Holmes E. Miller

THE MULTIPLE DIMENSIONS OF INFORMATION QUALITY

Information quality occurs along ten dimensions, is defined by the information‘s customer, and is constantly changing over time. IS managers must understand the dimensions and the dynamic nature of information quality to effectively use information as a product, as a component of their production processes, and as a vehicle for managerial planning and control.

DECISION MAKING WITH BELIEF STRUCTURES: AN APPLICATION IN RISK MANAGEMENT

This paper examines the problem of selecting an alternative in situations in which there exists uncertainty in our knowledge of the state of the world. We show how the ordered weighted averaging aggregation operators provide a unifying approach to selecting alternatives under uncertainty. In particular, we see how these operators provide a type of probability associated with our degree of optimism. We also show how the Dempster-Shafer belief structure provides a general framework for representing the information a decision maker has regarding relevant events. We then propose a methodology for decision making under uncertainty, integrating the ordered weighted averaging aggregation operators and the Dempster-Shafer belief structure. The proposed methodology is applied to a real world case involving risk management at one of the nation’s largest banks.

Information quality and market share in electronic commerce

Purpose – For electronic commerce applications, the importance of quality information is self‐evident, especially for firms offering information products where information either significantly augments a physical product or constitutes the product itself. Aims to use a probabilistic simulation model is used to explore the relationship between information quality and market share for firms offering an information product.Design/methodology/approach – In the Excel‐based Monte Carlo model information quality is represented as a function of four general quality attributes. For these attributes, quality gaps relative to best practices are calculated and these gaps are used to drive a volume model whose output includes market share. The model is used to examine various scenarios including cases dealing with differences in a firms initial information quality levels, differences in innovation rates and their variability, and differences in a firms proclivity to copy advances of competitors.Findings – The result...

Managing Customer Expectations

Abstract Users who have high expectations for a new system too often become disappointed once they discover the system does not live up to their dreams. Some IS managers circumvent this problem by purposely setting expectations too low. However, high expectations do not have to be a guarantee for disappointment and dissatisfaction.

A Monte Carlo simulation model of supply chain risk due to natural disasters

In this paper we present a model that simulates the effects of natural disaster risks for a hypothetical three tier supply chain. Drawing on concepts from reliability theory and capacity analysis, the model is structured such that diminutions of service capacity at nodes lower in the supply chain can affect higher tier nodes. The model is used to examine various scenarios, including examining correlation among node locations; the effectiveness of disaster recovery plans; and dual sourcing. In addition, the size of the lower tier of the supply chain is expanded and the ensuing results are compared to those for leaner supply chains.

Critical infrastructure and smart technology risk modelling using computational intelligence

We discuss the criticality of infrastructure in economic development and security, and identify various risks posed by smart technologies as applied to infrastructure. We provide a computational intelligence methodology, using attitudinal and fuzzy modelling, and illustrate its application as a risk modelling decision technology in the selection of smart technology in critical infrastructure.

A general methodology for decision making under uncertainty with a risk management application

The authors provide a general formulation for decision making under uncertainty. They discuss the role of the decision makers level of optimism in the selection of an alternative. It is shown that the ordered weighted averaging (OWA) operators play a central role in aggregating the payoffs to determine a value associated with each alternative. It is also shown that the Dempster-Shafer belief structure may provide a suitable framework for representing the information a decision maker has regarding the events. Using OWA operators, a methodology is provided for selecting the optimal alternative in decision making under uncertainty in which the knowledge about the uncertainty can be modeled by the belief structure. The methodology is applied to a case involving risk management at one of the USAs largest banks.<<ETX>>

Using Analytical Methods in Business Continuity Planning

Business continuity focuses on ensuring an organization can continue to provide services when faced with various crisis events. Part of the business continuity planning process involves: Business Impact Analysis; Risk Assessment; and Strategy Development. In practice, these activities often rely on ad hoc methods for collecting and analyzing data necessary for developing the business continuity plan. In this paper we discuss how various analytical methods that have been successfully used for addressing other problems, may be applied to the three phases of business continuity planning mentioned above.

Decision making with attitudinal based expected values

We provide the conceptual framework for attitudinal based expected values for continuous random variables. We introduce a new methodology to allow the decision maker to incorporate his own disposition with objective information in a risk environment. We provide an extension of the OWA operator to the case in which our argument is a continuous valued interval rather than a finite set of values. We look at some examples of this type of aggregation. We propose a method of incorporating the relative value of an alternatives intangible factors into the decision process.

Using the language of summarising statistics in dynamic decisions

Decision making under risk in a dynamic environment involves making a sequence of decisions which are interspersed with probabilistic events leading to an uncertain outcome. Decision makers typically use the expected value as a measure to summarise the value of a particular decision policy. We introduce a new methodology, using the language of summarising statistics, to allow the decision maker to assess the worth of a course of action. The decision maker may systematically align probabilistic elements with his own judgement. Summarising statistics provide descriptive measures to aid the decision makers understanding of a complex dynamic decision environment and to select a preferred sequence of decisions. We apply the Ordered Weighted Averaging operator as a basis for generating summarising statistics. We show how weight generating functions can induce different forms of Ordered Weighted Averaging operators which in turn revise event probabilities in the light of the decision makers disposition. We illustrate our methodology by determining a decision policy to counter cyberterrorism.