Conceptualising use for information systems (IS) success

This thesis conceptualises Use for IS (Information Systems) success. While Use in this study describes the extent to which an IS is incorporated into the user’s processes or tasks, success of an IS is the measure of the degree to which the person using the system is better off. For IS success, the conceptualisation of Use offers new perspectives on describing and measuring Use. We test the philosophies of the conceptualisation using empirical evidence in an Enterprise Systems (ES) context. Results from the empirical analysis contribute insights to the existing body of knowledge on the role of Use and demonstrate Use as an important factor and measure of IS success. System Use is a central theme in IS research. For instance, Use is regarded as an important dimension of IS success. Despite its recognition, the Use dimension of IS success reportedly suffers from an all too simplistic definition, misconception, poor specification of its complex nature, and an inadequacy of measurement approaches (Bokhari 2005; DeLone and McLean 2003; Zigurs 1993). Given the above, Burton-Jones and Straub (2006) urge scholars to revisit the concept of system Use, consider a stronger theoretical treatment, and submit the construct to further validation in its intended nomological net. On those considerations, this study re-conceptualises Use for IS success. The new conceptualisation adopts a work-process system-centric lens and draws upon the characteristics of modern system types, key user groups and their information needs, and the incorporation of IS in work processes. With these characteristics, the definition of Use and how it may be measured is systematically established. Use is conceptualised as a second-order measurement construct determined by three sub-dimensions: attitude of its users, depth, and amount of Use. The construct is positioned in a modified IS success research model, in an attempt to demonstrate its central role in determining IS success in an ES setting. A two-stage mixed-methods research design—incorporating a sequential explanatory strategy—was adopted to collect empirical data and to test the research model. The first empirical investigation involved an experiment and a survey of ES end users at a leading tertiary education institute in Australia. The second, a qualitative investigation, involved a series of interviews with real-world operational managers in large Indian private-sector companies to canvass their day-to-day experiences with ES. The research strategy adopted has a stronger quantitative leaning. The survey analysis results demonstrate the aptness of Use as an antecedent and a consequence of IS success, and furthermore, as a mediator between the quality of IS and the impacts of IS on individuals. Qualitative data analysis on the other hand, is used to derive a framework for classifying the diversity of ES Use behaviour. The qualitative results establish that workers Use IS in their context to orientate, negotiate, or innovate. The implications are twofold. For research, this study contributes to cumulative IS success knowledge an approach for defining, contextualising, measuring, and validating Use. For practice, research findings not only provide insights for educators when incorporating ES for higher education, but also demonstrate how operational managers incorporate ES into their work practices. Research findings leave the way open for future, larger-scale research into how industry practitioners interact with an ES to complete their work in varied organisational environments.

Variables governing the dynamics of attacker-defender systems in team sport

This project was a step forward in the examination and identification of key variables on the perception, decision making and action of team sport athletes through theoretical insights provided by the ecological dynamics perspective. The methodology drew on experiential knowledge of elite coaches to drive further empirical investigation into the specific task, environmental and personal constraints that shape the behaviour of athletes in specific performance contexts. The thesis has provided an effective rationale for further investigation into the emergent perception, decision making and action demanded of athletes in these unpredictable, fluent, fast-paced environments.

Managing knowledge for enterprise systems in indian organizations: Case study insights

Many Enterprise Systems (ES) projects have reported nil or detrimental impacts despite the substantial investment in the system. Having expected positive outcomes for the organization and its functions through the weighty spend, the effective management of ES-related knowledge has been suggested as a critical success factor for these ES projects in ES implementations. This paper suggests theoretical views purporting the importance of understanding on knowledge management for ES success. To explain the complex, dynamic and multifaceted of knowledge management, we adopt the concepts in Learning Network Theory. We then conceptualized the impact of knowledge management on ES by analyzing five case studies in several industries in India, based on the Knowledge-based Theory of the Firm that captures the performance of the system.

Some novel techniques of parameter estimation for the dynamical models in biological systems

Inverse problems based on using experimental data to estimate unknown parameters of a system often arise in biological and chaotic systems. In this paper, we consider parameter estimation in systems biology involving linear and non-linear complex dynamical models, including the Michaelis–Menten enzyme kinetic system, a dynamical model of competence induction in Bacillus subtilis bacteria and a model of feedback bypass in B. subtilis bacteria. We propose some novel techniques for inverse problems. Firstly, we establish an approximation of a non-linear differential algebraic equation that corresponds to the given biological systems. Secondly, we use the Picard contraction mapping, collage methods and numerical integration techniques to convert the parameter estimation into a minimization problem of the parameters. We propose two optimization techniques: a grid approximation method and a modified hybrid Nelder–Mead simplex search and particle swarm optimization (MH-NMSS-PSO) for non-linear parameter estimation. The two techniques are used for parameter estimation in a model of competence induction in B. subtilis bacteria with noisy data. The MH-NMSS-PSO scheme is applied to a dynamical model of competence induction in B. subtilis bacteria based on experimental data and the model for feedback bypass. Numerical results demonstrate the effectiveness of our approach.

Coupling hybrid-game strategies with particle swarm optimisation for multi-objective high lift systems design optimisation

This paper investigates the High Lift System (HLS) application of complex aerodynamic design problem using Particle Swarm Optimisation (PSO) coupled to Game strategies. Two types of optimization methods are used; the first method is a standard PSO based on Pareto dominance and the second method hybridises PSO with a well-known Nash Game strategies named Hybrid-PSO. These optimization techniques are coupled to a pre/post processor GiD providing unstructured meshes during the optimisation procedure and a transonic analysis software PUMI. The computational efficiency and quality design obtained by PSO and Hybrid-PSO are compared. The numerical results for the multi-objective HLS design optimisation clearly shows the benefits of hybridising a PSO with the Nash game and makes promising the above methodology for solving other more complex multi-physics optimisation problems in Aeronautics.

An integrated framework to assess financial reward systems in construction projects

Motivation is a major driver of project performance. Despite team member ability to deliver successful project outcomes if they are not positively motivated to pursue joint project goals, then performance will be constrained. One approach to improving the motivation of project organizations is by offering a financial reward for the achievement of set performance standards above a minimum required level. However, little investigation has been undertaken into the features of successful incentive systems as a part of an overall delivery strategy. With input from organizational management literature, and drawing on the literature covering psychological and economic theories of motivation, this paper presents an integrated framework that can be used by project organizations to assess the impact of financial reward systems on motivation in construction projects. The integrated framework offers four motivation indicators which reflect key theoretical concepts across both psychological and economic disciplines. The indicators are: (1) Goal Commitment, (2) Distributive Justice, (3) Procedural Justice, and (4) Reciprocity. The paper also interprets the integrated framework against the results of a successful Australian social infrastructure project case study and identifies key learning’s for project organizations to consider when designing financial reward systems. Case study results suggest that motivation directed towards the achievement of incentive goals is influenced not only by the value placed on the financial reward for commercial benefit, but also driven by the strength of the project initiatives that encourage just and fair dealings, supporting the establishment of trust and positive reciprocal behavior across a project team. The strength of the project relationships was found to be influenced by how attractive the achievement of the goal is to the incentive recipient and how likely they were to push for the achievement of the goal. Interestingly, findings also suggested that contractor motivation is also influenced by the fairness of the performance measurement process and their perception of the trustworthiness and transparency of their client. These findings provide the basis for future research on the impact of financial reward systems on motivation in construction projects. It is anticipated that such research will shed new light on this complex topic and further define how reward systems should be designed to promote project team motivation. Due to the unique nature of construction projects with high levels of task complexity and interdependence, results are expected to vary in comparison to previous studies based on individuals or single-entity organizations.

The HarmonyGrid : music, space and performance in grid music systems

This research explores music in space, as experienced through performing and music-making with interactive systems. It explores how musical parameters may be presented spatially and displayed visually with a view to their exploration by a musician during performance. Spatial arrangements of musical components, especially pitches and harmonies, have been widely studied in the literature, but the current capabilities of interactive systems allow the improvisational exploration of these musical spaces as part of a performance practice. This research focuses on quantised spatial organisation of musical parameters that can be categorised as grid music systems (GMSs), and interactive music systems based on them. The research explores and surveys existing and historical uses of GMSs, and develops and demonstrates the use of a novel grid music system designed for whole body interaction. Grid music systems provide plotting of spatialised input to construct patterned music on a two-dimensional grid layout. GMSs are navigated to construct a sequence of parametric steps, for example a series of pitches, rhythmic values, a chord sequence, or terraced dynamic steps. While they are conceptually simple when only controlling one musical dimension, grid systems may be layered to enable complex and satisfying musical results. These systems have proved a viable, effective, accessible and engaging means of music-making for the general user as well as the musician. GMSs have been widely used in electronic and digital music technologies, where they have generally been applied to small portable devices and software systems such as step sequencers and drum machines. This research shows that by scaling up a grid music system, music-making and musical improvisation are enhanced, gaining several advantages: (1) Full body location becomes the spatial input to the grid. The system becomes a partially immersive one in four related ways: spatially, graphically, sonically and musically. (2) Detection of body location by tracking enables hands-free operation, thereby allowing the playing of a musical instrument in addition to “playing” the grid system. (3) Visual information regarding musical parameters may be enhanced so that the performer may fully engage with existing spatial knowledge of musical materials. The result is that existing spatial knowledge is overlaid on, and combined with, music-space. Music-space is a new concept produced by the research, and is similar to notions of other musical spaces including soundscape, acoustic space, Smalley's “circumspace” and “immersive space” (2007, 48-52), and Lotis's “ambiophony” (2003), but is rather more textural and “alive”—and therefore very conducive to interaction. Music-space is that space occupied by music, set within normal space, which may be perceived by a person located within, or moving around in that space. Music-space has a perceivable “texture” made of tensions and relaxations, and contains spatial patterns of these formed by musical elements such as notes, harmonies, and sounds, changing over time. The music may be performed by live musicians, created electronically, or be prerecorded. Large-scale GMSs have the capability not only to interactively display musical information as music representative space, but to allow music-space to co-exist with it. Moving around the grid, the performer may interact in real time with musical materials in music-space, as they form over squares or move in paths. Additionally he/she may sense the textural matrix of the music-space while being immersed in surround sound covering the grid. The HarmonyGrid is a new computer-based interactive performance system developed during this research that provides a generative music-making system intended to accompany, or play along with, an improvising musician. This large-scale GMS employs full-body motion tracking over a projected grid. Playing with the system creates an enhanced performance employing live interactive music, along with graphical and spatial activity. Although one other experimental system provides certain aspects of immersive music-making, currently only the HarmonyGrid provides an environment to explore and experience music-space in a GMS.

An approximate Bayesian computation approach for estimating parameters of complex environmental processes in a cellular automata

Modelling an environmental process involves creating a model structure and parameterising the model with appropriate values to accurately represent the process. Determining accurate parameter values for environmental systems can be challenging. Existing methods for parameter estimation typically make assumptions regarding the form of the Likelihood, and will often ignore any uncertainty around estimated values. This can be problematic, however, particularly in complex problems where Likelihoods may be intractable. In this paper we demonstrate an Approximate Bayesian Computational method for the estimation of parameters of a stochastic CA. We use as an example a CA constructed to simulate a range expansion such as might occur after a biological invasion, making parameter estimates using only count data such as could be gathered from field observations. We demonstrate ABC is a highly useful method for parameter estimation, with accurate estimates of parameters that are important for the management of invasive species such as the intrinsic rate of increase and the point in a landscape where a species has invaded. We also show that the method is capable of estimating the probability of long distance dispersal, a characteristic of biological invasions that is very influential in determining spread rates but has until now proved difficult to estimate accurately.

Using Bayesian methods for the estimation of uncertainty in complex statistical models

The research objectives of this thesis were to contribute to Bayesian statistical methodology by contributing to risk assessment statistical methodology, and to spatial and spatio-temporal methodology, by modelling error structures using complex hierarchical models. Specifically, I hoped to consider two applied areas, and use these applications as a springboard for developing new statistical methods as well as undertaking analyses which might give answers to particular applied questions. Thus, this thesis considers a series of models, firstly in the context of risk assessments for recycled water, and secondly in the context of water usage by crops. The research objective was to model error structures using hierarchical models in two problems, namely risk assessment analyses for wastewater, and secondly, in a four dimensional dataset, assessing differences between cropping systems over time and over three spatial dimensions. The aim was to use the simplicity and insight afforded by Bayesian networks to develop appropriate models for risk scenarios, and again to use Bayesian hierarchical models to explore the necessarily complex modelling of four dimensional agricultural data. The specific objectives of the research were to develop a method for the calculation of credible intervals for the point estimates of Bayesian networks; to develop a model structure to incorporate all the experimental uncertainty associated with various constants thereby allowing the calculation of more credible credible intervals for a risk assessment; to model a single day’s data from the agricultural dataset which satisfactorily captured the complexities of the data; to build a model for several days’ data, in order to consider how the full data might be modelled; and finally to build a model for the full four dimensional dataset and to consider the timevarying nature of the contrast of interest, having satisfactorily accounted for possible spatial and temporal autocorrelations. This work forms five papers, two of which have been published, with two submitted, and the final paper still in draft. The first two objectives were met by recasting the risk assessments as directed, acyclic graphs (DAGs). In the first case, we elicited uncertainty for the conditional probabilities needed by the Bayesian net, incorporated these into a corresponding DAG, and used Markov chain Monte Carlo (MCMC) to find credible intervals, for all the scenarios and outcomes of interest. In the second case, we incorporated the experimental data underlying the risk assessment constants into the DAG, and also treated some of that data as needing to be modelled as an ‘errors-invariables’ problem [Fuller, 1987]. This illustrated a simple method for the incorporation of experimental error into risk assessments. In considering one day of the three-dimensional agricultural data, it became clear that geostatistical models or conditional autoregressive (CAR) models over the three dimensions were not the best way to approach the data. Instead CAR models are used with neighbours only in the same depth layer. This gave flexibility to the model, allowing both the spatially structured and non-structured variances to differ at all depths. We call this model the CAR layered model. Given the experimental design, the fixed part of the model could have been modelled as a set of means by treatment and by depth, but doing so allows little insight into how the treatment effects vary with depth. Hence, a number of essentially non-parametric approaches were taken to see the effects of depth on treatment, with the model of choice incorporating an errors-in-variables approach for depth in addition to a non-parametric smooth. The statistical contribution here was the introduction of the CAR layered model, the applied contribution the analysis of moisture over depth and estimation of the contrast of interest together with its credible intervals. These models were fitted using WinBUGS [Lunn et al., 2000]. The work in the fifth paper deals with the fact that with large datasets, the use of WinBUGS becomes more problematic because of its highly correlated term by term updating. In this work, we introduce a Gibbs sampler with block updating for the CAR layered model. The Gibbs sampler was implemented by Chris Strickland using pyMCMC [Strickland, 2010]. This framework is then used to consider five days data, and we show that moisture in the soil for all the various treatments reaches levels particular to each treatment at a depth of 200 cm and thereafter stays constant, albeit with increasing variances with depth. In an analysis across three spatial dimensions and across time, there are many interactions of time and the spatial dimensions to be considered. Hence, we chose to use a daily model and to repeat the analysis at all time points, effectively creating an interaction model of time by the daily model. Such an approach allows great flexibility. However, this approach does not allow insight into the way in which the parameter of interest varies over time. Hence, a two-stage approach was also used, with estimates from the first-stage being analysed as a set of time series. We see this spatio-temporal interaction model as being a useful approach to data measured across three spatial dimensions and time, since it does not assume additivity of the random spatial or temporal effects.

Cellulosic ethanol from sugarcane bagasse in Australia : exploring industry feasibility through systems analysis, techno-economic assessment and pilot plant development

Overcoming many of the constraints to early stage investment in biofuels production from sugarcane bagasse in Australia requires an understanding of the complex technical, economic and systemic challenges associated with the transition of established sugar industry structures from single product agri-businesses to new diversified multi-product biorefineries. While positive investment decisions in new infrastructure requires technically feasible solutions and the attainment of project economic investment thresholds, many other systemic factors will influence the investment decision. These factors include the interrelationships between feedstock availability and energy use, competing product alternatives, technology acceptance and perceptions of project uncertainty and risk. This thesis explores the feasibility of a new cellulosic ethanol industry in Australia based on the large sugarcane fibre (bagasse) resource available. The research explores industry feasibility from multiple angles including the challenges of integrating ethanol production into an established sugarcane processing system, scoping the economic drivers and key variables relating to bioethanol projects and considering the impact of emerging technologies in improving industry feasibility. The opportunities available from pilot scale technology demonstration are also addressed. Systems analysis techniques are used to explore the interrelationships between the existing sugarcane industry and the developing cellulosic biofuels industry. This analysis has resulted in the development of a conceptual framework for a bagassebased cellulosic ethanol industry in Australia and uses this framework to assess the uncertainty in key project factors and investment risk. The analysis showed that the fundamental issue affecting investment in a cellulosic ethanol industry from sugarcane in Australia is the uncertainty in the future price of ethanol and government support that reduces the risks associated with early stage investment is likely to be necessary to promote commercialisation of this novel technology. Comprehensive techno-economic models have been developed and used to assess the potential quantum of ethanol production from sugarcane in Australia, to assess the feasibility of a soda-based biorefinery at the Racecourse Sugar Mill in Mackay, Queensland and to assess the feasibility of reducing the cost of production of fermentable sugars from the in-planta expression of cellulases in sugarcane in Australia. These assessments show that ethanol from sugarcane in Australia has the potential to make a significant contribution to reducing Australia’s transportation fuel requirements from fossil fuels and that economically viable projects exist depending upon assumptions relating to product price, ethanol taxation arrangements and greenhouse gas emission reduction incentives. The conceptual design and development of a novel pilot scale cellulosic ethanol research and development facility is also reported in this thesis. The establishment of this facility enables the technical and economic feasibility of new technologies to be assessed in a multi-partner, collaborative environment. As a key outcome of this work, this study has delivered a facility that will enable novel cellulosic ethanol technologies to be assessed in a low investment risk environment, reducing the potential risks associated with early stage investment in commercial projects and hence promoting more rapid technology uptake. While the study has focussed on an exploration of the feasibility of a commercial cellulosic ethanol industry from sugarcane in Australia, many of the same key issues will be of relevance to other sugarcane industries throughout the world seeking diversification of revenue through the implementation of novel cellulosic ethanol technologies.

Multifractal characterisation and analysis of complex networks

Complex networks have been studied extensively due to their relevance to many real-world systems such as the world-wide web, the internet, biological and social systems. During the past two decades, studies of such networks in different fields have produced many significant results concerning their structures, topological properties, and dynamics. Three well-known properties of complex networks are scale-free degree distribution, small-world effect and self-similarity. The search for additional meaningful properties and the relationships among these properties is an active area of current research. This thesis investigates a newer aspect of complex networks, namely their multifractality, which is an extension of the concept of selfsimilarity. The first part of the thesis aims to confirm that the study of properties of complex networks can be expanded to a wider field including more complex weighted networks. Those real networks that have been shown to possess the self-similarity property in the existing literature are all unweighted networks. We use the proteinprotein interaction (PPI) networks as a key example to show that their weighted networks inherit the self-similarity from the original unweighted networks. Firstly, we confirm that the random sequential box-covering algorithm is an effective tool to compute the fractal dimension of complex networks. This is demonstrated on the Homo sapiens and E. coli PPI networks as well as their skeletons. Our results verify that the fractal dimension of the skeleton is smaller than that of the original network due to the shortest distance between nodes is larger in the skeleton, hence for a fixed box-size more boxes will be needed to cover the skeleton. Then we adopt the iterative scoring method to generate weighted PPI networks of five species, namely Homo sapiens, E. coli, yeast, C. elegans and Arabidopsis Thaliana. By using the random sequential box-covering algorithm, we calculate the fractal dimensions for both the original unweighted PPI networks and the generated weighted networks. The results show that self-similarity is still present in generated weighted PPI networks. This implication will be useful for our treatment of the networks in the third part of the thesis. The second part of the thesis aims to explore the multifractal behavior of different complex networks. Fractals such as the Cantor set, the Koch curve and the Sierspinski gasket are homogeneous since these fractals consist of a geometrical figure which repeats on an ever-reduced scale. Fractal analysis is a useful method for their study. However, real-world fractals are not homogeneous; there is rarely an identical motif repeated on all scales. Their singularity may vary on different subsets; implying that these objects are multifractal. Multifractal analysis is a useful way to systematically characterize the spatial heterogeneity of both theoretical and experimental fractal patterns. However, the tools for multifractal analysis of objects in Euclidean space are not suitable for complex networks. In this thesis, we propose a new box covering algorithm for multifractal analysis of complex networks. This algorithm is demonstrated in the computation of the generalized fractal dimensions of some theoretical networks, namely scale-free networks, small-world networks, random networks, and a kind of real networks, namely PPI networks of different species. Our main finding is the existence of multifractality in scale-free networks and PPI networks, while the multifractal behaviour is not confirmed for small-world networks and random networks. As another application, we generate gene interactions networks for patients and healthy people using the correlation coefficients between microarrays of different genes. Our results confirm the existence of multifractality in gene interactions networks. This multifractal analysis then provides a potentially useful tool for gene clustering and identification. The third part of the thesis aims to investigate the topological properties of networks constructed from time series. Characterizing complicated dynamics from time series is a fundamental problem of continuing interest in a wide variety of fields. Recent works indicate that complex network theory can be a powerful tool to analyse time series. Many existing methods for transforming time series into complex networks share a common feature: they define the connectivity of a complex network by the mutual proximity of different parts (e.g., individual states, state vectors, or cycles) of a single trajectory. In this thesis, we propose a new method to construct networks of time series: we define nodes by vectors of a certain length in the time series, and weight of edges between any two nodes by the Euclidean distance between the corresponding two vectors. We apply this method to build networks for fractional Brownian motions, whose long-range dependence is characterised by their Hurst exponent. We verify the validity of this method by showing that time series with stronger correlation, hence larger Hurst exponent, tend to have smaller fractal dimension, hence smoother sample paths. We then construct networks via the technique of horizontal visibility graph (HVG), which has been widely used recently. We confirm a known linear relationship between the Hurst exponent of fractional Brownian motion and the fractal dimension of the corresponding HVG network. In the first application, we apply our newly developed box-covering algorithm to calculate the generalized fractal dimensions of the HVG networks of fractional Brownian motions as well as those for binomial cascades and five bacterial genomes. The results confirm the monoscaling of fractional Brownian motion and the multifractality of the rest. As an additional application, we discuss the resilience of networks constructed from time series via two different approaches: visibility graph and horizontal visibility graph. Our finding is that the degree distribution of VG networks of fractional Brownian motions is scale-free (i.e., having a power law) meaning that one needs to destroy a large percentage of nodes before the network collapses into isolated parts; while for HVG networks of fractional Brownian motions, the degree distribution has exponential tails, implying that HVG networks would not survive the same kind of attack.

Using performance management systems as identity products

This paper explores how the effective use of performance management systems (PMS) essentialises collective identities through the use of textual performances. The discursive effect of PMS operates to simplify members’ logic to allow them to understand and negotiate the complex nature of collective performance. Two case studies, drawing on a qualitative study of the implementation of PMS in two public sector organisations, point to the unique contribution of symbolic effects of one popular PMS, the balanced scorecard (BSC). Findings suggest that the BSC visualising the trajectory of achieving organisational vision through multiple perspectives, measures and linkages is a valuable identity product to achieve organisational success. The case studies also provide an analysis that contrasts aspects of the diffusion and promotion of collective identities through the use of the BSC. This demonstrates that clear direction in the identity management process is an important factor in the design and implementation of successful PMS programs. The value of this paper is to heighten recognition of the symbolic agency of PMS, as it serves as a subtle mechanism for identity management, and also to foster the collaboration of communication specialists and management accountants to achieve common organisational goals.

Complex events in business processes

Flow-oriented process modeling languages have a long tradition in the area of Business Process Management and are widely used for capturing activities with their behavioral and data dependencies. Individual events were introduced for triggering process instantiation and activities. However, real-world business cases drive the need for also covering complex event patterns as they are known in the field of Complex Event Processing. Therefore, this paper puts forward a catalog of requirements for handling complex events in process models, which can be used as reference framework for assessing process definition languages and systems. An assessment of BPEL and BPMN is provided.