Research initiatives to improve the visibility and hence safety of road workers at night-time

Research has demonstrated the benefits that clothing incorporating retroreflective markers can provide in significantly improving visibility and reducing accidents, especially at night. Adding biomotion markings to standard vests can enhance the night-time conspicuity of roadway workers by capitalizing on perceptual capabilities.

ACE research briefing paper 005: Global Entrepreneurship Monitor (GEM) - Australia 2011 National Report

This report maps the current state of entrepreneurship in Australia using data from the Global Entrepreneurship Monitor (GEM) for the year 2011. Entrepreneurship is regarded as a crucial driver for economic well-being. Entrepreneurial activity in new and established firms drives innovation and creates jobs. Entrepreneurs also fuel competition thereby contributing indirectly to market and productivity growth along with improving competitiveness of the national economy. Given the economic landscape that exists as a result of the global financial crisis (GFC), it is probably more important than ever for us to understand the effects and drivers of entrepreneurial activity and attitudes in Australia. The central finding of this report is that entrepreneurship is certainly alive and well in Australia. With 10.5 per cent of the adult population involved in setting up a new business or owning a newly founded business as measured by the total entrepreneurial activity rate (TEA) in 2011, Australia ranks second only to the United States among the innovation-driven (developed) economies. Compared with 2010 the TEA rate has increased by 2.7 percentage points. Furthermore, in regard to employee entrepreneurial activity (EEA) rate in established firms, Australia ranks above average. According to GEM data, 5 per cent of the adult population is engaged in developing or launching new products, a new business unit or subsidiary for their employer. Further analysis of the GEM data also clearly shows that Australia compares well with other major economies in terms of the ‘quality’ of entrepreneurial activities being pursued. Indeed, it is not only the quantity of entrepreneurs but also the level of their aspirations and business goals that are important drivers for economic growth. On average, for each business started in Australia driven by the lack of alternatives for the founder to generate income from any other source, there are five other businesses started where the founders specifically want to take advantage of a business opportunity that they believe will increase their personal income or independence. With respect to innovativeness, 31 per cent of Australian new businesses offer products or services which they consider to be new to customers or where very few, or in some cases no, other businesses offer the same product or service. Both these indicators are higher than the average for innovation-driven economies. Somewhat below average is the international orientation of Australian entrepreneurs whereby only 12 per cent aim at having a substantial share of customers from international markets. So what drives this high quantity and quality of entrepreneurship in Australia? The analysis of the data suggests it is a combination of both business opportunities and entrepreneurial skills. It seems that around 50 per cent of the Australian population identify opportunities for a start-up venture and believe that they have the necessary skills to start a business. Furthermore, a large majority of the Australian population report that high media attention for entrepreneurship provides successful role models for prospective entrepreneurs. As a result, 12 per cent of our respondents have expressed the intention to start a business within the next three years. These numbers are all well above average when compared to the other major economies. With regard to gender, the GEM survey shows a high proportion of female entrepreneurs. Approximately 8.4 per cent of adult females are actually involved in setting up a business or have recently done so. Although this female TEA rate is slightly down from 2010, Australia ranks second among the innovation-driven economies. This paints a healthy picture of access to entrepreneurial opportunities for Australian women.

Accounting Research Journal Editorial : "ARJ – The First 25 Years"

In this 25th year of publication of the Accounting Research Journal we pay tribute to the efforts of the dedicated Editors who have successfully guided and developed the journal since its inception in 1988. After the rapid growth in accounting and finance research in the 1970s and 1980s the absence of outlets in Asia-Pacific region to publish novel, timely and applied research became increasingly apparent. In response to this gap, ARJ’s first volume was published in 1988 by the School of Accountancy at the Queensland Institute of Technology (QIT), which became the Queensland University of Technology (QUT) in the following year. The founding Editor was Myles McGregor-Lowndes and his editorship continued for three years until Scott Holmes took over as Editor in 1991. In 1992, Robert Faff joined Scott Holmes as Joint Editor, and their joint editorship continued for six years until Robert Faff took the reins as Editor in 1998. At that time Scott remained as Associate Editor and the editorial team was joined by Roger Willett as Consulting Editor and Chris Lambert as Associate Editor. This arrangement continued until 2002 when Tim Brailsford was newly appointed as Managing Editor. The editorship returned to QUT in 2008 and was taken on by Chris Ryan with our support as Co-editors. Since 2011 we have been the Joint Editors. Table 1 lists the individuals who have been involved in editing ARJ over the 25-year period and their roles...

A review of current statistical methodologies for in storage sampling and surveillance in the grains industry

Effective, statistically robust sampling and surveillance strategies form an integral component of large agricultural industries such as the grains industry. Intensive in-storage sampling is essential for pest detection, Integrated Pest Management (IPM), to determine grain quality and to satisfy importing nation’s biosecurity concerns, while surveillance over broad geographic regions ensures that biosecurity risks can be excluded, monitored, eradicated or contained within an area. In the grains industry, a number of qualitative and quantitative methodologies for surveillance and in-storage sampling have been considered. Primarily, research has focussed on developing statistical methodologies for in storage sampling strategies concentrating on detection of pest insects within a grain bulk, however, the need for effective and statistically defensible surveillance strategies has also been recognised. Interestingly, although surveillance and in storage sampling have typically been considered independently, many techniques and concepts are common between the two fields of research. This review aims to consider the development of statistically based in storage sampling and surveillance strategies and to identify methods that may be useful for both surveillance and in storage sampling. We discuss the utility of new quantitative and qualitative approaches, such as Bayesian statistics, fault trees and more traditional probabilistic methods and show how these methods may be used in both surveillance and in storage sampling systems.

Inquiries, empirical research and regulatory failure

The paper reveals that regulatory failure, often chronic, has characterised the regulatory environments of charities across time and locale. The analysis of the primary literature identifies common issues and suggested remedies pertaining to the regulatory failures of charities. These issues may well be appropriate for consideration by the commission and participants given their persistence in various inquiries for nearly four centuries. Such inquiries also considered other issues not directly referred to in this paper, to also include them would exponentially increase the already unwieldy size of this paper.

Postpositivist research in education : theorising social structure in critical and institutional ethnography

This chapter charts the theories and methods being adopted in an investigation of the 'micro-politics' of teacher education policy reception at a site of higher education in Queensland from 1980 to 1990. The paper combines insights and methods from critical ethnography with those from the institutional ethnography of feminist sociologist Dorothy Smith to link local policy activity at the institutional site to broader social structures and processes. In this way, enquiry begins with--and takes into account--the experiences of those groups normally excluded from mainstream and even critical policy analysis.

Bayesian hierarchical models in statistical quality control methods to improve healthcare in hospitals

Quality oriented management systems and methods have become the dominant business and governance paradigm. From this perspective, satisfying customers’ expectations by supplying reliable, good quality products and services is the key factor for an organization and even government. During recent decades, Statistical Quality Control (SQC) methods have been developed as the technical core of quality management and continuous improvement philosophy and now are being applied widely to improve the quality of products and services in industrial and business sectors. Recently SQC tools, in particular quality control charts, have been used in healthcare surveillance. In some cases, these tools have been modified and developed to better suit the health sector characteristics and needs. It seems that some of the work in the healthcare area has evolved independently of the development of industrial statistical process control methods. Therefore analysing and comparing paradigms and the characteristics of quality control charts and techniques across the different sectors presents some opportunities for transferring knowledge and future development in each sectors. Meanwhile considering capabilities of Bayesian approach particularly Bayesian hierarchical models and computational techniques in which all uncertainty are expressed as a structure of probability, facilitates decision making and cost-effectiveness analyses. Therefore, this research investigates the use of quality improvement cycle in a health vii setting using clinical data from a hospital. The need of clinical data for monitoring purposes is investigated in two aspects. A framework and appropriate tools from the industrial context are proposed and applied to evaluate and improve data quality in available datasets and data flow; then a data capturing algorithm using Bayesian decision making methods is developed to determine economical sample size for statistical analyses within the quality improvement cycle. Following ensuring clinical data quality, some characteristics of control charts in the health context including the necessity of monitoring attribute data and correlated quality characteristics are considered. To this end, multivariate control charts from an industrial context are adapted to monitor radiation delivered to patients undergoing diagnostic coronary angiogram and various risk-adjusted control charts are constructed and investigated in monitoring binary outcomes of clinical interventions as well as postintervention survival time. Meanwhile, adoption of a Bayesian approach is proposed as a new framework in estimation of change point following control chart’s signal. This estimate aims to facilitate root causes efforts in quality improvement cycle since it cuts the search for the potential causes of detected changes to a tighter time-frame prior to the signal. This approach enables us to obtain highly informative estimates for change point parameters since probability distribution based results are obtained. Using Bayesian hierarchical models and Markov chain Monte Carlo computational methods, Bayesian estimators of the time and the magnitude of various change scenarios including step change, linear trend and multiple change in a Poisson process are developed and investigated. The benefits of change point investigation is revisited and promoted in monitoring hospital outcomes where the developed Bayesian estimator reports the true time of the shifts, compared to priori known causes, detected by control charts in monitoring rate of excess usage of blood products and major adverse events during and after cardiac surgery in a local hospital. The development of the Bayesian change point estimators are then followed in a healthcare surveillances for processes in which pre-intervention characteristics of patients are viii affecting the outcomes. In this setting, at first, the Bayesian estimator is extended to capture the patient mix, covariates, through risk models underlying risk-adjusted control charts. Variations of the estimator are developed to estimate the true time of step changes and linear trends in odds ratio of intensive care unit outcomes in a local hospital. Secondly, the Bayesian estimator is extended to identify the time of a shift in mean survival time after a clinical intervention which is being monitored by riskadjusted survival time control charts. In this context, the survival time after a clinical intervention is also affected by patient mix and the survival function is constructed using survival prediction model. The simulation study undertaken in each research component and obtained results highly recommend the developed Bayesian estimators as a strong alternative in change point estimation within quality improvement cycle in healthcare surveillances as well as industrial and business contexts. The superiority of the proposed Bayesian framework and estimators are enhanced when probability quantification, flexibility and generalizability of the developed model are also considered. The empirical results and simulations indicate that the Bayesian estimators are a strong alternative in change point estimation within quality improvement cycle in healthcare surveillances. The superiority of the proposed Bayesian framework and estimators are enhanced when probability quantification, flexibility and generalizability of the developed model are also considered. The advantages of the Bayesian approach seen in general context of quality control may also be extended in the industrial and business domains where quality monitoring was initially developed.

Teachers’ detecting and reporting child abuse and neglect : research into the determinants of complex professional behavior

Child abuse and neglect is prevalent and entails significant costs to children, families and society. Teachers are responsible for significant proportions of official notifications to statutory child protection agencies. Hence, their accurate and appropriate reporting is crucial for well-functioning child protection systems. Approximately one-quarter of Australian teachers indicate never detecting a case of child maltreatment across their careers, while a further 13-15% admit to not reporting suspected cases in some circumstances. The detection and reporting of child abuse and neglect are complex decision-making behaviors, influenced by: the nature of the maltreatment itself; the characteristics of the teacher; the school environment; and the broader legislative and policy environment. In this chapter, the authors provide a background to teachers’ involvement in detecting and reporting child abuse and neglect, and an overview of the role of teachers is provided. Results are presented from three Australian studies that examine the unique contributions of: case; teacher; and contextual characteristics to detection and reporting behaviors. The authors conclude by highlighting the key implications for enhancing teacher training in child abuse and neglect, and outline future research directions.