Development of a “Disaster Management Literacy Hub” for Collecting, Creating, and Transmitting Disaster Management Content to Increase Disaster Management Literacy

2017 ◽  
Vol 12 (1) ◽  
pp. 42-56 ◽  
Author(s):  
Reo Kimura ◽  
◽  
Haruo Hayashi ◽  
Kosuke Kobayashi ◽  
Takahiro Nishino ◽  
...  

In this project, a “Disaster Management Literacy Hub” (DMLH) has been developed for collecting, creating, and transmitting various disaster management content over the Internet. The first screen of the DMLH lists disaster management content on tiles to allow users easily find relevant disaster management content using a keyword retrieval function. A user who registers an account can post disaster management content, create a first screen that is customized using the “favorite function,” and compile different disaster management content using the “binder function.” In addition to the “relay (hub) function” for disaster management content, a “creation function” was also implemented, such as the “message function” for posting short messages of approximately 200 letters and an image, and the “quiz creation function” for measuring the effects of disaster management literacy. In the course of modifying the prototype Disaster Management Literacy Hub, the system was evaluated by university student users in July 2016, approximately one year after the fully-fledged operation came into effect in August 2015. The results yielded the following findings: 1) the DMLH is meaningful because by using specific keywords, users retrieved items that had been difficult to find by means of general Internet search sites; 2) statistically significant improvement was shown for 15 disaster management literacy items in the evaluation; the DMLH leads to a proposal to create effective disaster management literacy improvement because students recognized three disaster management literacy categories: cases of disasters and disaster management, responses of familiar agents such as themselves and people to one, and responses of agents in the environment, including local communities and governments.

2008 ◽  
Vol 59 (7) ◽  
pp. 1009-1017 ◽  
Author(s):  
Kate R. Lorig ◽  
Philip L. Ritter ◽  
Diana D. Laurent ◽  
Kathryn Plant

Author(s):  
Md Kamruzzaman ◽  
Nurul I Sarkar ◽  
Jairo Gutierrez ◽  
Sayan Kumar Ray

2021 ◽  
Vol 3 (1) ◽  
pp. 14-25
Author(s):  
Sónia Ferreira ◽  
Sara Santos ◽  
Pedro Espírito Santo

The internet search trend has caused that online users are looking for more and more enriched information. The evolution of social media has been huge and users relate to social networks differently than they did before. Currently, there are more than 4 billion active users on social networks and brands are looking to showcase their products and services. Our research found the following factors that influence social media engagement: informativeness, self-connection and advertising stimulation. Through literature review, we propose a conceptual model that has been tested in the PLS-SEM. Data were collected from 237 consumers and our survey found that engagement in social media is explained by the variables identified by our model. Important contributions to brand theory and management will be found in this investigation.


2000 ◽  
Vol 1719 (1) ◽  
pp. 209-214
Author(s):  
Ho-Ling Hwang ◽  
David L. Greene ◽  
Shih-Miao Chin ◽  
Angela A. Gibson

Automated traffic data posted on the Internet by four cities have been continuously downloaded, processed, and archived for more than 1 year by an automated system developed by Oak Ridge National Laboratory and funded by the U.S. Bureau of Transportation Statistics. Although the experimental system is far from national in scale and scope, it has shown that automated collection and processing of local traffic data via the Internet for national purposes is feasible and practical. Strong seasonal patterns make it too early to estimate statistical models of traffic growth, but comparisons of the same months in 1998 and 1999 indicate changes ranging from 1 percent to 3 percent for the monitored systems. Direct measurements of delay on the monitored systems are lower than published estimates for previous years. Although some progress in the input of missing data has been made, missing data are still a major problem, and better methods are needed.


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