A Cost Model of Open Source Software Adoption

Introduction

Transformational governance supports new technologies that better increment the quality of the services and save resources¹. In this perspective, Open Source Software (OSS) appears to be an appealing solution. It is a technology free of charge, easy to get, transparent to the user, and ready for first use. As such, it has a big potential to support shared services (Janssen, Joha, & Weerakkody, 2007) within and among public bodies. Namely, shared services can benefit of open common standards and community development for interoperability, easy access, and maintenance. Furthermore, OSS is readily available with little initial expense and therefore it is an opportunity for public organizations that have limited budget and resources.

Embedding OSS in an existing system / environment is not always an easy task tough. Selection of the appropriate OSS, data conversion, and software configurations and customizations for a given operational environment are time-consuming activities that often result impracticable in public bodies by the limited resources. Furthermore, at our knowledge, there is still no established process with a clear sequence of steps that guides to a successful deployment of OSS. Altogether, the assimilation gap (Fichman & Kemerer, 1997) between the acquisition and making a system really operational could be quite significant in the case of OSS. Therefore moving towards OSS becomes a risky decision that requires a deep investigation on the future financial engagement of an organization (Arthur, 1989; Federal Financial Institution Examination Council, 2004; Katz, 1994).

In addition, like for any adoption of a new technology, migrating to a new software solution increases the uncertainty of the future economic asset of an organization. Costs of adoption, assimilation, and ownership are hard to forecast, may drastically vary over time and tend to stabilize only in the long term². In the specific case of a migration to OSS, the uncertainty is further amplified by the lack of empirical evidence of any positive return (Chau & Tam, 1997). The little understanding of the effects of a transition to OSS adds variability and unpredictability to any decision making process based on a direct economic evaluation. For these reasons, a migration toward OSS often does not follow a proper strategy of IT innovation but rather it is initiated by a need of compliance with new regulations and it is then let to the initiative of individual OSS promoters.

Moving to a new software configuration has further critical economic aspects. The theory of the increasing returns (Arthur, 1989) explains that the success of a product does not depend only on its technical superiority. A network effect occurs when the value of a product depends not only on its intrinsic technical value but also on the number of identical or compatible products already in use. A typical example of a network effect is the telephone—having the only telephone in the world would have been less valuable (who one could call?) than having a telephone in a network of 1 billion telephones already installed. In particular “network effects,” often cause “lock-in” situations (Katz & Shapiro, 1994; Economides 2000). More generally, an organization is locked into using a product by a complex combination of technical connectivity issues and liability and contractual agreements that bind it to a specific supplier, brand, or vendor even for future investments. An organization experiences software switching costs when is locked in a specific software configuration and decides to break it to move to a different solution.

Switching costs may become evident for a long time after a transition. As a migration to a new technology does not take place in isolation, switching costs are also experienced maintaining and supporting the infrastructures throughout its lifecycle.