A latent variable approach to the evaluation of CASE tools is used to assess user needs and applications. Responses are consistent with the taxonomy of upper and lower CASE tools. Results indicate the importance of analysis and prototyping features. Some existing tools are rated significantly higher than others in terms of these features. The study also reveals a link between organizational size and the demand for upper and lower CASE tool features. Smaller organizations use CASE tools in the design stage and rely on teamwork and collaboration facilities. Larger firms focus on lower CASE facilities such as prototyping to build completed systems.
Traditional static benefit-cost methods were useful when evaluating transaction processing systems. Strategic benefits are more difficult to evaluate, since they involve dynamic interactions between customers, suppliers, and rivals. In an attempt to gain a competitive advantage, there is a strong incentive to be the first implementor of new technology. However, information technology (IT) costs decline overtime, so there is an incentive to delay implementation. A model is developed that enables managers to evaluate this trade-off and choose the best implementation time. The model emphasizes competition between large firms in a regional (or national) market, interacting with firms in a local market. The model is illustrated with an application to the banking industry. It compares the implementation times of larger regional banks vis-à-vis smaller local banks, and shows how the banks might use technology to respond to various changes in the banking industry.
Auditors and systems analysts are increasingly called upon to determine the impact of a disaster striking the computer system. Current risk analysis methods rely on some variation of expected value analysis. The expected value method suffers from serious drawbacks in this application because probabilities of disaster are difficult to estimate and the loss distributions are likely to be highly skewed. This article presents an improved methodology for dealing with EDP risk analysis and contingency planning. It is based on the concept of stochastic dominance and it provides a more accurate comparison of the various contingency plans by dealing with estimates of the entire loss distribution. This methodology also focuses on the differences between contingency plans, rather than on the cause of the disaster. The application of this methodology is illustrated for the case of a hypothetical medium-sized bank using aggregated data.