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In the aftermath of disasters, humanitarian actors need to coordinate their activities based on accurate information about the disaster site, its surrounding environment, the victims and survivors and the supply of and demand for relief supplies. In this study, the authors examine the characteristics of radio frequency identification (RFID) technology and those of disaster relief operations to achieve information visibility and actor coordination for effective and efficient humanitarian relief operations.

Building on the contingent resource-based view (CRBV), the authors present a model of task-technology fit (TTF) that explains how the use of RFID can improve visibility and coordination. Survey data were collected from humanitarian practitioners in India, and partial least squares (PLS) analysis was used to analyze the model.

The characteristics of both RFID technology and disaster relief operations significantly influence TTF, and TTF predicts RFID usage in disaster relief operations, providing visibility and coordination. TTF is also a mediator between the characteristics of RFID technology and disaster relief operations and between visibility and coordination.

The many recent humanitarian disasters have demonstrated the critical importance of effective and efficient humanitarian supply chain and logistics strategies and operations in assisting disaster-affected populations. The active and appropriate use of technology, including RFID, can help make disaster response more effective and efficient.

Humanitarian actors value RFID technology because of its ability to improve the visibility and coordination of relief operations. This study brings a new perspective to the benefits of RFID technology and sheds light on its antecedents. The study thus expands the understanding of technology in humanitarian operations.

This study aims to identify how objective indicators of destination country risk differentiate business study abroad programs from those in other academic disciplines.

The authors trained a neural network model on six years of student-initiated inquiries about study abroad programs at a large US university. The model classified business versus nonbusiness study abroad programs using objective measures of destination country risk as the primary inputs.

The model correctly classifies business and nonbusiness study abroad programs with over 70% accuracy. Business programs were found to be 20% less likely to include destinations where the Centers for Disease Control and Prevention recommend nonroutine vaccinations and favor countries with higher Global Peace Index scores.

These results underscore the need to consider destination country risk in the design and administration of study abroad programs. An understanding of student preferences for lower risk destinations can contribute to improved planning, execution and student experiences in these programs.

Better planning and management of study abroad programs based on understanding of destination country risk can lead to enhanced student safety and experiences.

This study offers a unique perspective on understanding study abroad programs by focusing on objective measures of destination country risk rather than risk perceptions. It also is, to the best of the authors’ knowledge, the first to use a neural network to classify study abroad programs as business versus nonbusiness using objective measures of country-specify risk indicators.