The Strategic Problem of Information Security and Data Breaches

This paper considers the strategic uncertainties and impacts created by high-profile data breaches and discusses the unique strategic problem presented by information security breaches for organizational executives. Based on theory regarding strategic uncertainties, we develop a framework depicting a strategic perspective on breaches within and outside the firm. Then, within the major categories outlined by the framework, this research evaluates instances of 17 public disclosures of high-profile data breaches over the past four years. Based on our discussion of these 17 cases, we identify six major issues complicating strategic decision-making regarding security breaches and discuss guidance for managers

Case-Based Decision Theory

This paper suggests that view that decision-making under uncertainty is, at least party, case-based. We propose a model in which cases are assumed as primitives, and provide a simple axiomatization of a decision rules which chooses a "best" act based on its past performance in "similar" cases. Each act is evaluated by the sum--over cases in which it was chosen--of the product of the similarity of the past case to the problem at hand and the utility level that resulted from this act in the past. As in expected utility theory, both the utility and the similarity functions may be derived from preferences and the latter are represented by (the maximization of) a sum of products. However, there are some crucial differences between case-based decision theory and expected utility theory. In the former: -- every two acts are evaluated over completely different (and disjoint) histories of cases; -- neither probabilities nor states of the world are assumed as primitives. Moreover, the theory does not distinguish between certain and uncertain acts; -- the notions of "satisfiying" decisions and aspiration levels pop up naturally from the axiomatic derivation of case-based decisions. The paper also discusses various aspects, variations and applications of the basic model.

Study of the Types and Characteristics of Channel Shift of Omni Channel Service

Currently, we are living in an age in which the development of IT technology creates value by utilizing Internet and mobile platform. In the past, the consumers had an one-way consumption pattern which purchases after acquiring information via advertisement. And then, as the Internet improves, they have shown the aspect of consuming more actively, by searching and applying a variety of information acquired from Internet. While until then, online and offline belonged to the different area which delivers each different value, however presently, with vitalizing ICT and mobile environment, the boundary has begun to blurred. Accordingly, and a form of Omni Channel service where the physical place and Internet, mobile, the respective platform organically assimilate with each other has been established. Therefore, currently a number of distribution enterprises are providing various mobile-based O2O(Online to Offline) service as one strategy in order to support Omni Channel. In this case, currently a lot of enterprises have faced a problem of how to plan and design channels that support the users in each stage of decision making process for purchase and provide potential consumers with a consistent brand experience. Thus, the study would examine Omni Channel, which is a novel consuming trend, prior to solving the problem, and aims to draw the decision making process for purchase in Omni Channel service environment. On top of that, it would formalize Channel shift types of consumers and figure out their characteristics through customer journey map. Based on them, it aims to examine the characteristics of Omni Channel service by analyzing the cases of Omni Channel service in general. The study is meaningful as a preceding research in that it draws the Omni Channel service design strategy to maximize mobile users' experience in the future

Optimal randomized and non-randomized procedures for multinomial selection problems

Multinomial selection problem procedures are ranking and selection techniques that aim to select the best (most probable) alternative based upon a sequence of multinomial observations. The classical formulation of the procedure design problem is to find a decision rule for terminating sampling. The decision rule should minimize the expected number of observations taken while achieving a specified indifference zone requirement on the prior probability of making a correct selection when the alternative configurations are in a particular subset of the probability space called the preference zone. We study the constrained version of the design problem in which there is a given maximum number of allowed observations. Numerous procedures have been proposed over the past 50 years, all of them suboptimal. In this thesis, we find via linear programming the optimal selection procedure for any given probability configuration. The optimal procedure turns out to be necessarily randomized in many cases. We also find via mixed integer programming the optimal non-randomized procedure. We demonstrate the performance of the methodology on a number of examples. We then reformulate the mathematical programs to make them more efficient to implement, thereby significantly expanding the range of computationally feasible problems. We prove that there exists an optimal policy which has at most one randomized decision point and we develop a procedure for finding such a policy. We also extend our formulation to replicate existing procedures. Next, we show that there is very little difference between the relative performances of the optimal randomized and non-randomized procedures. Additionally, we compare existing procedures using the optimal procedure as a benchmark, and produce updated tables for a number of those procedures. Then, we develop a methodology that guarantees the optimal randomized and non-randomized procedures for a broad class of variable observation cost functions -- the first of its kind. We examine procedure performance under a variety of cost functions, demonstrating that incorrect assumptions regarding marginal observation costs may lead to increased total costs. Finally, we investigate and challenge key assumptions concerning the indifference zone parameter and the conditional probability of correct selection, revealing some interesting implications.PhDCommittee Co-Chair: Goldsman, David; Committee Co-Chair: Tovey, Craig; Committee Member: Alexopoulos, Christos; Committee Member: Kleywegt, Anton; Committee Member: Sanchez, Susa

Case-Based Reasoning Approach For Managing Water Quality Incidents In Distribution Systems

Access to safe drinking water is universally considered as a fundamental human right and customers regard a reliable supply of safe, clean water as the most important aspect of the water supply service. However, water quality failures do occur, with some of the hardest to understand and manage occurring within distribution systems. In the UK, a regulatory process is applied in which water companies must report on significant water quality incidents, their causes, actions, responses, and outcomes. The Drinking Water Inspectorate (DWI) assesses these reports on an annual basis and their findings are made publically available. It is hypothesised here that these reports form a valuable resource that can be ‘data mined’ for improved understanding and to help with future incident management. Developed in the late 1970s, case-based reasoning (CBR) is a knowledge-based problem-solving technique that relies on the reuse of past experience. It is based on the assumption that similar problems have similar solutions and hence new problems can be solved by reusing (and adapting) solutions. The WaterQualityCBR software system, reported on here, was developed as a decision support tool for water companies to deal more effectively with water quality incidents (e.g. water discolouration, contamination and loss of supply) by using information from previous incidents. The tool manipulates a database (compiled in XML) of past significant events from several years DWI reporting. The system can provide information at a strategic level, for example to help inform policy or water company guidance documents. In addition, a complete closed CBR cycle is possible for operational event management providing information from similar cases from the past and, importantly, ranking past actions in response to similar incidents. Examples are provided to illustrate both aspects of the software, demonstrating how the CBR methodology can support decision-making for water utilities in managing drinking water incidents

Reforming Public School Systems Through Sustained Union-Management Collaboration

Presents case studies of sustained collaboration between teachers' unions and management in school reform; common elements in initiating events, strategic priorities, supportive system infrastructure, and sustaining factors; and lessons learned

Knowledge data discovery and data mining in a design environment

Designers, in the process of satisfying design requirements, generally encounter difficulties in, firstly, understanding the problem and secondly, finding a solution [Cross 1998]. Often the process of understanding the problem and developing a feasible solution are developed simultaneously by proposing a solution to gauge the extent to which the solution satisfies the specific requirements. Support for future design activities has long been recognised to exist in the form of past design cases, however the varying degrees of similarity and dissimilarity found between previous and current design requirements and solutions has restrained the effectiveness of utilising past design solutions. The knowledge embedded within past designs provides a source of experience with the potential to be utilised in future developments provided that the ability to structure and manipulate that knowledgecan be made a reality. The importance of providing the ability to manipulate past design knowledge, allows the ranging viewpoints experienced by a designer, during a design process, to be reflected and supported. Data Mining systems are gaining acceptance in several domains but to date remain largely unrecognised in terms of the potential to support design activities. It is the focus of this paper to introduce the functionality possessed within the realm of Data Mining tools, and to evaluate the level of support that may be achieved in manipulating and utilising experiential knowledge to satisfy designers' ranging perspectives throughout a product's development