Unpacking user relations in an emerging ubiquitous computing environment : introducing the bystander

The move towards technological ubiquity is allowing a more idiosyncratic and dynamic working environment to emerge that may result in the restructuring of information communication technologies, and changes in their use through different user groups' actions. Taking a ‘practice’ lens to human agency, we explore the evolving roles of, and relationships between these user groups and their appropriation of emergent technologies by drawing upon Lamb and Kling's social actor framework. To illustrate our argument, we draw upon a study of a UK Fire Brigade that has introduced a variety of technologies in an attempt to move towards embracing mobile and ubiquitous computing. Our analysis of the enactment of such technologies reveals that Bystanders, a group yet to be taken as the central unit of analysis in information systems research, or considered in practice, are emerging as important actors. The research implications of our work relate to the need to further consider Bystanders in deployments other than those that are mobile and ubiquitous. For practice, we suggest that Bystanders require consideration in the systems development life cycle, particularly in terms of design and education in processes of use.

Computer modeling of diffusion in biological tissues

Molecular-level computer simulations of restricted water diffusion can be used to develop models for relating diffusion tensor imaging measurements of anisotropic tissue to microstructural tissue characteristics. The diffusion tensors resulting from these simulations can then be analyzed in terms of their relationship to the structural anisotropy of the model used. As the translational motion of water molecules is essentially random, their dynamics can be effectively simulated using computers. In addition to modeling water dynamics and water-tissue interactions, the simulation software of the present study was developed to automatically generate collagen fiber networks from user-defined parameters. This flexibility provides the opportunity for further investigations of the relationship between the diffusion tensor of water and morphologically different models representing different anisotropic tissues.

Investigating the success of operational business process management systems

Business process management systems (BPMS) belong to a class of enterprise information systems that are characterized by the dependence on explicitly modeled process logic. Through the process logic, it is relatively easy to manage explicitly the routing and allocation of work items along a business process through the system. Inspired by the DeLone and McLean framework, we theorize that these process-aware system features are important attributes of system quality, which in turn will elevate key user evaluations such as perceived usefulness, and usage satisfaction. We examine this theoretical model using data collected from four different, mostly mature BPM system projects. Our findings validate the importance of input quality as well as allocation and routing attributes as antecedents of system quality, which, in turn, determines both usefulness and satisfaction with the system. We further demonstrate how service quality and workflow dependency are significant precursors to perceived usefulness. Our results suggest the appropriateness of a multi-dimensional conception of system quality for future research, and provide important design-oriented advice for the design and configuration of BPMSs.

Learning opportunities : a partnership, connecting learning with projects

Major construction projects undertaken on university campuses are an ideal opportunity to connect learners in related disciplines to the real thing. How often do universities take that opportunity, make the connection and value add to projects being carried out? Discussion with students and academic staff will consistently generate enthusiasm for creating learning activities and resources related to projects. Some typical disciplines are project management, all fields of engineering, architecture, interior design and information technology. Some other areas that may not at first seem obvious are business, marketing, communication and public relations. The authors will provide a case study based on the new Queensland University of Technology (QUT) Science and Engineering Centre project of how the partnership between QUT and Leighton Contractors, the managing contractor, has delivered excellent learning opportunities through the design and construction phases of the Science and Engineering Centre project.

Indigenous/digital heterogeneities : an actor-network-theory approach

This thesis analysed the theoretical and ontological issues of previous scholarship concerning information technology and indigenous people. As an alternative, the thesis used the framework of actor-network-theory, especially through historiographical and ethnographic techniques. The thesis revealed an assemblage of indigenous/digital enactments striving for relevance and avoiding obsolescence. It also recognised heterogeneities- including user-ambivalences, oscillations, noise, non-coherences and disruptions - as part of the milieu of the daily digital lives of indigenous people. By taking heterogeneities into account, the thesis ensured that the data “speaks for itself” and that social inquiry is not overtaken by ideology and ontology.

Investigating ways of seeing IT research : a tool for facilitating effective research partnerships?

Effective research partnerships, both intra- and interdisciplinary, as well as academy-industry partnerships, rely on shared understandings of particular aspects of the research endeavour. Research partnerships are essentially learning partnerships, if we accept the argument that research may be seen as learning at the collective level. This paper establishes the need to investigate information technology (IT) researchers' varying ways of seeing certain aspects of their research world, in order to assist the process of forging effective research partnerships. We analyse the importance of facilitating effective partnerships in IT research, discuss our plan for investigating the collective consciousness of IT researchers, and explain some of the strategies to be used in the investigation.

Use of an on-line student response system : an analysis of adoption and continuance

The goal of this project was to initiate the use of an internet-based student response system in a large, first year chemistry class at a typical Australian university, and to verify its popularity and utility. A secondary goal was to influence other academic staff to adopt the system, initiating change at the discipline and Faculty level. The first goal was achieved with a high response rate using a commercial on-line system; however, the number of students engaging with the system dropped gradually during each class and over the course of the semester. Factors affecting student and staff adoption and continuance with technology are explored using established models.

Changing times, persistent paradigms : women and men at work in projects

The Australian economy is currently supported by a resources boom and work opportunities in traditionally male dominated fields of construction and engineering and information technology are at a premium. Yet despite more than 25 years of anti discrimination and equal employment opportunity legislation these industries still employ few women in operational or management roles. This paper investigates the issue of the low representation of women in project management and their different work and career experiences through interviews with male and female project managers.

A concept-based retrieval method for entity-oriented search

Entity-oriented retrieval aims to return a list of relevant entities rather than documents to provide exact answers for user queries. The nature of entity-oriented retrieval requires identifying the semantic intent of user queries, i.e., understanding the semantic role of query terms and determining the semantic categories which indicate the class of target entities. Existing methods are not able to exploit the semantic intent by capturing the semantic relationship between terms in a query and in a document that contains entity related information. To improve the understanding of the semantic intent of user queries, we propose concept-based retrieval method that not only automatically identifies the semantic intent of user queries, i.e., Intent Type and Intent Modifier but introduces concepts represented by Wikipedia articles to user queries. We evaluate our proposed method on entity profile documents annotated by concepts from Wikipedia category and list structure. Empirical analysis reveals that the proposed method outperforms several state-of-the-art approaches.

On structural considerations for governing the cloud

In this research, we suggest appropriate information technology (IT) governance structures to manage the cloud computing resources. The interest in acquiring IT resources a utility is gaining momentum. Cloud computing resources present organizations with opportunities to manage their IT expenditure on an ongoing basis, and are providing organizations access to modern IT resources to innovate and manage their continuity. However, cloud computing resources are no silver bullet. Organizations would need to have appropriate governance structures and policies in place to ensure its effective management and fit into existing business processes to leverage the promised opportunities. Using a mixed method design, we identified four possible governance structures for managing the cloud computing resources. These structures are a chief cloud officer, a cloud management committee, a cloud service facilitation centre, and a cloud relationship centre. These governance structures ensure appropriate direction of cloud computing resources from its acquisition to fit into the organizations business processes.

On governance structures for cloud resources and assessing their effectiveness

This research suggests information technology (IT) governance structures to manage cloud computing resources. The interest in acquiring IT resources as a utility from the cloud is gaining momentum. Cloud computing resources present organizations with opportunities to manage their IT expenditure on an ongoing basis, and are providing organizations access to modern IT resources to innovate and manage their continuity. However, cloud computing resources are no silver bullet. Organizations would need to have appropriate governance structures and policies in place to manage the cloud resources. The subsequent decisions from these governance structures will ensure effective management of cloud resources. This management will facilitate a better fit of cloud resources into organizations existing processes to achieve business (process-level) and financial (firm-level) objectives. Using a triangulation approach, we suggest four possible governance structures for managing the cloud computing resources. These structures are a chief cloud officer, a cloud management committee, a cloud service facilitation centre, and a cloud relationship centre. We also propose that these governance structures would relate to organizations cloud-related business objectives directly and indirectly to cloud-related financial objectives. Perceptive field survey data from actual and prospective cloud service adopters confirmed that the suggested structures would contribute directly to cloud-related business objectives and indirectly to cloud-related financial objectives.

Privacy oriented access control for electronic health records

Information privacy is a critical success/failure factor in information technology supported healthcare (eHealth). eHealth systems utilise electronic health records (EHR) as the main source of information, thus, implementing appropriate privacy preserving methods for EHRs is vital for the proliferation of eHealth. Whilst information privacy may be a fundamental requirement for eHealth consumers, healthcare professionals demand non-restricted access to patient information for improved healthcare delivery, thus, creating an environment where stakeholder requirements are contradictory. Therefore, there is a need to achieve an appropriate balance of requirements in order to build successful eHealth systems. Towards achieving this balance, a new genre of eHealth systems called Accountable-eHealth (AeH) systems has been proposed. In this paper, an access control model for EHRs is presented that can be utilised by AeH systems to create information usage policies that fulfil both stakeholders’ requirements. These policies are used to accomplish the aforementioned balance of requirements creating a satisfactory eHealth environment for all stakeholders. The access control model is validated using a Web based prototype as a proof of concept.

Building an augmented map for road risk assessment

Recent road safety statistics show that the decades-long fatalities decreasing trend is stopping and stagnating. Statistics further show that crashes are mostly driven by human error, compared to other factors such as environmental conditions and mechanical defects. Within human error, the dominant error source is perceptive errors, which represent about 50% of the total. The next two sources are interpretation and evaluation, which accounts together with perception for more than 75% of human error related crashes. Those statistics show that allowing drivers to perceive and understand their environment better, or supplement them when they are clearly at fault, is a solution to a good assessment of road risk, and, as a consequence, further decreasing fatalities. To answer this problem, currently deployed driving assistance systems combine more and more information from diverse sources (sensors) to enhance the driver's perception of their environment. However, because of inherent limitations in range and field of view, these systems' perception of their environment remains largely limited to a small interest zone around a single vehicle. Such limitations can be overcomed by increasing the interest zone through a cooperative process. Cooperative Systems (CS), a specific subset of Intelligent Transportation Systems (ITS), aim at compensating for local systems' limitations by associating embedded information technology and intervehicular communication technology (IVC). With CS, information sources are not limited to a single vehicle anymore. From this distribution arises the concept of extended or augmented perception. Augmented perception allows extending an actor's perceptive horizon beyond its "natural" limits not only by fusing information from multiple in-vehicle sensors but also information obtained from remote sensors. The end result of an augmented perception and data fusion chain is known as an augmented map. It is a repository where any relevant information about objects in the environment, and the environment itself, can be stored in a layered architecture. This thesis aims at demonstrating that augmented perception has better performance than noncooperative approaches, and that it can be used to successfully identify road risk. We found it was necessary to evaluate the performance of augmented perception, in order to obtain a better knowledge on their limitations. Indeed, while many promising results have already been obtained, the feasibility of building an augmented map from exchanged local perception information and, then, using this information beneficially for road users, has not been thoroughly assessed yet. The limitations of augmented perception, and underlying technologies, have not be thoroughly assessed yet. Most notably, many questions remain unanswered as to the IVC performance and their ability to deliver appropriate quality of service to support life-saving critical systems. This is especially true as the road environment is a complex, highly variable setting where many sources of imperfections and errors exist, not only limited to IVC. We provide at first a discussion on these limitations and a performance model built to incorporate them, created from empirical data collected on test tracks. Our results are more pessimistic than existing literature, suggesting IVC limitations have been underestimated. Then, we develop a new CS-applications simulation architecture. This architecture is used to obtain new results on the safety benefits of a cooperative safety application (EEBL), and then to support further study on augmented perception. At first, we confirm earlier results in terms of crashes numbers decrease, but raise doubts on benefits in terms of crashes' severity. In the next step, we implement an augmented perception architecture tasked with creating an augmented map. Our approach is aimed at providing a generalist architecture that can use many different types of sensors to create the map, and which is not limited to any specific application. The data association problem is tackled with an MHT approach based on the Belief Theory. Then, augmented and single-vehicle perceptions are compared in a reference driving scenario for risk assessment,taking into account the IVC limitations obtained earlier; we show their impact on the augmented map's performance. Our results show that augmented perception performs better than non-cooperative approaches, allowing to almost tripling the advance warning time before a crash. IVC limitations appear to have no significant effect on the previous performance, although this might be valid only for our specific scenario. Eventually, we propose a new approach using augmented perception to identify road risk through a surrogate: near-miss events. A CS-based approach is designed and validated to detect near-miss events, and then compared to a non-cooperative approach based on vehicles equiped with local sensors only. The cooperative approach shows a significant improvement in the number of events that can be detected, especially at the higher rates of system's deployment.

A fuzzy TOPSIS based multi criteria performance measurement model for lean supply chain

Increasing global competition, rapid technological changes, advances in manufacturing and information technology and discerning customers are forcing supply chains to adopt improvement practices that enable them to deliver high quality products at a lower cost and in a shorter period of time. A lean initiative is one of the most effective approaches toward achieving this goal. In the lean improvement process, it is critical to measure current and desired performance level in order to clearly evaluate the lean implementation efforts. Many attempts have tried to measure supply chain performance incorporating both quantitative and qualitative measures but failed to provide an effective method of measuring improvements in performances for dynamic lean supply chain situations. Therefore, the necessity of appropriate measurement of lean supply chain performance has become imperative. There are many lean tools available for supply chains; however, effectiveness of a lean tool depends on the type of the product and supply chain. One tool may be highly effective for a supply chain involved in high volume products but may not be effective for low volume products. There is currently no systematic methodology available for selecting appropriate lean strategies based on the type of supply chain and market strategy This thesis develops an effective method to measure the performance of supply chain consisting of both quantitative and qualitative metrics and investigates the effects of product types and lean tool selection on the supply chain performance Supply chain performance matrices and the effects of various lean tools over performance metrics mentioned in the SCOR framework have been investigated. A lean supply chain model based on the SCOR metric framework is then developed where non- lean and lean as well as quantitative and qualitative metrics are incorporated in appropriate metrics. The values of appropriate metrics are converted into triangular fuzzy numbers using similarity rules and heuristic methods. Data have been collected from an apparel manufacturing company for multiple supply chain products and then a fuzzy based method is applied to measure the performance improvements in supply chains. Using the fuzzy TOPSIS method, which chooses an optimum alternative to maximise similarities with positive ideal solutions and to minimise similarities with negative ideal solutions, the performances of lean and non- lean supply chain situations for three different apparel products have been evaluated. To address the research questions related to effective performance evaluation method and the effects of lean tools over different types of supply chains; a conceptual framework and two hypotheses are investigated. Empirical results show that implementation of lean tools have significant effects over performance improvements in terms of time, quality and flexibility. Fuzzy TOPSIS based method developed is able to integrate multiple supply chain matrices onto a single performance measure while lean supply chain model incorporates qualitative and quantitative metrics. It can therefore effectively measure the improvements for supply chain after implementing lean tools. It is demonstrated that product types involved in the supply chain and ability to select right lean tools have significant effect on lean supply chain performance. Future study can conduct multiple case studies in different contexts.

The efficiency of corpus-based distributional models for literature-based discovery on large data sets

This paper evaluates the efficiency of a number of popular corpus-based distributional models in performing discovery on very large document sets, including online collections. Literature-based discovery is the process of identifying previously unknown connections from text, often published literature, that could lead to the development of new techniques or technologies. Literature-based discovery has attracted growing research interest ever since Swanson's serendipitous discovery of the therapeutic effects of fish oil on Raynaud's disease in 1986. The successful application of distributional models in automating the identification of indirect associations underpinning literature-based discovery has been heavily demonstrated in the medical domain. However, we wish to investigate the computational complexity of distributional models for literature-based discovery on much larger document collections, as they may provide computationally tractable solutions to tasks including, predicting future disruptive innovations. In this paper we perform a computational complexity analysis on four successful corpus-based distributional models to evaluate their fit for such tasks. Our results indicate that corpus-based distributional models that store their representations in fixed dimensions provide superior efficiency on literature-based discovery tasks.