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There has been long‐term interest in the processes that affect human‐computer interaction, particularly those causing stress. Computer related stress has previously been correlated to general stress (Cohen, Kamarack, and Mermelstein 1983) and to somatic complaints (Derogatis et al. 1974). A negative correlation between perceived stress and academic performance has also been documented (Hudiburg and Jones 1991). This study compares computer related stress levels in three business student populations (239 students): juniors who have not begun their major course work, seniors who are MIS majors, and graduate MBA students. Using standard analysis of variance, we found that MIS students experienced a significantly higher number of stressors than the other two groups and a greater average severity of stress than the premajors. Human‐computer interaction is suspected to be more stressful for MIS students than other business students because their use of computers is greater and their grades are more heavily weighted toward computer work. This study further considers the various personality types/learning styles of these students and investigates how they might be accommodated to minimize anxiety. The Myers‐Briggs Type Indicator (MBTI) test and a computer assignment preference test were administered to the 239 students. Recommendations for different learner types, in terms of environment variables and computer assignment characteristics, were made.

A forecasting method involving construction and interpretation of the business cycle is presented. Definition and development of lead indicators are discussed. These tools provide management with short‐ to medium‐term forecasts of sales activity. Insights into the reasonableness of the forecasts and guidance for appropriate management actions are discussed. Data from an actual company are used to illustrate computation and interpretation procedures.

Presents a microcomputer‐based interactive decision support system (DSS) to assist managers to improve decision making for machine scheduling in a job shop environment. The system downloads data from a factory information system, and schedules work into the appropriate work centres. The system produces sequences using a rule set that incorporates set‐up time reduction, shortest processing time (SPT) sequencing, downstream requirements, and job due dates. The system automatically expedites where required, and provides an interactive interface to the decision maker. A SLAM II‐based computer simulation model guides system development and provides a test bed for policy decisions with regard to implementation of the DSS.