Application of Automaticity Theory in Construction
Publication: Journal of Management in Engineering
Volume 40, Issue 3
Abstract
Automaticity, an essential skill attribute, develops when an activity is performed without requiring attention. Despite its importance, little is known about the implications of automaticity in the construction industry. To address this gap, this study investigated the development of automaticity during a repetitive construction task. Recruiting 28 subjects to participate in a laboratory roofing-installation experiment repeated across four trial days, this study examined traditional metrics of the primary (installation) task’s duration and accuracy as well as nontraditional metrics of a concurrent secondary (memory) task’s score to examine performance measures diagnostically indicative of automaticity. The results revealed that there were significant improvements in the primary task’s mean duration and accuracy and the secondary task’s mean performance score from the experiment’s first trial day to every other day, an indication that, with repeated practice, automaticity-induced performance improves. Because these automatic performance measures provide an index for evaluating feature-based improvements indicative of automaticity, this study argues that such performance measures capture automaticity developing during repetitive construction activities. Given that practitioners are interested in training workers to achieve automaticity to increase their productivity and multitasking skills, the results of this study provide methods for testing training effectiveness and the extent to which workers have developed automaticity.
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Data Availability Statement
Some or all data, models, or codes that support the findings of this study are available from the corresponding author upon reasonable request.
Acknowledgments
The National Science Foundation is thanked for supporting the research reported in this paper through the Future of Work at the Human-Technology Frontier (FW-HTF) program. This paper was based on work supported by the National Science Foundation under Grant No. 2310210. Any opinions, findings, conclusions, or recommendations expressed in this material are those of the authors and do not necessarily reflect the views of the National Science Foundation.
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Published In
Journal of Management in Engineering
Volume 40 • Issue 3 • May 2024
Copyright
© 2024 American Society of Civil Engineers.
History
Received: Jul 17, 2023
Accepted: Nov 29, 2023
Published online: Mar 15, 2024
Published in print: May 1, 2024
Discussion open until: Aug 15, 2024
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