Determining the psychosocial predictors of living, living-related, and posthumous organ donation

The worldwide organ shortage occurs despite people’s positive organ donation attitudes. The discrepancy between attitudes and behaviour is evident in Australia particularly, with widespread public support for organ donation but low donation and communication rates. This problem is compounded further by the paucity of theoretically based research to improve our understanding of people’s organ donation decisions. This program of research contributes to our knowledge of individual decision making processes for three aspects of organ donation: (1) posthumous (upon death) donation, (2) living donation (to a known and unknown recipient), and (3) providing consent for donation by communicating donation wishes on an organ donor consent register (registering) and discussing the donation decision with significant others (discussing). The research program used extended versions of the Theory of Planned Behaviour (TPB) and the Prototype/Willingness Model (PWM), incorporating additional influences (moral norm, self-identity, organ recipient prototypes), to explicate the relationship between people’s positive attitudes and low rates of organ donation behaviours. Adopting the TPB and PWM (and their extensions) as a theoretical basis overcomes several key limitations of the extant organ donation literature including the often atheoretical nature of organ donation research, thefocus on individual difference factors to construct organ donor profiles and the omission of important psychosocial influences (e.g., control perceptions, moral values) that may impact on people’s decision-making in this context. In addition, the use of the TPB and PWM adds further to our understanding of the decision making process for communicating organ donation wishes. Specifically, the extent to which people’s registering and discussing decisions may be explained by a reasoned and/or a reactive decision making pathway is examined (Stage 3) with the novel application of the TPB augmented with the social reaction pathway in the PWM. This program of research was conducted in three discrete stages: a qualitative stage (Stage 1), a quantitative stage with extended models (Stage 2), and a quantitative stage with augmented models (Stage 3). The findings of the research program are reported in nine papers which are presented according to the three aspects of organ donation examined (posthumous donation, living donation, and providing consent for donation by registering or discussing the donation preference). Stage One of the research program comprised qualitative focus groups/interviews with university students and community members (N = 54) (Papers 1 and 2). Drawing broadly on the TPB framework (Paper 1), content analysed responses revealed people’s commonly held beliefs about the advantages and disadvantages (e.g., prolonging/saving life), important people or groups (e.g., family), and barriers and motivators (e.g., a family’s objection to donation), related to living and posthumous organ donation. Guided by a PWM perspective, Paper Two identified people’s commonly held perceptions of organ donors (e.g., altruistic and giving), non-donors (e.g., self-absorbed and unaware), and transplant recipients (e.g., unfortunate, and in some cases responsible/blameworthy for their predicament). Stage Two encompassed quantitative examinations of people’s decision makingfor living (Papers 3 and 4) and posthumous (Paper 5) organ donation, and for registering and discussing donation wishes (Papers 6 to 8) to test extensions to both the TPB and PWM. Comparisons of health students’ (N = 487) motivations and willingness for living related and anonymous donation (Paper 3) revealed that a person’s donor identity, attitude, past blood donation, and knowing a posthumous donor were four common determinants of willingness, with the results highlighting students’ identification as a living donor as an important motive. An extended PWM is presented in Papers Four and Five. University students’ (N = 284) willingness for living related and anonymous donation was tested in Paper Four with attitude, subjective norm, donor prototype similarity, and moral norm (but not donor prototype favourability) predicting students’ willingness to donate organs in both living situations. Students’ and community members’ (N = 471) posthumous organ donation willingness was assessed in Paper Five with attitude, subjective norm, past behaviour, moral norm, self-identity, and prior blood donation all significantly directly predicting posthumous donation willingness, with only an indirect role for organ donor prototype evaluations. The results of two studies examining people’s decisions to register and/or discuss their organ donation wishes are reported in Paper Six. People’s (N = 24) commonly held beliefs about communicating their organ donation wishes were explored initially in a TPB based qualitative elicitation study. The TPB belief determinants of intentions to register and discuss the donation preference were then assessed for people who had not previously communicated their donation wishes (N = 123). Behavioural and normative beliefs were important determinants of registering and discussing intentions; however, control beliefs influenced people’s registering intentions only. Paper Seven represented the first empirical test of the role of organ transplant recipient prototypes (i.e., perceptions of organ transplant recipients) in people’s (N = 465) decisions to register consent for organ donation. Two factors, Substance Use and Responsibility, were identified and Responsibility predicted people’s organ donor registration status. Results demonstrated that unregistered respondents were the most likely to evaluate transplant recipients negatively. Paper Eight established the role of organ donor prototype evaluations, within an extended TPB model, in predicting students’ and community members’ registering (n = 359) and discussing (n = 282) decisions. Results supported the utility of an extended TPB and suggested a role for donor prototype evaluations in predicting people’s discussing intentions only. Strong intentions to discuss donation wishes increased the likelihood that respondents reported discussing their decision 1-month later. Stage Three of the research program comprised an examination of augmented models (Paper 9). A test of the TPB augmented with elements from the social reaction pathway in the PWM, and extensions to these models was conducted to explore whether people’s registering (N = 339) and discussing (N = 315) decisions are explained via a reasoned (intention) and/or social reaction (willingness) pathway. Results suggested that people’s decisions to communicate their organ donation wishes may be better explained via the reasoned pathway, particularly for registering consent; however, discussing also involves reactive elements. Overall, the current research program represents an important step toward clarifying the relationship between people’s positive organ donation attitudes but low rates of organ donation and communication behaviours. Support has been demonstrated for the use of extensions to two complementary theories, the TPB and PWM, which can inform future research aiming to explicate further the organ donation attitude-behaviour relationship. The focus on a range of organ donation behaviours enables the identification of key targets for future interventions encouraging people’s posthumous and living donation decisions, and communication of their organ donation preference.

Empirical evaluation of a service analysis and design methodology

Service-orientation has gained widespread acceptance and is increasingly being employed as a paradigm for structuring both business and IT architectures. An earlier study of extant service analysis and design methodologies discovered a need for holistic approaches that equally account for both business and software services, which motivated the design of a new, consolidated service analysis and design methodology. A challenge in design-oriented research is to evaluate the utility of the newly created artefacts (here: the methodology), as they are often intended to become part of complex socio-technical systems. Therefore, after presenting a brief overview of the consolidated methodology, the paper discusses possible approaches for the “evaluate” phase of this design-science research process and presents the results of an empirical evaluation conducted in an Action Research study at one of Australia’s largest financial services providers.

Assessing information systems success models : empirical comparison (Research in Progress)

Information System (IS) success may be the most arguable and important dependent variable in the IS field. The purpose of the present study is to address IS success by empirically assess and compare DeLone and McLean’s (1992) and Gable’s et al. (2008) models of IS success in Australian Universities context. The two models have some commonalities and several important distinctions. Both models integrate and interrelate multiple dimensions of IS success. Hence, it would be useful to compare the models to see which is superior; as it is not clear how IS researchers should respond to this controversy.

The impact of “empirical facts” on legal scholarship and legal research training

Lawyers have traditionally viewed law as a closed system, and doctrinal research has been the research methodology used most widely in the profession. This reflects traditional concepts of legal reasoning. There is a wealth of reliable and valid social science data available to lawyers and judges. Judges in fact often refer to general facts about the world, society, institutions and human behaviour (‘empirical facts’). Legal education needs to prepare our students for this broader legal context. This paper examines how ‘empirical facts’ are used in Australian and other common law courts. Specifically, the paper argues that there is a need for enhanced training in non-doctrinal research methodologies across the law school curriculum. This should encompass a broad introduction to social science methods, with more attention being paid to a cross-section of methodologies such as content analysis, comparative law and surveys that are best applied to law.

Stochastic modelling of financial time series with memory and multifractal scaling

Financial processes may possess long memory and their probability densities may display heavy tails. Many models have been developed to deal with this tail behaviour, which reflects the jumps in the sample paths. On the other hand, the presence of long memory, which contradicts the efficient market hypothesis, is still an issue for further debates. These difficulties present challenges with the problems of memory detection and modelling the co-presence of long memory and heavy tails. This PhD project aims to respond to these challenges. The first part aims to detect memory in a large number of financial time series on stock prices and exchange rates using their scaling properties. Since financial time series often exhibit stochastic trends, a common form of nonstationarity, strong trends in the data can lead to false detection of memory. We will take advantage of a technique known as multifractal detrended fluctuation analysis (MF-DFA) that can systematically eliminate trends of different orders. This method is based on the identification of scaling of the q-th-order moments and is a generalisation of the standard detrended fluctuation analysis (DFA) which uses only the second moment; that is, q = 2. We also consider the rescaled range R/S analysis and the periodogram method to detect memory in financial time series and compare their results with the MF-DFA. An interesting finding is that short memory is detected for stock prices of the American Stock Exchange (AMEX) and long memory is found present in the time series of two exchange rates, namely the French franc and the Deutsche mark. Electricity price series of the five states of Australia are also found to possess long memory. For these electricity price series, heavy tails are also pronounced in their probability densities. The second part of the thesis develops models to represent short-memory and longmemory financial processes as detected in Part I. These models take the form of continuous-time AR(∞) -type equations whose kernel is the Laplace transform of a finite Borel measure. By imposing appropriate conditions on this measure, short memory or long memory in the dynamics of the solution will result. A specific form of the models, which has a good MA(∞) -type representation, is presented for the short memory case. Parameter estimation of this type of models is performed via least squares, and the models are applied to the stock prices in the AMEX, which have been established in Part I to possess short memory. By selecting the kernel in the continuous-time AR(∞) -type equations to have the form of Riemann-Liouville fractional derivative, we obtain a fractional stochastic differential equation driven by Brownian motion. This type of equations is used to represent financial processes with long memory, whose dynamics is described by the fractional derivative in the equation. These models are estimated via quasi-likelihood, namely via a continuoustime version of the Gauss-Whittle method. The models are applied to the exchange rates and the electricity prices of Part I with the aim of confirming their possible long-range dependence established by MF-DFA. The third part of the thesis provides an application of the results established in Parts I and II to characterise and classify financial markets. We will pay attention to the New York Stock Exchange (NYSE), the American Stock Exchange (AMEX), the NASDAQ Stock Exchange (NASDAQ) and the Toronto Stock Exchange (TSX). The parameters from MF-DFA and those of the short-memory AR(∞) -type models will be employed in this classification. We propose the Fisher discriminant algorithm to find a classifier in the two and three-dimensional spaces of data sets and then provide cross-validation to verify discriminant accuracies. This classification is useful for understanding and predicting the behaviour of different processes within the same market. The fourth part of the thesis investigates the heavy-tailed behaviour of financial processes which may also possess long memory. We consider fractional stochastic differential equations driven by stable noise to model financial processes such as electricity prices. The long memory of electricity prices is represented by a fractional derivative, while the stable noise input models their non-Gaussianity via the tails of their probability density. A method using the empirical densities and MF-DFA will be provided to estimate all the parameters of the model and simulate sample paths of the equation. The method is then applied to analyse daily spot prices for five states of Australia. Comparison with the results obtained from the R/S analysis, periodogram method and MF-DFA are provided. The results from fractional SDEs agree with those from MF-DFA, which are based on multifractal scaling, while those from the periodograms, which are based on the second order, seem to underestimate the long memory dynamics of the process. This highlights the need and usefulness of fractal methods in modelling non-Gaussian financial processes with long memory.

An empirical investigation of the level effect in Australian interest rates

An extensive literature examines the dynamics of interest rates, with particular attention given to the positive relationship between interest-rate volatility and the level of interest rates—the so-called level effect. This paper examines the interaction between the estimated level effect and competing parameterisations of interest-rate volatility for the Australian yield curve. We adopt a new methodology that estimates elasticity in a multivariate setting that explicitly accommodates the correlations that exist between various yield factors. Results show that significant correlations exist between the residuals of yield factors and that such correlations do indeed impact on model estimates. Within the multivariate setting, the level of the short rate is shown to be a crucial determinant of the conditional volatility of all three yield factors. Measures of model fit suggest that, in addition to the usual level effect, the incorporation of GARCH effects and possible regime shifts is important

Intuitive interaction, prior experience and aging : an empirical study

This paper describes an experiment undertaken to investigate intuitive interaction, particularly in older adults. Previous work has shown that intuitive interaction relies on past experience, and has also suggested that older people demonstrate less intuitive uses and slower times when completing set tasks with various devices. Similarly, this experiment showed that past experience with relevant products allowed people to use the interfaces of two different microwaves more quickly, although there were no significant differences between the different microwaves. It also revealed that certain aspects of cognitive decline related to aging, such as central executive function, have more impact on time, correct uses and intuitive uses than chronological age. Implications of these results and further work in this area are discussed.

Extraction of road lanes from high-resolution stereo aerial imagery based on maximum likelihood segmentation and texture enhancement

Accurate road lane information is crucial for advanced vehicle navigation and safety applications. With the increasing of very high resolution (VHR) imagery of astonishing quality provided by digital airborne sources, it will greatly facilitate the data acquisition and also significantly reduce the cost of data collection and updates if the road details can be automatically extracted from the aerial images. In this paper, we proposed an effective approach to detect road lanes from aerial images with employment of the image analysis procedures. This algorithm starts with constructing the (Digital Surface Model) DSM and true orthophotos from the stereo images. Next, a maximum likelihood clustering algorithm is used to separate road from other ground objects. After the detection of road surface, the road traffic and lane lines are further detected using texture enhancement and morphological operations. Finally, the generated road network is evaluated to test the performance of the proposed approach, in which the datasets provided by Queensland department of Main Roads are used. The experiment result proves the effectiveness of our approach.