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.
Open source software is becoming increasingly prominent, and the economic structure of open-source development is changing. In recent years, firms motivated by revenues from software services markets have become the primary contributors to open-source development. In this paper we study the role of services in open source software development and explore the choice between open source and proprietary software. Specifically, our economic model jointly analyzes the investment and pricing decisions of the originators of software and of subsequent open-source contributors.
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.