SOA Adoption Phases

The paper argues that attitudes to SOA follow a typical hype cycle from Technological Trigger, Peak of Inflated Expectations, and a Trough of Disillusionment to the more recent realization that SOA is a concept that may offer certain benefits but has several limitations. The main research question studies how the attitude to SOA changes in various phases of the hype cycle, how the SOA implementation cycle and an increase in business process maturity (BPMa) are interconnected and which factors influence the transition between the hype cycle phases. The paper shows that an organization’s success with implementing SOA depends on its ability to match the SOA implementation with an increase in BPMa. The dual purpose of implementing SOA is shown in the first framework: to assure the coherence of IT assets and to assure business/IT alignment. In the second framework, the interconnection of SOA and BPMa and its role in transiting through the hype cycle phases is outlined. The findings are analyzed using a longitudinal case study of a large Slovenian company.     

Granularity of Services

Service-oriented architectures are widely discussed as a design principle for application and enterprise architectures. Nevertheless, an adequate granularity of services has not yet been researched sufficiently from an economic perspective. The finer the granularity to realize the functions of a process, the higher the number of services is, and the more effort has to be directed towards composing them. In contrast, very coarse-grained services bear the disadvantages of higher implementation costs and lower reuse potential (e.g., in different processes). The aim of the decision model proposed in this paper is to determine an adequate granularity of services from an economical perspective. Thus, degrees of freedom, which often exist for the choice of granularity after a domain analysis, can be leveraged to realize a cost-efficient solution. We illustrate the applicability and practical benefits of the decision model with an example from the context of a financial services provider.     

Secure and Sustainable Benchmarking in Clouds

Cloud computing entails a novel security threat: The cloud service provider is entrusted with the data of all its customers. This may not be sustainable for highly confidential data. Encryption, or more generally cryptography, may provide a solution by computing on data encrypted by the customers. While this solution is theoretically appealing, it raises a number of research questions in information system design.     

ERP On Demand Platform

Platforms for SaaS-based enterprise applications are prospering and the number of on-demand ERP vendors is increasing. We combine both phenomena for the first time and illustrate how the integration of a sustainability benchmarking service into an on-demand ERP platform provides added value beyond pure cost savings. By applying a qualitative system dynamics approach we identify self-reinforcing mechanisms which allow a faster and more comprehensive market penetration compared to providing these services separately.     

Valuation of Discount Options in Software License Agreements

Many companies increasingly rely on licensed standard software for system software and applications. In addition to the regulation of usage conditions, software licensing agreements increasingly include services, such as software upgrades and user training, as a part of the contract or these are optional for a fee, which can be made use of by the licensee during the term of the contract at a reduced price or as a free service. This benefit entitlement is called a discount option and must be valued during the selection and designing of a contract. This paper describes the basic valuation issues as well as some weaknesses of previous approaches, and subsequently presents a model which, on the basis of the real option theory, enables an assessment of the discount options using mathematical methods. As the value of discount options can in many cases only be estimated by using analytical methods under certain conditions, a practical solution method is explained on the basis of numeric backwards induction. The procedure for applying the model and the achieved advances in knowledge are illustrated with an example.