Turbocharging Networks in the Public Sector

Networks have come to occupy a key position in the strategic armoury of the government, business and community sectors and now have impact on a broad array of policy and management arenas. An emphasis on relationships, trust and mutuality mean that networks function on a different operating logic to the conventional processes of government and business. It is therefore important that organizational members of networks are able to adopt the skills and culture necessary to operate successfully under these distinctive kinds of arrangements. Because networks function from a different operational logic to traditional bureaucracies, public sector organizations may experience difficulties in adapting to networked arrangements. Networks are formed to address a variety of social problems or meet capability gaps within organizations. As such they are often under pressure to quickly produce measurable outcomes and need to form rapidly and come to full operation quickly. This paper presents a theoretical exploration of how diverse types of networks are required for different management and policy situations and draws on a set of public sector case studies to understand/demonstrate how these various types of networked arrangements may be ‘turbo-charged’ so that they more quickly adopt the characteristics necessary to deliver required outcomes.

Property cash flow studies : focusing on model consistency and data accuracy

The high degree of variability and inconsistency in cash flow study usage by property professionals demands improvement in knowledge and processes. Until recently limited research was being undertaken on the use of cash flow studies in property valuations but the growing acceptance of this approach for major investment valuations has resulted in renewed interest in this topic. Studies on valuation variations identify data accuracy, model consistency and bias as major concerns. In cash flow studies there are practical problems with the input data and the consistency of the models. This study will refer to the recent literature and identify the major factors in model inconsistency and data selection. A detailed case study will be used to examine the effects of changes in structure and inputs. The key variable inputs will be identified and proposals developed to improve the selection process for these key variables. The variables will be selected with the aid of sensitivity studies and alternative ways of quantifying the key variables explained. The paper recommends, with reservations, the use of probability profiles of the variables and the incorporation of this data in simulation exercises. The use of Monte Carlo simulation is demonstrated and the factors influencing the structure of the probability distributions of the key variables are outline. This study relates to ongoing research into functional performance of commercial property within an Australian Cooperative Research Centre.