commercialization of platform technologies: launch timing and versioning strategy

Commercialization of Platform Technologies: LaunchTiming and Versioning Strategy*

Hemant K. BhargavaGraduate School of Management, University of California Davis, GH-3108, Davis, California 95616, USA, [email protected]

Byung Cho KimGraduate School of Management of Technology, Sogang University, 35 Baekbeom-ro (Shinsu-dong), Mapo-gu, Seoul, 121-742, Korea,

[email protected]

Daewon SunDepartment of Management, Mendoza College of Business, University of Notre Dame, Room 359, Notre Dame, Indiana 46556, USA,

[email protected]

M any emerging entrepreneurial applications and services connect two or more groups of users over Internet-basedinformation technologies. Commercial success of such technology products requires astute business practices

related to product line design, price discrimination, and launch timing. We examine these issues for a platform firm thatserves two markets—labeled as user and developer markets—such that the size of each market positively impacts partici-pation in the other. In addition, our model allows for sequential unfolding of consumer and developer participation, andfor uncertainty regarding developer participation. We demonstrate that product versioning is an especially attractive strat-egy for platform firms, that is, the trade-off between market size and margins is tilted in the direction of more versions.However, when expanding the product line carries substantial fixed costs (e.g., marketing cost, cost of additional plant,increased distribution cost), then the uncertainty in developer participation adversely impacts the firm’s ability to offermultiple versions. We show that for established firms with lower uncertainty about developer participation, the choice isessentially between an expanded or minimal product line. Startups and firms that are entering a new product categoryare more likely to benefit from a “wait and see” deferred expansion strategy.

Key words: technology commercialization; product launch strategy; platform technology; versioning; uncertaintyHistory: Received: September 2010; Accepted: January 2012 by Moren Levesque and Nitin Joglekar, after 3 revisions.

1. Introduction and Motivation

Technological innovation is an expensive and uncer-tain process which often requires high-end researchand development of new components, productionprocesses, and underlying technologies. Often, entre-preneurs and firms are unable to successfully commer-cialize their innovation despite having technologicallysophisticated products. Success requires clearingmany hurdles and adoption of astute business strate-gies (Christensen and Bower 1996, Daneels 2004,Moore 1991). Challenges include the “chicken andegg problem” (e.g., a new payment technology will beadopted only if accepted by sufficient number of mer-chants, but merchant adoption will itself depend on asufficient installed base of users), uncertainty in prod-uct design and compatibility (e.g., should—or will—all electric car technologies employ the same batterythat can be charged at every battery station, or willthe market be fragmented among multiple technology

formats?), the challenge of convincing consumers topay high (and definitive) up-front costs in return forsmall (and uncertain) benefits delivered over a longtime (e.g., residential solar power), and the growth vs.profitability dilemma (e.g., should a vendor of ane-book technology sacrifice margin and profits inreturn for high market share, to entice publisherstoward its technology?). This article examines thisfinal challenge, that is, the growth vs. profitabilitydilemma, for technology goods.Our research focuses on technology products that

operate as platforms in a two-sided market. These areproducts that exhibit positive cross-network effectsbetween two distinct networks of players, that is,market adoption on one network influences, anddepends on, the desirability of adoption on the othernetwork (Eisenmann 2007, Eisenmann et al. 2006). Forexample, video gaming consoles serve (i) gamers, bygiving them technology for playing complex videogames and (ii) game developers, by giving them a

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Vol. 0, No. 0, xxxx–xxxx 2012, pp. 1–15 DOI 10.1111/j.1937-5956.2012.01344.xISSN 1059-1478|EISSN 1937-5956|12|0|0001 © 2012 Production and Operations Management Society

platform for executing such games and reachingpotential buyers; hence a console platform thatattracts more game developers becomes more valu-able to gamers, and conversely, game developers areattracted to console platforms that have many gamers.Similarly, operating system platforms connect com-puter users with application software developers.More recently, smartphones have, as small comput-ers, become platforms for connecting phone userswith a variety of computational software and serviceapplications. As noted above, the launch of a platformtechnology presents the firm a tough challenge of bal-ancing customer growth and short-term profitability.Growth requires very low (or zero, or even negative)prices to propel interest in the future or on the otherside of the market, but these low prices lower thefirm’s short-term profit.This growth vs. profitability dilemma is common

for many startup entrepreneurial ventures, such asinformation applications (for mobile phones, tablets,and computers) which connect two or more groups ofusers over the Internet. Examples include (i) FiGuide.com, which provides personal financial services bycreating a financial planner and subscriber network,and (ii) Asana.com, which offers project managementtools for a manager and employee network. To illus-trate the dilemma, consider a startup firm which aimsto deliver and manage home exercise programs (HEP)on mobile phones. In contrast to the traditional print-based programs, the use of mobile phones can delivermultimedia content tailored to the patient, and it canalso track and transfer information to the clinician.Adoption involves a bidirectional loop betweenpatients (as users and direct beneficiary of the pro-gram) and clinicians (who prescribe the exercise pro-grams, customize and configure the application forthe patient, and track information about complianceand effectiveness). Because clinicians have very thinmargins and are unlikely to pay for the service, thefirm intends, at least initially, to generate revenue pri-marily from patients. Charging a high price topatients will generate the revenue that is desperatelyneeded to fund new applications but it will alsorestrict adoption, that, in turn, makes it difficult toentice clinicians to participate. This is the essence ofthe growth vs. profitability dilemma for such startupfirms.The tension between growth and profitability has

been discussed in the entrepreneurship literature.Firm growth is a common measure of success(Davidsson et al. 2008), but the wisdom is that toomuch growth must come at the expense of profitabil-ity (Markman and Gartner 2002, Ramezani et al.2002). Davidsson et al. (2009) examine the effective-ness of growth as a measure of business success froma Resource-Based View (RBV) and argue that sound

growth starts with achieving profitability. But gener-ally, growth and profitability are considered to be inconflict especially for products with network effects,the common belief being that firms can either havegrowth or achieve profitability. Many startup ven-tures respond to the tension between growth andprofitability by initially producing only a single ver-sion of their product (to avoid production complexityor perhaps to place their best foot forward to all theircustomers) and selling it at a relatively low priceneeded to accelerate growth. We argue that this maybe an unnecessarily extreme approach, and it magni-fies the conflict.Our research is founded on the proposition

that growth and profitability need not necessarilyoperate in conflict. Existing theory on market segmen-tation and product differentiation suggests versioning(i.e., an expanded product line with multiple, verti-cally differentiated, versions) as a way out especiallywhen network effects are present (Bhargava andChoudhary 2004, Jing 2007). This strategy is capturedin our model by giving the firm a choice to launchtwo versions of the product, a basic and a premiumone. The high-end version provides high margin,while the low-end, low-priced version delivers highmarket share and installed base necessary to generatesubstantial network effects. Yet this does not implythat vendors of new platform technology shouldlaunch the technology with an expanded, rather thanminimal, product line. Product line expansion istempered by the additional complexity and costs,including operations costs (additional plant, manag-ing multiple sets of inventory, increased complexityin distribution), marketing costs (data collectionand price optimization, segment development andmanagement, and advertising to multiple customersegments [Dhebar 1993, Villas-Boas 2004]), and canni-balization costs due to increased competition withinthe product line. This feature is captured in our modelas an incremental product line expansion cost, specifi-cally the one-time or fixed cost of executing on theexpansion strategy. The third feature of our model iscommon to platform goods, that is, that developerparticipation involves network effects and dependson having an installed base of end-users. The fourthand distinctive feature of our model is that devel-oper participation also has a random unpredictablecomponent. Although past literature has taken adeterministic rational expectations framework todescribing network effects (see e.g., Rochet and Tirole2006), we argue that platform firms face substantialuncertainty about whether or not they can secure par-ticipation by the developer market.Our research contributes to the entrepreneur-

ship literature by highlighting the launch strategyand timing problem for platform goods and other

Bhargava, Kim, and Sun: Commercialization of Platform Technologies: Launch Timing and Versioning Strategy2 Production and Operations Management 0(0), pp. 1–15, © 2012 Production and Operations Management Society

products that exhibit strong network effects. Intui-tively, platform firms may implement a minimalproduct line to avoid higher fixed costs at launch,and wait for substantial developer participationbefore expanding the product line. In contrast, weargue that the firm should be inclined to expand theproduct line early to increase its installed base andinduce a higher level of developer participation. Thenovel feature in this analysis is the role of the ran-dom component in developers’ participation deci-sions. Firms that develop platform products oftenhave little or no direct control over the number ofthird party applications; however, they can influencedevelopers through the design of their productlaunch strategy. We show that under networkeffects, early expansion is generally better thandeferred expansion. The exception is that the firmshould employ a “wait and see” (or deferred expan-sion) approach when developer participation isextremely uncertain (and expansion costs are high).The key insight, however, is that early expansion canbe useful even in the face of developer uncertainty:by expanding the user market available to applica-tion developers, it can drive developer participationhigh enough to the point where the gains exceed theexpansion costs. In other words, platform firms are(compared with products that do not exhibit cross-network effects) more likely to benefit from launch-ing multiple versions simultaneously rather thansequentially after observing developer participation.We note that in the technology industry, many

established firms also experience challenges typicallyfaced by startups. For instance, when Apple enteredthe entertainment market with the introduction of itsiPod music player in 2001, it was known as a maker ofcomputers. It had no footprint in home entertainmentproducts, lacked recognition as a music retailer, anddid not enjoy supply relationships with music provid-ers. These factors caused substantial uncertaintyregarding whether music firms—highly concernedabout piracy, and worried that digital music wouldamplify it—would in fact license their music for digi-tal distribution through iTunes. Other times, estab-lished firms want to retain a startup flavor to benefitfrom the innovations and breakthroughs that oftenemerge out of new thinking. For instance, Google,Intel, eBay, and other technology firms have internalorganizational structures (such as technology incuba-tors) and incentive schemes aimed at generating tech-nology startups. Hence our research has relevanceboth to established technology firms that are creatingnew products and entering new markets, and tostartup or entrepreneurial ventures.However, startups in the technology industry

should be cognizant about crucial differences that canlead to different strategies from those suited to estab-

lished firms (Joglekar and Levesque 2006, Shan et al.1994). Intuitively, the deferred (rather than early)expansion strategy is particularly suited to startupswhich are more weakly positioned with respect todeveloper participation. Established firms on theother hand—those that have a high fraction of earlyadopters and/or little uncertainty about developerparticipation—face a clearer choice between expan-sion (if costs are low) or not (for high expansion cost).Our model provides a rigorous foundation for under-standing how the expansion decision is influenced bythe interplay between intensity of network benefit,adoption characteristics, and uncertainty in developerparticipation. We demonstrate that despite suchuncertainty, and to some extent because of it, earlyexpansion can be desirable for startups. This isbecause versioning expands the early-stage installedbase, and this increase in market adoption reducesthe weight of the uncertain component in the extentof developer participation.

2. Literature Review

We discuss three streams of research that tie into ourwork: product launch timing, two-sided markets, andoptimal strategies for startup ventures.

2.1. Product Launch Timing and Two-SidedMarketSeveral researchers have studied the optimal timingof product launch. Ramdas (2003) provides a frame-work for examining a firm’s variety management anddiscusses strategies associated with variety-creatingdecisions. Carrillo (2005) examines the impact ofindustry clock-speed on pacing of new product devel-opment activities. Bag and Roy (2011) study distribu-tion of public goods with multiple providers andshow that total contribution generated in a sequentialmove game may be higher than in a simultaneousmove game under incomplete information. Aoki andPrusa (1997) find that sequential quality choice leadsto smaller quality investment and higher profit, but itlowers consumer and social surplus. Padmanabhanet al. (1997) study a firm’s new product launch strat-egy under consumer uncertainty regarding networkexternality. They argue that sequential launch withsequential provision of quality is optimal for a high-externality firm since under-provision of introductoryquality may serve as a signal of high externality.Moorthy and Png (1992) show that when a firm facesa serious threat from cannibalization, it may be opti-mal for the firm to serve high-valuation customersfirst and later introduce lower-quality version tocover low-valuation segment. We also examinesequential product launch, but unlike these priorstudies, we do so in the context of a two-sided market,

Bhargava, Kim, and Sun: Commercialization of Platform Technologies: Launch Timing and Versioning StrategyProduction and Operations Management 0(0), pp. 1–15, © 2012 Production and Operations Management Society 3

and specifically in the presence of uncertainty regard-ing developer participation.The literature on platforms has recognized that

several traditional business strategies such as pricingmust be modified in response to two-sided networkeffects. Rochet and Tirole (2003) model platformcompetition with two-sided markets and study pricesetting and surplus sharing under different gover-nance structures. Lee and O’Connor (2003) examinethe consumers’ consumption behavior and the corre-sponding new product launch strategy in the pres-ence of network effects. Parker and Van Alstyne(2005) provide insights to help understand interest-ing phenomena in the Internet economy, such as freeproducts and product coupling across markets.Rochet and Tirole (2006) provide a thorough reviewof the growing literature on platform competitionin two-sided markets. Armstrong (2006) examinesplatform pricing under competition in two-sidedmarkets and identifies the determinants of equilib-rium prices. One of the key findings in the monop-oly platform case is that subsidizing one user groupis desirable when the group’s demand elasticity ishigh and the external benefit realized by the othergroup is sufficient. Eisenmann et al. (2006) provide agood example of such a subsidy. They argue thatAdobe’s distribution of Acrobat Reader creates largeexternality from 500 million free users, which even-tually incentivizes enterprises to pay $299 for thecommercial version. Liu and Chintagunta (2009)discuss pricing issues under network effects from amarketing perspective. Eisenmann et al. (2011) exam-ine the strategic management of platform providersand discuss strategies for platform envelopment.Although the main focus of most existing studies isthe role of network externalities in two-sided mar-kets, our model incorporates uncertainty in applica-tion development as well as network externalities intwo-sided markets.

2.2. Optimal Strategies for Startup VenturesA notable stream of research in the entrepreneurshipliterature examines various aspects of the optimalentry strategy of a startup venture. One interestingtheme is a new venture’s retail channel selectionbetween virtual and bricks-and-mortar networksgiven the proliferation of the Internet (e.g., Rein-hardt and Levesque 2004). Closer to our article isentrepreneurs’ choice between early and delayedentry. Early studies find that early entry to theemerging economy generally yields higher profitthan deferred entry (DeCastro and Chrisman 1995).This result is somewhat consistent with our modelin the sense that early expansion has greater profitpotential than deferred expansion. More recently,Levesque and Shepherd (2004) examine a startup

venture’s optimal entry strategy in emerging anddeveloped markets, grounded on a stylized analyti-cal model, and find that companies entering emerg-ing markets have lower cost/benefit ratio fromusing a high mimicry entry strategy than the onesentering mature markets. Optimal timing of oppor-tunity exploitation is another relevant theme thathas been extensively studied in the knowledge man-agement literature (e.g., Choi et al. 2008, March1991). In general, it is believed that an optimal strat-egy for a startup company is to focus on explorationuntil it accumulates sufficient knowledge, andthen move to exploitation. Finally, Armstrong andLevesque (2002) extend Levesque (2000) by model-ing uncertainty for the amount of funding obtainedfor product development. Because the startups’financial ability is much more limited, their resultsindicate that startups are much more sensitive touncertainty than established firms.Although numerous aspects of a new venture’s

business strategies have been well studied, optimalproduct line expansion for platform technologieswhich posit two-sided markets with uncertainty hasnot been examined yet, to the best of our knowledge.We aim to bridge the gap in the literature by investi-gating the optimal product line expansion strategy fora startup venture with uncertainty. Inspired by real-world technology markets, we characterize the condi-tions under which the optimal timing for product lineexpansion is determined, and compare early anddeferred expansion strategies. Our results show thatwith versioning, firms can achieve both growth andprofitability, which will give guidance to entrepreneurswho want to commercialize platform technologies.

3. Overview of Model

The dominant approach to modeling two-sided mar-kets assumes simultaneous arrival of the two sides,so that the outcomes are resolved in a simultaneouscoordination game. But Hagiu (2006) argues thatthis representation may not be appropriate whenthe order of arrival of two sides is well defined, forexample in two-sided technology platforms such ascomputers, gaming consoles, or personal productiv-ity devices. Here, the first step tends to be device orplatform adoption by end-users (because the devicehas sufficient standalone features to be of valueeven without the second side of the market), andthe second step is entry by third-party developerswho provide additional applications to extend theutility of the platform device. Given this sequentialarrival of consumers and application developers inthe platform technology market, we develop a two-period model of customer purchase and developerparticipation.


3.1. Customer and Developer PreferencesPotential buyers arrive and exist in both periods. Inthe first period, the platform product is essentiallyviewed as a set of core standalone features (featureswhich are valuable by themselves, and do not dependon external applications) endowed by the platformfirm, and without a substantial network of third-partyapplication developers. For example, the initialiPhone released in June 2007 was an all-Apple prod-uct, endowed with several standalone features suchas voice-calling capabilities, in-built contact book,calendar, mail, and music capabilities. A softwaredevelopment kit (SDK), which enabled the creation ofthird-party applications, was released only in March2008, and the App Store was launched in July 2008,over a year after launch of the iPhone. Thus, purchasedecisions of first-period customers were based pri-marily on the product’s standalone features. Potentialdevelopers observe product adoption in the first per-iod, and by the start of the second period the marketobtains signals about developer participation. In thesecond period, therefore, customers make purchasedecisions based on both the standalone features andthird-party applications or product complements. TheiPhone illustrates this point well. Today, like withother platforms, customer choice between the iPhoneand similar products from competing firms (such asHTC, Google, Motorola) depends substantially on thesize of the respective applications (i.e., the App Storein the case of iPhone).Customers have heterogeneous preferences for the

platform product. We capture heterogeneity with aone-dimensional type parameter v, which representsthe customer’s marginal valuation of product quality.Product quality may be perceived as a collection offeatures and the level at which these are delivered.Higher quality may mean the inclusion of a greaternumber of useful features (e.g., inclusion of a cameraon a phone) or a premium level of a feature (e.g., a 5MP camera with zoom vs. a 2 MP camera). Custom-ers also value the platform more if it has a greaternumber of application developer participants (Eisen-mann et al. 2006, Jing 2007, Katz and Shapiro 1992).Thus, customers’ utility for the product is a combina-tion of its standalone features and third-party appli-cations or complements. This feature is capturedwith the additive utility function employed in theliterature (Bhargava and Choudhary 2004, Jing 2007,Katz and Shapiro 1992). Specifically, a type v cus-tomer’s valuation for a q-quality product when thenumber of complements is Q, is v·q + kQ, where krepresents the per-complement value. For simplicity,we assume that both first-period and second-periodcustomer arrivals have the same distribution of v,uniform on the [0,1] interval. This assumption is a

simplification, but we emphasize that the mainresults do not change even if the first period custom-ers on average have higher valuations (please refer toOnline Appendix S1 for the relaxation of thisassumption).Our model of customer behavior and purchase in

the two periods is based on theories of technologyadoption and diffusion in the marketing and infor-mation systems literatures. Moore (1991) proposed achasm framework for technology products, in whichtwo different segments of customers are clearlydefined. Customers in the early market, who arelabeled “technology enthusiasts” and “visionaries,”make an adoption decision in response to the natureand benefits of the innovation. They are more likerisk takers. Their perceived value from the platform,and their purchase decision, is based primarily onthe standalone features of the product. Moreover,note that because of the sequence of product launch,customer arrival, and developer participation, thesize of the developer network is negligible at thetime these early customers make their adoption deci-sion. Further, although such customers may antici-pate developer participation in later periods, theyhave a substantially high discount rate for futurebenefits.To summarize, the first period early adopters’ will-

ingness to pay for the product primarily depends onits standalone features, whereas the decision makingof second-period followers involves a combination ofstandalone features and developer participation. For-mally, we write the net utility of early adopters in thefirst period and followers in the second period,U1ðv; qÞ and U2ðv; qÞ, respectively—as

U1ðv; qÞ ¼ ðv � qÞ � pðqÞ andU2ðv; qÞ ¼ ðv � qþ kQÞ � qðqÞ; ð1Þ

where p(q) and ρ(q) are first and second periodprices for a q-quality product. Let q represent thefirm’s quality vector in period 1, and p the pricevector, and let D1ðq;pÞ be the realized demand forproduct version q. Then the total first-periodinstalled base of the firm in the user market isD ¼ P

q D1ðq;pÞ. The first-period cohort is thereforesplit into two parts: those with v larger than athreshold v who adopt the platform in the first per-iod, and those (with v\ v) who do not.The product’s adoption levels at this stage influence

and determine the extent of developer participation.If developers observe high product popularity, theyare more likely to sign on with the platform. This rela-tionship is normally modeled in the literature with adeterministic participation (or variety) function ofthe form Q ¼ D

/ where D is the demand for the


platform and φ is the cost of developing applications(Shy 2001). We maintain this classical assumptionabout the positive dependence from D to Q (and wenormalize φ to one since it is not the object of interestin this article). The novel feature of our model is theinclusion of uncertainty in the level of developer par-ticipation, beyond the dependence of this variable onthe installed base. That is, we argue that the extent ofdeveloper participation cannot fully be predicted bythe demand for the platform product and is influ-enced by other, possibly idiosyncratic, factors. Recentexamples of uncertainty in developer participation attime of launch include 3D TV. A lack of 3D contentwas identified as the most significant contributor tothe slow growth of 3D TV sales (Nuttall 2010). Despitepotential consumers’ belief that 3D TV will become anindustry standard in the near future, 3D TV is notvery attractive to them because it does not yet have asupporting eco-system (Mitra 2010). Thus, uncer-tainty in the future application development, that is,3D content, still remains a serious concern for poten-tial buyers. Therefore, this example demonstrates thatuncertainty in application development is critical fornew platform technologies.Formally, we conceive Q as c · D + e, where the

first component is proportional to the installed base ofusers and the second component is a random offset.We normalize the value of this random variable in thefirst period (where developers observe zero installedbase) to zero, hence by convention, Q represents thedeveloper base in the second period. For simplicity,we consider a distribution with just two atoms, corre-sponding to Favorable or Unfavorable developer par-ticipation. Our formalization of this uncertaintycomponent is consistent with other recent articles(Cachon and Lariviere 2001, Chen 2005, Dogan et al.2011, Ha and Tong 2008). To make the notation con-cise, we write the two cases as B (“B”est case, withprobability h) and W (or “W”orst case). We normalizethe random component in the worst-case to zero, toget

whereD is the observed installed base for the productat the start of the second period.Additional customers enter in the second period,

that is, after developer participation is realized. Theseare more risk-averse decision makers, afraid of beinglocked in a not-yet-standard technology, but willingto make a decision after uncertainty about developerparticipation is resolved. These “late adopters” or

“laggards” are influenced by the previous number ofadopters, which is a widely adopted assumption inthe literature on diffusion modeling (see, e.g., Maha-jan and Muller 1998, Mahajan et al. 1990). Thisassumption implies, when applied to a two-sidedplatform product, that second-period customers makedecisions based on the observed level of developerparticipation (which in turn depends on the adoptionlevel in the first period). Van den Bulte and Joshi(2007), who provide a detailed review of various theo-ries motivating a two-segment structure, also deploya two-segment model containing “influentials” whoare more in touch with innovations than the othersand “imitators” whose adoption decisions are ofteninfluenced by others’ decisions. The two-segmentstructure was also empirically verified by manyresearchers (see, e.g., Joshi et al. 2009, Moe and Fader2002).We normalize a few parameters to simplify the

exposition and analysis. First, like Moorthy and Png(1992), we focus on product expansion as a businessstrategy rather than driven by technologicalimprovement, in which case the firm may introducehigher quality products over time. Thus, we assumethat the firm’s technological capabilities remain con-stant, and that cost and other parameters induce itto offer the highest quality version in the first perioditself. Since the quality level of the high-end productis constrained exogenously, we can set this to one.Then we denote valuations (for just the productfeatures) for the low-quality product as av, wherea ∈ (0,1) represents a quality degradation parame-ter. This formulation is employed frequently inthe versioning literature (see, e.g., Deneckere andMcAfee 1996); it implies that each user has constantmarginal valuations (CMV) for product quality, andthat the type parameter v represents this marginalvaluation. Second, let j denote the mass of first-period customers, and let us normalize the mass ofcustomers who exogenously arrive in the secondperiod to one.

3.2. Structure of Game and Solution FrameworkThe sequence of events unfolds as follows. In the firststage the firm chooses its initial product line strategy.With respect to our research objectives, we limit theproduct qualities that the firm can pick to two levels,L and H, where the high quality H is exogenouslygiven and constrained by the technology innovationlevel of the firm. Hence the key question for the firm

Q ¼ c �Dþ n if application development is High (probability hÞc �Dþ 0 if application development is Low (probability 1� hÞ

�ð2Þ


is whether and when to include an additional, lowerquality, version in the product line. We assume fullcompatibility between the two versions. That is, anyapplication that works for one version works for theother. For simplicity, we also assume that thedifference of production costs for the high and lowquality products is negligible, which is applicable tomany information goods and other inferior/damagedgoods. In section 4.3, we demonstrate that our mainfindings still hold even with different marginal costs.Hence the firm’s strategy space has two points, {H}and {L,H}, because launching only {L} in the first per-iod is strictly dominated by launching {H} whenthere is no difference in production cost (this strategydoes become feasible under positive costs, which wediscuss in section 4.3). At this point, as indicated inEquation (2) the firm is uncertain about the full extentof developer participation, although it is aware thatparticipation levels will depend positively on itsinstalled base. In this stage, the firm’s target market(on the user side) primarily consists of early adopterswho value the product for its technological featuresand care little about third-party applications. At theend of the first period, as adoption levels materialize,developers begin participating in the eco-system, andthe extent of participation level Q is observed by thefirm and second period customers. In the second per-iod, the firm can reconfigure its product line and itsets second-period prices, targeting the product tosecond-period customers or “followers” who make apurchase decision after observing developer partici-pation.Formally, we write the strategy space for the firm’s

first-period decision problem as the vector ½K1; pL; pH�where K1 is either {H} or {L,H} (as noted above, weadd {H,L} in section 4.3). If K1 ¼ fHg then pL is vacu-ous, and pH must satisfy the constraint 0 � pH \ 1. IfK1 ¼ fL;Hg then the firm incurs a fixed product lineexpansion cost g, and its prices must satisfy0 � pL

a \ pH � pL1� a \ 1. In the first case, first-period

installed base is D ¼ jð1 � pHÞ, while in the second(where the firm expands its product line) it isD ¼ jð1 � pL

a Þ. The first-period profits for the twocases are

P1ðfHg; pHÞ ¼ jpHð1� pHÞ; ð3Þ

P1ðfL;Hg; pL; pHÞ ¼ j pH 1� pH � pL1� a

� ��

þpLpH � pL1� a

� pLa

� �� g:

ð4Þ

Let K2 denote the second period product line.Given the product line it is offering, the firm picksprices to maximize the current period profits. Let usfirst consider the firm’s optimal operating profit for

offering a particular product line ({H} or {L,H}),that is, profit without considering any applicableproduct line expansion cost g. This term is the profitafter optimizing the product prices corresponding tothe level of developer participation (high or low),and is represented using the notation given inTable 1.To solve the second-period problem, the only rele-

vant inputs from the first period are K1 and D. Firstconsider the case where the firm had already chosenan expanded product line in the first period (i.e.,K1 ¼ fL;Hg). Should it continue offering both prod-ucts in the second period?

PROPOSITION 1 (NO PRODUCT EXPANSION COST). Whenproduct line expansion is costless, the firm’s optimalsecond-period strategy, for all kQ > 0, is to sell both ver-sions. The incremental profit from versioning (given a

developer participation level Q) is DPversioning2 ¼ k2Q2ð1�aÞ

4aand this incremental profit increases in Q.

This result implies that, when K1 ¼ fL;Hg, sellingboth 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 thebase case (see, e.g., Bhargava and Choudhary 2001,Deneckere and McAfee 1996). It is the inclusion ofutility from third-party complements, which kick inin the second period, that makes versioning an attrac-tive strategy in the second period (in the absence ofadditional costs for product line expansion), matchingprior results under network effects (Bhargava andChoudhary 2004, Jing 2007).If, however, the firm chose a minimal first-period

product line K1 ¼ fHg, then the additional expan-sion cost g destroys the inevitability of versioning inthe second period. Specifically, because DPversioning

2

increases in Q, versioning will be attractive onlywhen developer participation Q is sufficiently highor, equivalently, expansion cost is low enough. Recallthat Q is a random variable with best-case andworst-case realizations, B and W, respectively, both

of which are functions of D. Let PB=W2 ðD; fL;HgÞ be

the optimal second-period profit if the firm expandsthe product line (adds L) and PB=W

2 ðD; fHgÞ if it

Table 1 Notation for Optimal Operating Second-Period Profit (NotConsidering g)

Developer Participation

Low/worst Case (W) High/best Case (B)

Offer H only PW2 ðD; fHgÞ PB

2 ðD; fHgÞOffer {L,H} PW

2 ðD; fL;HgÞ PB2 ðD; fL;HgÞ


continues with an H-only strategy. For each of thetwo cases B and W (which are resolved prior to thefirm’s second-period action), the firm’s second per-iod profit is therefore the maximum of these twoprofit terms.At the beginning of the game, therefore, the firm

picks its product line and prices to maximize itsexpected profit over the two periods. To complete ournotation, define E½P2ðK1;DÞ� to be the firm’s first-period expectation of its second-period profit if itenters the second period with an existing product lineK1 and installed base D. Using the notation fromTable 1, we have

E½P2ðfL;Hg;DÞ� ¼ hPB2 ðD; fL;HgÞ

þ ð1� hÞPW2 ðD; fL;HgÞ; ð5Þ

E½P2ðfHg;DÞ�¼hmaxnPB

2 ðD;fL;HgÞ�g;

PB2 ðD;fHgÞ

o

þð1�hÞmaxnPW

2 ðD;fL;HgÞ�g;

PW2 ðD;fHgÞ

o:

ð6Þ

Combining the second-period actions under the Band W realizations (under K1 ¼ fHg) the firm willeither (i) not launch L regardless of the level of Q(i.e., even if Q is high, case B), (ii) launch L only if Qis high (note that launch L only if Q is low wouldtrivially 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 Wcases). Of these we eliminate case (iii) because of thefollowing result.

PROPOSITION 2 (BENEFITS OF EARLY EXPANSION). If prod-uct expansion is foreseen as inevitable in period 2 (i.e.,launch L even if Q is low), then expanding in period 1itself is optimal.

Equation (6) can therefore be replaced with

E½P2ðfHg;DÞ� ¼ ð1� hÞPW2 ðD; fHgÞ

þ hmax PB2 ðD; fHgÞ;�

PB2 ðD; fL;HgÞ � gg:

ð7Þ

Our main objective is to compare the profitability ofK1¼fHg with K1¼fL;Hg. The total expected profit,

at the start of first period, from a decision to setK1;pL;pH is PðK1;pL;pHÞ¼P1ðK1;pL;pHÞþE½P2ðK1;DÞ�where D¼DðpL;pHÞ¼jð1� pL

a Þ or jð1�pHÞ as appro-priate. For the two possible first-period product linedecisions, we have the optimal profit under eachstrategy as

� Early expansion of product line (K1 ¼ fL;Hg),then

Pearly ¼maxpL;pH

PðfL;Hg;pL;pHÞ¼max

pL;pHP1ðfL;Hg;pL;pHÞð þE½P2ðfL;Hg;DÞ�Þ: ð8Þ

� Defer decision to expand product line (K1 ¼fHg), then

Pdefer ¼ maxpH

PðfHg; pHÞ¼ max

pHP1ðfHg; pHÞ þ E½P2ðfHg;DÞ�ð Þ: ð9Þ

3.3. Optimal Product Line and PricesThis section specifies the optimal solutionsemploying the solution framework described insection 3.2. While we provide a complete analysisof this two-period problem using the solutionframework specified above in Online Appendix S2,these solution details are needed to analyze theimpact of network effects and uncertainty on thefirm’s product line expansion strategy. Solvingseparately the three optimization problems (onefor Equation (8) and two implied by the “max”term inside Equation (9)), we obtain

LEMMA 1 (OPTIMAL PRICES AND PROFIT OF EARLY EXPAN-

SION). Under a first-period product line {L,H}, optimalfirst-period prices are

pearlyL ¼ a 2a2 � ack� ck2ðcjþ hnÞ� �

4a2 � c2jk2;

pearlyH ¼ a2ð4� 2ckÞ � c2jk2 � ack2ðcjþ 2hnÞ

8a2 � 2c2jk2;

and the optimal total profit under early expansion is

Pearly¼ a 4að1�4gþjþcjkþ2hknÞþk2 c2jð1þjÞþ4chjnþ4hn2� ��

16a2�4c2jk2�c2jk2 að1�4gþjÞþð1�hÞhk2n2� �

16a3�4ac2jk2: ð10Þ


LEMMA 2 (OPTIMAL PRICES AND PROFITS OF DEFERRED

EXPANSION). For the two deferred expansion strategies:

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

pdefer; H�onlyH ¼ 2� ckð1þ cjkþ hknÞ

4� c2jk2; ð11Þ

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

pdefer;expandH ¼að2�ckð1þcð1�hÞjkÞÞ�chk2ðcjþnÞ

4a�c2ðað1�hÞþhÞjk2 ; ð13Þ

The optimal launch strategy can now be determinedby comparing the total-profits under the three strate-gies specified in Equations (10), (12), and (14). Wedo this next.

4. Results

Lemma 1 and 2 provided the optimal prices andprofits conditional on each of the three product linestrategies, (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 versioningan attractive strategy, but high product line expan-sion costs can force the firm to follow an H onlystrategy. Our goal in this section is to examine andelaborate on these ideas with rigor and precision.Moreover, higher network benefits (e.g., throughgreater value per-developer or through higher levelsof developer participation) make versioning moreattractive in the second period; but at the same time,early expansion increases the levels of developer par-ticipation. In additional, we seek to inquire about theimpact of uncertainty in developer participation onthe expansion strategy. On the one hand, the firmmight wish to delay the expansion decision until thelevel of developer participation—and, consequently,

the level of consumer surplus available to extract—becomes better known. Alternately, the firmmay wantto expand the product line—and installed base—early,in order to drive higher levels of developer participa-tion and increase the available surplus in the secondperiod. This section investigates these two forces.

4.1. Optimal Product Expansion Strategy

PROPOSITION 3 (OPTIMAL PRODUCT EXPANSION STRATEGY).Pairwise comparison of the three expansion strategy yieldscutoff points (gHD, gDE, and gHE specified in Equations(25), (26), and (27) in the online supplement) such that

Pdefer; expand [Pdefer; H�only , g\gHD; ð15Þ

Pearly [Pdefer; expand , g\gDE; ð16Þ

Pearly [Pdefer; H�only , g\gHE: ð17Þ

Combining these results yields the optimal expansionstrategy as:

1. If g\ minfgHD; gDEg (low expansion cost), thenearly expansion is optimal.

2. When expansion cost is moderate, that is,minfgHD; gDEg\ g\ maxfgHD; gDEg,(a) If gHD \ g\ gDE,

(i) if g\ gHE, early expansion is optimal.(ii) otherwise (i.e., g [ gHE), defer, H-only is

optimal.

(b) If gDE \ g\ gHD, the firm’s optimal strategy isto defer the expansion decision to the secondperiod, and then expand only if developer par-ticipation is high (case B).

3. If g [ maxfgHD; gDEg, the optimal strategy is tosell product H only.

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

Pdefer; H�only ¼ 4þ 4hknð2þ knÞ þ j 4þ ck 4þ hkn 4� cð1� hÞk2n� �� 4 4� c2jk2� � : ð12Þ

Pdefer; expand ¼ hk2 4cjnþ 4n2 � c2jð1� 4ghþ ð1� hÞknðkn� 2ÞÞ� �16a� 4c2ðað1� hÞ þ hÞjk2

þ a 4ð1þ jþ cjkÞ þ h c2jk2 þ 2 4� c2ð1� hÞjk2� �ðkn� 2gÞ� �� 16a� 4c2ðað1� hÞ þ hÞjk2 :

ð14Þ


product line improves second-period profit. For a tra-ditional good, the firm would have no reason tolaunch L early (in the first period) as discussed in theBenchmark case in Online Appendix S3. However,the presence of network effects induces the firm tolaunch L early in the first period itself and benefitfrom higher installed base, which causes greaterdeveloper participation and creates greater value forconsumers in the second period. For instance, afterlaunching the relatively expensive iPhone at the endof June 2007, Apple faced a relatively low expansioncost of adding the iPod Touch, which is just theiPhone minus the calling feature. Importantly, such adevice had the promise of increasing the overallinstalled base of devices that could run iPhone apps,which made the platform very attractive to potentialapplication developers. Indeed after launchingiPhone at the end of June 2007 (and with no prior foot-print in the world of mobile phones), Apple quicklyadded an iPod Touch to the product line in September2007. The iPod Touch was priced much lower thanthe iPhone and also did not carry a recurring monthlycellular service fee. Industry estimates (around Janu-ary 2010) were that the installed base of the iPhoneOS platform was nearly doubled by addition of theiPod Touch, referred to as a “stealth device” for theplatform.1

As g becomes higher, however, the firm’s expan-sion strategy becomes more conservative: it defersexpansion in the first period, observes developer par-ticipation, and then incurs expansion costs only if Q ishigh enough to guarantee high gains from versioning.This leads to a sequential or delayed expansion of theproduct line, if favorable market circumstancesemerge. The evolution of Apple’s iPod music playeris a good example, because of the higher expansioncosts associated with a Windows version of the prod-uct and with multiple form factors for the product.

Apple launched the iPod in October 2001 with a mini-mal product line, a Mac-only iPod in a single design(with 5GB and 10GB disks). Only after observingiTunes’ roaring success did Apple branch into anexpanded product line with a Windows version of theiPod and additional form factors such as the iPodMini and iPod Shuffle. These moves, which involvedsubstantial fixed costs of product line expansion,enormously increased the iPod installed base butwere deferred until Apple had observed high devel-oper participation and the consequent assurances of asuccessful product category.Pushing further into the impact of uncertainty, we

examine how the firm’s strategy shifts as the degreeof uncertainty in developer participation changes. Todo this, we frame the cut-off points for the optimalstrategies as a combination of g and ξ; this is becausea change in ξ alone (which measures difference in Qbetween the W and B scenarios) affects both reserva-tion prices and the extent of uncertainty in participa-tion. Figure 1 illustrates the impact of uncertainty. Atξ = 0, the firm’s optimal strategy is either to expandearly if g is very low or not to expand at all if g ishigher; the “wait and see” approach of deferringexpansion has no value due to lack of uncertainty,and early expansion is always superior to deferredexpansion. This same policy remains in force as ξincreases beyond 0 (because the uncertain componentof Q remains small relative to the overall value),except that the early expansion is optimal for a higherrange of expansion costs due to increase in second-period reservation prices. But, as depicted in Figure 1,as ξ increases even further, the firm’s optimal policyshifts and introduces a new element: defer andexpand (only if developer participation is high) for“moderate” expansion costs. The reason is that forsuch high ξ, the uncertain component of second-per-iod reservation prices is substantial (relative to the

(a) (b)

Figure 1 Impact of Uncertainty about Developer Participation on Expansion Strategy


mean), hence the uncertainty effect becomes domi-nant 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 ~n [ 0 such that for lowlevel of uncertainty in developer participation—that is,n\ ~n—the optimal strategy is either to expand the prod-uct early (if expansion cost is very low, g\ gDEðnÞ) ornot at all. For higher levels of uncertainty in developerparticipation, however, there is a moderate region ofexpansion cost such that the optimal expansion strategyis a “wait and see” approach (expand in the second periodonly if high developer participation is observed); hence forn [ ~n, early expansion is optimal if g\ gDEðnÞ, H-onlyis optimal if g [ gHDðnÞ, and deferred expansion is opti-mal when g 2 ½gDEðnÞ; gHDðnÞ�.

What is notable about this analysis is that thereexists a range of expansion costs for which earlyexpansion is optimal even though, had the firmfollowed an H-only strategy in the first period, expan-sion would not have been optimal in the secondperiod despite the removal of uncertainty in devel-oper participation. This result is surprising becauseintuition suggests that uncertainty in developer par-ticipation (combined with relatively high expansioncosts) makes early expansion unattractive; if the firmexpands at all, it should do so in the second periodand only if developer participation is high. The reasonfor the result is the intricate feedback loop betweenthe firm’s product line, installed base, and developerparticipation. Note that the incremental secondperiod profit gain from versioning is influenced bythe installed base D. Under an H-only product line—compared with early expansion—D is relativelysmall, leading to smaller incremental gain and henceversioning is attractive only for relatively low g. If,however, the firm “sub-optimally” expanded theproduct line early and has higher installed base inperiod 1, then the second-period incremental gainfrom versioning is higher than before, justifying thedecision to incur the expansion cost in period 1.Implementation of this strategy can raise a startup’sshort-term cash needs. But the costs of financing thesecash requirements can be more than offset by thespill-over effect of increased D on second period prof-its that can generate enough gains to pay back theloan (and interest) in the second period, even thoughthis same action is unprofitable in the full-informationsetting of the second period.An alternative way to examine the impact of uncer-

tainty is to alter ξ and h at the same time, becausechanging ξ alone implies higher developer marketsize on average. We conducted a numerical study,

maintaining the average market size fixed but chang-ing the uncertainty parameter h. Note that if the aver-age market size is kept fixed (by adjusting ξ), a lowerh corresponds to a higher standard deviation of Q.Therefore, Figure 1 shows the pure effect of uncer-tainty about developer participation, uncompoundedby the effect of market size. As depicted in Figure 1,there exists a threshold ~h such that (i) when h [ ~h,the optimal product launch strategy is either early orno expansion, and (ii) when h\ ~h, the deferredexpansion strategy becomes attractive. To understand(i), consider an extreme case where h is very high(close to one) and recall Proposition 2. Since highdeveloper participation is very likely, the firm mightas well expand early if expansion is optimal at all (i.e.,product line expansion cost is low enough). For (ii),consider a low h. Now, ξ is relatively large becausewe maintained the average market size. Hence, thefirm is better off with early expansion when productline expansion cost is low, and no expansion when itis very high. For moderate expansion cost, the “waitand see” (deferred strategy) becomes very attractivebecause the pay-off is relatively large.

4.2. Managerial Implications for Startups vs.Established FirmsAlthough many of our examples have focused onestablished firms entering new product categories(e.g., Apple iPhone), our findings also have importantimplications for a startup that needs to commercializea platform product. Now we extend our discussion totechnology startups and explain how they can inter-pret our findings. As discussed, many technology-oriented firms frequently face uncertainty in applica-tion development in two-sided markets. For instance,OpenTable has created a market for connecting restau-rants and diners; it provides technology to enablerestaurant discovery, reservations, and other applica-tions. A big challenge for OpenTable was to obtainsufficient numbers of restaurants (correspondingly,diners) into the network, to convince diners (corre-spondingly, restaurants) to use the system.To differentiate and compare startups and estab-

lished firms, we analyze the effect of two parametersin our model: c, the ability to attract developers and j,the size of early adopters. First, in our model, the like-lihood of participation is captured via the parameter cin the participation function Q = c· D + ξ. It is widelyaccepted in the literature on software developmentthat the reputation of a software founder is critical torecruiting developers (West and O’Mahony 2005).Therefore, we assume that c is lower for startups rela-tive to the value for established firms: an OpenTablefaces greater challenges in obtaining participants thanan Amazon might face in convincing publishers toprovide e-books for the Kindle (or, e.g., Apple to


convince developers to write applications for theiPad). Figure 2 illustrates the variation in product lineexpansion strategies for startups vs. established firms.In all three figures, startups are to the left side of thex axis, that is, lower ability to attract developers, andsmaller size of early adopters, whereas establishedfirms are to the right side of the x axis, that is, greaterability to attract developers and larger size of earlyadopters. When expansion costs are low, then evenstartups should consider early expansion as a way toincrease the installed base and position itself betterfor the second period. OpenTable addressed thisproblem by having multiple levels of nonlinear pric-ing structures for small vs. large restaurants, both theinitial one-time costs and the continuing fees for pro-viding customers to the restaurant. It also offersrestaurants a choice (and different price levels)between using their own reservation technology andpaying only for customer reservations vs. paying forreservation software and hardware as well. Thisexample also indicates a useful strategy that manyfirms can follow: create product variety and segmentcustomers through differentiation in pricing (which isrelatively less expensive to administer) rather thandesigning multiple physical products. For the HEPexample discussed in the Introduction section, ourresults suggest that the firm can offer (i) a low-endpatient HEP with a minimal fee (or even free) thatmight have fewer features and (ii) a high-end with ahigher fee that could communicate more data, andmore real-time communication, to clinicians. But theresult also suggests that when product line expansioncosts are higher, early expansion is less attractive tostartups than to established firms. As an additionalconsequence, this finding also suggests that a startupshould carefully build its business strategy to attractmore developers—that is, raise its c—by providingdevelopment tools or incentives.The second differentiating parameter for startups

and established firms is j. Specifically, startups andestablished firms may also differ in their ability toattract early adopters who purchase the product

based solely on core standalone features and do notrequire substantial developer participation beforetheir purchase decision. Therefore, we assume thatstartups are likely to have lower j relative to estab-lished firms, consistent with the existing theory in themarketing literature that a firm with a better reputa-tion has a higher chance to get early adoptions of itsproduct (Herbig and Milewicz 1995). Figure 2 illus-trates the effect of j on the expansion strategy. First,note that as j increases, total profit increases in all ofthe strategies because of the increase in total marketsize. However, compare deferred expansion againstno expansion. In both cases, the first period action isthe same (H-only), but deferred expansion can exploithigher j more because higher j leads to higher D andhigher Q, hence higher reservation prices in thesecond period. Thus, the deferred expansion profitgrows faster than no expansion profit as j increases.For the early expansion profit—which partially sacri-fices short-term (first-period) profit not only havebetter position in the second period—higher jincreases the first-period sacrifice, but it also delivershigher D and Q, leading to greater gain in the secondperiod. Therefore, the desirability of early expansionincreases with j. This point is reinforced in Figure 2,which demonstrates that an established firm is morelikely to follow early expansion; and, if expansioncosts are too high, it might just choose not to expandat all (this is because with higher j, Q becomes morecertain, reducing the benefit from a “wait and see”approach). In contrast, a startup is more likely to finddeferred expansion attractive because the uncertaincomponent of Q carries greater weight when j issmall.

4.3. Role of Marginal CostsAlthough the assumption of same (or zero) marginalcost is realistic for information goods and widelyaccepted in the literature, relaxing this assumptionmay add reality since some hardware platforms mayhave different marginal costs, for example, Sony’sinclusion of a Blu-Ray player in the PS3 gaming

(a) (b) (c)

Figure 2 Optimal Expansion Strategies for Startups vs. Established Firms


console substantially raised the per-unit costs of theconsole. In this section, we investigate whether or notour main analysis and findings can be applicable todifferent marginal costs by relaxing same marginalcost assumptions. We start with checking the robust-ness of our main findings, that is, comparisonbetween early and deferred expansion strategies. Tobetter reflect the reality that marginal cost increaseswith product quality, we assume different levels ofmarginal costs for H and L, then normalize L’s mar-ginal cost to be zero. We denote H’s positive marginalcost with c. The result is summarized in the followingproposition.

PROPOSITION 5 (OPTIMAL STRATEGY WITH DIFFERENT

MARGINAL COSTS). There exists a unique cut-off point ~gsuch that

1. When g\ ~g, early expansion outperforms defer,expand strategy,

2. When g [ ~g, there exists a unique cut-off point ~nsuch that defer, expand strategy is preferred whenthe level of favorable developer participation is low(n\ ~n) and early expansion is optimal when it ishigh (n [ ~n).

This result demonstrates that deferred expansioncould be a viable strategy even with differentmarginal costs. Next we examine the impact of differ-ent marginal costs on optimal product launch strat-egy. Specifically, we compare two different defer,expand strategies, that is, launch H first then expandlater (H,Then L) vs. introduce L first, then exp-and later (L,Then H). With same marginal cost, trivi-ally we see that H,Then L dominates L,Then H. But,more generally, the optimal expansion strategydepends on the difference in marginal cost structuresbetween the low and high quality versions. LetPH;Then L (PL;Then H) be the profit function of H,ThenL (L,Then H).

PROPOSITION 6 (OPTIMAL SEQUENTIAL LAUNCH WITH

DIFFERENT MARGINAL COSTS). Let g(·) be the profit differ-ence (PL;Then H � PH;Then L).If gð�Þjc¼1 [ 0, there exists a unique cutoff point ~c

where H,Then L is preferred if c\~c. Otherwise (c [ ~c),L,Then H is preferred. If gð�Þjc¼1 \ 0, H,Then L isalways preferred.

This result indicates that marginal costs primarilyinfluence the sequence of product launch and version-ing, rather than the timing of the expansion decision.Specifically, when the incremental cost of the Hversion is quite high, then the firm may employ asequential expansion strategy when it launches thelower-quality version L first, and then launches themore expensive product if positive market conditions

emerge. This is in contrast to the equal-cost casewhere launching H first is always optimal. The reasonwhy the firm might want to launch L first is that itwants to sustain enough market share in the firstperiod in order to create enough incentives for highdeveloper participation. Launching the higher-costversion, H, first, would force the firm to either sacri-fice margin in the first period or obtain a much lowermarket share if it sets a high price.

5. Concluding Remarks

Many innovative platform products have beenlaunched in the last two decades, including Xbox,PlayStation, Palm Pilot, Microsoft Platform products,iPhone, iPod, and iPad. Firms have deployed differ-ent ways of introducing new platform products. Thisobservation inspired us to investigate optimal prod-uct launch and pricing strategies for a firm that wantsto launch a new platform product when it is uncertainabout third-party application development. Althoughprior studies mostly considered uncertainty aboutuser adoption, our article is novel in its considerationof uncertainty in application development. This factoris relevant because the level of developer participa-tion plays a critical role in consumers’ purchase deci-sions. The consideration of developer participationuncertainty leads to the novel finding that deferredexpansion can often be the optimal product launchstrategy. We demonstrated that a technology-orientedstartup needs to pay special attention to the level ofexpansion cost, the number of early adopters (or tech-nology enthusiasts), and the likelihood of applicationdevelopers’ participation. For a startup with a tech-nology product that wants to expand rapidly in atwo-sided market, our study suggests some importantpractical guidelines, including (i) increase the aware-ness of the product to make customers purchase it inearly stages and (ii) provide some incentives or con-venient development tools to application developersfor fast-growing applications.Our analysis has several limitations that demand

some consideration. First, we modeled customers asarriving in two periods, and constrained the first-period customers to either buy in the first period orvanish from the market. Relaxing this assumptionmight reduce second period profits, and it appears toshift the strategic choice of expansion timing awayfrom deferred expansion. However, we believe it doesnot materially affect the results. Second, in computingthe first-period customers’ utility for the product, ourmodel ignored the anticipated value from having acollection of third-party developers in the laterperiod. This view is reasonable if the first-periodcustomers apply a very high discount rate for futurebenefits of this sort, that is, when they are technology


innovators who purchase a new technology based pri-marily on visible standalone features. The assumptionwas also motivated by reasons of computational trac-tability (which is harmed by the inclusion of suchanticipated benefits). However, we conducted numer-ical experiments and confirmed that our main find-ings still hold: with anticipated benefits, more earlyadopters would purchase the product in the firstperiod, but this does not change a firm’s selection ofoptimal product launch strategy. Despite these limita-tions, we hope that the conceptual insights providedin this article will be of value to a spectrum of firmsthat develop new platform technologies.This article can be improved in additional ways,

which motivate a few directions for future research.First, future research can focus more on technologystartups. It is generally more difficult for startups toattract developers and early adopters. Therefore,many startups would be keenly interested in theeffect of other pricing/marketing strategies thatcould help attract more early adopters (e.g., freedistribution, free trial-version) and/or improvedevelopers’ participation (e.g., increasing technologyinvestment for App development, providing incen-tives to developers, and identifying optimal contractmechanism). Second, although we assumed that themanufacturer already acquired the innovative tech-nology with R&D investment (i.e., development costwas sunk), it would be useful to examine how uncer-tainty in developer participation impacts the level ofinnovation. This problem might be particularlyimportant to many startups because of their limitedresources. Note that with limited resources, identify-ing an appropriate level of innovation is a veryimportant decision problem. Third, additional inves-tigation of the two product line expansion strategieswith more general assumptions (e.g., continuous dis-tribution for uncertainty, relaxing two assumptionsmentioned above) would improve our understand-ing of the dynamics of platform product launchstrategies. Fourth, product line expansion is oftendictated by technological improvement, a factorignored in our model and other studies of sequentialvs. simultaneous versioning (Moorthy and Png1992). It would be useful to examine how the expan-sion strategy is impacted by the interplay betweentechnological improvement and other factors consid-ered in this article. Considering the fact that manystartups gradually improve their technologies overtime, this extension will shed more light on startups’technology commercialization strategies.

Acknowledgments

The authors thank the guest editors (Prof. Moren Levesqueand Prof. Nitin Joglekar), senior editor, and two referees for

their contribution in improving the clarity and quality ofthis article.

Notes

*Author names are listed alphabetically and all authorscontributed equally.1See http://gigaom.com/apple/ipod-touch-now-outselling-iphone/.

ReferencesAoki, R., T. J. Prusa. 1997. Sequential versus simultaneous choice

with endogenous quality. Internat. J. Indust. Organ. 15: 103–121.

Armstrong, M. 2006. Competition in two-sided markets. RAND J.Econ. 37: 668–691.

Armstrong, M. J., M. Levesque. 2002. Timing and quality deci-sions for entrepreneurial product development. Eur. J. Oper.Res. 141: 88–106.

Bag, P. K., S. Roy. 2011. On sequential and simultaneous contribu-tions under incomplete information. Int. J. Game Theory 40(1):119–145.

Bhargava, H., V. Choudhary. 2001. Information good and verticaldifferentiation. J. Manage. Inf. Syst. 18(2): 89–106.

Bhargava, H., V. Choudhary. 2004. Economics of an informationintermediary with aggregation benefits. Inf. Syst. Res. 15(1):22–36.

Cachon, G., M. Lariviere. 2001. Contracting to assure supply:How to share demand forecasts in a supply chain. Manage.Sci. 47(5): 629–646.

Carrillo, J. 2005. Industry clockspeed and the pace of new productdevelopment. Prod. Oper. Manag. 14(2): 125–141.

Chen, F. 2005. Salesforce incentives market information and pro-duction/inventory planning. Manage. Sci. 51(1): 60–75.

Choi, Y., M. Levesque, D. A. Shephert. 2008. When should entre-preneurs expedite or delay opportunity exploitation. J. Bus.Ventur. 23: 333–355.

Christensen, C. M., J. Bower. 1996. Customer power strategicinvestment and the failure of leading firms. Strategic Manage.J. 17(3): 197–218.

Daneels, E. 2004. Disruptive technology reconsidered: A critiqueand research agenda. J. Prod. Innov. Manage. 21(4): 246–258.

Davidsson, P., P. Steffens, J. Fitzsimmons. 2008. Performanceassessment in entrepreneurship research: Is there a pro-growth bias? Available at: http://eprints.qut.edu.au/archive/00012040 (accessed February 21, 2012).

Davidsson, P., P. Steffens, J. Fitzsimmons. 2009. Growing profit-able or growing from profits: Putting the horse in front of thecart? J. Bus. Ventur. 24(4): 388–406.

DeCastro, J. O., J. J. Chrisman. 1995. Order of market entry com-petitive strategy and financial performance. J. Bus. Ventur. 33:165–177.

Deneckere, R. J., R. P. McAfee. 1996. Damaged goods. J. Econom.Manage. Strategy 5(2): 149–174.

Dhebar, A. 1993. Cambridge Software Corp. Harvard BusinessSchool, Cambridge, MA.

Dogan, K., Y. Ji, V. S. Mookerjee, S. Radhakrishnan. 2011. Manag-ing the versions of a software product under variable andendogenous demand. Inf. Syst. Res. 22(1): 5–21.

Eisenmann, T. 2007. Platform-Mediated Networks: Definitions andCore Concepts. Harvard Business School, Cambridge, MA.

Eisenmann, T., G. Parker, M. Van Alstyne. 2006. Strategies fortwo-sided markets. Harv. Bus. Rev. 84(10): 92–101.


Eisenmann, T., G. Parker, M. Vanalstyne. 2011. Platform envelop-ment. Strategic Manage. J., forthcoming.

Ha, A. Y., S. Tong. 2008. Contracting and information sharingunder supply chain competition. Manage. Sci. 54: 701–715.

Hagiu, A. 2006. Pricing and commitment by two-sided platforms.RAND J. Econ. 37(3): 720–737.

Herbig, P., J. Milewicz. 1995. The relationship of reputation andcredibility to brand success. J. Consumer Market. 12: 5–10.

Jing, B. 2007. Network externalities and market segmentation in amonopoly. Econ. Lett. 95: 7–13.

Joglekar, N., M. Levesque. 2006. Marketing, R&D, and startup val-uation. IEEE Trans. Eng. Manage. 56: 229–242.

Joshi, Y. V., D. J. Reibstein, J. Zhang. 2009. Optimal entry timingin markets with social influence. Manage. Sci. 55: 926–939.

Katz, M. L., C. Shapiro. 1992. Product introduction with networkexternalities. J. Indust. Econ. 401: 55-83.

Lee, Y., G. C. O’Connor. 2003. New product launch strategy fornetwork effects products. J. Acad. Market. Sci. 31(3): 241–255.

Levesque, M. 2000. Effects of funding and its return on productquality in new ventures. IEEE Trans. Eng. Manage. 47(1): 98–105.

Levesque, M., D. A. Shepherd. 2004. Entrepreneurs’ choice ofentry strategy in emerging and developed markets. J. Bus.Ventur. 19: 29–54.

Liu, H., P. K. Chintagunta. 2009. Pricing under network effects.V. R., Rao ed. Handbook of Pricing Research in Marketing.Edward Elgar Publishing Inc., Northampton, MA, 435–450.

Mahajan, V., E. Muller. 1998. When is it worthwhile targeting themajority instead of the innovators in a new product launch?J. Market. Res. 35: 488–495.

Mahajan, V., E. Muller, F. M. Bass 1990. New product diffusionmodels in marketing: A review and directions for research.J. Market. 54: 1–26.

March, J. G. 1991. Exploration and exploitation in organizationallearning. Organ. Sci. 2: 71–87.

Markman, G., W. Gartner. 2002. Is extraordinary growth profit-able? A study of Inc. 500 high-growth companies. Entrepre-neurship Theory Practice 27: 65–75.

Mitra, K. 2010. One D too many. Bus. Today. October 3. Availableat: http://businesstoday.intoday.in/story/one-d-too-many/1/9439.html (accessed February 21,2012).

Moe, W. W., P. S. Fader. 2002. Using advance purchase orders toforecast new product sales. Market. Sci. 21: 347–364.

Moore, G. 1991. Crossing the Chasm. Harper Business, New York.

Moorthy, K. S., I. P. L. Png. 1992. Market segmentation cannibal-ization and the timing of product introductions. Manage. Sci.38: 345–359.

Nuttall, C. 2010. Lack of content leaves 3D TV sales flat. Finan.Times. December 16. Available at: http://www.ft.com/cms/

s/0/13ae797c-093f-11e0-ada6-00144feabdc0.html#axzz1n0tlqtEJ(accessed February 21, 2012).

Padmanabhan, V., S. Rajiv, K. Srinivasan. 1997. New productsupgrades and new releases: A rational for sequential productintroduction. J. Market. Res. 34: 456–472.

Parker, G., M. Van Alstyne. 2005. Two-sided network effects: Atheory of information product design. Manage. Sci. 51: 1494–1504.

Ramdas, K. 2003. Managing product variety: An integrativereview and research directions. Prod. Oper. Manag. 12: 79–100.

Ramezani, C. A., L. Soenen, A. Jung. 2002. Growth corporate prof-itability and value creation. Financ. Analysts J. 62: 56–67.

Reinhardt, G., M. Levesque. 2004. A new entrant’s decision onvirgual versus bricks-and-mortar retailing. J. Electron. Com-merce Res. 3: 136–152.

Rochet, J. C., J. Tirole. 2003. Platform competition in two-sidedmarkets. J. Eur. Econ. Assoc. 1: 990–1029.

Rochet, J., J. Tirole. 2006. Two-sided markets: A progress report.RAND J. Econ. 37: 645–667.

Shan, W., G. Walker, B. Kogut. 1994. Interfirm cooperation andstartup innovation in biotechnology industry. Strategic Man-age. J. 15: 387–394.

Shy, O. 2001. The Economics of Network Industries. Cambridge Uni-versity Press, Cambridge, UK.

Van den Bulte, C., Y. V. Joshi. 2007. New product diffusion withinfluentials and imitators. Market. Sci. 26: 400–421.

Villas-Boas, J. M. 2004. Communication strategies and productline design. Market. Sci. 23: 304–316.

West, J., S. O’Mahony. 2005. Contrasting community building insponsored and community founded open source projects.Proceedings of the 38th Hawaii International Conference onSystem Sciences, Hawaii, January.

Supporting InformationAdditional Supporting Information may be found in theonline version of this article:

Appendix S1: Asymmetric Valuations Across Time

Appendix S2: Technical Details for Lemma 1 and 2

Appendix S3: Benchmark: No Network Effect

Please note: Wiley-Blackwell is not responsible for the con-tent or functionality of any supporting materials suppliedby the authors. Any queries (other than missing material)should be directed to the corresponding author for thearticle.
