7,228 research outputs found

    Competitive Positioning in International Logistics: Identifying a System of Attributes Through Neural Networks and Decision Trees

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    Firms involved in international logistics must develop a system of service attributes that give them a way to be profitable and to satisfy customers’ needs at the same time. How customers trade-off these various attributes in forming satisfaction with competing international logistics providers has not been explored well in the literature. This study explores the ocean freight shipping sector to identify the system of attributes that maximizes customers’ satisfaction. Data were collected from shipping managers in Singapore using personal interviews to identify the chief concerns in choosing and evaluating ocean freight services. The data were then examined using neural networks and decision trees, among other approaches to identify the system of attributes that is connected with customer satisfaction. The results illustrate the power of these methods in understanding how industrial customers with global operations process attributes to derive satisfaction. Implications are discussed

    Investigating motor skill in closed-loop myoelectric hand prostheses:Through speed-accuracy trade-offs

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    An Exploratory Study of Patient Falls

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    Debate continues between the contribution of education level and clinical expertise in the nursing practice environment. Research suggests a link between Baccalaureate of Science in Nursing (BSN) nurses and positive patient outcomes such as lower mortality, decreased falls, and fewer medication errors. Purpose: To examine if there a negative correlation between patient falls and the level of nurse education at an urban hospital located in Midwest Illinois during the years 2010-2014? Methods: A retrospective crosssectional cohort analysis was conducted using data from the National Database of Nursing Quality Indicators (NDNQI) from the years 2010-2014. Sample: Inpatients aged ≥ 18 years who experienced a unintentional sudden descent, with or without injury that resulted in the patient striking the floor or object and occurred on inpatient nursing units. Results: The regression model was constructed with annual patient falls as the dependent variable and formal education and a log transformed variable for percentage of certified nurses as the independent variables. The model overall is a good fit, F (2,22) = 9.014, p = .001, adj. R2 = .40. Conclusion: Annual patient falls will decrease by increasing the number of nurses with baccalaureate degrees and/or certifications from a professional nursing board-governing body

    Critical success factors for accounting information systems data quality

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    Quality information is critical to organisations’ success in today’s highly competitive environment. Accounting information systems (AIS) as a discipline within information systems require high quality data. However, empirical evidence suggests that data quality is problematic in AIS. Therefore, knowledge of critical factors that are important in ensuring data quality in accounting information systems is desirable. A literature review evaluates previous research work in quality management, data quality, and accounting information systems. It was found that there was a gap in the literature about critical success factors for data quality in accounting information systems. Based on this gap in the literature and the findings of the exploratory stage of the research, a preliminary research model for factors influence data quality in AIS was developed. A framework for understanding relationships between stakeholder groups and data quality in accounting information systems was also developed. The major stakeholders are information producers, information custodians, information managers, information users, and internal auditors. Case study and survey methodology were adopted for this research. Case studies in seven Australian organisations were carried out, where four of them were large organisations and the other three are small to medium organisations (SMEs). Each case was examined as a whole to obtain an understanding of the opinions and perspectives of the respondents from each individual organisation as to what are considered to be the important factors in the case. Then, cross-case analysis was used to analyze the similarities and differences of the seven cases, which also include the variations between large organisations and small to medium organisations (SMEs). Furthermore, the variations between five different stakeholder groups were also examined. The results of the seven main case studies suggested 26 factors that may have impact on data quality in AIS. Survey instrument was developed based on the findings from case studies. Two large-scale surveys were sent to selected members of Australian CPA, and Australian Computer Society to further develop and test the research framework. The major findings from the survey are: 1. respondents rated the importance of the factors consistent higher than the actual performance of those factors. 2. There was only one factor, ‘audit and reviews’, that was found to be different between different sized organisations. 3. Four factors were found to be significantly different between different stakeholder groups: user focus, measurement and reporting, data supplier quality management and audit and reviews. 4. The top three critical factors for ensuring data quality in AIS were: top management commitment, education and training, and the nature of the accounting information systems. The key contribution of this thesis is the theoretical framework developed from the analysis of the findings of this research, which is the first such framework built upon empirical study that explored factors influencing data quality in AIS and their interrelationships with stakeholder groups and data quality outcomes. That is, it is now clear which factors impact on data quality in AIS, and which of those factors are critical success factors for ensuring high quality information outcomes. In addition, the performance level of factors was also incorporated into the research framework. Since the actual performance of factors has not been highlighted in other studies, this research adds new theoretical insights to the extant literature. In turn, this research confirms some of the factors mentioned in the literature and adds a few new factors. Moreover, stakeholder groups of data quality in AIS are important considerations and need more attention. The research framework of this research shows the relationship between stakeholder groups, important factors and data quality outcomes by highlighting stakeholder groups’ influence on identifying the important factors, as well as the evaluation of the importance and p erformance of the factors

    Improving Data Quality in Primary Care: Modelling, Measurement, and the Design of Interventions

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    In an era where governments around the world invest heavily in data collection and data management, poor-quality data is expensive and has many direct and indirect costs. While there are different types of data quality challenges, some of the more complex data quality problems depend on the design and production processes involved in generating data. Therefore, it is important to design systems that support better data quality. This involves understanding what quality means in a specific context, understanding how it can be measured, and identifying ways to encourage better data quality behaviours. Healthcare is not immune to the challenges of data quality and can be classified as a complex socio-technical system by virtue of its characteristics. As such, the study of healthcare data quality and its improvement is well suited for the domain of systems design and human factors engineering. Cognitive Work Analysis (CWA) is especially well suited for this task, as it can be used to better understand the context and workflow of users in complex socio-technical domains. It is a conceptual framework that facilitates the analysis of factors that shape human-information interaction and has been used in healthcare for over 20 years. The approach is work-centred, rather than user-centred, and it analyses the constraints and goals that shape information behaviour in the work environment. I used CWA as a framework to help me analyse the problem of data quality in healthcare. My research uses an instrumental case study approach to understand data quality in primary care. My goal was to answer three questions: In primary care, how are individual users influenced by their environment to input high-quality data? What techniques could be used to design systems that persuade users to enter higher-quality data? Is it possible to improve data quality in primary care by persuading users with the user interface of information systems in these complex socio-technical systems? The scope of work included modelling data quality, defining and measuring data quality in a primary care system, establishing design concepts that could improve data quality through persuasion, and testing the viability of some design concepts. I began analysing this problem by creating an abstraction hierarchy of patient treatment with medical records. This model can be used to represent patient treatment from a primary care perspective. The model helped explain the patient treatment ecosystem and how data is generated through patient encounters. After creating my model to represent patient treatment, I incorporated it into two CWAs of data quality and data codification. The first model represented codification in the primary care ecosystem, whereas the second model represented codification in community hospitals. After developing abstraction hierarchies for both domains, I analysed similar tasks from each system with control task analysis, strategies analysis, and worker competencies analysis. The tasks that I analysed related specifically to data codification: in primary care, I modelled the record encounter task performed by clinicians at a Family Health Team (FHT), and in the community hospital, I modelled the abstract task performed by health information management professionals. I used the same record encounter task at the FHT as a continuing focus of my case study. I used both models of codification to perform a comparison. My goal was to identify the differences between the ecosystems and tasks that were present in primary care and the community hospital. Comparing CWA models is not a well-defined process in the literature, and I developed an approach to conduct this comparison based on seminal works. I used the approach to systematically compare each phase of my CWA models. I found that the analysis of both system domains in parallel enabled a richer understanding of each environment that may not have been achieved independently. In addition, I discovered that a rich environment exists around data codification processes, and this context influences and distinguishes the actions of users. While the tasks in both domains were seemingly similar, they took place with different priorities and required different competencies. After building and comparing models, I investigated the summarizing task in primary care more closely by analysing data within a FHT’s reporting database. The goal of this study was to understand data quality tradeoffs between timeliness, validity, completeness, and use in primary care users. Data quality measures and metrics were developed through interviews with a focus group of managers. After analysing data quality measures for 196,967 patient encounters, I created baselines, modelled each measure with logit binomial regression to show correlations, characterized tradeoffs, and investigated data quality interactions. Based on the analysis, I found a positive relationship between validity and completeness, and a negative relationship between timeliness and use. Use of data and reductions in entry delay were positively associated with completeness and validity. These results suggested that if users are not provided with sufficient time to record data as part of their regular workflow, they will prioritize their time to spend more time with patients. As a measurement of the effectiveness of a system, the negative correlation between use and timeliness points to a self-reinforcing data repository that provides users with little external value. These findings were consistent with the modelling work and also provided useful insight to study data quality improvements within the system. I used my measures from the data analysis to select design priorities and behaviour changes that should, according to my ongoing case study, improve data quality. Then I developed several design concepts by combining CWA, a framework for behaviour change, and a design framework for persuasive systems. The design concepts adopted different persuasion principles to change specific behaviours. To test the validity of my design concepts, I worked with a FHT to implement some of my proposed interventions during a field study. This involved the introduction of a non-invasive summary screen into the user workflow. After the summary screen had been deployed for eight weeks, I received secondary data from the FHT to analyse. First, I performed a pre-post measurement of several data quality measures by doing a simple paired t-test. To further understand the results, I borrowed from healthcare quality improvement methodologies and used statistical process control charts to understand the overall context of the measures. The average delay per entry was reduced by 3.35 days, and the percentage of same-day entries increased by 10.3%. The number of records that were complete dropped by 4.8%. Changes to entry accuracy and report generation were not significant. Several additional insights could be extracted by looking at each the XmR chart for each variable and discussing the trends with the FHT. Feedback was also collected from users through an online survey. Through the use of a case study spanning several years, I was able to reach the following conclusions: data codification and data quality are manufactured within complex socio-technical systems and users are heavily influenced by a variety of factors within their ecosystem; persuasive design, informed with data from a CWA, is an effective technique for creating ecologically relevant persuasive designs; and data quality in primary care can be improved through the use of these designs in the system’s user interface. There are interesting opportunities to apply the results of my work to other jurisdictions. A strength of this work lies in its usefulness for international readers to draw comparisons between different systems and health care environments throughout the world

    Requirements engineering: a review and research agenda

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    This paper reviews the area of requirements engineering. It outlines the key concerns to which attention should be devoted by both practitioners, who wish to "reengineer" their development processes, and academics, seeking intellectual challenges. It presents an assessment of the state-of-the-art and draws conclusions in the form of a research agenda

    Congress' Wicked Problem: Seeking Knowledge Inside the Information Tsunami

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    The lack of shared expert knowledge capacity in the U.S. Congress has created a critical weakness in our democratic process. Along with bipartisan cooperation, many contemporary and urgent questions before our legislators require nuance, genuine deliberation and expert judgment. Congress, however, is missing adequate means for this purpose and depends on outdated and in some cases antiquated systems of information referral, sorting, communicating, and convening. Congress is held in record low esteem by the public today. Its failings have been widely analyzed and a multitude of root causes have been identified. This paper does not put forward a simple recipe to fix these ailments, but argues that the absence of basic knowledge management in our legislature is a critical weakness. Congress struggles to make policy on complex issues while it equally lacks the wherewithal to effectively compete on substance in today's 24 hour news cycle.This paper points out that Congress is not so much venal and corrupt as it is incapacitated and obsolete. And, in its present state, it cannot serve the needs of American democracy in the 21st Century.The audience for this paper is those who are working in the open government, civic technology and transparency movements as well as other foundations, think tanks and academic entities. It is also for individuals inside and outside of government who desire background about Congress' current institutional dilemmas, including lack of expertise

    Focus Groups for Artifact Refinement and Evaluation in Design Research

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    Focus groups to investigate new ideas are widely used in many research fields. The use of focus groups in design research poses interesting opportunities and challenges. Traditional focus group methods must be adapted to meet two specific goals of design research. For the refinement of an artifact design, exploratory focus groups (EFGs) study the artifact to propose improvements in the design. The cycle of build and evaluate using EFGs continues until the artifact is released for field test in the application environment. Then, the field test of the design artifact may employ confirmatory focus groups (CFGs) to establish the utility of the artifact in field use. Rigorous investigation of the artifact requires multiple CFGs to be run with opportunities for quantitative and qualitative data collection and analyses across the multiple CFGs. In this paper, we discuss the adaptation of focus groups to design research projects. We demonstrate the use of both EFGs and CFGs in a design research project in the health care field

    Chronic Stress and Reproductive Function in Female Childhood Cancer Survivors

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    Reproductive dysfunction is reported as a major concern for childhood cancer survivors (CCS) and is highly correlated with quality of life in this population. Few predictors of post-treatment reproductive function in CCS have been identified. CCS report high levels of psychological stress. Psychological stress activates the hypothalamic-pituitary-adrenal axis, which can disrupt reproductive function. The purpose of this exploratory study was to explore the relationship between perceived stress, biomarkers of hypothalamic-pituitary-adrenal activity, gonadotropin levels, and anti-Müllerian hormone levels in female CCS. This exploratory cross-sectional study included female cancer survivors (ages 16-35) treated for pediatric cancer at the Royal Hospital for Sick Children in Edinburgh, Scotland. Perceived stress was measured using the Perceived Stress Scale (PSS-10). Hypothalamic-pituitary-adrenal activity (HPA) was measured using salivary and hair cortisol levels. Ovarian function was measured using serum gonadotropin levels and serum anti-Müllerian hormone levels. Latent growth curve modeling was used to determine diurnal cortisol slope and intercept. Bayesian structural equation modeling was used to explore the relationship between perceived stress, biomarkers of HPA activity and ovarian function. Twenty-four female (mean age 21.79 ± 5.68) CCS were included in the study. We found an inverse association between perceived stress and ovarian function and a positive association between biomarkers of HPA activity and ovarian function. The findings from this study suggest that perceived stress is negatively associated with ovarian function and that threshold cortisol levels are required for healthy ovarian function in female childhood cancer survivors

    PRIVACY ASSURANCE AND NETWORK EFFECTS IN THE ADOPTION OF LOCATION-BASED SERVICES: AN IPHONE EXPERIMENT

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    The use of geospatially aware mobile devices and applications is increasing, along with the potential for the unethical use of personal location information. For example, iPhone apps often ask users if they can collect location data in order to make the program more useful. The purpose of this research is to empirically examine the significance of this new and increasingly relevant privacy dimension. Through a simulation experiment, we examine how the assurance of location information privacy (as well as mobile app quality and network size) influences users\u27 perceptions of location privacy risk and the utility associated with the app which, in turn, affects their adoption intentions and willingness-to-pay for the app. The results indicate that location privacy assurance is of great concern and that assurance is particularly important when the app’s network size is low or if its quality cannot be verified
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