Diffusion of corporate governance practices : the Dutch response to pressures for shareholder value

As part of a larger literature focused on identifying and relating the antecedents and consequences of diffusing organizational practices/ideas, recent research has debated the international adoption of a shareholder-value-orientation (SVO). The debate has financial economists characterizing the adoption of an SVO as performance-enhancing and thus inevitable, with behavioral scientists disputing both claims, invoking institutional differences. This study seeks to provide some resolution to the debate (and advance current understanding on the diffusion of practices/ideas) by developing a socio-political perspective that links the antecedents and consequences of an SVO. In particular, we introduce the notion of misaligned elites and misfitted practices in our analysis of how and why differences in the technical and cultural preferences of major owners will influence a firm’s adoption and (un)successful implementation of an SVO among the largest 100 corporations in the Netherlands from 1992-2006. We conclude with a discussion of the implications of our perspective and our findings for future research on corporate governance and the diffusion of organizational practices/ideas.

Monitoring mesenchymal stem cell cultures using image processing and pattern recognition techniques

Stem cells have attracted tremendous interest in recent times due to their promise in providing innovative new treatments for a great range of currently debilitating diseases. This is due to their potential ability to regenerate and repair damaged tissue, and hence restore lost body function, in a manner beyond the body's usual healing process. Bone marrow-derived mesenchymal stem cells or bone marrow stromal cells are one type of adult stem cells that are of particular interest. Since they are derived from a living human adult donor, they do not have the ethical issues associated with the use of human embryonic stem cells. They are also able to be taken from a patient or other donors with relative ease and then grown readily in the laboratory for clinical application. Despite the attractive properties of bone marrow stromal cells, there is presently no quick and easy way to determine the quality of a sample of such cells. Presently, a sample must be grown for weeks and subject to various time-consuming assays, under the direction of an expert cell biologist, to determine whether it will be useful. Hence there is a great need for innovative new ways to assess the quality of cell cultures for research and potential clinical application. The research presented in this thesis investigates the use of computerised image processing and pattern recognition techniques to provide a quicker and simpler method for the quality assessment of bone marrow stromal cell cultures. In particular, aim of this work is to find out whether it is possible, through the use of image processing and pattern recognition techniques, to predict the growth potential of a culture of human bone marrow stromal cells at early stages, before it is readily apparent to a human observer. With the above aim in mind, a computerised system was developed to classify the quality of bone marrow stromal cell cultures based on phase contrast microscopy images. Our system was trained and tested on mixed images of both healthy and unhealthy bone marrow stromal cell samples taken from three different patients. This system, when presented with 44 previously unseen bone marrow stromal cell culture images, outperformed human experts in the ability to correctly classify healthy and unhealthy cultures. The system correctly classified the health status of an image 88% of the time compared to an average of 72% of the time for human experts. Extensive training and testing of the system on a set of 139 normal sized images and 567 smaller image tiles showed an average performance of 86% and 85% correct classifications, respectively. The contributions of this thesis include demonstrating the applicability and potential of computerised image processing and pattern recognition techniques to the task of quality assessment of bone marrow stromal cell cultures. As part of this system, an image normalisation method has been suggested and a new segmentation algorithm has been developed for locating cell regions of irregularly shaped cells in phase contrast images. Importantly, we have validated the efficacy of both the normalisation and segmentation method, by demonstrating that both methods quantitatively improve the classification performance of subsequent pattern recognition algorithms, in discriminating between cell cultures of differing health status. We have shown that the quality of a cell culture of bone marrow stromal cells may be assessed without the need to either segment individual cells or to use time-lapse imaging. Finally, we have proposed a set of features, that when extracted from the cell regions of segmented input images, can be used to train current state of the art pattern recognition systems to predict the quality of bone marrow stromal cell cultures earlier and more consistently than human experts.

Designing for engagement towards healthier lifestyles through food image sharing : the case of I8DAT

This paper introduces the underlying design concepts of I8DAT, a food image sharing application that has been developed as part of a three-year research project – Eat, Cook, Grow: Ubiquitous Technology for Sustainable Food Culture in the City (http://www.urbaninformatics .net/projects/food) – exploring urban food practices to engage people in healthier, more environmentally and socially sustainable eating, cooking, and growing food in their everyday lives. The key aim of the project is to produce actionable knowledge, which is then applied to create and test several accessible, user-centred interactive design solutions that motivate user-engagement through playful and social means rather than authoritative information distribution. Through the design and implementation processes we envisage to integrate these design interventions to create a sustainable food network that is both technical and socio-cultural in nature (technosocial). Our primary research locale is Brisbane, Australia, with additional work carried out in three reference cities with divergent geographic, socio-cultural, and technological backgrounds: Seoul, South Korea, for its global leadership in ubiquitous technology, broadband access, and high population density; Lincoln, UK, for the regional and peri-urban dimension it provides, and Portland, Oregon, US, for its international standing as a hub of the sustainable food movement.

The determinants of corporate sustainability performance

This paper investigates the factors that drive high levels of corporate sustainability performance (CSP), as proxied by membership of the Dow Jones Sustainability World Index. Using a stakeholder framework, we examine the incentives for US firms to invest in sustainability principles and develop a number of hypotheses that relate CSP to firm-specific characteristics. Our results indicate that leading CSP firms are significantly larger, have higher levels of growth and a higher return on equity than conventional firms. Contrary to our predictions, leading CSP firms do not have greater free cash flows or lower leverage than other firms.

Reframing the development of corporate learning

Due to increasing recognition by industry that partnerships with universities can lead to more effective knowledge and skills acquisition and deployment, corporate learning programmes are currently experiencing a resurgence of interest. Rethinking of corporations’ approaches to what has traditionally been classed as ‘training’ has resulted in a new focus on learning and the adoption of philosophies that underlie the academic paradigm. This paper reports on two studies of collaboration between major international engineering corporations and an Australian university, the aim of which was to up-skill the workforce in response to changing markets. The paper highlights the differences between the models of learning adopted in such collaboration and those in more conventional, university-based environments. The learning programmes combine the ADDIE (analysis, design, develop, implement and evaluate) development and workplace learning models. Adaptations that have added value for industry partners and recommendations as to how these can be evolved to cope with change are discussed. The learning is contextualised by industry- based subject matter experts working in close collaboration with university experts and learning designers to develop programmes that are reflective of current and future needs in the organisation. Results derived from user feedback indicate that the learning programmes are effectively aligned with the needs of the industry partners whilst simultaneously upholding academic ideals. In other words, it is possible to combine academic and more traditional approaches to develop corporate learning programmes that satisfy requirements in the workplace. Emerging from the study, a new conceptual framework for the development of corporate learning is presented.

An analysis of the nature and effectiveness of corporate governance in smaller listed Australian companies

The objective of this thesis is to investigate the corporate governance attributes of smaller listed Australian firms. This study is motivated by evidence that these firms are associated with more regulatory concerns, the introduction of ASX Corporate Governance Recommendations in 2004, and a paucity of research to guide regulators and stakeholders of smaller firms. While there is an extensive body of literature examining the effectiveness of corporate governance, the literature principally focuses on larger companies, resulting in a deficiency in the understanding of the nature and effectiveness of corporate governance in smaller firms. Based on a review of agency theory literature, a theoretical model is developed that posits that agency costs are mitigated by internal governance mechanisms and transparency. The model includes external governance factors but in many smaller firms these factors are potentially absent, increasing the reliance on the internal governance mechanisms of the firm. Based on the model, the observed greater regulatory intervention in smaller companies may be due to sub-optimal internal governance practices. Accordingly, this study addresses four broad research questions (RQs). First, what is the extent and nature of the ASX Recommendations that have been adopted by smaller firms (RQ1)? Second, what firm characteristics explain differences in the recommendations adopted by smaller listed firms (RQ2), and third, what firm characteristics explain changes in the governance of smaller firms over time (RQ3)? Fourth, how effective are the corporate governance attributes of smaller firms (RQ4)? Six hypotheses are developed to address the RQs. The first two hypotheses explore the extent and nature of corporate governance, while the remaining hypotheses evaluate its effectiveness. A time-series, cross-sectional approach is used to evaluate the effectiveness of governance. Three models, based on individual governance attributes, an index of six items derived from the literature, and an index based on the full list of ASX Recommendations, are developed and tested using a sample of 298 smaller firms with annual observations over a five-year period (2002-2006) before and after the introduction of the ASX Recommendations in 2004. With respect to (RQ1) the results reveal that the overall adoption of the recommendations increased from 66 per cent in 2004 to 74 per cent in 2006. Interestingly, the adoption rate for recommendations regarding the structure of the board and formation of committees is significantly lower than the rates for other categories of recommendations. With respect to (RQ2) the results reveal that variations in rates of adoption are explained by key firm differences including, firm size, profitability, board size, audit quality, and ownership dispersion, while the results for (RQ3) were inconclusive. With respect to (RQ4), the results provide support for the association between better governance and superior accounting-based performance. In particular, the results highlight the importance of the independence of both the board and audit committee chairs, and of greater accounting-based expertise on the audit committee. In contrast, while there is little evidence that a majority independent board is associated with superior outcomes, there is evidence linking board independence with adverse audit opinion outcomes. These results suggest that board and chair independence are substitutes; in the presence of an independent chair a majority independent board may be an unnecessary and costly investment for smaller firms. The findings make several important contributions. First, the findings contribute to the literature by providing evidence on the extent, nature and effectiveness of governance in smaller firms. The findings also contribute to the policy debate regarding future development of Australia’s corporate governance code. The findings regarding board and chair independence, and audit committee characteristics, suggest that policy-makers could consider providing additional guidance for smaller companies. In general, the findings offer support for the “if not, why not?” approach of the ASX, rather than a prescriptive rules-based approach.

Improved image quality for x-ray CT imaging of gel dosimeters

Purpose: This study provides a simple method for improving precision of x-ray computed tomography (CT) scans of irradiated polymer gel dosimetry. The noise affecting CT scans of irradiated gels has been an impediment to the use of clinical CT scanners for gel dosimetry studies. Method: In this study, it is shown that multiple scans of a single PAGAT gel dosimeter can be used to extrapolate a ‘zero-scan’ image which displays a similar level of precision to an image obtained by averaging multiple CT images, without the compromised dose measurement resulting from the exposure of the gel to radiation from the CT scanner. Results: When extrapolating the zero-scan image, it is shown that exponential and simple linear fits to the relationship between Hounsfield unit and scan number, for each pixel in the image, provides an accurate indication of gel density. Conclusions: It is expected that this work will be utilised in the analysis of three-dimensional gel volumes irradiated using complex radiotherapy treatments.

The impact of the Malaysian code on corporate governance: compliance, institutional investors and stock performance

In 2001, the Malaysian Code on Corporate Governance (MCCG) became an integral part of the Bursa Malaysia Listing Rules, which requires all listed firms to disclose the extent of compliance with the MCCG. Our panel analysis of 440 firms from 1999 to 2002 finds that corporate governance reform in Malaysia has been successful, with a significant improvement in governance practices. The relationship between ownership by the Employees Provident Fund (EPF) and corporate governance has strengthened during the period subsequent to the reform, in line with the lead role taken by the EPF in establishing the Minority Shareholders Watchdog Group. The implementation of MCCG has had a substantial effect on shareholders' wealth, increasing stock prices by an average of about 4.8%. Although there is no evidence that politically connected firms perform better, political connections do have a significantly negative effect on corporate governance, which is mitigated by institutional ownership.

Corporate governance, accounting and finance: a review

We review accounting and finance research on corporate governance (CG). In the course of our review, we focus on a particularly vexing issue, namely endogeneity in the relationships between CG and other matters of concern to accounting and finance scholars, and suggest ways to deal with it. Given the advent of large commercial CG databases, we also stress the importance of how CG is measured and in particular, the construction of CG indices, which should be sensitive to local institutional arrangements, and the need to capture both internal and external aspects of governance. The ‘stickiness’ of CG characteristics provides an additional challenge to CG scholars. Better theory is required, for example, to explain whether various CG practices substitute for each other or are complements. While a multidisciplinary approach to developing better theory is never without its difficulties, it could enrich the current body of knowledge in CG. Despite the vastness of the existing CG literature, these issues do suggest a number of avenues for future research.

Pattern recognition using invariants defined from higher order spectra : 2-D image inputs

A new algorithm for extracting features from images for object recognition is described. The algorithm uses higher order spectra to provide desirable invariance properties, to provide noise immunity, and to incorporate nonlinearity into the feature extraction procedure thereby allowing the use of simple classifiers. An image can be reduced to a set of 1D functions via the Radon transform, or alternatively, the Fourier transform of each 1D projection can be obtained from a radial slice of the 2D Fourier transform of the image according to the Fourier slice theorem. A triple product of Fourier coefficients, referred to as the deterministic bispectrum, is computed for each 1D function and is integrated along radial lines in bifrequency space. Phases of the integrated bispectra are shown to be translation- and scale-invariant. Rotation invariance is achieved by a regrouping of these invariants at a constant radius followed by a second stage of invariant extraction. Rotation invariance is thus converted to translation invariance in the second step. Results using synthetic and actual images show that isolated, compact clusters are formed in feature space. These clusters are linearly separable, indicating that the nonlinearity required in the mapping from the input space to the classification space is incorporated well into the feature extraction stage. The use of higher order spectra results in good noise immunity, as verified with synthetic and real images. Classification of images using the higher order spectra-based algorithm compares favorably to classification using the method of moment invariants

Particulate texture image analysis with applications

Texture analysis and textural cues have been applied for image classification, segmentation and pattern recognition. Dominant texture descriptors include directionality, coarseness, line-likeness etc. In this dissertation a class of textures known as particulate textures are defined, which are predominantly coarse or blob-like. The set of features that characterise particulate textures are different from those that characterise classical textures. These features are micro-texture, macro-texture, size, shape and compaction. Classical texture analysis techniques do not adequately capture particulate texture features. This gap is identified and new methods for analysing particulate textures are proposed. The levels of complexity in particulate textures are also presented ranging from the simplest images where blob-like particles are easily isolated from their back- ground to the more complex images where the particles and the background are not easily separable or the particles are occluded. Simple particulate images can be analysed for particle shapes and sizes. Complex particulate texture images, on the other hand, often permit only the estimation of particle dimensions. Real life applications of particulate textures are reviewed, including applications to sedimentology, granulometry and road surface texture analysis. A new framework for computation of particulate shape is proposed. A granulometric approach for particle size estimation based on edge detection is developed which can be adapted to the gray level of the images by varying its parameters. This study binds visual texture analysis and road surface macrotexture in a theoretical framework, thus making it possible to apply monocular imaging techniques to road surface texture analysis. Results from the application of the developed algorithm to road surface macro-texture, are compared with results based on Fourier spectra, the auto- correlation function and wavelet decomposition, indicating the superior performance of the proposed technique. The influence of image acquisition conditions such as illumination and camera angle on the results was systematically analysed. Experimental data was collected from over 5km of road in Brisbane and the estimated coarseness along the road was compared with laser profilometer measurements. Coefficient of determination R2 exceeding 0.9 was obtained when correlating the proposed imaging technique with the state of the art Sensor Measured Texture Depth (SMTD) obtained using laser profilometers.

Aerial image analysis using spiking neural networks with application to power line corridor monitoring

Trees, shrubs and other vegetation are of continued importance to the environment and our daily life. They provide shade around our roads and houses, offer a habitat for birds and wildlife, and absorb air pollutants. However, vegetation touching power lines is a risk to public safety and the environment, and one of the main causes of power supply problems. Vegetation management, which includes tree trimming and vegetation control, is a significant cost component of the maintenance of electrical infrastructure. For example, Ergon Energy, the Australia’s largest geographic footprint energy distributor, currently spends over $80 million a year inspecting and managing vegetation that encroach on power line assets. Currently, most vegetation management programs for distribution systems are calendar-based ground patrol. However, calendar-based inspection by linesman is labour-intensive, time consuming and expensive. It also results in some zones being trimmed more frequently than needed and others not cut often enough. Moreover, it’s seldom practicable to measure all the plants around power line corridors by field methods. Remote sensing data captured from airborne sensors has great potential in assisting vegetation management in power line corridors. This thesis presented a comprehensive study on using spiking neural networks in a specific image analysis application: power line corridor monitoring. Theoretically, the thesis focuses on a biologically inspired spiking cortical model: pulse coupled neural network (PCNN). The original PCNN model was simplified in order to better analyze the pulse dynamics and control the performance. Some new and effective algorithms were developed based on the proposed spiking cortical model for object detection, image segmentation and invariant feature extraction. The developed algorithms were evaluated in a number of experiments using real image data collected from our flight trails. The experimental results demonstrated the effectiveness and advantages of spiking neural networks in image processing tasks. Operationally, the knowledge gained from this research project offers a good reference to our industry partner (i.e. Ergon Energy) and other energy utilities who wants to improve their vegetation management activities. The novel approaches described in this thesis showed the potential of using the cutting edge sensor technologies and intelligent computing techniques in improve power line corridor monitoring. The lessons learnt from this project are also expected to increase the confidence of energy companies to move from traditional vegetation management strategy to a more automated, accurate and cost-effective solution using aerial remote sensing techniques.

Fundus image registration algorithms for a pc-based image processing system

We have developed digital image registration program for a MC 68000 based fundus image processing system (FIPS). FIPS not only is capable of executing typical image processing algorithms in spatial as well as Fourier domain, the execution time for many operations has been made much quicker by using a hybrid of "C", Fortran and MC6000 assembly languages.