CAN HUMANS DETECT ERRORS IN DATA - IMPACT OF BASE RATES, INCENTIVES, AND GOALS

There is strong evidence that data items stored in organizational data bases have a significant rate of errors. If undetected in use, those e rrors in stored data may significantly affect business outcomes. Publi shed research suggests that users of information systems tend to be in effective in detecting data errors. However, in this paper it is argue d that, rather than accepting poor human error detection performance, MIS researchers need to develop better theories of human error detecti on and to improve their understanding of the conditions for improving performance. This paper applies several theory bases (primarily signal detection theory but also a theory of individual task performance, th eories of effort and accuracy in decision making, and theories of goal s and incentives) to develop a set of propositions about successful hu man error detection. These propositions are tested in a laboratory set ting. The results present a strong challenge to earlier assertions tha t humans are poor detectors of data errors. The findings of the two la boratory experiments show that explicit error detection goals and ince ntives can modify error detection performance. These findings provide an improved understanding of conditions under which users detect data errors. They indicate it is possible to influence detection behavior i n organizational settings through managerial directives, training, and incentives.