A classification model for human error in collaborative systems

A Classification Model for Human Error in Collaborative Systems The primary focus of the work reported in this thesis is to investigate and provide a means by which the occurrence of human error in collaborative systems can be better understood. The thesis suggests that much can be gained from looking at human error from a collaborative perspective as opposed to more traditional cognitive and behavioural approaches. The work is motivated through the failure of many human error analysis methodologies to fully capture and model the impact collaboration can have on the occurrence of human error. The basis of the work is the premise that human error can be examined and understood using accepted models of collaboration. This thesis describes the development of a classification model for understanding human error in collaborative systems. It describes how a model of collaborative human error was conceived and how its elements were developed into a classification mechanism. The classification model was developed and tested through its application and examination to a series of reported and observed examples of collaborative human error. Through the development of the classification model a structured approach was developed to support its application. This structured approach incorporated a framework of standard techniques that were adapted for the research. The issues raised in the research provide a means by which the complex nature of collaborative human errors can be broken down, enabling them to be understood and described. The model describes collaborative human error on three levels examining social issues, such as regulations, rules, beliefs, goals and historical factors; environmental issues, such as opportunities, interests and plans; and, issues of local interaction performed by user to complete a task. This unified approach provides a manageable way to investigate erroneous environments.