Browse by author
Lookup NU author(s): Dr David GolightlyORCiD
This work is licensed under a Creative Commons Attribution 4.0 International License (CC BY 4.0).
This paper presents a cognitive task analysis to derive models of decision making for rail maintenance processes. Maintenance processes are vital for safe and continuous availability of rail assets and services. These processes are increasingly embracing the ‘Intelligent Infrastructure’ paradigm, which uses automated analysis to predict asset state and potential failure. Understanding the cognitive processes of maintenance operators is critical to underpin design and acceptance of Intelligent Infrastructure. A combination of methods, including observation, interview and an adaption of critical decision method, was employed to elicit the decision-making strategies of operators in three different types of maintenance control centre, with three configurations of pre-existing technology. The output is a model of decision making, based on Rasmussen’s decision ladder, that reflects the varying role of automation depending on technology configurations. The analysis also identifies which types of fault were most challenging for operators and identifies the strategies used by operators to manage the concurrent challenges of information deficiencies (both underload and overload). Implications for design are discussed.
Author(s): Dadashi N, Golightly D, Sharples S
Publication type: Article
Publication status: Published
Journal: Cognition, Technology and Work
Year: 2021
Volume: 23
Pages: 255-271
Print publication date: 01/05/2021
Online publication date: 20/06/2020
Acceptance date: 08/06/2020
Date deposited: 08/06/2020
ISSN (print): 1435-5558
ISSN (electronic): 1435-5566
Publisher: Springer Verlag
URL: https://doi.org/10.1007/s10111-020-00636-x
DOI: 10.1007/s10111-020-00636-x
Altmetrics provided by Altmetric
