What makes the difference in decision making in high reliability organizations – Profession or Work Experience?

Autoren: Klostermann, Mählmann, Kluge

Kategorie: Menschengerechte Gestaltung digitaler Assistenten: Psychologische Konsequenzen der Interaktion zwischen Mensch, Roboter und Automation

Chair: Ontrup

Beitragskurzfassung:
Automation can be both a curse and a blessing for the human operator (Onnasch, Wickens, & Manzey, 2014). According to Onnasch et al. (2014) there is an increased likelihood of negative influences on the operator and as a result a greater likelihood of incidents when there is more automation present. This can be explained by the deduction of the operators involvement and leaving the operator out of the loop in cases of system failure (Onnasch et al., 2014). As a consequence the decision-making ability is impaired even in the presence of salient cues leaving the operator in an elusive safety of having control (Lützhöft & Dekker, 2002). Both industries, the maritime and aviation industry, are highly automated. However, there is a longer history of training critical incidents and skills including decision-making in the aviation industry (Helmreich & Foushee, 2010). Therefore, the aim was to investigate the relation between decision-making in critical incidents for the maritime and aviation industry. According to the Naturalistic Decision Making (NDM) experienced people have more resources to reach appropriate decisions under high dynamicity and uncertainty (Klein, 2008). In line with the NDM, it was further assessed whether decision-making could be predicted by experience.
A quantitative online study was implemented and 235 participants (N = 125 from the shipping industry, N = 110 from the aviation industry) completed the questionnaire. The questionnaire regarded the demography, decision-making and experience of participants. Two simple linear regression models were conducted to investigate the relations between decision making as dependent variable and experience and training history as independent variables separately. Both experience and training history were contributing significantly to decision making, however, the explained variance was small. Further, a change in decision-making was not predicted whether someone had a longer history of training (aviation) than a shorter history of training (maritime). A discussion of the results and valuable implications for training designs in high reliability organizations regarding automation can be derived.

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Klostermann, Mählmann, Kluge