The influence of role models on students' entrepreneurial intentions

The Smart State initiative requires both improved education and training, panicularly in technical fields, plus entrepreneurship to commercialise new ideas. In this study, we propose an entrepreneurial intentions model as a guide to examine the educational choices and entrepreneurial intentions of first-year University students, focusing on the effect of role models. A survey of over 1000 first-year University students revealed that the most enterprising students were choosing to study in the disciplines of information technology and business, economics and law, or selecting dualdegree programs that include business. The role models most often identified for their choice of field of study were parents,followed by teachers and peers, with females identifying more role models than males. For entrepreneurship, students' role models were parents andpeers,followed by famous persons and teachers. Males and females identified similar numbers of role models, but males found starting a business more desirable and more feasible, and reponed higher entrepreneurial intention. The implications of these findings for Smart State policy are discussed.

Context and models for the analysis of individual and group needs

This paper summarizes the papers presented in the thematic stream Models for the Analysis of Individual and Group Needs, at the 2007 IAEVG-SVP-NCDA Symposium: Vocational Psychology and Career Guidance Practice: An International Partnership. The predominant theme which emerged from the papers was that theory and practice need to be positioned within their contexts. For this paper, context has been formulated as a dimension ranging from the individual’s experience of himself or herself in conversations, including interpersonal transactions and body culture, through to broad higher levels of education, work, nation, and economy.

A review of models of spatial innovation infrastructures in support of high growth SMEs

This Paper first provides a review and analysis of the recent trends on innovation infrastructures developed in industrialised countries to promote innovation and competitiveness for high growth SMEs. It specifically aims to examine various spatial models developed to support provision of innovation infrastructure for high growth sector.

Life cycle modelling and design knowledge development in 3D virtual environments : final report

Experience plays an important role in building management. “How often will this asset need repair?” or “How much time is this repair going to take?” are types of questions that project and facility managers face daily in planning activities. Failure or success in developing good schedules, budgets and other project management tasks depend on the project manager's ability to obtain reliable information to be able to answer these types of questions. Young practitioners tend to rely on information that is based on regional averages and provided by publishing companies. This is in contrast to experienced project managers who tend to rely heavily on personal experience. Another aspect of building management is that many practitioners are seeking to improve available scheduling algorithms, estimating spreadsheets and other project management tools. Such “micro-scale” levels of research are important in providing the required tools for the project manager's tasks. However, even with such tools, low quality input information will produce inaccurate schedules and budgets as output. Thus, it is also important to have a broad approach to research at a more “macro-scale.” Recent trends show that the Architectural, Engineering, Construction (AEC) industry is experiencing explosive growth in its capabilities to generate and collect data. There is a great deal of valuable knowledge that can be obtained from the appropriate use of this data and therefore the need has arisen to analyse this increasing amount of available data. Data Mining can be applied as a powerful tool to extract relevant and useful information from this sea of data. Knowledge Discovery in Databases (KDD) and Data Mining (DM) are tools that allow identification of valid, useful, and previously unknown patterns so large amounts of project data may be analysed. These technologies combine techniques from machine learning, artificial intelligence, pattern recognition, statistics, databases, and visualization to automatically extract concepts, interrelationships, and patterns of interest from large databases. The project involves the development of a prototype tool to support facility managers, building owners and designers. This final report presents the AIMMTM prototype system and documents how and what data mining techniques can be applied, the results of their application and the benefits gained from the system. The AIMMTM system is capable of searching for useful patterns of knowledge and correlations within the existing building maintenance data to support decision making about future maintenance operations. The application of the AIMMTM prototype system on building models and their maintenance data (supplied by industry partners) utilises various data mining algorithms and the maintenance data is analysed using interactive visual tools. The application of the AIMMTM prototype system to help in improving maintenance management and building life cycle includes: (i) data preparation and cleaning, (ii) integrating meaningful domain attributes, (iii) performing extensive data mining experiments in which visual analysis (using stacked histograms), classification and clustering techniques, associative rule mining algorithm such as “Apriori” and (iv) filtering and refining data mining results, including the potential implications of these results for improving maintenance management. Maintenance data of a variety of asset types were selected for demonstration with the aim of discovering meaningful patterns to assist facility managers in strategic planning and provide a knowledge base to help shape future requirements and design briefing. Utilising the prototype system developed here, positive and interesting results regarding patterns and structures of data have been obtained.

Generative and evolutionary models for design

The paper describes three design models that make use of generative and evolutionary systems. The models describe overall design methods and processes. Each model defines a set of tasks to be performed by the design team, and in each case one of the tasks requires a generative or evolutionary design system. The architectures of these systems are also broadly described.

The influence of role models on students' entrepreneurial intentions

The Smart State initiative requires both improved education and training, particularly in technical fields, plus entrepreneurship to commercialise new ideas. In this study, we propose an entrepreneurial intentions model as a guide to examine the educational choices and entrepreneurial intentions of first-year University students, focusing on the effect of role models. A survey of over 1000 first -year University students revealed that the most enterprising students were choosing to study in the disciplines of information technology and business, economics and law, or selecting dual degree programs that include business. The role models most often identified for their choice of field of study were parents, followed by teachers and peers, wish females identifying more role models than males. For entrepreneurship, students' role models were parents and peers, followed by famous persons and teachers. Males and females identified similar numbers of role models, but males found starting a business more desirable and more feasible, and reported higher entrepreneurial intention. The implications of these findings for Smart State policy are discussed.

Life Cycle Modelling and Design Knowledge Development in 3D Virtual Environments

Experience plays an important role in building management. “How often will this asset need repair?” or “How much time is this repair going to take?” are types of questions that project and facility managers face daily in planning activities. Failure or success in developing good schedules, budgets and other project management tasks depend on the project manager's ability to obtain reliable information to be able to answer these types of questions. Young practitioners tend to rely on information that is based on regional averages and provided by publishing companies. This is in contrast to experienced project managers who tend to rely heavily on personal experience. Another aspect of building management is that many practitioners are seeking to improve available scheduling algorithms, estimating spreadsheets and other project management tools. Such “micro-scale” levels of research are important in providing the required tools for the project manager's tasks. However, even with such tools, low quality input information will produce inaccurate schedules and budgets as output. Thus, it is also important to have a broad approach to research at a more “macro-scale.” Recent trends show that the Architectural, Engineering, Construction (AEC) industry is experiencing explosive growth in its capabilities to generate and collect data. There is a great deal of valuable knowledge that can be obtained from the appropriate use of this data and therefore the need has arisen to analyse this increasing amount of available data. Data Mining can be applied as a powerful tool to extract relevant and useful information from this sea of data. Knowledge Discovery in Databases (KDD) and Data Mining (DM) are tools that allow identification of valid, useful, and previously unknown patterns so large amounts of project data may be analysed. These technologies combine techniques from machine learning, artificial intelligence, pattern recognition, statistics, databases, and visualization to automatically extract concepts, interrelationships, and patterns of interest from large databases. The project involves the development of a prototype tool to support facility managers, building owners and designers. This Industry focused report presents the AIMMTM prototype system and documents how and what data mining techniques can be applied, the results of their application and the benefits gained from the system. The AIMMTM system is capable of searching for useful patterns of knowledge and correlations within the existing building maintenance data to support decision making about future maintenance operations. The application of the AIMMTM prototype system on building models and their maintenance data (supplied by industry partners) utilises various data mining algorithms and the maintenance data is analysed using interactive visual tools. The application of the AIMMTM prototype system to help in improving maintenance management and building life cycle includes: (i) data preparation and cleaning, (ii) integrating meaningful domain attributes, (iii) performing extensive data mining experiments in which visual analysis (using stacked histograms), classification and clustering techniques, associative rule mining algorithm such as “Apriori” and (iv) filtering and refining data mining results, including the potential implications of these results for improving maintenance management. Maintenance data of a variety of asset types were selected for demonstration with the aim of discovering meaningful patterns to assist facility managers in strategic planning and provide a knowledge base to help shape future requirements and design briefing. Utilising the prototype system developed here, positive and interesting results regarding patterns and structures of data have been obtained.

Open Specification for BIM : Sydney Opera House Case Study

Executive Summary The objective of this report was to use the Sydney Opera House as a case study of the application of Building Information Modelling (BIM). The Sydney opera House is a complex, large building with very irregular building configuration, that makes it a challenging test. A number of key concerns are evident at SOH: • the building structure is complex, and building service systems - already the major cost of ongoing maintenance - are undergoing technology change, with new computer based services becoming increasingly important. • the current “documentation” of the facility is comprised of several independent systems, some overlapping and is inadequate to service current and future services required • the building has reached a milestone age in terms of the condition and maintainability of key public areas and service systems, functionality of spaces and longer term strategic management. • many business functions such as space or event management require up-to-date information of the facility that are currently inadequately delivered, expensive and time consuming to update and deliver to customers. • major building upgrades are being planned that will put considerable strain on existing Facilities Portfolio services, and their capacity to manage them effectively While some of these concerns are unique to the House, many will be common to larger commercial and institutional portfolios. The work described here supported a complementary task which sought to identify if a building information model – an integrated building database – could be created, that would support asset & facility management functions (see Sydney Opera House – FM Exemplar Project, Report Number: 2005-001-C-4 Building Information Modelling for FM at Sydney Opera House), a business strategy that has been well demonstrated. The development of the BIMSS - Open Specification for BIM has been surprisingly straightforward. The lack of technical difficulties in converting the House’s existing conventions and standards to the new model based environment can be related to three key factors: • SOH Facilities Portfolio – the internal group responsible for asset and facility management - have already well established building and documentation policies in place. The setting and adherence to well thought out operational standards has been based on the need to create an environment that is understood by all users and that addresses the major business needs of the House. • The second factor is the nature of the IFC Model Specification used to define the BIM protocol. The IFC standard is based on building practice and nomenclature, widely used in the construction industries across the globe. For example the nomenclature of building parts – eg ifcWall, corresponds to our normal terminology, but extends the traditional drawing environment currently used for design and documentation. This demonstrates that the international IFC model accurately represents local practice for building data representation and management. • a BIM environment sets up opportunities for innovative processes that can exploit the rich data in the model and improve services and functions for the House: for example several high-level processes have been identified that could benefit from standardized Building Information Models such as maintenance processes using engineering data, business processes using scheduling, venue access, security data and benchmarking processes using building performance data. The new technology matches business needs for current and new services. The adoption of IFC compliant applications opens the way forward for shared building model collaboration and new processes, a significant new focus of the BIM standards. In summary, SOH current building standards have been successfully drafted for a BIM environment and are confidently expected to be fully developed when BIM is adopted operationally by SOH. These BIM standards and their application to the Opera House are intended as a template for other organisations to adopt for the own procurement and facility management activities. Appendices provide an overview of the IFC Integrated Object Model and an understanding IFC Model Data.