Unexpected Events in Nigerian Construction Projects: A Case of Four Construction Companies

In Nigeria, 50% to 70% of construction projects are delayed due to unexpected events that are linked to lapses in performance, near misses, and surprises. While researchers have theorized on the impact of mindfulness and information systems management (ISM) on unexpected events, information is lacking on how project teams can combine ISM and mindfulness in response to unexpected events in construction projects. The purpose of this case study was to examine how project teams can combine mindfulness with ISM in response to unexpected events during the execution phase of Nigerian construction projects. The framework of High Reliability Theory revealed that unexpected events could be minimized by mindfulness defined by 5 cognitive processes: preoccupation with failure, reluctance to simplify, sensitivity to operations, commitment to resilience, and deference to expertise. In-depth semi-structured interviews elicited the views of 24 project experts on team behaviors, tactics, and processes for combining mindfulness with ISM. Data analysis was conducted by open coding to identify and reduce data into themes, and axial coding was used to identify and isolate categories. Findings were that project teams could combine mindfulness with ISM in response to unexpected events by integrating effective risk, team, and communication management with appropriate training and technology infrastructure. If policymakers, project clients, and practitioners adopt practices suggested in this study, the implications for social change are that project management practices, organizational learning, and the performance of construction projects may improve, construction wastes may be reduced, and taxpayers may derive optimum benefits from public funds committed to construction projects

Towards understandable explanations for document analysis systems

smartFIX is a product portfolio for knowledge based extraction of data from any document format. The system automatically determines the document type and extracts all relevant data for the respective business process. Data that is unreliably recognized is forwarded to a verification workplace for manual checking. In general, users have no difficulties to interpret the document data and wonder why the system needs additional input. For that reason, we implemented an explanation component that is used to justify extraction results, thus, increasing confidence of users. The component is using a semantic log making it possible to provide understandable explanations. We illustrate the benefits of that kind of technology in contrast to the current smartFIX Log Viewer by means of a preliminary user experiment