A Comparison Of Evaluation Techniques For Decision Analysis Involving Large Attribute Sets

Abstract

This study investigates the effectiveness of two multi-attribute evaluation techniques under conditions of high information load that is caused by large attribute sets.  One hundred and sixty-five respondents were randomly assigned to two groups: the first one used a holistic, point allocation-based method to evaluate a list of 20 job attributes, while the second employed a triad-based technique that decomposed the evaluation task. The results suggest that the decomposed method produced more reliable results and was deemed easier to use, even though it took longer to complete the task.