Commercializing of Platform Technologies

7/29/2019 Commercializing of Platform Technologies

1/47Electronic copy available at: http://ssrn.com/abstract=1692252

Commercialization of Platform Technologies:

Launch Timing and Versioning Strategy

Hemant K. Bhargava

[email protected]

Graduate School of Management

University of California Davis

Byung Cho Kim

[email protected]

Graduate School of Management of Technology

Sogang University

Daewon Sun

[email protected]

Department of Management

University of Notre Dame

January 11, 2012

Author names are listed alphabetically and all authors contributed equally.

Mailing Address: Hemant K. Bhargava, GH-3108, University of California Davis, Davis, CA 95616; Telephone:530-754-5961; Fax: 530-752-2924.

Mailing Address: Byung Cho Kim, Graduate School of Management of Technology, Sogang University, 35Baekbeom-ro (Shinsu-dong), Mapo-gu, Seoul 121-742, Korea; Telephone: 82-2-705-7986; Fax: 82-2-3274-4808.

Mailing Address: Daewon Sun, Room 359, Mendoza CoB, Notre Dame, IN 46556; Telephone: 574-631-0982;Fax: 574-631-5127.



Commercialization of Platform Technologies:Launch Timing and Versioning Strategy

Abstract

Many emerging entrepreneurial applications and services connect two or more groups ofusers over Internet-based information technologies. Commercial success of such platform tech-

nology products requires adoption of astute business practices related to product line design,

price discrimination, and launch timing. We examine these issues for a platform firm that

serves two markets, labeled as user and developer markets, with the latter emerging after the

user market is proven. While the size of each market positively impacts participation in the

other, our model allows for uncertainty regarding developer participation. We demonstrate

that product versioning is an especially attractive strategy for platform firms, i.e., the tradeoff

between market size and margins is tilted in the direction of more versions. However, when ex-

panding the product line carries substantial fixed costs (e.g., marketing cost, cost of additionalplant, managing multiple sets of inventory, increased distribution cost) then the uncertainty in

developer participation adversely impacts the firms ability to offer multiple versions. We show

that for established firms with lower uncertainty about developer participation, the choice is

essentially between an expanded or minimal product line. Startups and firms that are entering

a new product category are more likely to benefit from a wait and see deferred expansion

strategy. Still, we demonstrate that uncertainty in developer participation can make early

expansion desirable because it expands the installed base and, with the consequent increase

in developer participation levels, increases the long-term incremental gain from product line

expansion.

Keywords: Technology Commercialization, Product Launch Strategy, Platform Technology,

Versioning, Uncertainty



1 Introduction and Motivation

Technological innovation is an expensive and uncertain process which often requires high-end research

and development of new components, production processes, and underlying technologies. Often,

entrepreneurs and firms are unable to successfully commercialize their innovation despite having

technologically sophisticated products. Success requires clearing many hurdles and adoption of

astute business strategies (Christensen and Bower, 1996; Daneels, 2004; Moore, 1991). Challenges

include the chicken and egg problem (e.g., a new payment technology will be adopted only if

accepted by sufficient number of merchants, but merchant adoption will itself depend on a sufficient

installed base of users), uncertainty in product design and compatibility (e.g., shouldor willall

electric car technologies employ the same battery that can be charged at every battery station, or

will the market be fragmented among multiple technology formats?), the challenge of convincing

consumers to pay high (and definitive) up-front costs in return for small (and uncertain) benefits

delivered over a long time (e.g., residential solar power), and the growth vs. profitability dilemma

(e.g., should a vendor of an e-book technology sacrifice margin and profits in return for high market

share, in order to entice publishers towards its technology?). This article examines this final challenge,

i.e., the growth vs. profitability dilemma, for technology goods.

Our research focuses on technology products that operate as platforms in a two-sided market.

These are products that exhibit positive cross-network effects between two distinct networks of

players, i.e., market adoption on one network influences, and depends on, the desirability of adoption

on the other network (Eisenmann et al., 2006; Eisenmann, 2007). For example, video gaming

consoles serve (i) gamers, by giving them technology for playing complex video games and (ii)

game developers, by giving them a platform for executing such games and reaching potential buyers;

hence a console platform that attracts more game developers becomes more valuable to gamers, and

conversely, game developers are attracted to console platforms that have many gamers. Similarly,

operating system platforms connect computer users with application software developers. More

recently, smartphones have, as small computers, become platforms for connecting phone users with

1


4/47

a variety of computational software and service applications. As noted above, the launch of a

platform technology presents the firm a tough challenge of balancing customer growth and short-

term profitability. Growth requires very low (or zero, or even negative) prices in order to propel

interest in the future or on the other side of the market, but these low prices lower the firms

short-term profit.

This growth vs. profitability dilemma is common for many startup entrepreneurial ventures,

such as information applications (for mobile phones, tablets, and computers) which connect two or

more groups of users over the Internet. Examples include i) FiGuide.com, which provides personal

financial services by creating a financial planner and subscriber network, and ii) Asana.com, which

offers project management tools for a manager and employee network. To illustrate the dilemma,

consider a startup firm which aims to deliver and manage home exercise programs (HEP) on mobile

phones. In contrast to the traditional print-based programs, the use of mobile phones can deliver

multimedia content tailored to the patient, and it can also track and transfer information to the

clinician. Adoption involves a bidirectional loop between patients (as users and direct beneficiary

of the program) and clinicians (who prescribe the exercise programs, customize and configure the

application for the patient, and track information about compliance and effectiveness). Because

clinicians have very thin margins and are unlikely to pay for the service, the firm intends, at least

initially, to generate revenue primarily from patients. Charging a high price to patients will generate

the revenue that is desperately needed to fund new applications but it will also restrict adoption;

that, in turn, makes it difficult to entice clinicians to participate. This is the essence of the growth

vs. profitability dilemma for such startup firms.

The tension between growth and profitability has been discussed in the entrepreneurship litera-

ture. Firm growth is a common measure of success (Davidsson et al., 2008), but the wisdom is thattoo much growth must come at the expense of profitability (Ramezani et al., 2002; Markman and

Gartner, 2002). Davidsson et al. (2009) examine the effectiveness of growth as a measure of busi-

ness success from a Resource-Based View (RBV) and argue that sound growth starts with achieving

2


5/47

profitability. But generally, growth and profitability are considered to be in conflict especially for

products with network effects, the common belief being that firms can either have growth or achieve

profitability. Many startup ventures respond to the tension between growth and profitability by ini-

tially producing only a single version of their product (to avoid production complexity or perhaps to

place their best foot forward to all their customers) and selling it at a relatively low price needed to

accelerate growth. We argue that this may be an unnecessarily extreme approach, and it magnifies

the conflict.

Our research is founded on the proposition that growth and profitability need not necessarily

operate in conflict. Existing theory on market segmentation and product differentiation suggests

versioning (i.e., an expanded product line with multiple, vertically-differentiated, versions) as a way

out especially when network effects are present (Bhargava and Choudhary, 2004; Jing, 2007). This

strategy is captured in our model by giving the firm a choice to launch two versions of the product,

a basic and a premium one. The high-end version provides high margin, while the low-end, low-

priced version delivers high market share and installed base necessary to generate substantial network

effects. Yet this does not imply that vendors of new platform technology should launch the technology

with an expanded, rather than minimal, product line. Product line expansion is tempered by the

additional complexity and costs, including operations costs (additional plant, managing multiple

sets of inventory, increased complexity in distribution), marketing costs (data collection and price

optimization, segment development and management, and advertising to multiple customer segments

(Dhebar, 1993; Villas-Boas, 2004)), and cannibalization costs due to increased competition within

the product line. This feature is captured in our model as an incremental product line expansion

cost, specifically the one-time or fixed cost of executing on the expansion strategy. The third feature

of our model is common to platform goods, i.e., that developer participation involves network effectsand depends on having an installed base of end-users. The fourth and distinctive feature of our model

is that developer participation also has a random unpredictable component. While past literature

has taken a deterministic rational expectations framework to describing network effects (see e.g.,

3


6/47

Rochet and Tirole, 2006), we argue that platform firms face substantial uncertainty about whether

or not they can secure participation by the developer market.

Our research contributes to the entrepreneurship literature by highlighting the launch strategy

and timing problem for platform goods and other products that exhibit strong network effects.

Intuitively, platform firms may implement a minimal product line in order to avoid higher fixed costs

at launch, and wait for substantial developer participation before expanding the product line. In

contrast, we argue that the firm should be inclined to expand the product line early in order to

increase its installed base and induce a higher level of developer participation. The novel feature

in this analysis is the role of the random component in developers participation decisions. Firms

that develop platform products often have little or no direct control over the number of third party

applications, however they can influence developers through the design of their product launch

strategy. We show that under network effects, early expansion is generally better than deferred

expansion. The exception is that the firm should employ a wait and see (or deferred expansion)

approach when developer participation is extremely uncertain (and expansion costs high). The key

insight, however, is that early expansion can be useful even in the face of developer uncertainty: by

expanding the user market available to application developers, it can drive developer participation

high enough to the point where the gains exceed the expansion costs. In other words, platform

firms are (compared with products that do not exhibit cross-network effects) more likely to benefit

from launching multiple versions simultaneously rather than sequentially after observing developer

participation.

We note that in the technology industry, many established firms also experience challenges

typically faced by startups. For instance, when Apple entered the entertainment market with the

introduction of its iPod music player in 2001, it was known as a maker of computers. It had nofootprint in home entertainment products, lacked recognition as a music retailer, and did not enjoy

supply relationships with music providers. These factors caused substantial uncertainty regarding

whether music firmshighly concerned about piracy, and worried that digital music would amplify

4


7/47

itwould in fact license their music for digital distribution through iTunes. Other times, established

firms want to retain a startup flavor in order to benefit from the innovations and breakthroughs that

often emerge out of new thinking. For instance, Google, Intel, eBay, and other technology firms

have internal organizational structures (such as technology incubators) and incentive schemes aimed

at generating technology startups. Hence our research has relevance both to established technology

firms that are creating new products and entering new markets, and to startup or entrepreneurial

ventures.

However, startups in the technology industry should be cognizant about crucial differences that

can lead to different strategies from those suited to established firms (Shan et al., 1994; Joglekar and

Levesque, 2006). Intuitively, the deferred (rather than early) expansion strategy is particularly suited

to startups which are more weakly positioned with respect to developer participation. Established

firms on the other handthose who have a high fraction of early adopters and/or little uncertainty

about developer participationface a clearer choice between expansion (if costs are low) or not (for

high expansion cost). Our model provides a rigorous foundation for understanding how the expansion

decision is influenced by the interplay between intensity of network benefit, adoption characteristics,

and uncertainty in developer participation. We demonstrate that despite such uncertainty, and to

some extent because of it, early expansion can be desirable for startups. This is because versioning

expands the early-stage installed base, and this increase in market adoption reduces the weight of

the uncertain component in the extent of developer participation.

2 Literature Review

We discuss three streams of research that tie into our work: product launch timing, two-sided

markets, and optimal strategies for startup ventures.

5


8/47

2.1 Product Launch Timing and Two-Sided Market

Several researchers have studied the optimal timing of product launch. Ramdas (2003) provides

a framework for examining a firms variety management and discusses strategies associated with

variety-creating decisions. Carrillo (2005) examines the impact of industry clock-speed on pacing of

new product development activities. Bag and Roy (2010) study distribution of public goods with

multiple providers and show that total contribution generated in a sequential move game may be

higher than in a simultaneous move game under incomplete information. Aoki and Prusa (1997)

find that sequential quality choice leads to smaller quality investment and higher profit, but it

lowers consumer and social surplus. Padmanabhan et al. (1997) study a firms new product launch

strategy under consumer uncertainty regarding network externality. They argue that sequential

launch with sequential provision of quality is optimal for a high-externality firm since under-provision

of introductory quality may serve as a signal of high externality. Moorthy and Png (1992) show

that when a firm faces a serious threat from cannibalization, it may be optimal for the firm to

serve high-valuation customers first and later introduce lower-quality version to cover low-valuation

segment. We also examine sequential product launch, but unlike these prior studies, we do so in the

context of a two-sided market, and specifically in the presence of uncertainty regarding developer

participation.

The literature on platforms has recognized that several traditional business strategies such as

pricing must be modified in response to two-sided network effects. Rochet and Tirole (2003) model

platform competition with two-sided markets and study price setting and surplus sharing under

different governance structures. Lee and OConnor (2003) examine the consumers consumption

behavior and the corresponding new product launch strategy in the presence of network effects.

Parker and Van Alstyne (2005) provide insights to help understand interesting phenomena in the

Internet economy, such as free products and product coupling across markets. Rochet and Tirole

(2006) provide a thorough review of the growing literature on platform competition in two-sided

markets. Armstrong (2006) examines platform pricing under competition in two-sided markets and

6


9/47

identifies the determinants of equilibrium prices. One of the key findings in the monopoly platform

case is that subsidizing one user group is desirable when the groups demand elasticity is high and

the external benefit realized by the other group is sufficient. Eisenmann et al. (2006) provide a good

example of such a subsidy. They argue that Adobes distribution of Acrobat Reader creates large

externality from 500 million free users, which eventually incentivizes enterprises to pay $299 for the

commercial version. Liu and Chintagunta (2009) discuss pricing issues under network effects from

a marketing perspective. Eisenmann et al. (2011) examine the strategic management of platform

providers and discuss strategies for platform envelopment. While the main focus of most existing

studies is the role of network externalities in two-sided markets, our model incorporates uncertainty

in application development as well as network externalities in two-sided markets.

2.2 Optimal Strategies for Startup Ventures

A notable stream of research in the entrepreneurship literature examines various aspects of the op-

timal entry strategy of a startup venture. One interesting theme is a new ventures retail channel

selection between virtual and bricks-and-mortar networks given the proliferation of the Internet (e.g.,

Reinhardt and Levesque, 2004). Closer to our paper is entrepreneurs choice between early and de-

layed entry. Early studies find that early entry to the emerging economy generally yields higher

profit than deferred entry (DeCastro and Chrisman, 1995). This result is somewhat consistent with

our model in the sense that early expansion has greater profit potential than deferred expansion.

More recently, Levesque and Shepherd (2004) examine a startup ventures optimal entry strategy in

emerging and developed markets, grounded on a stylized analytical model, and find that companies

entering emerging markets have lower cost/benefit ratio from using a high mimicry entry strategy

than the ones entering mature markets. Optimal timing of opportunity exploitation is another rele-

vant theme that has been extensively studied in the knowledge management literature (e.g., March,

1991; Choi et al., 2008). In general, it is believed that an optimal strategy for a startup company

is to focus on exploration until it accumulates sufficient knowledge, and then move to exploitation.

7


10/47

Finally, Armstrong and Levesque (2002) extend Levesque (2000) by modeling uncertainty for the

amount of funding obtained for product development. Because the startups financial ability is

much more limited, their results indicate that startups are much more sensitive to uncertainty than

established firms.

While numerous aspects of a new ventures business strategies have been well studied, optimal

product line expansion for platform technologies which posit two-sided markets with uncertainty has

not been examined yet, to the best of our knowledge. We aim to bridge the gap in the literature

by investigating the optimal product line expansion strategy for a startup venture with uncertainty.

Inspired by real-world technology markets, we characterize the conditions under which the optimal

timing for product line expansion is determined, and compare early and deferred expansion strategies.

Our results show that with versioning, firms can achieve both growth and profitability, which will

give guidance to entrepreneurs who want to commercialize platform technologies.

3 Overview of Model

The dominant approach to modeling two-sided markets assumes simultaneous arrival of the two

sides, so that the outcomes are resolved in a simultaneous coordination game. But Hagiu (2006)

argues that this representation may not be appropriate when the order of arrival of two sides is

well defined, for example in two-sided technology platforms such as computers, gaming consoles, or

personal productivity devices. Here, the first step tends to be device or platform adoption by end-

users (because the device has sufficient standalone features to be of value even without the second

side of the market), and the second step is entry by third-party developers who provide additional

applications to extend the utility of the platform device. Given this sequential arrival of consumers

and application developers in the platform technology market, we develop a two-period model of

customer purchase and developer participation.

8


11/47

3.1 Customer and Developer Preferences

Potential buyers arrive and exist in both periods. In the first-period, the platform product is essentially

viewed as a set of core standalone features (features which are valuable by themselves, and do not

depend on external applications) endowed by the platform firm, and without a substantial network

of third-party application developers. For example, the initial iPhone released in June 2007 was an

all-Apple product, endowed with several standalone features such as voice-calling capabilities, in-

built contact book, calendar, mail, and music capabilities. A software development kit (SDK), which

enabled the creation of third-party applications, was released only in March 2008, and the App Store

was launched in July 2008, over a year after launch of the iPhone. Thus, purchase decisions of first-

period customers were based primarily on the products standalone features. Potential developers

observe product adoption in the first period, and by the start of the second period the market

obtains signals about developer participation. In the second period, therefore, customers make

purchase decisions based on both the standalone features and third-party applications or product

complements. The iPhone illustrates this point well. Today, like with other platforms, customer

choice between the iPhone and similar products from competing firms (such as HTC, Google,

Motorola) depends substantially on the size of the respective applications (i.e., the App Store in the

case of iPhone) .

Customers have heterogeneous preferences for the platform product. We capture heterogeneity

with a one-dimensional type parameter v, which represents the customers marginal valuation of

product quality. Product quality may be perceived as a collection of features and the level at which

these are delivered. Higher quality may mean the inclusion of a greater number of useful features

(e.g., inclusion of a camera on a phone) or a premium level of a feature (e.g., a 5 MP camera with

zoom vs. a 2 MP camera). Customers also value the platform more if it has a greater number of

application developer participants (Eisenmann et al., 2006; Katz and Shapiro, 1992; Jing, 2007).

Thus, customers utility for the product is a combination of its standalone features and third-party

applications or complements. This feature is captured with the additive utility function employed in

9


12/47

the literature (Katz and Shapiro, 1992; Bhargava and Choudhary, 2004; Jing, 2007). Specifically,

a type v customers valuation for a q-quality product when the number of complements is Q, is

v q+ Q, where represents the per-complement value. For simplicity, we assume that both first-

period and second-period customer arrivals have the same distribution of v, uniform on the [0, 1]

interval. This assumption is a simplification, but we emphasize that the main results do not change

even if the first period customers on average have higher valuations (please refer to Appendix A in

the online supplement for the relaxation of this assumption).

Our model of customer behavior and purchase in the two periods is based on theories of tech-

nology adoption and diffusion in the marketing and information systems literatures. Moore (1991)

proposed a chasm framework for technology products, in which two different segments of customers

are clearly defined. Customers in the early market, who are labeled technology enthusiasts and

visionaries, make an adoption decision in response to the nature and benefits of the innovation.

They are more like risk takers. Their perceived value from the platform, and their purchase decision,

is based primarily on the standalone features of the product. Moreover, note that because of the

sequence of product launch, customer arrival and developer participation, the size of the developer

network is negligible at the time these early customers make their adoption decision. Further, while

such customers may anticipate developer participation in later periods, they have a substantially

high discount rate for future benefits.

To summarize, the first period early adopters willingness to pay for the product primarily de-

pends on its standalone features, while the decision-making of second-period followers involves a

combination of standalone features and developer participation. Formally, we write the net util-

ity of early adopters in the first period and followers in the second period, U1(v, q) and U2(v, q),

respectivelyas

U1(v, q) = (v q) p(q) and U2(v, q) = (v q+ Q) (q), (1)

where p(q) and (q) are first and second period prices for a q-quality product. Let q represent the

10


13/47

firms quality vector in period 1, and p the price vector, and let D1(q; p) be the realized demand

for product version q. Then the total first-period installed base of the firm in the user market is

D =

q D1(q; p). The first-period cohort is therefore split into two parts: those, with v larger than

a threshold v who adopt the platform in the first period, and those (with v < v) who do not.

The products adoption levels at this stage influence and determine the extent of developer

participation. If developers observe high product popularity, they are more likely to sign on with the

platform. This relationship is normally modeled in the literature with a deterministic participation

(or variety) function of the form Q = D

where D is the demand for the platform and is the cost

of developing applications (Shy, 2001). We maintain this classical assumption about the positive

dependence from D to Q (and we normalize to 1 since it is not the object of interest in this

paper). The novel feature of our model is the inclusion of uncertainty in the level of developer

participation, beyond the dependence of this variable on the installed base. That is, we argue that

the extent of developer participation cannot fully be predicted by the demand for the platform

product and is influenced by other, possibly idiosyncratic, factors. Recent examples of uncertainty in

developer participation at time of launch include 3D TV. A lack of 3D content was identified as the

most significant contributor to the slow growth of 3D TV sales (Nuttall, 2010). Despite potential

consumers belief that 3D TV will become an industry standard in the near future, 3D TV is not

much attractive to them because it does not yet have a supporting eco-system (Mitra, 2010). Thus,

uncertainty in the future application development, i.e., 3D content, still remain a serious concern for

potential buyers. Therefore, this example demonstrates that uncertainty in application development

is critical for new platform technologies.

Formally, we conceive Q as D + , where the first component is proportional to the in-

stalled base of users and the second component is a random offset. We normalize the value of thisrandom variable in the first period (where, developers observe zero installed base) to zero, hence

by convention, Q represents the developer base in the second period. For simplicity, we consider a

distribution with just two atoms, corresponding to Favorable or Unfavorable developer participation.

11


14/47

Our formalization of this uncertainty component is consistent with other recent papers (Cachon

and Lariviere, 2001; Chen, 2005; Dogan et al., 2010; Ha and Tong, 2008). To make the notation

concise, we write the two cases as B (Best case, with probability ) and W (or Worst case).

We normalize the random component in the worst-case to zero, to get

Q =

D + if application development is High (probability )

D + 0 if application development is Low (probability 1 )(2)

where D is the observed installed base for the product at the start of the second period.

Additional customers enter in the second period, i.e., after developer participation is realized.

These are more risk-averse decision makers, afraid of being locked in a not-yet-standard technology,

but willing to make a decision after uncertainty about developer participation is resolved. These late

adopters or laggards are influenced by the previous number of adopters which is a widely adopted

assumption in the literature on diffusion modeling (see, e.g., Mahajan et al., 1990; Mahajan and

Muller, 1998). This assumption implies, when applied to a two-sided platform product, that second-

period customers make decisions based on the observed level of developer participation (which in

turn depends on the adoption level in the first period). Van den Bulte and Joshi (2007), who

provide a detailed review of various theories motivating a two-segment structure, also deploy a two-

segment model containing influentials who are more in touch with innovations than the others and

imitators whose adoption decisions are often influenced by others decisions. The two-segment

structure was also empirically verified by many researchers (see, e.g., Moe and Fader, 2002; Joshi

et al., 2009).

We normalize a few parameters to simplify the exposition and analysis. First, like Moorthy and

Png (1992), we focus on product expansion as a business strategy rather than driven by technological

improvement, in which case the firm may introduce higher quality products over time. Thus, we

assume that the firms technological capabilities remain constant, and that cost and other parameters

induce it to offer the highest quality version in the first period itself. Since the quality-level of the

12


15/47

high-end product is constrained exogenously, we can set this to 1. Then we denote valuations (for

just the product features) for the low quality product as v, where (0, 1) represents a quality

degradation parameter. This formulation is employed frequently in the versioning literature (see, e.g.,

Deneckere and McAfee, 1996); it implies that each user has constant marginal valuations (CMV)

for product quality, and that the type parameter v represents this marginal valuation. Second,

let denote the mass of first-period customers, and let us normalize the mass of customers who

exogenously arrive in the second period to 1.

3.2 Structure of Game and Solution Framework

The sequence of events unfolds as follows. In the first stage the firm chooses its initial product line

strategy. With respect to our research objectives, we limit the product qualities that the firm can

pick to two levels, L and H, where the high quality H is exogenously given and constrained by the

technology innovation level of the firm. Hence the key question for the firm is whether and when

to include an additional, lower quality, version in the product line. We assume full compatibility

between the two versions. That is, any application that works for one version works for the other.

For simplicity, we also assume that the difference of production costs for the high and low quality

products is negligible, which is applicable to many information goods and other inferior/damaged

goods. In 4.3, we demonstrate that our main findings still hold even with different marginal costs.

Hence the firms strategy space has two points, {H} and {L, H}, because launching only {L} in

the first period is strictly dominated by launching {H} when there is no difference in production cost

(this strategy does become feasible under positive costs, which we discuss in 4.3). At this point,

as indicated in Eq. 2 the firm is uncertain about the full extent of developer participation, though it

is aware that participation levels will depend positively on its installed base. In this stage, the firms

target market (on the user side) primarily consists of early adopters who value the product for its

technological features and care little about third-party applications. At the end of the first period,

as adoption levels materialize, developers begin participating in the eco-system, and the extent of

13


16/47

developer participationlow/worst case (W) high/best case (B)

offer H only W2 (D; {H}) B2 (D; {H})

offer {L, H} W2 (D; {L, H})

B2 (D; {L, H})

Table 1: Notation for optimal operating second-period profit (not considering ).

participation level Q is observed by the firm and second-period customers. In the second-period,

the firm can reconfigure its product line and it sets second-period prices, targeting the product to

second-period customers or followers who make a purchase decision after observing developer

participation.

Formally, we write the strategy space for the firms first-period decision problem as the vector

[1, pL, pH] where 1 is either {H} or {L, H} (as noted above, we add {H, L} in 4.3). If1 = {H}

then pL is vacuous, and pH must satisfy the constraint 0 pH < 1. If 1 = {L, H} then the firm

incurs a fixed product line expansion cost , and its prices must satisfy 0 pL 0, is to sell both versions. The incremental profit from

versioning (given a developer participation levelQ) isversioning2 =

2Q2(1)4

and this incremental

profit increases in Q.

This result implies that, when 1 = {L, H}, selling both products in the second period as well

is optimal. Recall that our base-case utility function U(v, q) = v q (i.e., utility for core product

standalone features) was chosen such that versioning is not optimal in the base case (see, e.g.,

Bhargava and Choudhary, 2001; Deneckere and McAfee, 1996). It is the inclusion of utility from

third-party complements, which kick-in in the second period, that makes versioning an attractive

strategy in the second period (in the absence of additional costs for product line expansion), matching

prior results under network effects (Bhargava and Choudhary, 2004; Jing, 2007).

If, however, the firm chose a minimal first-period product line 1 = {H}, then the additional

expansion cost destroys the inevitability of versioning in the second period. Specifically, because

versioning2 increases in Q, versioning will be attractive only when developer participation Q is

sufficiently high or, equivalently, expansion cost is low enough. Recall that Q is a random variable

with best-case and worst-case realizations B and W respectively, both of which are functions of

D. Let B/W2 (D; {L, H}) be the optimal second-period profit if the firm expands the product line

(adds L) and B/W2 (D; {H}) if it continues with an H-only strategy. For each of the two cases B

and W (which are resolved prior to the firms second-period action), the firms second period profit

is therefore the maximum of these two profit terms.

At the beginning of the game, therefore, the firm picks its product line and prices to maximize

its expected profit over the two periods. To complete our notation, define E[2(1, D)] to be

15


18/47

the firms first-period expectation of its second-period profit if it enters the second period with an

existing product line 1 and installed base D. Using the notation from Table 1, we have

E[2({L, H}, D)] =

B

2 (D; {L, H}) + (1 )

W

2 (D; {L, H}), (5)

E[2({H}, D)] = max{B2 (D; {L, H}) ,

B2 (D; {H})}

+(1 )max{W2 (D; {L, H}) , W2 (D; {H})}. (6)

Combining the second-period actions under the B and W realizations (under 1 = {H}) the

firm will either (i) not launch L regardless of the level of Q (i.e., even if Q is high, case B), (ii)

launch L only if Q is high (note that launch L only if Q is low would trivially be inferior to launch

L even if Q is low), or (iii) launch L even if Q is low (i.e., in both B and W cases). Of these we

eliminate case (iii) because of the following result.

Proposition 2 (Benefits of Early Expansion) If product expansion is foreseen as inevitable in

period 2 (i.e., launch L even if Q is low), then expanding in period 1 itself is optimal.

Eq. 6 can therefore be replaced with

E[2({H}, D)] = (1 )W2 (D; {H}) + max{

B2 (D; {H}),

B2 (D; {L, H}) }. (7)

Our main objective is to compare the profitability of 1 = {H} with 1 = {L, H}. The total

expected profit, at the start of first period, from a decision to set 1, pL, pH is (1, pL, pH) =

1(1, pL, pH)+E[2(1, D)] where D = D(pL, pH) =

1 pL

or (1pH) as appropriate. For

the two possible first-period product line decisions, we have the optimal profit under each strategy

as

Early expansion of product line (1 = {L, H}), then

early = maxpL,pH

({L, H}, pL, pH) = maxpL,pH

1({L, H}, pL, pH) + E[2({L, H}, D)]

. (8)

16


19/47

Defer decision to expand product line (1 = {H}), then

defer = maxpH

({H}, pH) = maxpH

1({H}, pH) + E[2({H}, D)]

. (9)

3.3 Optimal Product Line and Prices

This section specifies the optimal solutions employing the solution framework described in 3.2.

While we provide a complete analysis of this two-period problem using the solution framework

specified above in Appendix B in the online supplement, these solution details are needed in order to

analyze the impact of network effects and uncertainty on the firms product line expansion strategy.

Solving separately the three optimization problems (one for Eq. 8 and two implied by the max

term inside Eq. 9), we obtain

Lemma 1 (Optimal Prices and Profit of Early Expansion) Under a first-period product line{L, H}, optimal first-period prices are

pearlyL =

(22 2( + ))

42 22,

pearlyH =

2(4 2) 22 2( + 2)

82 222,

and the optimal total profit under early expansion is

early = (4(1 4 + + + 2) + 2 (2(1 + ) + 4+ 42))

162 422

22 ((1 4 + ) + (1 )22)

163 422. (10)

Lemma 2 (Optimal Prices and Profits of Deferred Expansion) For the two deferred expan-sion strategies:

1. Under a defer, H-only strategy, optimal first-period price and total expected profits are

pdefer, H-onlyH =

2 (1 + + )

4 22, (11)

defer, H-only =4 + 4(2 + ) + (4 + (4 + (4 (1 )2)))

4 (4 22).

(12)

17


20/47

2. Under a defer, expand strategy, optimal first-period price and total expected profits are

pdefer, expandH =

(2 (1 + (1 ))) 2( + )

4 2((1 ) + )2, (13)

defer, expand =2 (4 + 42 2(1 4 + (1 )( 2)))

16 42((1 ) + )2+

(4(1 + + ) + (22 + 2(4 2(1 )2) ( 2)))

16 42((1 ) + )2.

(14)

The optimal launch strategy can now be determined by comparing the total profits under the three

strategies specified in Eqs. 10, 12, and 14. We do this next.

4 Results

Lemma 1 and 2 provided the optimal prices and profits conditional on each of the three product line

strategies, (i) expand early ({L, H} in period 1 itself), (ii) expand late (H in period 1, add L in period

2), and (iii) sell H only. Intuitively, as explained earlier, the second-period network effects make

versioning an attractive strategy, but high product line expansion costs can force the firm to follow

an H only strategy. Our goal in this section is to examine and elaborate on these ideas with rigor and

precision. Moreover, higher network benefits (e.g., through greater value per-developer or through

higher levels of developer participation) make versioning more attractive in the second period; but

at the same time, early expansion increases the levels of developer participation. Additionally, we

seek to inquire about the impact of uncertainty in developer participation on the expansion strategy.

On the one hand, the firm might wish to delay the expansion decision until the level of developer

participationand, consequently, the level of consumer surplus available to extractbecomes better

known. Alternately, the firm may want to expand the product lineand installed baseearly, in

order to drive higher levels of developer participation and increase the available surplus in the second

period. This section investigates these two forces.

18


21/47

4.1 Optimal Product Expansion Strategy

Proposition 3 (Optimal Product Expansion Strategy) Pairwise comparison of the three ex-pansion strategy yields cutoff points (HD , DE, and HE specified in Eqs. 25, 26, & 27) suchthat

defer, expand > defer, H-only < HD , (15)

early > defer, expand < DE, (16)

early > defer, H-only < HE. (17)

Combining these results yields the optimal expansion strategy as

1. If < min{HD , DE} (low expansion cost), then early expansion is optimal.

2. When expansion cost is moderate, i.e., min{HD , DE} < < max{HD , DE},

(a) IfHD < < DE,i. if < HE, early expansion is optimal,

ii. otherwise (i.e., > HE), defer, H-only is optimal.

(b) IfDE < < HD , the firms optimal strategy is to defer the expansion decision to thesecond period, and then expand only if developer participation is high (case B).

3. If > max{HD , DE}, the optimal strategy is to sell productH only.

When fixed cost of launching L is very small, then the firm knows that having L in the second-

period product line improves second-period profit. For a traditional good, the firm would have no

reason to launch L early (in the first period) as discussed in the Benchmark case in Appendix C in the

online supplement . However, the presence of network effects induces the firm to launch L early in the

first period itself and benefit from higher installed base which causes greater developer participation

and creates greater value for consumers in the second period. For instance, after launching the

relatively expensive iPhone at the end of June 2007, Apple faced a relatively low expansion cost

of adding the iPod Touch, which is just the iPhone minus the calling feature. Importantly, such a

device had the promise of increasing the overall installed base of devices that could run iPhone apps,

which made the platform very attractive to potential application developers. Indeed after launching

iPhone at the end of June 2007 (and with no prior footprint in the world of mobile phones), Apple

quickly added an iPod Touch to the product line in September 2007. The iPod Touch was priced

19


22/47

much lower than the iPhone and also did not carry a recurring monthly cellular service fee. Industry

estimates (around January 2010) were that the installed base of the iPhone OS platform was nearly

doubled by addition of the iPod Touch, referred to as a stealth device for the platform.1

As becomes higher, however, the firms expansion strategy becomes more conservative: it

defers expansion in the first period, observes developer participation and then incurs expansion costs

only if Q is high enough to guarantee high gains from versioning. This leads to a sequential or

delayed expansion of the product line, if favorable market circumstances emerge. The evolution

of Apples iPod music player is a good example, because of the higher expansion costs associated

with a Windows version of the product and with multiple form factors for the product. Apple

launched iPod in October 2001 with a minimal product line, a Mac-only iPod in a single design

(with 5GB and 10GB disks). Only after observing iTunes roaring success did Apple branch into

an expanded product line with a Windows version of the iPod and additional form factors such as

the iPod Mini and iPod Shuffle. These moves, which involved substantial fixed costs of product line

expansion, enormously increased the iPod installed base but were deferred until Apple had observed

high developer participation and the consequent assurances of a successful product category.

Pushing further into the impact of uncertainty, we examine how the firms strategy shifts as the

degree of uncertainty in developer participation changes. To do this, we frame the cutoff points

for the optimal strategies as a combination of and ; this is because a change in alone (which

measures difference in Q between the W and B scenarios) affects both reservation prices and the

extent of uncertainty in participation. Figure 1(a) illustrates the impact of uncertainty. At = 0,

the firms optimal strategy is either to expand early if is very low or not to expand at all if is

higher; the wait and see approach of deferring expansion has no value due to lack of uncertainty,

and early expansion is always superior to deferred expansion. This same policy remains in forces as increases beyond 0 (because the uncertain component of Q remains small relative to the overall

value), except that the early expansion is optimal for a higher range of expansion costs due to

1See http://gigaom.com/apple/ipod-touch-now-outselling-iphone/.

20


23/47

arly

xpansion

Early

ExpansionNo

Expansion

No

Expansion

Defer,

Expand

Expansion Cost h

RandomOffset

x

(a)

arly

xpansion

arly

xpansion

o

xpansion

o

xpansion

efer,

xpand

Expansion Cost h

Probabilityq

(b)

Figure 1: Impact of Uncertainty about Developer Participation on Expansion Strategy

increase in second-period reservation prices. But, as depicted in Figure 1(a), as increases even

further, the firms optimal policy shifts and introduces a new element: defer and expand (only if

developer participation is high) for moderate expansion costs. The reason is that for such high ,

the uncertain component of second-period reservation prices is substantial (relative to the mean),

hence the uncertainty effect becomes dominant in the expansion policy and leads to the use of a

wait and see approach to product line expansion. The formal result is stated below.

Proposition 4 (Cutoff Point for Defer, Expand Strategy) There exists a unique > 0 such

that for low level of uncertainty in developer participationi.e., < the optimal strategy

is either to expand the product early (if expansion cost is very low, < DE()) or not at all.

For higher levels of uncertainty in developer participation, however, there is a moderate region of

expansion cost such that the optimal expansion strategy is a wait and see approach (expand in

the second period only if high developer participation is observed); hence for > , early expansion

is optimal if < DE(), H-only is optimal if > HD (), and deferred expansion is optimal when

[DE(), HD ()].

21


24/47

What is notable about this analysis is that there exists a range of expansion costs for which

early expansion is optimal even though, had the firm followed an H-only strategy in the first period,

expansion would not have been optimal in the second-period despite the removal of uncertainty

in developer participation. This result is surprising because intuition suggests that uncertainty

in developer participation (combined with relatively high expansion costs) makes early expansion

unattractive; if the firm expands at all, it should do so in the second period and only if developer

participation is high. The reason for the result is the intricate feedback loop between the firms

product line, installed base and developer participation. Note that the incremental second period

profit gain from versioning is influenced by the installed base D. Under an H-only product line

compared with early expansionD is relatively small, leading to smaller incremental gain and hence

versioning is attractive only for relatively low . If, however, the firm suboptimally expanded the

product line early and has higher installed base in period 1, then the second-period incremental gain

from versioning is higher than before, justifying the decision to incur the expansion cost in period

1. Implementation of this strategy can raise a startups short-term cash needs. But the costs of

financing these cash requirements can be more than offset by the spillover effect of increased D

on second-period profits that can generate enough gains to pay back the loan (and interest) in the

second period, even though this same action is unprofitable in the full-information setting of the

second period.

An alternative way to examine the impact of uncertainty is to alter and at the same time,

because changing alone implies higher developer market size on average. We conducted a numerical

study, maintaining the average market size fixed but changing the uncertainty parameter . Note

that if the average market size is kept fixed (by adjusting ), a lower corresponds to a higher

standard deviation ofQ. Therefore, Figure 1(b) shows the pure effect of uncertainty about developerparticipation, unconpounded by the effect of market size. As depicted in Figure 1(b), there exists

a threshold such that (i) when > , the optimal product launch strategy is either early or no

expansion, and (ii) when < , the deferred expansion strategy becomes attractive. To understand

22


25/47

(i), consider an extreme case where is very high (close to 1) and recall Proposition 2. Since high

developer participation is very likely, the firm might as well expand early if expansion is optimal at all

(i.e., product line expansion cost is low enough). For (ii), consider a low . Now, is relatively large

because we maintained the average market size. Hence, the firm is better off with early expansion

when product line expansion cost is low, and no expansion when it is very high. For moderate

expansion cost, the wait and see (deferred strategy) becomes very attractive because the payoff

is relatively large.

4.2 Managerial Implications for Startups vs. Established Firms

While many of our examples have focused on established firms entering new product categories

(e.g., Apple iPhone), our findings also have important implications for a startup that needs to

commercialize a platform product. Now we extend our discussion to technology startups and explain

how they can interpret our findings. As discussed, many technology-oriented firms frequently face

uncertainty in application development in two-sided markets. For instance, OpenTable has created a

market for connecting restaurants and diners; it provides technology to enable restaurant discovery,

reservations, and other applications. A big challenge for OpenTable was to obtain sufficient numbers

of restaurants (correspondingly, diners) into the network, in order to convince diners (correspondingly,

restaurants) to use the system.

In order to differentiate and compare startups and established firms, we analyze the effect of two

parameters in our model: , the ability to attract developers and , the size of early adopters. First, in

our model, the likelihood of participation is captured via the parameter in the participation function

Q = D + . It is widely accepted in the literature on software development that the reputation

of a software founder is critical to recruiting developers (West and OMahony, 2005). Therefore, we

assume that is lower for startups relative to the value for established firms: an OpenTable faces

greater challenges in obtaining participants than an Amazon might face in convincing publishers to

provide e-books for the Kindle (or, e.g., Apple to convince developers to write applications for the

23


26/47

iPad).

No

Expansion

Defer,

Expand

Early

Expansion

Abilityto Attract Developers g

ExpansionCosth

(a)

TotalExpecte

dProfit

Size of Early Adopters

Early

Defer

No

(b)

No

Expansion

Early

Expansion

Defer,

Expand

Size of Early Adopters k

ExpansionCosth

rl

r

(c)

Figure 2: Optimal Expansion Strategies for Startups vs. Established Firms

Figure 2(a) illustrates the variation in product line expansion strategies for startups vs. established

firms. In all three figures, startups are to the left side of the x axis, i.e., lower ability to attract

developers and smaller size of early adopters, while established firms are to the right side of the x

axis, i.e., greater ability to attract developers and larger size of early adopters. When expansion costs

are low, then even startups should consider early expansion as a way to increase the installed base and

position itself better for the second period. OpenTable addressed this problem by having multiple

levels of nonlinear pricing structures for small vs. large restaurants, both the initial one-time costs and

the continuing fees for providing customers to the restaurant. It also offers restaurants a choice (and

different price levels) between using their own reservation technology and paying only for customer

reservations vs. paying for reservation software and hardware as well. This example also indicates a

useful strategy that many firms can follow: create product variety and segment customers through

differentiation in pricing (which is relatively less expensive to administer) rather than designing

multiple physical products. For the HEP example discussed in the Introduction section, our results

suggest that the firm can offer i) a low-end patient HEP with a minimal fee (or even free) that

might have fewer features and ii) a high-end with a higher fee that could communicate more data,

and more real-time communication, to clinicians. But the result also suggests that when product

line expansion costs are higher, early expansion is less attractive to startups than to established

24


27/47

firms. As an additional consequence, this finding also suggests that a startup should carefully build

its business strategy to attract more developersi.e., raise its by providing development tools

or incentives.

The second differentiating parameter for startups and established firms is . Specifically, startups

and established firms may also differ in their ability to attract early adopters who purchase the product

based solely on core standalone features and do not require substantial developer participation before

their purchase decision. Therefore, we assume that startups are likely to have lower relative to

established firms, consistent with the existing theory in the marketing literature that a firm with a

better reputation has a higher chance to get early adoptions of its product (Herbig and Milewicz,

1995). Figure 2(b) illustrates the effect of on the expansion strategy. First, note that as

increases, total profit increases in all of the strategies because of the increase in total market size.

However, compare deferred expansion against no expansion. In both cases, the first period action

is the same (H-only), but deferred expansion can exploit higher more because higher leads to

higher D and higher Q, hence higher reservation prices in the second period. Thus, the deferred

expansion profit grows faster than no expansion profit as increases. For the early expansion

profitwhich partially sacrifices short-term (first-period) profit in order to have better position in

the second periodhigher increases the first-period sacrifice, but it also delivers higher D and Q,

leading to greater gain in second period. Therefore, the desirability of early expansion increases with

. This point is reinforced in Figure 2(c) which demonstrates that an established firm is more likely

to follow early expansion; and, if expansion costs are too high, it might just choose not to expand at

all (this is because with higher , Q becomes more certain, reducing the benefit from a wait and

see approach). In contrast, a startup is more likely to find deferred expansion attractive because

the uncertain component of Q carries greater weight when is small.

25


28/47

4.3 Role of Marginal Costs

While the assumption of same (or zero) marginal cost is realistic for information goods and widely

accepted in the literature, relaxing this assumption may add reality since some hardware platforms

may have different marginal costs, e.g., Sonys inclusion of a Blu-Ray player in the PS3 gaming

console substantially raised the per-unit costs of the console. In this section, we investigate whether

our main analysis and findings can be applicable to different marginal costs by relaxing same marginal

cost assumption. We start with checking the robustness of our main findings, i.e., comparison

between early and deferred expansion strategies. In order to better reflect reality that marginal

cost increases with product quality, we assume different levels of marginal costs for H and L, then

normalize Ls marginal cost to be zero. We denote Hs positive marginal cost with c. The result is

summarized in the following proposition.

Proposition 5 (Optimal Strategy with Different Marginal Costs) There exists a unique cut-off point such that

1. When < , early expansion outperforms defer, expand strategy,

2. When > , there exists a unique cutoff point such that defer, expand strategy is preferredwhen the level of favorable developer participation is low ( < ) and early expansion is optimal

when it is high ( > ).

This result demonstrates that deferred expansion could be a viable strategy even with different

marginal costs. Next we examine the impact of different marginal costs on optimal product launch

strategy. Specifically, we compare two different defer, expand strategies, i.e., launch H first then

expand later (H, Then L) vs. introduce L first, then expand later (L, Then H). With same

marginal cost, trivially we see that H, Then L dominates L, Then H. But, more generally, the

optimal expansion strategy depends on the difference in marginal cost structures between the low

and high quality versions. Let H, Then L (L, Then H) be the profit function of H, Then L

(L, Then H).

26


29/47

Proposition 6 (Optimal Sequential Launch with Different Marginal Costs) Let g() be the

profit difference (L,Then H H,Then L). If g()|c=1 > 0, there exists a unique cutoff point c

whereH, Then L is preferred ifc < c. Otherwise (c > c), L, Then H is preferred. Ifg()|c=1 < 0,

H, Then L is always preferred.

This result indicates that marginal costs primarily influence the sequence of product launch and

versioning, rather than the timing of the expansion decision. Specifically, when the incremental cost

of the H version is quite high, then the firm may employ a sequential expansion strategy when it

launches the lower-quality version L first, and then launches the more expensive product if positive

market conditions emerge. This is in contrast to the equal-cost case where launching H first is

always optimal. The reason why the firm might want to launch L first is that it wants to sustain

enough market share in the first period in order to create enough incentives for high developer

participation. Launching the higher-cost version, H, first, would force the firm to either sacrifice

margin in the first period or obtain a much lower market share if it sets a high price.

5 Concluding Remarks

Many innovative platform products have been launched in the last two decades, including Xbox,

PlayStation, Palm Pilot, Microsoft Platform products, iPhone, iPod, and iPad. Firms have deployed

different ways of introducing new platform products. This observation inspired us to investigate

optimal product launch and pricing strategies for a firm that wants to launch a new platform product

when it is uncertain about third-party application development. While prior studies mostly considered

uncertainty about user adoption, our paper is novel in its consideration of uncertainty in application

development. This factor is relevant because the level of developer participation plays a critical role

in consumers purchase decisions. The consideration of developer participation uncertainty leads

to the novel finding that deferred expansion can often be the optimal product launch strategy.

We demonstrated that a technology-oriented startup needs to pay special attention to the level

27


30/47

of expansion cost, the number of early adopters (or technology enthusiasts), and the likelihood of

application developers participation. For a startup with a technology product that wants to expand

rapidly in a two-sided market, our study suggests some important practical guidelines, including

i) increase the awareness of the product to make customers purchase it in early stages and ii)

provide some incentives or convenient development tools to application developers for fast-growing

applications.

Our analysis has several limitations that demand some consideration. First, we modeled cus-

tomers as arriving in two periods, and constrained the first-period customers to either buy in the first

period or vanish from the market. Relaxing this assumption might reduce second period profits, and

it appears to shift the strategic choice of expansion timing away from deferred expansion. However,

we believe it does not materially affect the results. Second, in computing the first-period customers

utility for the product, our model ignored the anticipated value from having a collection of third-

party developers in the later period. This view is reasonable if the first-period customers apply a

very high discount rate for future benefits of this sort, e.g., when they are technology innovators

who purchase a new technology based primarily on visible standalone features. The assumption was

also motivated by reasons of computational tractability (which is harmed by the inclusion of such

anticipated benefits). However, we conducted numerical experiments and confirmed that our main

findings still hold: with anticipated benefits, more early adopters would purchase the product in the

first period, but this does not change a firms selection of optimal product launch strategy. Despite

these limitations, we hope that the conceptual insights provided in this paper will be of value to a

spectrum of firms that develop new platform technologies.

This paper can be improved in additional ways, which motivate a few directions for future re-

search. First, future research can focus more on technology startups. It is generally more difficult forstartups to attract developers and early adopters. Therefore, many startups would be keenly inter-

ested in the effect of other pricing/marketing strategies that could help attract more early adopters

(e.g., free distribution, free trial-version) and/or improve developers participation (e.g., increasing

28


31/47

technology investment for App development, providing incentives to developers, and identifying op-

timal contract mechanism). Second, while we assumed that the manufacturer already acquired the

innovative technology with R&D investment (i.e., development cost was sunk), it would be useful

to examine how uncertainty in developer participation impacts the level of innovation. This problem

might be particularly important to many startups because of their limited resources. Note that

with limited resources, identifying an appropriate level of innovation is a very important decision

problem. Third, further investigation of the two product line expansion strategies with more gen-

eral assumptions (e.g., continuous distribution for uncertainty, relaxing two assumptions mentioned

above) would improve our understanding of the dynamics of platform product launch strategies.

Fourth, product line expansion is often dictated by technological improvement, a factor ignored in

our model and other studies of sequential vs. simultaneous versioning (Moorthy and Png, 1992).

It would be useful to examine how the expansion strategy is impacted by the interplay between

technological improvement and other factors considered in this paper. Considering the fact that

many startups gradually improve their technologies over time, this extension will shed more light on

startups technology commercialization strategies.

Acknowledgement

The authors thank the guest editors (Prof. Moren Levesque & Prof. Nitin Joglekar), senior editor,

and two referees for their contribution in improving the clarity and quality of this paper.

References

Aoki, R. and T. J. Prusa (1997): Sequential versus simultaneous choice with endogenous

quality, International Journal of Industrial Organization, 15, 103121.

Armstrong, M. (2006): Competition in two-sided markets, RAND Journal of Economics, 37,668691.

Armstrong, M. and M. Levesque (2002): Timing and Quality Decisions for EntrepreneurialProduct Development, European Journal of Operational Research, 141, 88106.

29


32/47

Bag, P. K. and S. Roy (2010): On sequential and simultaneous contributions under incompleteinformation, International Journal of Game Theory, Forthcoming.

Bhargava, H. and V. Choudhary (2001): Information good and vertical differentiation,Journal of Management Information Systems, 18, 89106.

(2004): Economics of an information intermediary with aggregation benefits, InformationSystems Research, 15, 2236.

Cachon, G. and M. Lariviere (2001): Contracting to assure supply: how to share demandforecasts in a supply chain, Management Science, 47, 629646.

Carrillo, J. (2005): Industry clockspeed and the pace of new product development, Productionand Operations Management, 14, 125141.

Chen, F. (2005): Salesforce Incentives, Market Information, and Production/Inventory Planning,Management Science, 51, 6075.

Choi, Y., M. Levesque, and D. Shephert (2008): When should entrepreneurs expedite ordelay opportunity exploitation, Journal of Business Venturing, 23, 333355.

Christensen, C. and J. Bower (1996): Customer power, strategic investment, and thefailure of leading firms, Strategic Management Journal, 17, 197218.

Daneels, E. (2004): Disruptive technology reconsidered: A critique and research agenda,Journal of Product Innovation Management, 21, 246258.

Davidsson, P., P. Steffens, and J. Fitzsimmons (2008): Performance assess-ment in entrepreneurship research: is there a pro-growth bias? Downloadable at from

http://eprints.qut.edu.au/archive/00012040.

(2009): Growing profitable or growing from profits: Putting the horse in front of the cart?Journal of Business Venturing, 24, 388406.

DeCastro, J. and J. Chrisman (1995): Order of market entry, competitive strategy andfinancial performance, Journal of Business Venturing, 33, 165177.

Deneckere, R. and R. McAfee (1996): Damaged goods, Journal of Economics and Man-agement Strategy, 5, 149174.

Dhebar, A. (1993): Cambridge Software Corp. Harvard Business School.

Dogan, K., Y. Ji, V. S. Mookerjee, and S. Radhakrishnan (2010): Managing theversions of a software product under variable and endogenous demand, Information SystemsResearch, Forthcoming.

Eisenmann, T. (2007): Platform-Mediated Networks: Definitions and Core Concepts, HarvardBusiness School.

30


33/47

Eisenmann, T., G. Parker, and M. Van Alstyne (2006): Strategies for two-sided mar-kets, Harvard Business Review, 84, 92101.

(2011): Platform Envelopment, Strategic Management Journal, Forthcoming.

Ha, A. Y. and S. Tong (2008): Contracting and Information Sharing Under Supply Chain

Competition, Management Science, 54, 701715.

Hagiu, A. (2006): Pricing and commitment by two-sided platforms, RAND Journal of Eco-nomics, 37, 720737.

Herbig, P. and J. Milewicz (1995): The Relationship of Reputation and Credibility to BrandSuccess, Journal of Consumer Marketing, 12, 510.

Jing, B. (2007): Network Externalities and Market Segmentation in a Monopoly, EconomicLetters, 95, 713.

Joglekar, N. and M. Levesque (2006): Marketing, R&D, and startup valuation, IEEETransactions on Engineering Management, 56, 229242.

Joshi, Y. V., D. J. Reibstein, and J. Zhang (2009): Optimal entry timing in markets withsocial influence, Management Science, 55, 926939.

Katz, M. L. and C. Shapiro (1992): Product Introduction with Network Externalities, TheJournal of Industrial Economics, 40, 5583.

Lee, Y. and G. OConnor (2003): New product launch strategy for network effects products,Journal of the Academy of Marketing Science, 31, 241255.

Levesque, M. (2000): Effects of Funding and Its Return on Product Quality in New Ventures,IEEE Transactions on Engineering Management, 47, 98105.

Levesque, M. and D. Shepherd (2004): Entrepreneurs choice of entry strategy in emergingand developed markets, Journal of Business Venturing, 19, 2954.

Liu, H. and P. Chintagunta (2009): Pricing under network effects, in Handbook of pricingresearch in marketing, Edward Elgar Publishing, Inc., MA, USA, 435450.

Mahajan, V. and E. Muller (1998): When is it worthwhile targeting the majority instead ofthe innovators in a new product launch? Journal of Marketing Research, 35, 488495.

Mahajan, V., E. Muller, and F. M. Bass (1990): New product diffusion models inmarketing: A review and directions for research, Journal of Marketing, 54, 126.

March, J. (1991): Exploration and Exploitation in Organizational Learning, Organization Sci-ence, 2, 7187.

Markman, G. and W. Gartner (2002): Is extraordinary growth profitable? A study of Inc.500 high-growth companies, Entrepreneurship Theory and Practice, 27, 6575.

31


34/47

Mitra, K. (2010): One D too many, Business Today. October 3, 2010.

Moe, W. W. and P. S. Fader (2002): Using advance purchase orders to forecast new productsales, Marketing Science, 21, 347364.

Moore, G. (1991): Crossing the chasm, New York: Harper Business.

Moorthy, K. S. and I. P. L. Png (1992): Market segmentation, cannibalization and thetiming of product introductions, Management Science, 38, 345359.

Nuttall, C. (2010): Lack of content leaves 3D TV sales flat, Financial Times. December 16,2010.

Padmanabhan, V., S. Rajiv, and K. Srinivasan (1997): New products, upgrades, andnew releases: A rational for sequential product introduction, Journal of Marketing Research, 34,456472.

Parker, G. and M. Van Alstyne(2005): Two-sided network effects: A theory of informationproduct design. Management Science, 51, 14941504.

Ramdas, K. (2003): Managing product variety: An integrative review and research directions,Production and Operations Management, 12, 79100.

Ramezani, C., L. Soenen, and A. Jung (2002): Growth, corporate profitability, and valuecreation, Financial Analysts Journal, 62, 5667.

Reinhardt, G. and M. Levesque (2004): A new entrants decision on virgual versus bricks-and-mortar retailing, Journal of Electronic Commerce Research, 3, 136152.

Rochet, J. and J. Tirole (2003): Platform competition in two-sided markets, Journal ofthe European Econmic Association, 1, 9901029.

(2006): Two-sided markets: a progress report, RAND Journal of Economics, 37, 645667.

Shan, W., G. Walker, and B. Kogut (1994): Interfirm cooperation and startup innovationin biotechnology industry, Strategic Management Journal, 15, 387394.

Shy, O. (2001): The economics of network industries, Cambridge University Press.

Van den Bulte, C. and Y. V. Joshi (2007): New product diffusion with influentials andimitators, Marketing Science, 26, 400421.

Villas-Boas, J. M. (2004): Communication Strategies and Product Line Design, MarketingScience, 23, 304316.

West, J. and S. OMahony (2005): Contrasting Community Building in Sponsored and Com-munity Founded Open Source Projects, Proceedings of the 38th Hawaii International Conferenceon System Sciences.

32


35/47

Online Supplement for

Commercialization of Platform Technologies:Launch Timing and Versioning Strategy

A Asymmetric Valuations Across Time

Our formulation assumed that the aggregate customer valuations were identical in both periods, with

v being uniformly distributed on [0, 1] in both periods. However, it could be also true that the early

adopters in the market are likely to be the ones with higher valuations for the product (especially so,

if they are willing to purchase without any third-party applications); therefore assuming that early

adopters and followers have the same valuation for the product is a simplification. In this section,

we relax this assumption to see if the main results still hold.

Consider the case where valuations of first period customers (early adopters) are higher than that

of second period customers (followers). This can be achieved by setting the first period customers

valuations as uniformly distributed on [0, b] where b > 1. We examine the impact on our earlier

finding about the linkage between the expansion strategy and the uncertainty about developer

participation (recall Figure 1(a)).

Solid: b=1

Dashed: b=3

Expansion Cost h

RandomOffsetx

Figure 3: Effect of Asymmetric Customer Valuations on Optimal Expansion Strategy

1


36/47

Figure 3 illustrates the impact of this relaxation: shift in the cutoff lines for the different strategies

(the solid lines represent the base case b = 1 while the dashed lines represent b = 3). The main

impact is that early expansion becomes more attractive. The intuitive explanation for this observation

is that, as b increases above 1, the product becomes more attractive in the first period, leading to

higher market share and increasing the likelihood of high developer participation. Therefore, early

expansion is preferred for a higher threshold of expansion cost. Overall, our main finding holds with

the relaxation, and the impact is in a predictable way.

B Technical Details for Lemma 1 and 2

We organize our analysis in terms of the two high-level strategic choices that the firm faces at the

start of the game, whether to expand the product line early or to defer the expansion decision to

the second period after uncertainty about developer participation is resolved.

B.1 Early Expansion of Product Line

In this case, because the firm incurs its product line expansion cost in the first period and enters

the second period with {L, H}, the second period decision is simply a pricing problem for the

two products. The optimal second-period prices will depend on the realized level of developer

participation, hence we obtain

1. Case B (high level of developer participation): Taking derivatives of the second-period profit

term against L and H, the second-period prices are solutions to the system of simultaneous

equations representing the first-order conditions. We have the optimal prices

earlyL =

+

1 pL

+

2,

earlyH =

1 +

1 pL

+

2,

2


37/47

and they yield the second-period profit as

B2 (D, {L, H}) = + 2

1 pL

+

+ 2

1 pL

+ 2

4. (18)

2. Case W (low level of developer participation): By setting = 0 for the optimal solutions

of Case B, we can obtain L and H, and the system of simultaneous equations yields the

second-period profit as

W2 (D, {L, H}) = + 2

1 pL

+ 2

1 pL2

4. (19)

Combining these two profit terms (Eq. 18 and Eq. 19) into Eq. 5 and then into Eq. 8, the optimal

expected total profit, conditioned on early expansion, is

early = maxpL,pH

pH

1 pHpL1

+ pLpHpL1

pL

+

B2 (D; {L, H}) + (1 ) W2 (D; {L, H})

(20)

where D =

1 pL

. Solving it optimally yields Lemma 1.

B.2 Deferred Expansion Decision

Now, consider the case where the firm launches only H in the first period. In order to compute

expected second-period profit under this strategy (E[2({L, H}, D)], Eq. 7), we need to solve

second-period price-optimization problems and compute the following three terms, W2 (D; {H}),

3


38/47

B2 (D; {H}), and B2 (D; {L, H}).

B2 (D; {H}) =(1 + (D+ ))2

4, (21)

B2 (D; {L, H}) =(1 + 2(D+ )) + 2(D+ )2

4 , (22)

W2 (D; {H}) =(1 + (D))2

4. (23)

Combining these three terms into Eq. 7 and then substituting into Eq. 9, the total expected

profit from a deferred expansion strategy is (after replacing D with (1 pH))

defer = maxpH

(pH(1 pH))) + (1 )(1+((1pH)))

2

4

+ max

(1+((1pH)+))2

4,

(1+2((1pH)+))+2((1pH)+)

2

4

. (24)

Solving separately the two optimization problems implied by the max term inside Eq. 24, we

obtain Lemma 2.

C Benchmark: No Network Effect

The main goal of our paper is to isolate the impact of the platform characteristics (i.e., cross-network

effects) and developer participation uncertainty on the firms product launch and timing decision.

In order to see this, we describe two benchmark cases representing zero network effects and/or zero

uncertainty about developer participation.

1. First, set = 0, i.e., consumer valuations for the product do not depend in any way on

developer participation (this setting also, in effect, makes developer uncertainty irrelevant).

Due to this, the two periods collapse into one, and with the linear utility form U(v, q) = v q,

it follows from past literature that versioning is not optimal for the firm (see, e.g., Bhargava

4


39/47

and Choudhary, 2001; Deneckere and McAfee, 1996).

2. Second, set = 0, i.e., developer participation does not depend on installed base. Moreover,

let = 1, i.e., there is no uncertainty about developer participation. Now, second-period

customers utility has the form vq + k due to which it follows from Bhargava and Choudhary

(2004) that versioning is optimal in period 2, i.e., the firms second period product line is

{L, H}. Normally, the uncertainty about developer participation should make the firm cautious

about incurring fixed costs needed to expand its product line early. But, it can trivially be

seen that, even in the absence of this uncertainty (and even though the cost of launching L

is the same in either period) the firm has no incentive to launch L in the first period.

What these two benchmark cases demonstrate is that (a) when the user side of the platform

does not perceive cross-network benefits (i.e., users do not care about developer participation), then

versioning is not optimal for the firm, and (b) when the developer side does not care about installed

base, then the firm has no reason to expand its product line early despite its fore-knowledge about

developer participation and about the positive impact of developer participation on valuations of

second-period consumers.

D Other Proofs

Proof of Proposition 1. Note that the incremental profit from versioning is

versioning2 =

2Q2(1 )

4> 0.

By taking first derivative of this profit with respect to Q, we have

versioning2

Q=

2Q(1 )

2> 0.

5


40/47

Proof of Proposition 2. We first consider early expansion. Recall from Lemma 1 that the

profit under early expansion is as follows:

early

=

(4(1 4 + + + 2) + 2 (2(1 + ) + 4+ 42))

162 422

22 ((1 4 + ) + (1 )22)

163 422.

Suppose a firm foresees product expansion inevitable in period 2. Then, following the same logic

described in Appendix B, we can derive total expected profit as

defer =2(4 16 + (2 + )2 + 8) 2(1 )42

4

(4

22

)+

2(2(4 1) + 4+ 42)

4(4 22).

By taking difference of these two profits, we have

early defer =(1 )22(2 + + 2)2

4(4 22)(42 22).

Because 42

> 2

is a necessary condition for positive prices, the profit difference is always

positive. Therefore, it is optimal to expand in period 1 when product expansion is inevitable.

Proof of Proposition 3. Recall from Lemma 1 that the profit under early expansion is as

follows:

early = (4(1 4 + + + 2) + 2 (2(1 + ) + 4+ 42))

162 422

22 ((1 4 + ) + (1 )22)

163 422.

6


41/47

Also, recall from Lemma 2 that the profits are

defer, H-only =4 + 4(2 + ) + (4 + (4 + (4 (1 )2)))

4 (4 22),

defer, expand = 2

(4 + 42

2

(1 4 + (1 )( 2)))16 42((1 ) + )2

+

(4(1 + + ) + (22 + 2(4 2(1 )2) ( 2)))

16 42((1 ) + )2.

We first compare defer, expand with defer, H-only. We find the cutoff point in terms of at

which defer, expand defer, H-only = 0. Note that the first derivative of profit difference with

respect to is , implying that profit difference is monotonically decreasing in . The detailed

expression of HD is

HD =(1 )2(4+ (2 + (1 (1 ))))2

4(4 22)(4 2((1 ) + )2). (25)

Because 42 > 2 (a necessary condition for positive prices), we see that 4 > 22 and

4 > 2((1 ) + )2). Therefore, HD > 0, implying that defer, expand > defer, H-only

when < HD , while defer, expand < defer, H-only when > HD .

We now compare early expansion with deferred expansion. The profit difference is denoted with

f() (i.e., f() = early defer, expand). Note that f() = 1 + < 0, meaning that f()

is a monotonically decreasing in . Next, we find the cutoff point of at which profit difference

becomes zero. We denote this cutoff point with DE. Note that

DE =(1 )22(43(1 + (1 )(1 + )) + 2(1 )242)

4(1 )(42 22)(4 2((1 ) + )2)+

(1 )22(2((4 + (1 )(1 + )) + 4(2 + (1 )))

4(1 )(42 22)(4 2((1 ) +

Commercializing of Platform Technologies

Documents

Transcript of Commercializing of Platform Technologies