The banquet staff at a north Texas hotel were responsible for setting up 11 different functions (e.g., buffet dinners) for conferences and meetings. The functions were often set up late and items were often omitted. An analysis suggested that performance problems were the result of weak antecedents, inefficient work procedures, inadequate training and a lack of motivating consequences. An intervention consisting of task checklists, feedback, goal setting, monetary bonuses, training and job aids was designed to enhance the accuracy and timeliness of function setups. Performance increased from an average of 68.8% on the quality measure (accuracy plus timeliness) in baseline, to 99.7% during the intervention phase. Performance decreased to 82.3% during a follow-up phase in which parts of the intervention were discontinued by hotel management. Performance increased to 99.3% with the reintroduction of the intervention phase.

A popular approach to operating organizations in the 1990s is the implementation of work teams. The current literature offers little information on the use of performance management techniques in work team settings. This case study examined the effects of employing a performance improvement strategy on employee performance in a work team environment comprised of part-time graduate students. The performance improvement strategy included composing job descriptions, job aids (e.g., work organization charts), task request logs and posting weekly and monthly performance feedback. Improvements were observed in some aspects of team performance. Some of the improvement was due to task clarification and improved scheduling produced by the antecedent interventions. Performance feedback had little effect on measured performance but seemed to facilitate discussion and problem-solving.

The purpose of this study is to determine which of three forecasting methods provides the most accurate short-term forecasts, in terms of absolute and mean absolute percentage error, for a unique set of data. The study applies three forecasting techniques--the Box-Jenkins or ARIMA method, cycle regression analysis, and multiple regression analysis--to quarterly sales tax revenue data. The final results show that, with varying success, each model identifies the direction of change in the future, but does not closely identify the period to period fluctuations. Indeed, each model overestimated revenues for every period forecasted. Cycle regression analysis, with a mean absolute percentage error of 7.21, is the most accurate model. Multiple regression analysis has the smallest absolute percentage error of 3.13.