A practical approach to goal modelling for time-constrained projects

Goal modelling is a well known rigorous method for analysing problem rationale and developing requirements. Under the pressures typical of time-constrained projects its benefits are not accessible. This is because of the effort and time needed to create the graph and because reading the results can be difficult owing to the effects of crosscutting concerns. Here we introduce an adaptation of KAOS to meet the needs of rapid turn around and clarity. The main aim is to help the stakeholders gain an insight into the larger issues that might be overlooked if they make a premature start into implementation. The method emphasises the use of obstacles, accepts under-refined goals and has new methods for managing crosscutting concerns and strategic decision making. It is expected to be of value to agile as well as traditional processes.

Intellectual property management in M&A transactions

Purpose – The purpose of this paper is to focus on the intellectual property (IP) aspects of mergers and acquisitions (M&A) transactions. Design/methodology/approach – A holistic approach is proposed that embraces IP as an integral part of the M&A process in the form of a roadmap for the strategic and purposeful management of IP assets in M&A deals. Findings – Addressing IP issues in transactions in isolation can lead to undesirable consequences (e.g. considerable costs for unplanned purchases of rights). IP due diligence and IP integration project processes can be automated using collaborative software solutions. Thereby, risk can be reduced through the creation of a high level of transparency and predefined responsibilities. Practical implications – Although the proposed IP management framework is based on intellectual property matters relevant to German jurisdiction, conclusions derived and the proposed roadmap are transferable to other jurisdictions and hence have a broader relevance. Evidence for this is provided by the successful application of the roadmap in the context of the multinational transaction stated above. Originality/value – This paper is based on the authors' collective experience, insight and reflected observation of M&A practice gained in advisory M&A roles in management consultancies over a period of more than a decade. The paper summarizes the authors' reflections and observations and brings these into the context of the current management literature. Moreover, this paper builds on earlier published research.

A practical comparison of group-sequential and adaptive designs

Sequential methods provide a formal framework by which clinical trial data can be monitored as they accumulate. The results from interim analyses can be used either to modify the design of the remainder of the trial or to stop the trial as soon as sufficient evidence of either the presence or absence of a treatment effect is available. The circumstances under which the trial will be stopped with a claim of superiority for the experimental treatment, must, however, be determined in advance so as to control the overall type I error rate. One approach to calculating the stopping rule is the group-sequential method. A relatively recent alternative to group-sequential approaches is the adaptive design method. This latter approach provides considerable flexibility in changes to the design of a clinical trial at an interim point. However, a criticism is that the method by which evidence from different parts of the trial is combined means that a final comparison of treatments is not based on a sufficient statistic for the treatment difference, suggesting that the method may lack power. The aim of this paper is to compare two adaptive design approaches with the group-sequential approach. We first compare the form of the stopping boundaries obtained using the different methods. We then focus on a comparison of the power of the different trials when they are designed so as to be as similar as possible. We conclude that all methods acceptably control type I error rate and power when the sample size is modified based on a variance estimate, provided no interim analysis is so small that the asymptotic properties of the test statistic no longer hold. In the latter case, the group-sequential approach is to be preferred. Provided that asymptotic assumptions hold, the adaptive design approaches control the type I error rate even if the sample size is adjusted on the basis of an estimate of the treatment effect, showing that the adaptive designs allow more modifications than the group-sequential method.