Topic Maps For Improving Services In Disaster Operations Management

Disaster operations management is an increasingly important application area for the developing techniques of service science. This paper examines the use of topic maps, a semantic technology, within this environment, and provides a preliminary discussion of the benefits that its implementation can provide in the capture and exchange of contextual information. The discussion is motivated by a look at the different phases of disaster operations management in a services context, and focuses on the need for effective and relevant information exchange as an important part of the services process. As the amount and complexity of information increases within such processes, semantic technologies are becoming increasingly important as a means representing and managing contextual information. This paper seeks to help further the understanding of the relevance of such tools as part of the study of service science

Human-AI Teaming During an Ongoing Disaster: How Scripts Around Training and Feedback Reveal this is a Form of Human-Machine Communication

Humans play an integral role in identifying important information from social media during disasters. While human annotation of social media data to train machine learning models is often viewed as human-computer interaction, this study interrogates the ontological boundary between such interaction and human-machine communication. We conducted multiple interviews with participants who both labeled data to train machine learning models and corrected machine-inferred data labels. Findings reveal three themes: scripts invoked to manage decision-making, contextual scripts, and scripts around perceptions of machines. Humans use scripts around training the machine—a form of behavioral anthropomorphism—to develop social relationships with them. Correcting machine-inferred data labels changes these scripts and evokes self-doubt around who is right, which substantiates the argument that this is a form of human-machine communication

Research questions to facilitate the future development of European long-term ecosystem research infrastructures : A horizon scanning exercise

Distributed environmental research infrastructures are important to support assessments of the effects of global change on landscapes, ecosystems and society. These infrastructures need to provide continuity to address long-term change, yet be flexible enough to respond to rapid societal and technological developments that modify research priorities. We used a horizon scanning exercise to identify and prioritize emerging research questions for the future development of ecosystem and socio-ecological research infrastructures in Europe. Twenty research questions covered topics related to (i) ecosystem structures and processes, (ii) the impacts of anthropogenic drivers on ecosystems, (iii) ecosystem services and socio-ecological systems and (iv), methods and research infrastructures. Several key priorities for the development of research infrastructures emerged. Addressing complex environmental issues requires the adoption of a whole-system approach, achieved through integration of biotic, abiotic and socio-economic measurements. Interoperability among different research infrastructures needs to be improved by developing standard measurements, harmonizing methods, and establishing capacities and tools for data integration, processing, storage and analysis. Future research infrastructures should support a range of methodological approaches including observation, experiments and modelling. They should also have flexibility to respond to new requirements, for example by adjusting the spatio-temporal design of measurements. When new methods are introduced, compatibility with important long-term data series must be ensured. Finally, indicators, tools, and transdisciplinary approaches to identify, quantify and value ecosystem services across spatial scales and domains need to be advanced.Peer reviewe

The roles of prior experience and the location on the severity of supply chain disruptions

This study examines relationships between the location of supply chain disruptions (SCDs) within the supply chain, a firm’s experience with SCDs, and the disruption severity. Using organisational learning theory, we propose that an organisation’s prior experience with SCDs will reduce the negative influence of future disruptions. However, the location of disruption occurrence (internal to the firm vs. external to the firm) also plays a vital role in the severity of future disruptions. We consider two measures of SCD severity to quantify the extent of negative influence on firms: (1) the initial loss of return on assets (ROA) and (2) the total loss of ROA over time. We empirically evaluate the performance of 262 publicly traded U.S. firms that experienced an SCD. Our study shows that the influence of internal and external SCDs on firms can be different when firms do and do not have experience with similar events. More specifically, the results show that when firms have not experienced a similar event in the past, internal SCDs are associated with a higher disruption severity than are external SCDs. The results also show that prior experience significantly decreases the disruption severity suffered by firms after internal SCDs