No abstract

Disaster recovery sites are an important mechanism in continuous IT system operations. Such mechanisms can sustain IT availability and reduce business losses during natural or human-made disasters. Concerning the cost and risk aspects, the IT disaster-recovery site selection problems are multi-criterion decision making (MCDM) problems in nature. For such problems, the decision aspects include the availability of the service, recovery time requirements, service performance, and more. The importance and complexities of IT disaster recovery sites increases with advances in IT and the categories of possible disasters. The modern IT disaster recovery site selection process requires further investigation. However, very few researchers tried to study related issues during past years based on the authors' extremely limited knowledge. Thus, this paper aims to derive the aspects and criteria for evaluating and selecting a modern IT disaster recovery site. A hybrid MCDM framework consisting of the Decision Making Trial and Evaluation Laboratory (DEMATEL) and the Analytic Network Process (ANP) will be proposed to construct the complex influence relations between aspects as well as criteria and further, derive weight associated with each aspect and criteria. The criteria with higher weight can be used for evaluating and selecting the most suitable IT disaster recovery sites. In the OPEN ACCESSSustainability 2015, 7 6150 future, the proposed analytic framework can be used for evaluating and selecting a disaster recovery site for data centers by public institutes or private firms.

Flood hazards have become increasingly common and serious over the last few centuries. Volunteers can observe instant flood information in their local environment, which presents a great opportunity to gather flood information. The information provided by individual volunteers is too much for them to truly understand. Corporate volunteers can offer more accurate and truthful information due to their understanding of the roles and requirements of specific tasks. Past studies of factors influencing the success of corporate volunteers in flood disaster are limited. Thus, this research aims to derive the factors that enable corporate volunteers to successfully integrate the flood information to help reduce the number of injuries and deaths being caused by flood disasters. This research used the information success model and the Public-Private Partnership (PPP) model to develop an analytic framework. The nature of flood disaster management problems is inherently complex, time-bound, and multifaceted. Therefore, we proposed a novel hybrid multi-criteria decision-making (MCDM) model to address the key influence factors and the cause-effect relationships between factors. An empirical study in Taiwanese public flood disaster inquiry and notification systems was used to verify the effectiveness of the proposed methodology. The research results can serve as guidelines for improving the government's policies and the public sector in the context of corporate volunteer involvement in flood disaster inquiry and notification and in relation to other natural and manmade disasters.

With growing scientific evidence showing the harmful impact of air pollution on the environment and individuals’ health in modern societies, public concern about air pollution has become a central focus of the development of air pollution prevention policy. Past research has shown that social media is a useful tool for collecting data about public opinion and conducting analysis of air pollution. In contrast to statistical sampling based on survey approaches, data retrieved from social media can provide direct information about behavior and capture long-term data being generated by the public. However, there is a lack of studies on how to mine social media to gain valuable insights into the public’s pro-environmental behavior. Therefore, research is needed to integrate information retrieved from social media sites into an established theoretical framework on environmental behaviors. Thus, the aim of this paper is to construct a theoretical model by integrating social media mining into a value-belief-norm model of public concerns about air pollution. We propose a hybrid method that integrates text mining, topic modeling, hierarchical cluster analysis, and partial least squares structural equation modelling (PLS-SEM). We retrieved data regarding public concerns about air pollution from social media sites. We classified the topics using hierarchical cluster analysis and interpreted the results in terms of the value-belief-norm theoretical framework, which encompasses egoistic concerns, altruistic concerns, biospheric concerns, and adaptation strategies regarding air pollution. Then, we used PLS-SEM to confirm the causal relationships and the effects of mediation. An empirical study based on the concerns of Taiwanese social media users about air pollution was used to demonstrate the feasibility of the proposed framework in general and to examine gender differences in particular. Based on the results of the empirical studies, we confirmed the robust effects of egoistic, altruistic, and biospheric concerns of public impact on adaptation strategies. Additionally, we found that gender differences can moderate the causal relationship between egoistic concerns, altruistic concerns, and adaptation strategies. These results demonstrate the effectiveness of enhancing perceptions of air pollution and environmental sustainability by the public. The results of the analysis can serve as a basis for environmental policy and environmental education strategies.