Emphasizing the rank positions in a distance-based aggregation procedure

This paper deals with the problem of aggregating individual preferences in order to obtain a social order. In particular, a preference aggregation procedure is proposed for those cases in which the decision-makers express their preferences by means of a ranking of alternatives. Among the most commonly applied methods for this purpose are those based on distance measures between individual and collective preferences, which look for the solution that minimizes the disagreement across decision-makers. This class of procedures may include weighting factors in order to emphasize the relative importance of the individuals. In the model proposed here, a weighted disagreement function that computes the differences between alternatives differentiating the rank positions of the alternatives is developed. The proposed disagreement function weighs the differences between orders depending on the ordinal position that the alternative occupies.     

An extended tuning method for cost-sensitive regression and forecasting

In many real-world regression and forecasting problems, over-prediction and under-prediction errors have different consequences and incur asymmetric costs. Such problems entail the use of cost-sensitive learning, which attempts to minimize the expected misprediction cost, rather than minimize a simple measure such as mean squared error. A method has been proposed recently for tuning a regular regression model post hoc so as to minimize the average misprediction cost under an asymmetric cost structure. In this paper, we build upon that method and propose an extended tuning method for cost-sensitive regression. The previous method becomes a special case of the method we propose. We apply the proposed method to loan charge-off forecasting, a cost-sensitive regression problem that has had a bearing on bank failures over the last few years. Empirical evaluation in the loan charge-off forecasting domain demonstrates that the method we have proposed can further lower the misprediction cost significantly.     

Decision support for Cybersecurity risk planning

Security countermeasures help ensure the confidentiality, availability, and integrity of information systems by preventing or mitigating asset losses from Cybersecurity attacks. Due to uncertainty, the financial impact of threats attacking assets is often difficult to measure quantitatively, and thus it is difficult to prescribe which countermeasures to employ. In this research, we describe a decision support system for calculating the uncertain risk faced by an organization under cyber attack as a function of uncertain threat rates, countermeasure costs, and impacts on its assets. The system uses a genetic algorithm to search for the best combination of countermeasures, allowing the user to determine the preferred tradeoff between the cost of the portfolio and resulting risk. Data collected from manufacturing firms provide an example of results under realistic input conditions.     

The adoption of mobile healthcare by hospital's professionals: An integrative perspective

The adoption rate of mobile healthcare is relatively low in the hospital. In practice, a study of how healthcare professionals adopt mobile services to support their work is imperative. An integration of TAM and TPB, concerning both technological and organizational aspects, is important for understanding the adoption of mobile healthcare. However, mobile healthcare is a wireless device which is often used in a voluntary motive. Service provisions for pervasive and timely usage and individual psychological states are critical in determining its use. Accordingly, perceived service availability (PSA) and personal innovativeness in IT (PIIT) may be the important drivers to be included in TAM and TPB. This study thus proposed such a research framework from a broader and integrative perspective. The empirical examination showed high predictive power for adoption intention and the influential role of these important variables.     

Operational causes of bankruptcy propagation in supply chain

With the increasing interdependence among supply chain members, bankruptcy of a supply chain member may be caused by operational decisions of other members. To investigate how bankruptcy occurs and propagates in supply chain networks, we build a multi-agent simulation model for a two-stage supply chain that consists of multiple upstream manufacturers and multiple downstream retailers. Based on the developed simulation model, we study impacts of various operational parameters and decisions, such as horizontal competition among retailers, order allocation strategies of retailers, wholesale price of manufacturers, characteristics of market demand and number of retailers, on bankruptcy propagation. Since many operational decisions of a firm are made under financial constraints, we also investigate the linkage between firm's operational risks and financial decisions (e.g., the maximal risk of cash flow that a member is willing to take, and the up-front payment proportion of retailers in a two-period payment policy). Experimental results reveal that operational interactions between supply chain members and operational decisions made by supply chain members are important causes of bankruptcy propagation, but impacts of these operational parameters and decisions depend on financial decisions. These observations indicate that supply chain members can moderately hedge their operational risk through financial decisions.     

A framework for dynamic multiple-criteria decision making

The classic multiple-criteria decision making (MCDM) model assumes that, when taking a decision, the decision maker has defined a fixed set of criteria and is presented with a clear picture of all available alternatives. The task then reduces to computing the score of each alternative, thus producing a ranking, and choosing the one that maximizes this value.However, most real-world decisions take place in a dynamic environment, where the final decision is only taken at the end of some exploratory process. Exploration of the problem is often beneficial, in that it may unveil previously unconsidered alternatives or criteria, as well as render some of them unnecessary.In this paper we introduce a flexible framework for dynamic MCDM, based on the classic model, that can be applied to any dynamic decision process and which is illustrated by means of a small helicopter landing example. In addition, we outline a number of possible applications in very diverse fields, to highlight its versatility.     

Mining churning behaviors and developing retention strategies based on a partial least squares (PLS) model

In a very competitive mobile telecommunication business environment, marketing managers need a business intelligence model that allows them to maintain an optimal (at least a near optimal) level of churners very effectively and efficiently while minimizing the costs throughout their marketing programs. As a first step toward optimal churn management program for marketing managers, this paper focuses on building an accurate and concise predictive model for the purpose of churn prediction utilizing a partial least squares (PLS)-based methodology on highly correlated data sets among variables. A preliminary experiment demonstrates that the presented model provides more accurate performance than traditional prediction models and identifies key variables to better understand churning behaviors. Further, a set of simple churn marketing programs—device management, overage management, and complaint management strategies—is presented and discussed.     

An elusive antecedent of superior firm performance: The knowledge management factor

Knowledge management (KM) concepts, principles, and technologies provide a foundation for understanding and building systems for acquiring, assimilating, selecting, generating, and emitting knowledge—a crucial resource of the firm. In the knowledge management community, it is commonly contended that knowledge, and capabilities for processing it, comprise a major resource that can differentiate one firm from another in the sense of yielding better performance or a competitive edge. However, aside from anecdotes, there has been little to substantiate this contention. Can any empirical link be discovered between a firm's KM success and that firm's financial performance? To develop an answer to this question, we use an independent research company's reports of firms judged to be highly successful in their KM initiatives, plus related data reported by COMPUSTAT. As an initial investigation of the linkage between KM performance and firm performance, as measured by financial ratios, this study uses the Matched Sample Comparison Group methodology to evaluate research hypotheses. The analysis reveals a heretofore elusive antecedent of firm performance—evidence that superior KM performance is indeed a predictor of superior bottom-line performance. This study contributes to the information systems (IS) literature by demonstrating that KM, a basic foundation for IS, is an important factor to consider from the standpoint of achieving strong financial performance. As such, it suggests that KM furnishes an important context for understanding designs, applications, and possibilities for IS with respect to achieving such performance. In the context of devising and executing KM initiatives, both technological and human treatments of knowledge need to be cultivated and integrated in ways that lead to superior KM performance. This study also contributes to the management literature by going beyond anecdotes and case studies in buttressing the proposition that a firm's KM competencies are an important ingredient in that firm's performance. It solidifies the raison d'etre for investigating KM phenomena and methods (computer-based and human), both within and across modern organizations. It gives practicing managers evidence that bottom-line benefits are indeed associated with superior KM strategy and execution.     

Formal modelling of organisational goals based on performance indicators

Every organisation exists or is created for the achievement of one or more goals. To ensure continued success, the organisation should monitor its performance with respect to the formulated goals. In practice the performance of an organisation is often evaluated by estimating its performance indicators. In most existing approaches for organisation modelling the relation between performance indicators and goals remains implicit. This paper proposes a formal framework for modelling goals based on performance indicators and defines mechanisms for establishing goal satisfaction, which enable evaluation of organisational performance. Methodological and analysis issues related to goals are also discussed in the paper. The described framework is a part of a general framework for organisation modelling and analysis.