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