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Vol. 0, No. 0, xxxx–xxxx 2012, pp. 1–15 ISSN 1059-1478|EISSN 1937-5956|12|0|0001

DOI 10.1111/j.1937-5956.2012.01344.x © 2012 Production and Operations Management Society

Commercialization of Platform Technologies: Launch Timing and Versioning Strategy* Hemant K. Bhargava Graduate School of Management, University of California Davis, GH-3108, Davis, California 95616, USA, hemantb@ucdavis.edu

Byung Cho Kim Graduate School of Management of Technology, Sogang University, 35 Baekbeom-ro (Shinsu-dong), Mapo-gu, Seoul, 121-742, Korea, bckim@sogang.ac.kr

Daewon Sun Department of Management, Mendoza College of Business, University of Notre Dame, Room 359, Notre Dame, Indiana 46556, USA, dsun@nd.edu

any emerging entrepreneurial applications and services connect two or more groups of users over Internet-based information 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 that serves two markets—labeled as user and developer markets—such that the size of each market positively impacts participation in the other. In addition, our model allows for sequential unfolding of consumer and developer participation, and for uncertainty regarding developer participation. We demonstrate that product versioning is an especially attractive strategy 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 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.

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Key words: technology commercialization; product launch strategy; platform technology; versioning; uncertainty History: Received: September 2010; Accepted: January 2012 by Moren Le´vesque and Nitin Joglekar, after 3 revisions.

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, to entice publishers toward its technology?). This article examines this final challenge, that is, 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, that is, market adoption on one network influences, and depends on, the desirability of adoption on the other network (Eisenmann 2007, Eisenmann et al. 2006). 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

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., should—or will— all electric car technologies employ the same battery that can be charged at every battery station, or will the market be fragmented among multiple technology 1

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

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 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 to propel interest in the future or on the other side of the market, but these low prices lower the firm’s 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 printbased 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 literature. Firm growth is a common measure of success (Davidsson et al. 2008), but the wisdom is that too much growth must come at the expense of profitability (Markman and Gartner 2002, Ramezani et al. 2002). Davidsson et al. (2009) examine the effectiveness of growth as a measure of business success from a Resource-Based View (RBV) and argue that sound

growth starts with achieving 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 initially 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, that is, that developer participation involves network effects and 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. Although past literature has taken a deterministic rational expectations framework to describing network effects (see e.g., 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

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

products that exhibit strong network effects. Intuitively, platform firms may implement a minimal product line 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 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 are 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 crossnetwork 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 no footprint 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 firms—highly concerned about piracy, and worried that digital music would amplify it—would in fact license their music for digital distribution through iTunes. Other times, established firms want to retain a startup flavor 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 estab-

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lished firms (Joglekar and Le´vesque 2006, Shan et al. 1994). 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 hand—those that have a high fraction of early adopters and/or little uncertainty about developer participation—face 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. 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 firm’s 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 (2011) 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 firm’s new product launch strategy under consumer uncertainty regarding network externality. They argue that sequential launch with sequential provision of quality is optimal for a highexternality 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,

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

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 O’Connor (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 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 group’s 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 Adobe’s 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. Although 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 optimal entry strategy of a startup venture. One interesting theme is a new venture’s retail channel selection between virtual and bricks-and-mortar networks given the proliferation of the Internet (e.g., Reinhardt and Le´vesque 2004). Closer to our article is entrepreneurs’ choice between early and delayed 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, Le´vesque and Shepherd (2004) examine a startup

venture’s 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 relevant theme that has been extensively studied in the knowledge management literature (e.g., Choi et al. 2008, March 1991). 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. Finally, Armstrong and Le´vesque (2002) extend Le´vesque (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. Although numerous aspects of a new venture’s 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 realworld 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 twoperiod model of customer purchase and developer participation.

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

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 product’s 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 customer’s 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, Jing 2007, Katz and Shapiro 1992). 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 the literature (Bhargava and Choudhary 2004, Jing 2007, Katz and Shapiro 1992). Specifically, a type v customer’s valuation for a q-quality product when the number of complements is Q, is v·q + kQ, where k 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

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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 Online Appendix S1 for the relaxation of this assumption). Our model of customer behavior and purchase in the two periods is based on theories of technology 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, although 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 depends on its standalone features, whereas the decision making of second-period followers involves a combination of standalone features and developer participation. Formally, we write the net utility of early adopters in the first period and followers in the second period, U1 ðv; qÞ and U2 ðv; qÞ, respectively—as U1 ðv; qÞ ¼ ðv  qÞ  pðqÞ and U2 ðv; qÞ ¼ ðv  q þ kQÞ  qðqÞ;

ð1Þ

where p(q) and ρ(q) are first and second period prices for a q-quality product. Let q represent the firm’s quality vector in period 1, and p the price vector, and let D1 ðq; pÞ be the realized demand for product version q. Then the total first-period installed P base of the firm in the user market is D ¼ q D1 ðq; pÞ. 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 product’s 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 ¼ D / where D is the demand for the

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platform and φ is the cost of developing applications (Shy 2001). We maintain this classical assumption about the positive dependence from D to Q (and we normalize φ to one since it is not the object of interest in this article). 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 very attractive to them because it does not yet have a supporting eco-system (Mitra 2010). Thus, uncertainty in the future application development, that is, 3D content, still remains 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 c · D + e, where the first component is proportional to the installed base of users and the second component is a random offset. We normalize the value of this random 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. Our formalization of this uncertainty component is consistent with other recent articles (Cachon and Lariviere 2001, Chen 2005, Dogan et al. 2011, Ha and Tong 2008). To make the notation concise, we write the two cases as B (“B”est case, with probability h) and W (or “W”orst case). We normalize the random component in the worst-case to zero, to get  Q¼

cDþn cDþ0

“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 and Muller 1998, Mahajan et al. 1990). 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., Joshi et al. 2009, Moe and Fader 2002). 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 firm’s 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 high-end product is constrained exogenously, we can set this to one. Then we denote valuations (for just the product features) for the low-quality product as av, where a ∈ (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 j denote the mass of firstperiod customers, and let us normalize the mass of customers who exogenously arrive in the second period to one.

if application development is High (probability hÞ if application development is Low (probability 1  hÞ

where D is the observed installed base for the product at the start of the second period. Additional customers enter in the second period, that is, 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

ð2Þ

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

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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 section 4.3, we demonstrate that our main findings 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 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 section 4.3). At this point, as indicated in Equation (2) the firm is uncertain about the full extent of developer participation, although it is aware that participation levels will depend positively on its installed base. In this stage, the firm’s 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 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 firm’s first-period decision problem as the vector ½K1 ; pL ; pH  where K1 is either {H} or {L,H} (as noted above, we add {H,L} in section 4.3). If K1 ¼ fHg then pL is vacuous, and pH must satisfy the constraint 0  pH \ 1. If K1 ¼ fL; Hg then the firm incurs a fixed product line expansion cost g, and its prices must satisfy p p p 0  aL \ H1  a L \ 1. In the first case, first-period installed base is D ¼ jð1  pH Þ, while in the second (where the firm expands its product line) it is p D ¼ jð1  aL Þ. The first-period profits for the two cases are P1 ðfHg; pH Þ ¼ jpH ð1  pH Þ;   pH  pL  P1 ðfL; Hg; pL ; pH Þ ¼ j pH 1  p  p 1 pa  H L L  þpL  g: 1a a

ð3Þ

ð4Þ

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

Table 1

Notation for Optimal Operating Second-Period Profit (Not Considering g) Developer Participation

Offer H only Offer {L,H}

Low/worst Case (W)

High/best Case (B)

PW 2 ðD; fHgÞ PW 2 ðD; fL; HgÞ

PB2 ðD; fHgÞ PB2 ðD; fL; HgÞ

offering a particular product line ({H} or {L,H}), that is, profit without considering any applicable product line expansion cost g. This term is the profit after optimizing the product prices corresponding to the level of developer participation (high or low), and is represented using the notation given in Table 1. To solve the second-period problem, the only relevant inputs from the first period are K1 and D. First consider the case where the firm had already chosen an expanded product line in the first period (i.e., K1 ¼ fL; Hg). Should it continue offering both products in the second period? PROPOSITION 1 (NO PRODUCT EXPANSION COST). When product line expansion is costless, the firm’s optimal second-period strategy, for all kQ > 0, is to sell both versions. The incremental profit from versioning (given a versioning

developer participation level Q) is DP2 and this incremental profit increases in Q.

ð1  aÞ ¼ k Q 4a 2

2

This result implies that, when K1 ¼ fL; Hg, 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 K1 ¼ fHg, then the additional expansion cost g destroys the inevitability of versioning in versioning the second period. Specifically, because DP2 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 B=W of which are functions of D. Let P2 ðD; fL; HgÞ be the optimal second-period profit if the firm expands B=W the product line (adds L) and P2 ðD; fHgÞ if it

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continues with an H-only strategy. For each of the two cases B and W (which are resolved prior to the firm’s second-period action), the firm’s 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 E½P2 ðK1 ; DÞ to be the firm’s firstperiod expectation of its second-period profit if it enters the second period with an existing product line K1 and installed base D. Using the notation from Table 1, we have E½P2 ðfL; Hg; DÞ ¼ hPB2 ðD; fL; HgÞ hÞPW 2 ðD; fL; HgÞ;

ð5Þ

þ ð1  n E½P2 ðfHg;DÞ ¼ hmax PB2 ðD;fL;HgÞ  g; o PB2 ðD;fHgÞ n ð6Þ þ ð1  hÞmax PW ðD;fL;HgÞ  g; 2 o PW 2 ðD;fHgÞ :

Combining the second-period actions under the B and W realizations (under K1 ¼ fHg) 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. Equation (6) can therefore be replaced with E½P2 ðfHg; DÞ

¼ ð1  hÞPW 2 ðD; fHgÞ  B þ h max P2 ðD; fHgÞ; PB2 ðD; fL; HgÞ  gg:

P

¼

 Early expansion of product line (K1 ¼ fL; Hg), then Pearly ¼ max PðfL; Hg; pL ; pH Þ pL ;pH

¼ maxðP1 ðfL; Hg; pL ; pH Þ þ E½P2 ðfL; Hg; DÞÞ: ð8Þ pL ;pH

 Defer decision to expand product line (K1 ¼ fHg), then Pdefer ¼ max PðfHg; pH Þ pH

¼ maxðP1 ðfHg; pH Þ þ E½P2 ðfHg; DÞÞ:

3.3. Optimal Product Line and Prices This section specifies the optimal solutions employing the solution framework described in section 3.2. While we provide a complete analysis of this two-period problem using the solution framework specified above in Online Appendix S2, these solution details are needed to analyze the impact of network effects and uncertainty on the firm’s product line expansion strategy. Solving separately the three optimization problems (one for Equation (8) and two implied by the “max” term inside Equation (9)), we obtain LEMMA 1 (OPTIMAL PRICES AND PROFIT OF EARLY EXPANUnder a first-period product line {L,H}, optimal first-period prices are SION).

early

ð7Þ

early

pH

¼ ¼

  a 2a2  ack  ck2 ðcj þ hnÞ 4a2  c2 jk2

;

a2 ð4  2ckÞ  c2 jk2  ack2 ðcj þ 2hnÞ ; 8a2  2c2 jk2

and the optimal total profit under early expansion is

   a 4að1  4g þ j þ cjk þ 2hknÞ þ k2 c2 jð1 þ jÞ þ 4chjn þ 4hn2 16a2  4c2 jk2

ð9Þ

pH

pL

Our main objective is to compare the profitability of K1 ¼ fHg with K1 ¼ fL;Hg. The total expected profit,

early

at the start of first period, from a decision to set K1 ;pL ;pH is PðK1 ;pL ;pH Þ ¼ P1 ðK1 ;pL ;pH Þ þ E½P2 ðK1 ;DÞ p where D ¼ DðpL ;pH Þ ¼ jð1  aL Þ or jð1  pH Þ as appropriate. For the two possible first-period product line decisions, we have the optimal profit under each strategy as



  c2 jk2 að1  4g þ jÞ þ ð1  hÞhk2 n2 16a3  4ac2 jk2

:

ð10Þ

Bhargava, Kim, and Sun: Commercialization of Platform Technologies: Launch Timing and Versioning Strategy

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LEMMA 2 (OPTIMAL PRICES AND PROFITS OF DEFERRED EXPANSION). For the two deferred expansion strategies: 1. Under a defer, H-only strategy, optimal first-period price and total expected profits are defer; Honly

pH

¼

2  ckð1 þ cjk þ hknÞ ; 4  c2 jk2

P

defer; Honly

ð11Þ

    4 þ 4hknð2 þ knÞ þ j 4 þ ck 4 þ hkn 4  cð1  hÞk2 n   ¼ : 4 4  c2 jk2

2. Under a defer, expand strategy, optimal first-period price and total expected profits are 2 defer;expand að2ckð1þcð1hÞjkÞÞchk ðcjþnÞ ¼ ; pH 2 2

4ac ðað1hÞþhÞjk

P

defer; expand

¼

the level of consumer surplus available to extract— becomes better known. Alternately, the firm may want to expand the product line—and installed base—early, in order to drive higher levels of developer participation and increase the available surplus in the second period. This section investigates these two forces.

ð13Þ

ð12Þ

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

  hk2 4cjn þ 4n2  c2 jð1  4gh þ ð1  hÞknðkn  2ÞÞ 16a  4c2 ðað1  hÞ þ hÞjk2     a 4ð1 þ j þ cjkÞ þ h c2 jk2 þ 2 4  c2 ð1  hÞjk2 ðkn  2gÞ

ð14Þ



þ

16a  4c2 ðað1  hÞ þ hÞjk2

The optimal launch strategy can now be determined by comparing the total-profits under the three strategies specified in Equations (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. In additional, 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 participation—and, consequently,

:

Pdefer; expand [ Pdefer; Honly , g\gHD ;

ð15Þ

Pearly [ Pdefer; expand , g\gDE ;

ð16Þ

Pearly [ Pdefer; Honly , g\gHE :

ð17Þ

Combining these results yields the optimal expansion strategy as: 1. If g \ minfgHD ; gDE g (low expansion cost), then early expansion is optimal. 2. When expansion cost is moderate, that is, minfgHD ; gDE g \ g \ maxfgHD ; gDE g, (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 is to defer the expansion decision to the second period, and then expand only if developer participation is high (case B). 3. If g [ maxfgHD ; gDE g, the optimal strategy is to sell product H only. When fixed cost of launching L is very small, then the firm knows that having L in the second-period

10

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

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 Online Appendix S3. 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 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 g becomes higher, however, the firm’s 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 Apple’s 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 the 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 firm’s strategy shifts as the degree of uncertainty in developer participation changes. To do this, we frame the cut-off points for the optimal strategies as a combination of g and ξ; this is because a change in ξ 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 illustrates the impact of uncertainty. At ξ = 0, the firm’s optimal strategy is either to expand early if g is very low or not to expand at all if g 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 force as ξ 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 increase in secondperiod reservation prices. But, as depicted in Figure 1, as ξ increases even further, the firm’s 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 ξ, the uncertain component of second-period reservation prices is substantial (relative to the

Figure 1 Impact of Uncertainty about Developer Participation on Expansion Strategy

(a)

(b)

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

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 ~ n [ 0 such that for low level of uncertainty in developer participation—that is, n\~ n—the optimal strategy is either to expand the product early (if expansion cost is very low, g \ gDE ðnÞ) 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 n[~ n, early expansion is optimal if g \ gDE ðnÞ, H-only is optimal if g [ gHD ðnÞ, and deferred expansion is optimal when g 2 ½gDE ðnÞ; gHD ðnÞ. 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 firm’s 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 expansion—D is relatively small, leading to smaller incremental gain and hence versioning is attractive only for relatively low g. If, however, the firm “sub-optimally” 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 startup’s short-term cash needs. But the costs of financing these cash requirements can be more than offset by the spill-over 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 ξ and h at the same time, because changing ξ alone implies higher developer market size on average. We conducted a numerical study,

11

maintaining the average market size fixed but changing the uncertainty parameter h. Note that if the average market size is kept fixed (by adjusting ξ), a lower h corresponds to a higher standard deviation of Q. Therefore, Figure 1 shows the pure effect of uncertainty about developer participation, uncompounded by 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 or no expansion, and (ii) when h \ ~h, the deferred expansion strategy becomes attractive. To understand (i), consider an extreme case where h is very high (close to one) 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 h. Now, ξ 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 pay-off is relatively large. 4.2. Managerial Implications for Startups vs. Established Firms Although 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 technologyoriented 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, to convince diners (correspondingly, restaurants) to use the system. To differentiate and compare startups and established firms, we analyze the effect of two parameters in our model: c, the ability to attract developers and j, the size of early adopters. First, in our model, the likelihood of participation is captured via the parameter c in the participation function Q = c· D + ξ. It is widely accepted in the literature on software development that the reputation of a software founder is critical to recruiting developers (West and O’Mahony 2005). Therefore, we assume that c 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

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Figure 2 Optimal Expansion Strategies for Startups vs. Established Firms

(a)

convince developers to write applications for the iPad). Figure 2 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, that is, lower ability to attract developers, and smaller size of early adopters, whereas established firms are to the right side of the x axis, that is, 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 firms. As an additional consequence, this finding also suggests that a startup should carefully build its business strategy to attract more developers—that is, raise its c—by providing development tools or incentives. The second differentiating parameter for startups and established firms is j. Specifically, startups and established firms may also differ in their ability to attract early adopters who purchase the product

(b)

(c)

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 j 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 illustrates the effect of j on the expansion strategy. First, note that as j 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 j more because higher j 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 j increases. For the early expansion profit—which partially sacrifices short-term (first-period) profit not only have better position in the second period—higher j increases the first-period sacrifice, but it also delivers higher D and Q, leading to greater gain in the second period. Therefore, the desirability of early expansion increases with j. This point is reinforced in Figure 2, 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 j, 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 j is small. 4.3. Role of Marginal Costs Although 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, for example, Sony’s inclusion of a Blu-Ray player in the PS3 gaming

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

console substantially raised the per-unit costs of the console. In this section, we investigate whether or not our main analysis and findings can be applicable to different marginal costs by relaxing same marginal cost assumptions. We start with checking the robustness of our main findings, that is, comparison between early and deferred expansion strategies. To better reflect the reality that marginal cost increases with product quality, we assume different levels of marginal costs for H and L, then normalize L’s marginal cost to be zero. We denote H’s 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 ~g such that 1. When g \ ~ g, early expansion outperforms defer, expand strategy, 2. When g [ ~ g, there exists a unique cut-off point ~n such that defer, expand strategy is preferred when the level of favorable developer participation is low (n \ ~ n) and early expansion is optimal when it is high (n [ ~ n). 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, that is, 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 PH;Then L (PL;Then H ) be the profit function of H,Then L (L,Then H). PROPOSITION 6 (OPTIMAL SEQUENTIAL LAUNCH WITH DIFFERENT MARGINAL COSTS). Let g(·) be the profit difference (PL;Then H  PH;Then L ). If gðÞjc¼1 [ 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 gðÞjc¼1 \ 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

13

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. Although prior studies mostly considered uncertainty about user adoption, our article 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 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 customers as arriving in two periods, and constrained the firstperiod 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, that is, when they are technology

14

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

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 firm’s selection of optimal product launch strategy. Despite these limitations, we hope that the conceptual insights provided in this article will be of value to a spectrum of firms that 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 technology startups. It is generally more difficult for startups to attract developers and early adopters. Therefore, many startups would be keenly interested 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 technology investment for App development, providing incentives to developers, and identifying optimal contract mechanism). Second, although 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, additional investigation of the two product line expansion strategies with more general 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 article. Considering the fact that many startups gradually improve their technologies over time, this extension will shed more light on startups’ technology commercialization strategies.

their contribution in improving the clarity and quality of this article.

Notes *Author names are listed alphabetically and all authors contributed equally. 1 See http://gigaom.com/apple/ipod-touch-now-outsellingiphone/.

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Supporting Information Additional Supporting Information may be found in the online 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 content or functionality of any supporting materials supplied by the authors. Any queries (other than missing material) should be directed to the corresponding author for the article.


Commercialization of Platform Technologies: Launch Timing and Versioning Strategy