Gillian M. Nicholls

Breakeven Volatility for Real Option Valuation

Abstract: Financial and real options values depend on several variables, including the volatility parameter. Real option volatility arises from the uncertainty of forecasts, and thus, is more subjective than financial option volatility. Difficulties in estimating the volatility parameter inhibit confidence in the resulting option value. This article reviews current estimation methods and proposes a reverse methodology. Real options analysis determines an option value which, combined with project NPV, creates an Expanded Net Present Value (ENPV) such that ENPV exceeding zero justifies a project. At breakeven (ENPV = 0), the minimum option value that justifies the project equals NPV. This breakeven option value determines an implied breakeven volatility.

Determining When Simplified Agile Project Management Is Right for Small Teams

Abstract: Agile project management was developed in the information technology industry as a way of increasing efficiency, productivity, and responsiveness to customers. Many projects with highly uncertain scopes, tasks, task precedence relationships, and/or resources are difficult to handle with traditional planning tools. Better planning in such cases does not lead to better outcomes. An agile approach that maximizes productivity and quality within the resource and time constraints can be superior whether or not the project is IT related. This article addresses when a simplified agile project management methodology can be appropriate for small teams both in academia and in industry.

Team Member Perceptions of Alternative Decision Analysis Approaches

Abstract This research evaluated team members’ perceptions of alternative decision analysis approaches when they were asked to analyze a simple or complex data set under a time constraint. There has been significant work done to understand what criteria should be included in this class of problems. However, less work has been done examining the selection of a specific decision analysis approach. This is an area of concern since research has shown that selecting a different decision analysis approach can change the results even when the same data is analyzed. This research effort is a preliminary look at perceptions of alternative decision analysis approaches. A mixed method study analyzing quantitative and qualitative data was completed. Teams were assigned to use one of five different decision analysis approaches, including Weighted Sum, SMART, TOPSIS, AHP, and ELECTRE I. After applying the approach to either a simple or complex data set, team members were surveyed to determine perceptions including comfort with the approach, its results, and ease/difficulty of explaining the approach to coworkers. Discussions were also held with team members to gain additional insights. The study indicated that team members preferred less technically challenging decision analysis approaches, such as Weighted Sum and to a lesser degree SMART, when they were using a simple data set because they found the approach easier to use and to explain to others. The more technically challenging decision analysis approaches, such as AHP and ELECTRE I, were preferred with a complex data set because they provided better insight into the problem. Teams in this study did selected different alternatives when they used different decision analysis approaches. Demographic data was also collected but was not found to influence results in this study.