Natural disasters cause enormous damage to countries all over the world. To deal with these common problems, different activities are required for disaster management at each phase of the crisis. There are three groups of activities as follows: (1) make sense of the situation and determine how best to deal with it, (2) deploy the necessary resources, and (3) harmonize as many parties as possible, using the most effective communication channels. Current technological improvements and developments now enable people to act as real-time information sources. As a result, inundation with crowdsourced data poses a real challenge for a disaster manager. The problem is how to extract the valuable information from a gigantic data pool in the shortest possible time so that the information is still useful and actionable. This research proposed an actionable-data-extraction process to deal with the challenge. Twitter was selected as a test case because messages posted on Twitter are publicly available. Hashtag, an easy and very efficient technique, was also used to differentiate information. A quantitative approach to extract useful information from the tweets was supported and verified by interviews with disaster managers from many leading organizations in Thailand to understand their missions. The information classifications extracted from the collected tweets were first performed manually, and then the tweets were used to train a machine learning algorithm to classify future tweets. One particularly useful, significant, and primary section was the request for help category. The support vector machine algorithm was used to validate the results from the extraction process of 13,696 sample tweets, with over 74 percent accuracy. The results confirmed that the machine learning technique could significantly and practically assist with disaster management by dealing with crowdsourced data.

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The study investigated the role of human resource development climate (HRDC) and self-efficacy as predictors of work engagement amongst IT-sector employees of India and Thailand. In addition, it also made an attempt to unfurl the mechanism underlying the proposed relationship by proposing and testing a model with self-efficacy as an intervening variable. Work engagement levels among IT-sector employees in Thailand were found be slightly higher than those among the employees in India. Both HRDC and self-efficacy were found to be significant predictors of work engagement. The results for self-efficacy as a mediator and a moderator of the proposed relationship between HRDC and work engagement are reported and discussed. Article building on the theoretical framework of the job-demands resources model, the social cognitive theory and the conservation of resources (COR) theory produces cross-national knowledge about work engagement and predictors.

<span lang="EN-US">The national digital identity platform is a robust scheme that enables individuals and entities to prove who they are to digitally access critical information or services. However, </span><span lang="EN-US">c</span><span lang="EN-US">urrent digital identity systems do not sufficiently consider delegation between entities from the viewpoint of dynamic authorizers and permissions. This study aims to understand the pain points and expectations of end-users and service providers in the Thai national digital identity platform, to design </span><span lang="EN-US">a dynamic delegation model </span><span lang="EN-US">and develop an innovative delegation application to test user acceptance. The research utilizes semi-structured interviews with 3 digital identity experts, two focus groups, one with 6 service providers, and the other one with 6 end-users. Based on results from the data analysis and conceptual prototype design, validated by experts, the proposed prototype is practical and suitable for developing a digital </span><span lang="EN-US">delegation </span><span lang="EN-US">mobile web application that is convenient, safe, secure, and reliable utilizing blockchain technology under the Thai national digital identity platform. </span><span lang="EN-US">The technology acceptance model</span><span lang="EN-US"> was used to test the application acceptance </span><span lang="EN-US">with </span><span lang="EN-US">42 participants</span><span lang="EN-US">. </span><span lang="EN-US">The result reveals that both person and businesses intend to adopt the digital delegation mobile web application.</span><span lang="EN-US"> </span><span lang="EN-US">Use cases of the application include users give their power to trusted entities and Government Agency to provide services to the citizens via the authorized delegatee.</span>

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