Who really pays for urban infrastructure? : the impact of developer infrastructure charges on housing affordability in Brisbane, Australia

This thesis advances the understanding of the impact of developer infrastructure charges on housing affordability in Brisbane, Australia through the development of an econometric model and empirical analysis. The results indicate substantial on-passing of these government charges to purchasers of both new and existing homes, thus negatively impacting housing affordability across the whole community. The results of this thesis will inform policy makers and assist in the development of evidence based policy related to housing affordability and funding of urban infrastructure. Being generic, the econometric model is expected to be a tool that is suitable for estimating similar house price effects in other housing markets.

Three of a kind? The special case of Australia's Island Councils

In common with many other countries, Australian local government policymakers have focussed heavily on improving financial sustainability and operational efficiency through structural change and other modes of systemic reform. However, this system-wide approach cannot adequately deal with small island councils due to their sui generis characteristics. In an effort to fill this gap in the literature, this article examines the financial sustainability of Australia’s three island councils – Flinders, Kangaroo and King – over the period 2008–2013 in order to determine whether alternative organisational arrangements may be better suited to their unique circumstances. In so doing, our study contributes to the literature by providing the first empirical analysis of the financial viability of Australia’s island councils while considering the need for an alternative organisation entity in an effort to enhance their long-term financial sustainability.

The Times They Are A Changin': The Effect of Institutional Change on Cooperative Behaviour at 26,000ft over Sixty Years

In 2006, Sir Edmund Hillary lambasted the modern climbing fraternity for abandoning other climbers to a slow frozen death on Everest, claiming that in his day they would never leave someone to die. This followed the controversial death of David Sharp, passed by an estimated 40 climbers who were more interested in the summit than the life of a fellow human being. But was this stinging criticism true or just the faded recollections of a former climbing giant? This book investigates that claim through a narrative analysis, which combines the empirical analysis of Hawley and Salisbury's Himalayan Expedition Database with the anecdotal evidence provided by a plethora of newspaper articles and books. While there is evidence supporting the claim that commercialization is to blame for the breakdown of pro-social behaviour, the results cannot conclude if it is the commercial climber or the operator driving the problem and that the Sherpa are the saving grace.

Determining reverse salient types and evolutionary dynamics of technology systems with performance disparities

Technological system evolution is marked by the uneven evolution of constituent sub-systems. Subsequently, system evolution is hampered by the resulting state of unevenness, or reverse salience, which results from the presence of the sub-system that delivers the lowest level of performance with respect to other sub-systems, namely, the reverse salient. In this paper, we develop absolute and proportional performance gap measures of reverse salience and, in turn, derive a typology of reverse salients that distinguishes alternative dynamics of change in the evolving system. We subsequently demonstrate the applicability of the measures and the typology through an illustrative empirical study of the PC (personal computer) technological system that functions as a gaming platform. Our empirical analysis demonstrates that patterns of temporal dynamics can be distinguished with the measurement of reverse salience, and that distinct paths of technological system evolution can be identified as different types of reverse salients emerge over time.

Virtual community, purchasing behaviour, and emotional well-being

The benefits of virtual communities in increasing firms' profits, instilling knowledge in consumers, and enhancing consumers' social experience and enjoyment are widely recognised. However, relatively little is known about how the use of a virtual community could influence consumers' emotional well-being. This study examines the relationships among virtual community features (structural and experiential routes) as antecedents of virtual community engagement, including quality of use of virtual communities (time spent online and level of information exchange), electronic word-of-mouth (eWOM) purchasing behaviour, and consumers' emotional experience. Furthermore, by extending the cultural perspective to virtual community engagement, this study examines the role of collectivistic values on the aforementioned relationships. The proposed hypotheses are tested on the basis of data collected from 286 members of different virtual communities in Taiwan. The results partially support the theory that features of virtual communities influenced the quality of use, which then has a subsequent effect on consumer eWOM purchasing and emotional well-being. The results of the empirical analysis add credence to the proposed relationships. The role of collectivistic values is also partially supported. A detailed discussion of the findings and limitations of this study is provided.

Semi-supervised document clustering via loci

Document clustering is one of the prominent methods for mining important information from the vast amount of data available on the web. However, document clustering generally suffers from the curse of dimensionality. Providentially in high dimensional space, data points tend to be more concentrated in some areas of clusters. We take advantage of this phenomenon by introducing a novel concept of dynamic cluster representation named as loci. Clusters’ loci are efficiently calculated using documents’ ranking scores generated from a search engine. We propose a fast loci-based semi-supervised document clustering algorithm that uses clusters’ loci instead of conventional centroids for assigning documents to clusters. Empirical analysis on real-world datasets shows that the proposed method produces cluster solutions with promising quality and is substantially faster than several benchmarked centroid-based semi-supervised document clustering methods.

Popper or Kuhn: Truth and the progress of science

The questions of whether science pursues truth as correspondence to reality and whether science in fact progresses towards attaining a truthful understanding of physical reality are fundamental and contested in the philosophy of science. On one side of the debate stands Popper, who argues that science is objective, necessarily assumes a correspondence theory of truth, and inevitably progresses toward truth as physical theories develop, gaining a more truthful understanding of reality through progressively more sophisticated empirical analysis. Conversely Kuhn, influenced by postmodern philosophy, argues that ultimate truth cannot be attained since no objective metaphysical reality exists and it cannot be known, and consequently the notion of scientific objectivity and "progress" is a myth, marred by philosophical and ideological value judgments. Ultimately, Kuhn reduces so-called scientific progress through the adoption of successive paradigms to leaps of "faith". This paper seeks a reconciliation of the two extremes, arguing that Popper is correct in the sense that science assumes a correspondence theory of truth and may progress toward truth as physical theories develop, while simultaneously acknowledging with Kuhn that science is not purely objective and free of value judgments. The notion of faith is also critical, for it was the acknowledgement of God's existence as the creator and instituter of observable natural laws which allowed the development of science and the scientific method in the first place. Therefore, accepting and synthesising the contentions that science is to some extent founded on faith, assumes and progresses toward truth, and is subject to value judgments is necessary for the progress of science.

R1SVM: A randomised nonlinear approach to large-scale anomaly detection

The problem of unsupervised anomaly detection arises in a wide variety of practical applications. While one-class support vector machines have demonstrated their effectiveness as an anomaly detection technique, their ability to model large datasets is limited due to their memory and time complexity for training. To address this issue for supervised learning of kernel machines, there has been growing interest in random projection methods as an alternative to the computationally expensive problems of kernel matrix construction and sup-port vector optimisation. In this paper we leverage the theory of nonlinear random projections and propose the Randomised One-class SVM (R1SVM), which is an efficient and scalable anomaly detection technique that can be trained on large-scale datasets. Our empirical analysis on several real-life and synthetic datasets shows that our randomised 1SVM algorithm achieves comparable or better accuracy to deep auto encoder and traditional kernelised approaches for anomaly detection, while being approximately 100 times faster in training and testing.

R1STM: One-class support tensor machine with randomised kernel

Identifying unusual or anomalous patterns in an underlying dataset is an important but challenging task in many applications. The focus of the unsupervised anomaly detection literature has mostly been on vectorised data. However, many applications are more naturally described using higher-order tensor representations. Approaches that vectorise tensorial data can destroy the structural information encoded in the high-dimensional space, and lead to the problem of the curse of dimensionality. In this paper we present the first unsupervised tensorial anomaly detection method, along with a randomised version of our method. Our anomaly detection method, the One-class Support Tensor Machine (1STM), is a generalisation of conventional one-class Support Vector Machines to higher-order spaces. 1STM preserves the multiway structure of tensor data, while achieving significant improvement in accuracy and efficiency over conventional vectorised methods. We then leverage the theory of nonlinear random projections to propose the Randomised 1STM (R1STM). Our empirical analysis on several real and synthetic datasets shows that our R1STM algorithm delivers comparable or better accuracy to a state-of-the-art deep learning method and traditional kernelised approaches for anomaly detection, while being approximately 100 times faster in training and testing.

Reasoning about discontinuance of information system use

While many studies have explored conditions and consequences of information systems adoption and use, few have focused on the final stages of the information system lifecycle. In this paper, I develop a theoretical and an initial empirical contribution to understanding individuals’ intentions to discontinue the use of an information system. This understanding is important because it yields implications about maintenance, retirement, and users’ switching decisions, which ultimately can affect work performance, system effectiveness, and return on technology investments. In this paper, I offer a new conceptualization of factors determining users’ intentions to discontinue the use of information systems. I then report on a preliminary empirical test of the model using data from a field study of information system users in a promotional planning routine in a large retail organization. Results from the empirical analysis provide first empirical support for the theoretical model. I discuss the work’s implications for theory on information systems continuance and dual-factor logic in information system use. I also provide suggestions for managers dealing with cessation of information systems and broader work routine change in organizations due to information system end-of-life decisions.