The organisational precursors to human automation interaction issues in safety-critical domains: the case of an automated alarm system from the air traffic management domain

Rozzi, Simone (2016) The organisational precursors to human automation interaction issues in safety-critical domains: the case of an automated alarm system from the air traffic management domain. PhD thesis, Middlesex University. [Thesis]

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Much has been written about the side effects of automation in complex safety-critical domains, such as air traffic management, aviation, nuclear power generation, and healthcare. Here, human factors and safety researchers have long acknowledged that the potential of automation to increase cost-effectiveness, quality of service and safety, is accompanied by undesired side effects or issues in human automation interaction (HAI). Such HAI issues may introduce the potential for increased confusion, uncertainty, and frustration amongst sharp end operators, i.e. the users of automation. These conditions may result in operators to refuse to use the automation, in impaired ability of operators to control the hazardous processes for which they are responsible, and in new, unintended paths to safety failure.

The present thesis develops a qualitative framework of the organisational precursors to HAI issues (OPHAII) that can be found in safety-critical domains. Organisational precursors denote those organisational and managerial conditions that, although distant in time and space from the operational environment, may actually influence the quality of HAI found there. Such precursors have been extensively investigated by organisational safety (OS) scholars in relation to the occurrence of accidents and disasters—although not HAI issues. Thus, the framework’s development is motivated by the intent to explore the theoretical gap lying at the intersection between the OS area and the current perspectives on the problem—the human computer interaction (HCI) and the system lifecycle ones. While considering HAI issues as a design problem or a failure in human factors integration and/or safety assurance respectively, both perspectives, in fact, ignore, the organisational roots of the problem.

The OPHAII framework was incrementally developed based on three qualitative studies: two successive, historical, case studies coupled with a third corroboratory expert study. The first two studies explored the organisational precursors to a known HAI issue: the nuisance alert problem relative to an automated alarm system from the air traffic management domain. In particular, the first case study investigated retrospectively the organisational response to the nuisance alert problem in the context of the alarm’s implementation and improvement in the US between 1977 and 2006. The second case study has a more contemporary focus, and examined at the organisational response to the same problem within two European Air Navigation Service Providers between 1990 and 2010. The first two studies produced a preliminary version of the framework. The third study corroborated and refined this version by subjecting it to the criticism from a panel of 11 subject matter experts.

The resulting framework identifies three classes of organisational precursors: (i) the organisational assumptions driving automation adoption and improvement; (2) the availability of specific organisational capabilities for handling HAI issues; and (3) the control of implementation quality at the boundary between the service provider and the software manufacturer. These precursors advance current understanding of the organisational factors involved in the (successful and problematic) handling of HAI issues within safety-critical service provider organisations. Its dimensions support the view that HAI issues can be seen as and organisational phenomenon—an organisational problem that can be the target of analysis and improvements complementary to those identified by the HCI and the system lifecycle perspectives.

Item Type: Thesis (PhD)
Research Areas: A. > School of Science and Technology
A. > School of Science and Technology > Computer Science
B. > Theses
Item ID: 21259
Depositing User: Jennifer Basford
Date Deposited: 09 Feb 2017 16:16
Last Modified: 29 Nov 2022 21:40

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