Since the 1990s there has been an increasing interest in knowledge, knowledge management, and the knowledge economy due to recognition of its economic value. Processes of globalization and developments in information and communications technologies have triggered transformations in the ways in which knowledge is shared, produced, and used to the extent that the 21st century was forecasted to be the knowledge century. Organizational learning has also been accepted as critical for organizational performance. A key question that has emerged is how knowledge can be “captured” by organizations. This focus on knowledge and learning demands an engagement with what knowledge means, where it comes from, and how it is affected by and used in different contexts. An inclusive definition is to say that knowledge is acquired theoretical, practical, embodied, and intuitive understandings of a situation. Knowledge is also located socially, geographically, organizationally, and it is specialized; so it is important to examine knowledge in less abstract terms. The specific case engaged with in this article is knowledge in hazardous industry and its role in industrial disaster prevention.
In hazardous industries such as oil and gas production, learning and expertise are identified as critical ingredients for disaster prevention. Conversely, a lack of expertise or failure to learn has been implicated in disaster causation. The knowledge needs for major accident risk management are unique. Trial-and-error learning is dangerously inefficient because disasters must be prevented before they occur. The temporal, geographical, and social scale of decisions in complex sociotechnical systems means that this cannot only be a question of an individual’s expertise, but major accident risk management requires that knowledge is shared across a much larger group of people. Put another way, in this context knowledge needs to be collective. Incident reporting systems are a common solution, and organizations and industries as a whole put substantial effort into gathering information about past small failures and their causes in an attempt to learn how to prevent more serious events. However, these systems often fall short of their stated goals. This is because knowledge is not collective by virtue of being collected and stored. Rather, collective knowing is done in the context of social groups and it relies on processes of sensemaking.
Christopher B. Mayhorn and Michael S. Wogalter
Warnings are risk communication messages that can appear in a variety of situations within the healthcare context. Potential target audiences for warnings can be very diverse and may include health professionals such as physicians or nurses as well as members of the public. In general, warnings serve three distinct purposes. First, warnings are used to improve health and safety by reducing the likelihood of events that might result in personal injury, disease, death, or property damage. Second, they are used to communicate important safety-related information. In general, warnings likely to be effective should include a description of the hazard, instructions on how to avoid the hazard, and an indication of the severity of consequences that might occur as a result of not complying with the warning. Third, warnings are used to promote safe behavior and reduce unsafe behavior. Various regulatory agencies within the United States and around the globe may take an active role in determining the content and formatting of warnings.
The Communication-Human Information Processing (C-HIP) model was developed to describe the processes involved in how people interact with warnings and other information. This framework employs the basic stages of a simple communication model such that a warning message is sent from one entity (source) through some channel(s) to another (receiver). Once warning information is delivered to the receiver, processing may be initiated, and if not impeded, will continue through several stages including attention switch, attention maintenance, comprehension and memory, beliefs and attitudes, and motivation, possibly ending in compliance behavior. Examples of health-related warnings are presented to illustrate concepts. Methods for developing and evaluating warnings such as heuristic evaluation, iterative design and testing, comprehension, and response times are described.