortium (IIC). However, in recent years, they
have determined that their business is contracting due to poor performance and they retreated to aconventional application specific model. While we mentioned the difficulties with the type 1 model in
B2B business in the previous section, more detailed feasibility research is required.
16

Finally, there is the possibility of advancing from a type 2 platform (producer ecosystem type) to a
type 1 platform (movement to the left from type 2). If the SAP ERP system can encourage user
community activity (e.g., sharing of SAP system user experience), and bring forth consumer-side
direct network effects (consumer-side networking), then a stronger platform closer to type 1 is possible.
Although this example of a type 1 platform will clearly work for internet platformers like GAFA and
BAT, whether it can be established for B2B business remains unclear. If the consumer side of a type 2
platform consists of corporate customers (B2B business), corporations might compete with one
another, making it difficult to expect consumer-side direct network effects as in B2C business.
６． Implications to Japan’s industrial competitiveness and future research agenda
In this study, we propose the typology of platform models, type 1 (internet platformer type), type 2(producer ecosystem type) and type 3 (IoT data type), depending on the existence of direct and/or
indirect network effects. In addition, we examined the economic advantages of four business models,
adding in the conventional supply chain (pipeline model), and their relationship to advancements inthe digital economy. From our results, we found that the pipeline model seen in the automobile industry
is unlikely to be replaced by a type 2 producer ecosystem through economic digitization. There israther a chance for a pipeline producer (typical Japanese manufacturer) to further strengthen its
competitiveness by moving to the type 3 platform (IoT data use type).
However, we have identified that the risk associated with type 3 model, when it is integrated into a
more superordinate concept service model, then it may be exposed to competition with type 1 players(internet platformers such as GAFA and BAT). In addition, there is the possibility of producer
ecosystem (type 2) platformer owners upgrading to type 1 by deriving consumer side network effects.
The type 1 and type 2 business types greatly differ from B2C and B2B. Yet, there are examples of type1 players moving into B2B business (for example, Amazon’s logistics business and Tencent’s blockchain-based smart contract business), which may cast disruptive changes in the competitive landscapeof traditional manufacturing industries.
In this paper, we focused on the relationship between digitization and platform models, describing
their relationship to manufacturing competitiveness, but there are other critical problems related toJapan’s industrial competitiveness that are worthy of discussion. We conclude this paper with future
research agenda to disentangle a whole question.
The first one is related to questioning the possibility of type 1 platform for B2B business. In current
type 1 internet platforms, big data on consumer personal information produces indirect network effects(spillover effects) for producers. Large volumes of personal data can be used for advertising and otherforms of marketing. However, if the consumer side consists of business users (for example, users ofKomatsu and GE capital goods), then data usage is limited to specific equipment (Komatsu
construction machinery and GE jet engines), and there is little benefit to opening up to producers (other17

businesses) and pursuing ecosystem-based innovation. Other competitor companies would have theindustrial domain knowledge needed to use relevant data, and allowing such data access is not a goodcompetitive strategy. GE put out the Predix platform concept, presumably an attempt to move forwardwith their “industrial internet” type 1 platform concept. What they attempted was not data access, butbuilding an ecosystem connecting the producer side and consumer side, in which an analytical module
for analyzing big data on industrial machinery (functions such as asset performance management(APM) for efficiently maintaining heavy equipment and driving support systems) would serve as ashared management resource. However, since producers were not allowed data access in theirrelationship to data (machinery) owners, indirect network effects were limited and operations ceased
before reaching the necessary tipping point.
In the world of AI/Big Data/IoT, is  in figure 4 (integrated servitization) really impossible? If not,an appropriate strategy would be for capital and durable goods manufacturers to construct a solid type3 platform to prevent business entry by internet platformers based on type 1 personal information. In
Germany, there is movement towards networking and standardization of IoT applications (mobility,
smart production systems, smart city, etc.), based upon the CPS (cyber physical system) concept of asystem fusing digital and physical things (ACATECH, 2015). There is the possibility that this industryacademia collaboration will convert the B2B world, in which information is split between devices andproducts, into an integrated and horizontal world like the internet. While keeping in mind the
movement of such forth industrial revolution, it is important to pursue research into the possibility ofrealizing a type 1 platform in the IoT by analyzing indirect network effects and exploring governancestructures.
To study the above problem, there needs to be a theoretical framework of balancing two factor, (A)incentives to attract diverse producers on the producer side (supply side), and (B) the appeal(reservation prices) of consumer side goods and services in a diverse ecosystem. Regarding (A), therelative size of platform function added-value to producer added-value has an impact on overall
ecosystems. A team lead by prof. Gawer of UCL conducted a comprehensive survey of global platformbusinesses, and divided them into three categories: (1) asset heavy types in which producer
management resources are a major contributor (GE’s Predix, Samsung’s Tizen, etc.), (2) contrasting
asset light types in which shared platform management resources are important (Google, Uber, etc.),and (3) mixed types that fall in between (Apple, Amazon, etc.) (Evans and Gawer 2015). The heavier
the producer side assets, the less the centripetal pull of platform functions, and the less likelihood of
forming type 1 or type 2 platforms. These differences between platform types lead to the question of
how shared management resources can be separated (modularized) from the supply structure of aplatform business. Furthermore, they interrogate what needs to be studied, particularly regarding
product architecture and related industrial architecture.
In addition, the platform openness in (A) also has an impact. For example, Bourdreau (2010) defines18

the openness of mobile data devices (handheld computers) according to the OS intellectual property
license policy of platform owners to device manufacturers, e.g., no external licensing (in-house device
development), licensing only to specific partners, and unspecified and indiscriminate licensing), andshows product development (innovation) and performance as being an inverted U-shaped relationship.
In addition, there are also examples of defining openness by the ratio of sharing profits from overall
ecosystems of platforms and producers, and conducting model analysis of the relationship between
openness and innovation performance (Parker et al., 2017; Parker and Van Alstyne, 2018). Increasing
openness (producer share) benefits producers in that they have an increased incentive for development.
However, increasing the number of producers creates greater competition between producers, which
may impair development. Consequently, an optimal level of openness (intermediate openness that is
not completely open or closed) is being derived from model analysis.
Furthermore, the root question in (B) is how much the profits expected from goods and services
supplied in an ecosystem can be raised through development competition between diverse producers
as seen above. This point is closely related to research into manufacturing servitization throughdigitization. As seen with GE’s Predix and Komatsu’s KOMTRAX, there is the possibility of aplatform model with B2B manufacturing by adding value-added service using digital data to products
themselves. While using the above findings, work needs to be done to answer the question of whether
there is a business model balancing both (A) and (B) like type 1 and typ2 platform models (manifesting
positive feedback effects through indirect network effects).
The second research topic is related to the Japan’s economic and innovation systems in relation to
the advancement of digital platforms. The Japanese economic system is described as one in which
non-market mechanisms, such as long-term business relationships between companies and stable labor
relations, hold greater importance than market mechanisms, such as product and labor markets (Halland Soskice, 2001). The result has generally been a relative advantage in cumulative innovation, yetweakness in dealing with disruptive innovation (Motohashi, 2014; Kwon and Motohashi, 2017). In
the world of type 1 internet platformers, which have been rapidly growing through the use of personaldata, Japanese companies have been completely left behind. Substantial network effects by GAFA are
substantiated already, which may be irreversible unfortunately. However, Japanese companies havemanaged to be relatively competitive due to type 3 platforms that utilize data for manufacturingindustries, such as automobiles and machine tools. Thus, the focus of future research should be toidentify strategies for exploiting the strengths of the Japanese system amid continued digital andphysical advancements, and to identify the system innovations needed to overcome its weaknesses.
If we assume continued standardization towards CPS, then there is a greater chance that the platform
model will materialize in the B2B field as well. In the platform model, portions that can manifest froma core of shared management resources and producer ecosystems need to be separated. However, thisseparation requires an environment in which interfaces are open but not completely modular. Platform
19

owners should maintain a certain amount of control, while producers engage in permission-lessinnovation. Integration capability is required to create business models that coordinate ecosystems(Helfat and Raubitschek, 2018). Japanese companies have excellent integration capability fordeveloping complex machine systems. However, they may have many deficiencies in their capability
to generate ecosystem dynamism among different industry players. In addition, there is the questionof whether they can keep up with the speed at which manufacturing platformization proceeds through
CPS.
Furthermore, compared to American companies that have been moving ahead thanks to the rise ofinternet platformers and digitization, and German companies fusing cyber and physical aspects in
manufacturing, Japanese companies are unfortunately still behind in digital transformation. The
aforementioned RIETI survey of digitization and open innovation in small and mid-sized
manufacturing companies comparatively analyzed Japan and Germany with the cooperation of theGerman Leibniz Centre for European Economic Research (Leibniz-Zentrum für Europäische
Wirtschaftsforschung: ZEW), and found that Japanese company efforts towards digitization fall shortof those of their German counterparts (Motohashi and Rammer, 2020). However, simultaneously,
Japanese companies were found to be slightly ahead of German companies in co-innovation with
suppliers and other business partners. In the current Japanese economic system, further research isneeded to understand the strong fundamental bonds in network structures underlying economictransactions, how to utilize them within platform models that fall between market transactions andpipelines, and whether innovation is needed to overcome these obstacles.

References
Acatech (2015), Living in a network world: Integrated research agenda, Cyber-Physical-System,
Eva Geisberger and Manfred Broy ed, Acatech Study March 2015, Germany
Asanuma, B. (1997), Innovative adaption mechanisms in Japanese corporate organization –structure and function of long-term business relationships, Toyo Keizai Inc., June 1997
Baldwin, C. Y. (2019) “Platform Systems vs. Step Processes—The Value of Options and thePower of Modularity,” HBS Working Paper (January 2019)
Baldwin, C. and Clark, K. (2001), Design Rule: The Power of Modularity, MIT Press, Cambridge
MA
Boudreau, K. (2010), Open Platform Strategies and Innovation: Granting Access vs. Devolving
Control, Management Science, 56 (10), 1849–1872.
Ceccagnoli, M., Forman, C., Huang, P. and D. J. Wu (2012), Cocreation of Value in Platform
Ecosystem: The Case of Enterprise Software, MIS Quarterly, 36 (1), 263-290.
Cerf, V. (2012), Keep the Internet Open, New York Times, May 24, 201220

Evans, P. and Gawer, A. (2015), The Rise of the Platform Enterprise: A Global Survey, The
Emerging Platform Economy Series No. 1, The Center for Global Enterprise
Gawer, A. and M. Cusumano (2013), Industry Platforms and Ecosystem Innovation, Journal of
Production Innovation Management, 31 (3): 417-433.
Hall, P. and Soskice, D. (2001), Varieties of Capitalism: The Institutional Foundations ofComparative Advantage. Oxford University Press.
Helfat, C. and R. Raubitschek (2018), Dynamic and integrative capabilities for profiting frominnovation in digital platform-based ecosystems, Research Policy, 47, 1391-1399.
Iansiti, M. and R. Levien (2004), The Keystone Advantage: What the New Dynamics of Business
Ecosystems Mean for Strategy, Innovation, and Sustainability, Harvard Business School
Press, Boston, MA
Ikeda, N. (2002), Digitization and Modularization, Modularization: Essence of new industrial
architecture, Chapter 4 (Masahiko, A., Ando, H., eds.), Toyo Keizai Inc., March 2002
Jacobides, M., Cennamo, C. and A. Gawer (2018), Towards a theory of ecosystem, Strategic
Management Journal, 39, 2255-2276.
Katz, M. and C. Shapiro (1985), Network Externalities, Competition and Compatibility, American
Economic Journal, 75 (3), 424-440.
Keidanren, The 21st Century Public Policy Institute (2017), Innovation ecosystems research: Howto make money from open innovation, 21st Century Policy Research Report, February 2017
Kinukawa, S., Tanaka, T., Nishio, K. and Motohashi, K. (2015), “Innovation trends and casestudies using big data,” RIETI Policy Discussion Paper Series 15-P-015
Kwon, S. and K. Motohashi (2017), How institutional arrangements in the National Innovation
System affect industrial competitiveness: A study of Japan and the U.S. with multiagent
simulation", Technological Forecasting and Social Change, 115, 221–235.
Mayer-Schonberger, V. and K. Cukier (2013), Big Data: A revolution that will transform how welive, work and think, John Murray Publisher, Great Britain
McAfee, A. and E. Brynjolfsson (2017), Harnessing our Digital Future: Machine, Platform andCrowd, W. W. Norton & Company, New York
Milgrom, P. and J. Roberts (1990), Rationalizability and learning in games with strategic
complementarity, Econometrica, 58 (6), 1255-1278.
Motohashi, K. (2014), The sun rises again: Revitalizing industrial competitiveness, Nikkei
Publishing, Inc., February 2014
Motohashi, K. (2016), “Actual state of big data activity and innovation in the Japanesemanufacturing industry,” RIETI Policy Discussion Paper Series 16-P-012
Motohashi, K. and Rammer, C. (2020), Digitization and New Product Development in
Manufacturing SMEs: A Comparative Study of Germany and Japan, RIETI Policy
21

Discussion Paper 20-P-007.
Parker, G. and M. Alstyne (2018), Innovation, Openness and Platform Control, Management
Science 64 (7): 3015-3022.
Parker, G. and M. Alstyne (2005), Two sided Network Effects: A Theory of Information Product
Design, Management Science, 51 (10), 1494-1504.
Parker, G., M. Alstyne and X. Jiang (2017), Platform Ecosystems: How Developers Invert theFirm, MIS Quarterly, 41 (1), 255-266.
Rysman, M. (2009), The Economics of Two sided Markets, Journal of Economic Perspectives, 23(3), 125–143.
van Alstyne, M., Parker, G. G. and Choudary, S. P. (2016), Pipelines, Platforms and the New Rulesof Strategy, Harvard Business Review, April 2016
Vendrell-Herrero, F., Bustinza O., Parry, G. and N. Georgantzis (2017), Servitization,
digitization and supply chain interdependency, International Marketing Management,
60 (2011) 69-81.
Whitney, D. (1996), Why Mechanical Design Cannot be like VLSI Design, Research in
Engineering Design, 8, 125-138.

22