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Firm Selection Based on Logistics Risk Factors: A Multiple Criteria Decision Making Approach
No full textLogistics firms are exposed several domestic and global risks such as variability in demand, shipment damages, earthquakes, and terrorist attacks. The purpose of this study is to provide an approach for selecting a logistics firm based on logistics risk factors according to the manufacturing firms' perspectives. An analytical network process method is utilized to determine risk factors' importance levels and VIKOR method is used to select the logistics firm. The study results show that the most important logistics risk factors are customer and supplier, and the least risky logistic firm is B. It can be concluded that the proposed model enables managers to examine potential risks influencing their logistics activities and select the logistics firm having minimum risk
Proximate Breast Cancer Factors Using Data Mining Classification Techniques
No full textBreast cancer is the most common of all cancers and is the leading cause of cancer deaths in women worldwide. The classification of breast cancer data can be useful to predict the outcome of some diseases or discover the genetic behavior of tumors. Data mining technology helps in classifying cancer patients and this technique helps to identify potential cancer patients by simply analyzing the data. This study examines the determinant factors of breast cancer and measures the breast cancer patient data to build a useful classification model using a data mining approach. In this study of 2397 women, 1022 (42.64%) were diagnosed with breast cancer. Among the four main learning techniques such as: Random Forest, Naive Bayes, Classification and Regression Model (CART), and Boosted Tree model were used for the study. The Random Forest technique had the better accuracy value of 0.9892(95%CI,0.9832 -0.9935) and a sensitivity value of about 92%. This means that the Random Forest learning model is the best model to classify and predict breast cancer based on associated factors
How Do CIOs Become CEOs?
No full textThe role of information systems and technologies in any modern organization has become increasingly important. Concepts such as digital transformation and disruptive technologies have become the strategic directions for new and established companies. The role of the Chief Information Officer (CIO) has been long established. This case-based research article examines how and when a CIO becomes the CEO. This is particularly relevant as organizations embrace technology-based strategies to compete, and in many cases, to survive
National Corporate Governance Codes and IT Governance Transparency in Annual Reports
No full textIT governance is concerned with the oversight of IT assets, their contribution to business value and the mitigation of IT-related risks. Emerging research calls for more board level engagement in IT governance and identifies profound consequences for digitized organizations in case the board is not involved. Against this context, this article analyses how corporate governance codes are guiding boards to provide transparency on how they treat IT governance. The findings show that only the South African corporate governance code, King III, contains a significant amount of IT (governance)-related content. As a second objective, this article builds on these findings by providing an exploratory insight in the contemporary state of IT governance transparency in Belgian and South African companies. This way, the influence of the national corporate governance code on IT governance transparency is explored. The authors' findings show that South African firms tend to be more concerned with IT governance transparency in their annual reports than Belgian firms, given a comparable IT strategic role and ownership structure. Accordingly, the case is made for including more IT (governance)-related guidance in national corporate governance codes, as this might enable companies to be more transparent about their IT governance
Demystifying the Communication-Driven Usefulness Hypothesis: The Case of Healthcare Insurance Applications
No full textHealthcare insurance applications are increasingly vital to and have gained popularity with consumers. Previous information systems research featured perceived ease of use and perceived usefulness as key independent variables to explain behavioural intention impacting the use of information systems. In today's environment, however, many consumers already rely on websites and mobile applications as a key means of communication with healthcare insurance providers. Examining the data from 333 survey respondents, this study reports that perceived ease of use and perceived usefulness are strongly influenced by three communication content variables (information quality, interaction ease, and provider competence). Importantly, consumers may judge applications' ease of use based on the quality of communication contents. Once applications reach some maturity, the prominence of communication quality may drive their use more significantly than before
Applying Horner's Rule to Optimize Lightweight MDS Matrices
No full textThis article is concerned with the problem of constructing lightweight MDS matrices. The authors present a new construction of 4 × 4 MDS matrices over GL(F2, m) for any integer m. They give sufficient and necessary conditions to determine whether the construction is an MDS matrix. Further, for any even number m ≥ 4, they construct lightweight MDS matrices in this structure. Applying Horner's rule to implement MDS matrices, the authors constructions need only 8+4×3×m XOR operations
Community-Centered Urban Sensing: Smart Engaged Planning and Design in a Dysfunctional Urban Context
No full textThe authors examine the problem of integrating urban sensing into engaged planning. The authors ask whether enhanced urban data and analysis can enhance resident engagement in planning and design, rather than hinder it, even when current urban planning and design practices are dysfunctional. The authors assess the outcomes of a planning and design effort in Charlottesville, Virginia, USA. Community-Centered Urban Sensing is a participatory urban sensing initiative developed by urban planners and designers, architects, landscape architects, and technologists at the University of Virginia to address the need for actionable information on the urban environment through community-engaged urban data collection and analysis. These findings address how technological urbanism moves from data to action, as well as its potential for marginalization. Finally, the authors discuss a conceptualization of smart and engaged planning that accounts for urban dysfunction. The smart cities paradigm should encompass modes and methods that function even when local urban systems are dysfunctional
Changing Hearts and Minds: Getting Administrative Support for Delivery of Care
No full textBy the nature of their occupation, first responders witness numerous traumatic events. Most of the time, their training and desire to help others allows them to respond professionally and appropriately. However, there are certain events that, for whatever reason, affect first responders in a more dramatic way, leading to emotional and behavioral changes that affect their interpersonal relationships and occupational functioning. Even if they recognize the need, first responders often are hesitant to reveal their distress and/or seek professional help to superiors, believing that they will be perceived as unfit for duty. In this chapter, the need for administrators to understand and accept the severe impact of traumatic events is discussed. Consistent with the individualized nature of traumatic responses, a tiered strategy of intervention is proposed. Finally, drawing from an occupational health perspective, a model that conceptualizes and responds to exposure to traumatic events as an occupational hazard is discussed
Detecting and Distinguishing Adaptive and Non-Adaptive Steganography by Image Segmentation
No full textThis article describes how blind steganalysis aiming at uncovering the existence of hidden data in digital images remains an open problem. Conventional spatial image steganographic algorithms hide data into pixels spreading evenly in the entire cover image, while the content-adaptive algorithms prefer the textural areas and edge regions. In this article, the impact of image content on blind steganalysis is discussed and a practical and extensible approach to distinguish the different types of steganography and construct blind steganalytic detector is proposed. Through the technique of image segmentation, the images are segmented into sub-images with different levels of texture. The classifier only cares for the sub-images which can help modeling the statistical detectability and is trained on sub-images instead of the entire image. Experimental results show the authors' scheme can recognize the type of steganographic methods reliably. The further steps to improve capacity of blind steganalysis based on image segmentation are also mentioned and achieve better performance than ordinary blind steganalysis
The Role of Faculty Development in Online Universities
No full textFaculty development continues to be an important topic in online education. As online program and course offerings are growing and adopted in popularity with many traditional brick and mortar universities, it is causing many institutions to look at how they are providing support, training, guidance, and professional development to the faculty who are teaching in these programs. This chapter will explore how one faculty development department has instituted several best practices to support their faculty at an online institution