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Titolo:
CAN HUMANS DETECT ERRORS IN DATA - IMPACT OF BASE RATES, INCENTIVES, AND GOALS
Autore:
KLEIN BD; GOODHUE DL; DAVIS GB;
Indirizzi:
UNIV MICHIGAN DEARBORN MI 48128 UNIV GEORGIA,TERRY COLL BUSINESS ATHENS GA 30602 UNIV MINNESOTA,CARLSON SCH MANAGEMENT MINNEAPOLIS MN 55455
Titolo Testata:
Management information systems quarterly
fascicolo: 2, volume: 21, anno: 1997,
pagine: 169 - 194
SICI:
0276-7783(1997)21:2<169:CHDEID>2.0.ZU;2-5
Fonte:
ISI
Lingua:
ENG
Soggetto:
SIGNAL-DETECTION THEORY; DATA QUALITY; INFORMATION-SYSTEMS; TASK-PERFORMANCE; RATE FALLACY; ACCURACY; PREDICTIONS; INSTRUCTION; COMMITMENT; PSYCHOLOGY;
Keywords:
INFORMATION ATTRIBUTES; USER BEHAVIOR; DATA;
Tipo documento:
Review
Natura:
Periodico
Settore Disciplinare:
Physical, Chemical & Earth Sciences
CompuMath Citation Index
Science Citation Index Expanded
Citazioni:
101
Recensione:
Indirizzi per estratti:
Citazione:
B.D. Klein et al., "CAN HUMANS DETECT ERRORS IN DATA - IMPACT OF BASE RATES, INCENTIVES, AND GOALS", Management information systems quarterly, 21(2), 1997, pp. 169-194

Abstract

There is strong evidence that data items stored in organizational databases have a significant rate of errors. If undetected in use, those errors in stored data may significantly affect business outcomes. Published research suggests that users of information systems tend to be ineffective in detecting data errors. However, in this paper it is argued that, rather than accepting poor human error detection performance,MIS researchers need to develop better theories of human error detection and to improve their understanding of the conditions for improvingperformance. This paper applies several theory bases (primarily signal detection theory but also a theory of individual task performance, theories of effort and accuracy in decision making, and theories of goals and incentives) to develop a set of propositions about successful human error detection. These propositions are tested in a laboratory setting. The results present a strong challenge to earlier assertions that humans are poor detectors of data errors. The findings of the two laboratory experiments show that explicit error detection goals and incentives can modify error detection performance. These findings providean improved understanding of conditions under which users detect dataerrors. They indicate it is possible to influence detection behavior in organizational settings through managerial directives, training, and incentives.

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Documento generato il 13/07/20 alle ore 17:16:31