Structuring IS Outsourcing Contracts for Mutual Gain: An Approach to Analyzing Performance Incentive Schemes

As information systems managers come under increasing pressure to improve the cost performance of information processing, outsourcing has become an important management strategy. Although information systems outsourcing is now a major industry, it is still a new decision problem for many managers. As managers gain more and more experience with IS outsourcing, satisfaction with vendor performance is becoming a major issue. Key to managing outsourcing relationships is the outsourcing contract. These contracts assign responsibilities and rewards for the parties. However, improperly or incompletely written contracts have lead to adverse problems. How then are managers to choose from a set of options that which is most appropriate for their firm? Outsourcing problems are complex and entail considerable implications for the strategy of the firm. Although many articles have appeared on outsourcing, few have extended the discussion beyond simple cost-benefit analysis. Contracts that encourage vendor performance and discourage under-performance are clearly of interest to managers. In this paper, an approach to analyzing incentive schemes and structuring outsourcing contracts for the mutual gain of the parties is presented. The approach provides managers with a strategy and techniques for analyzing some of the more subtle issues they may face when dealing with complex outsourcing decision problems

Quality Dimensions for B2C E-Commerce

Organizations have still not realized the full potential of e-commerce. One factor that is likely to influence the further adoption of e-commerce is the quality of the e-commerce system as system quality impacts user satisfaction and hence use of the system. However, in order to improve the quality of any systems, one first needs to identify measures to assess quality. Although other researchers have recognized the need for such measures, they have primarily focused on a single specific aspect of e-commerce systems, typically the user interface. In this paper we identify the key components of e-commerce systems and synthesize existing research related to quality of these components to arrive at a comprehensive list of quality dimensions, which in turn provide measures to assess the quality of e-commerce systems

Generating consensus priority point vectors: a logarithmic goal programming approach

The analytic hierarchy process (Saaty, The Analytic Hierarchy Process: Planning, Priority Setting, Resource Allocation, NewYork: McGraw-Hill 1980) is a popular technique for addressing multiple-criteria decision-making problems (MCDMs). Various techniques have been proposed for using the AHP in group situations. Fundamental to the AHP is the generation of priority point vectors from matrices of pairwise comparison data. In this paper, we present a logarithmic goal programming model for generating the ‘consensus’ priority point vector from the set of individual priority point vectors. Scope and purpose Within modern organizations, multiple-criteria decision-making problems (MCDMs) often occur within a group context, and individual priorities for decision alternatives must be synthesized into a single set of priorities which represents the consensus opinion for the group. This requires a process for aggregating individual priorities into a set of group priorities. In this paper, we examine the use of the analytic hierarchy process (AHP) MCDM technique for the group situation, and present an approach for aggregating individual priorities into a set of group ‘consensus’ priorities

Using formal MS/OR modeling to support disaster recovery planning

All organizations are susceptible to a non-zero risk of experiencing out-of-course events, whether natural or man-made, that can lead to internal “disasters” with respect to business operations. Different types of events (e.g. flood, earthquake, fire, theft, computer failure) have implications for the operations of modern organizations. Hence, there is a critical need for planning and recovery strategies for the effects of disasters. Disaster recovery plans (DRPs) aim at ensuring that organizations can function effectively during and following the occurrence of a disaster. As such, they possess cost, performance, reliability, and complexity characteristics that make their development and selection non-trivial. To date, there has been little modeling of disaster recovery issues in the MS/OR literature. We believe that many of the issues involved can benefit from the application of quantitative decision-making techniques. Consequently, in this paper our contribution is prescriptive rather than descriptive in nature and we propose the use of mathematical modeling as a decision support tool for successful development of a DRP. In arriving at a final DRP, decision-makers must consider a number of options or subplans and select a subset of these subplans for inclusion in the final plan. We present a mathematical programming model which helps the decision maker to select among competing subplans, a subset of subplans which maximizes the “value” of the recovery capability of a recovery strategy. We use hypothetical situations to illustrate how this technique can be used to support the planning process