We investigate the impact of the intergenerational nature of services, via backward compatibility , on the adoption of multigenerational platforms. We consider a mobile Internet platform that has evolved over several generations and for which users download complementary services from third-party providers. These services are often intergenerational: newer platform generations are backward compatible with respect to services released under earlier generation platforms. In this paper, we propose a model to identify the main drivers of consumers' choice of platform generation, accounting for (i) the migration from older to newer platform generations, (ii) the indirect network effect on platform adoption due to same-generation services, and (iii) the effect on platform adoption due to the consumption of intergenerational services via backward compatibility. Using data on mobile Internet platform adoption and services consumption for the time period of 2001Ð2007 from a major wireless carrier in an Asian country, we estimate the three effects noted above. We show that both the migration from older to newer platform generations and the indirect network effects are significant. The surprising finding is that intergenerational services that connect subsequent generations of platforms essentially engender backward compatibility with two opposing effects. Whereas an intergenerational service may accelerate the migration to the subsequent platform generations, it may also, perhaps unintentionally, provide a fresh lease on life for earlier generation platforms due to the continued use of earlier generation services on newer platform generations.
In this paper, we explore the economics of <i>free</i> under perpetual licensing. In particular, we focus on two emerging software business models that involve a free component: <i>feature-limited freemium</i> (<i>FLF</i>) and <i>uniform seeding</i> (<i>S</i>). Under <i>FLF</i>, the firm offers the basic software version for free, while charging for premium features. Under <i>S</i>, the firm gives away for free the full product to a <i>percentage</i> of the addressable market <i>uniformly</i> across consumer types. We benchmark their performance against a conventional business model under which software is sold as a bundle (labeled as “charge for everything” or <i>CE</i>) without free offers. In the context of consumer bounded rationality and information asymmetry, we develop a unified two-period consumer valuation learning framework that accounts for both word-of-mouth (WOM) effects and experience-based learning, and use it to compare and contrast the three business models. Under both constant and dynamic pricing, for moderate strength of WOM signals, we derive the equilibria for each model and identify optimality regions. In particular, <i>S</i> is optimal when consumers significantly underestimate the value of functionality and cross-module synergies are weak. When either cross-module synergies are stronger or initial priors are higher, the firm decides between <i>CE</i> and <i>FLF</i>. Furthermore, we identify nontrivial switching dynamics from one optimality region to another depending on the initial consumer beliefs about the value of the embedded functionality. For example, there are regions where, ceteris paribus, <i>FLF</i> is optimal when the prior on <i>premium</i> functionality is either relatively low or high, but not in between. We also demonstrate the robustness of our findings with respect to various parameterizations of cross-module synergies, strength of WOM effects, and number of periods. We find that stronger WOM effects or more periods lead to an expansion of the seeding optimality region in parallel with a decrease in the seeding ratio. Moreover, under <i>CE</i> and dynamic pricing, second period price may be decreasing in the initial consumer valuation beliefs when WOM effects are strong and the prior is relatively low. However, this is not the case under weak WOM effects. We also discuss regions where price skimming and penetration pricing are optimal. Our results provide key managerial insights that are useful to firms in their business model search and implementation.
By software vendors offering, via the cloud, software-as-a-service (SaaS) versions of traditionally on-premises application software, security risks associated with usage become more diversified. This can greatly increase the value associated with the software. In an environment where negative security externalities are present and users make complex consumption and patching decisions, we construct a model that clarifies whether and how SaaS versions should be offered by vendors. We find that the existence of version-specific security externalities is sufficient to warrant a versioned outcome, which has been shown to be suboptimal in the absence of security risks. In high security-loss environments, we find that SaaS should be geared to the middle tier of the consumer market if patching costs and the quality of the SaaS offering are high, and geared to the lower tier otherwise. In the former case, when security risk associated with each version is endogenously determined by consumption choices, strategic interactions between the vendor and consumers may cause a higher tier consumer segment to prefer a lower inherent quality product. Relative to on-premises benchmarks, we find that software diversification leads to lower average security losses for users when patching costs are high. However, when patching costs are low, surprisingly, average security losses can increase as a result of SaaS offerings and lead to lower consumer surplus. We also investigate the vendor’s security investment decision and establish that, as the market becomes riskier, the vendor tends to increase investments in an on-premises version and decrease investments in a SaaS version. On the other hand, in low security-loss environments, we find that SaaS is optimally targeted to a lower tier of the consumer market, average security losses decrease, and consumer surplus increases as a result. Security investments increase for both software versions as risk increases in these environments.
Firms nowadays are increasingly proactive in trying to strategically capitalize on consumer networks and social interactions. In this paper, we complement an emerging body of research on the engineering of word-of-mouth effects by exploring a different angle through which firms can strategically exploit the value-generation potential of the user network. Namely, we consider how software firms should optimize the strength of network effects at utility level by adjusting the level of embedded social media features in tandem with the right market seeding and pricing strategies in the presence of seeding disutility. We explore two opposing seeding cost models where seeding-induced disutility can be either positively or negatively correlated with customer type. We consider both complete and incomplete information scenarios for the firm. Under complete information, we uncover a complementarity relationship between seeding and building social media features that holds for both disutility models. When the cost of any of these actions increases, rather than compensating by a stronger action on the other dimension to restore the overall level of network effects, the firm will actually scale back on the other initiative as well. Under incomplete information, this complementarity holds when seeding disutility is negatively correlated with customer type but may not always hold in the other disutility model, potentially leading to fundamentally different optimal strategies. We also discuss how our insights apply to asymmetric networks.
Wireless telecommunications have become over time a ubiquitous tool that not only sustains our increasing need for flexibility and efficiency, but also provides new ways to access and experience both utilitarian and hedonic information goods and services. This paper explores the parallel market evolution of the two main categories of wireless services-voice and data-in leading technology markets, inspecting the differences and complex interactions between the associated adoption processes. We propose a model that addresses specific individual characteristics of these two services and the stand-alone/add-on relationship between them. In particular, we acknowledge the distinction between the nonoverlapping classes of basic consumers, who only subscribe to voice plans, and sophisticated consumers, who adopt both services. We also account for the fact that, unlike voice services, data services rapidly evolved over time due to factors such as interface improvement, gradual technological advances in data transmission speed and security, and the increase in volume and diversity of the content and services ported to mobile Internet. Moreover, we consider the time gap between the market introduction of these services and allow for different corresponding consumer learning curves. We test our model on the Japanese wireless market. The empirical analysis reveals several interesting results. In addition to an expected one-way effect of voice on data adoption at the market potential level, we do find two-way codiffusion effects at the speed of adoption level. We also observe that basic consumers impact the adoption of wireless voice services in a stronger way compared to sophisticated consumers. This, in turn, leads to a decreasing average marginal network effect of voice subscribers on the adoption of wireless voice services. Furthermore, we find that the willingness of voice consumers to consider adopting data services is positively related to both time and penetration of 3G-capable handsets among voice subscribers.
In this paper we explore the underlying consumer heterogeneity in competitive markets for subscription-based information technology services that exhibit network effects. Insights into consumer heterogeneity with respect to a given service are paramount in forecasting future subscriptions, understanding the impact of price and information dissemination on market penetration growth, and predicting the adoption path for complementary products that target the same customers as the original service. Employing a continuous-time utility model, we capture the behavior of a continuum of consumers who are differentiated by their intrinsic valuations from using the service. We study service subscription patterns under both perfect and imperfect information dissemination. In each case, we first specify the conditions under which consumer rational behavior supported by the utility model can explain a general observed adoption path, and if so, we explicitly derive the analytical closed-form expression for the consumer valuation distribution. We further explore the impact of awareness and distribution skewness on adoption. In particular, we highlight the practical forecasting importance of understanding the information dissemination process in the market as observed past adoption may be explained by several distinct awareness and heterogeneity scenarios that may lead to divergent adoption paths in the future. Moreover, we show that in the later part of the service lifecycle the subscription decision for new customers can be driven predominantly by information dissemination instead of further price markdowns. We also extend our results to time-varying consumer valuation scenarios. Furthermore, based on our framework, we advance a set of heuristic methods to be applied to discrete-time real industry data for estimation and forecasting purposes. In an empirical exercise, we apply our methodology to the Japanese mobile voice services market and provide relevant managerial insights from the analysis.