A Multiple-Task Measurement Framework for Assessing Maximum-Typical Performance.
This study presents a novel measurement framework for assessing and predicting maximum and typical performance. The proposed measurement approach addresses the need for organizations to assess maximum and typical performance changes over time in complex job settings requiring coordination of multipl...
| Published in: | Human Performance Vol. 20; no. 3; pp. 241 - 259 |
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| Main Authors: | , , , |
| Format: | Article |
| Published: |
Taylor & Francis Ltd
2007
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| Subjects: | |
| Online Access: | View this record in EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ssf&AN=25354392&site=ehost-live header: @attributes: shortDbName: ssf uiTerm: 25354392 longDbName: Social Sciences Full Text (H.W. Wilson) uiTag: AN controlInfo: bkinfo: jinfo: jid: 08959285 7LX jtl: Human Performance issn: 08959285 maglogo: N pubinfo: dt: 2007 vid: 20 iid: 3 pid: 377 pub: Taylor & Francis Ltd artinfo: ui: 25354392 10.1080/08959280701333115 ppf: 241 ppct: 18 formats: tig: atl: A Multiple-Task Measurement Framework for Assessing Maximum-Typical Performance. aug: au: Mangos, PhillipM. Steele-Johnson, Debra LaHuis, David White III, EdwardD. affil: Naval Air Warfare Center, Training Systems Division Wright State University Air Force Institute of Technology su: Occupations Employee attitudes Individual differences Performance Task analysis sug: subj: Occupations Employee attitudes Individual differences Performance Task analysis ab: This study presents a novel measurement framework for assessing and predicting maximum and typical performance. The proposed measurement approach addresses the need for organizations to assess maximum and typical performance changes over time in complex job settings requiring coordination of multiple tasks with changing priorities. We present results of an experiment in which participants engaged in a complex task with multiple task elements and instructions to either maximize a different task element in each of four performance blocks (variable-priority condition) or treat all task elements with equal priority (stable-priority condition). We estimated growth curves corresponding to each task element and calculated the area under each growth curve as a summary performance index. Growth curves corresponding to the maximized, high-priority task element in the variable-priority condition reflected maximum performance, whereas those corresponding to the deemphasized, lower priority elements reflected typical performance. We compared the shape of the maximum and typical growth curves in the variable-priority condition to their corresponding performance trajectories in the stable-priority condition. In addition, we tested the moderating influence of individual differences in action-state orientation on the obtained maximum and typical performance estimates. Results indicated support for the proposed measurement framework in terms of its usefulness for inducing sustained levels of maximum performance and for identifying and correcting sources of the maximum-typical performance discrepancy. pubtype: Academic Journal doctype: Article src: R language: English refInfo: copyright: @attributes: flag: N holdings: @attributes: islocal: N |
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