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.

Error, Bias, and Long-Branch Attraction in Data for Two Chloroplast Photosystem Genes in Seed Plants

Sequences of two chloroplast photosystem genes, psaA and psbB, together comprising about 3,500 bp, were obtained for all five major groups of extant seed plants and several outgroups among other vascular plants. Strongly supported, but significantly conflicting, phylogenetic signals were obtained in parsimony analyses from partitions of the data into first and second codon positions versus third positions. In the former, both genes agreed on a monophyletic gymnosperms, with Gnetales closely related to certain conifers. In the latter, Gnetales are inferred to be the sister group of all other seed plants, with gymnosperms paraphyletic. None of the data supported the modern ‘‘anthophyte hypothesis,’’ which places Gnetales as the sister group of flowering plants. A series of simulation studies were undertaken to examine the error rate for parsimony inference. Three kinds of errors were examined: random error, systematic bias (both properties of finite data sets), and statistical inconsistency owing to long-branch attraction (an asymptotic property). Parsimony reconstructions were extremely biased for third-position data for psbB. Regardless of the true underlying tree, a tree in which Gnetales are sister to all other seed plants was likely to be reconstructed for these data. None of the combinations of genes or partitions permits the anthophyte tree to be reconstructed with high probability. Simulations of progressively larger data sets indicate the existence of long-branch attraction (statistical inconsistency) for third-position psbB data if either the anthophyte tree or the gymnosperm tree is correct. This is also true for the anthophyte tree using either psaA third positions or psbB first and second positions. A factor contributing to bias and inconsistency is extremely short branches at the base of the seed plant radiation, coupled with extremely high rates in Gnetales and nonseed plant outgroups. M. J. Sanderson,* M. F. Wojciechowski,*† J.-M. Hu,* T. Sher Khan,* and S. G. Brady

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.

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.

Estimating indoor air quality using integrated 3D CAD building models

The indoor air quality (IAQ) in buildings is currently assessed by measurement of pollutants during building operation for comparison with air quality standards. Current practice at the design stage tries to minimise potential indoor air quality impacts of new building materials and contents by selecting low-emission materials. However low-emission materials are not always available, and even when used the aggregated pollutant concentrations from such materials are generally overlooked. This paper presents an innovative tool for estimating indoor air pollutant concentrations at the design stage, based on emissions over time from large area building materials, furniture and office equipment. The estimator considers volatile organic compounds, formaldehyde and airborne particles from indoor materials and office equipment and the contribution of outdoor urban air pollutants affected by urban location and ventilation system filtration. The estimated pollutants are for a single, fully mixed and ventilated zone in an office building with acceptable levels derived from Australian and international health-based standards. The model acquires its dimensional data for the indoor spaces from a 3D CAD model via IFC files and the emission data from a building products/contents emissions database. This paper describes the underlying approach to estimating indoor air quality and discusses the benefits of such an approach for designers and the occupants of buildings.