The Sendai Framework for Disaster Risk Reduction 2015-2030 finds that, despite progress in disaster risk reduction over the last decade “evidence indicates that exposure of persons and assets in all countries has increased faster than vulnerability has decreased, thus generating new risk and a steady rise in disaster losses” (p.4, UNISDR 2015). Fostering cooperation among relevant stakeholders and policy makers to “facilitate a science-policy interface for effective decisionmaking in disaster risk management” is required to achieve two priority areas for action, understanding disaster risk and enhancing disaster preparedness (p. 13, p. 23, UNISDR 2015). In other topic areas, the term science-policy interface is used interchangeably with the term boundary organisation. Both terms are usually used refer to systematic collaborative arrangements used to manage the intersection, or boundary, between science and policy domains, with the aim of facilitating the joint construction of knowledge to inform decision-making. Informed by complexity theory, and a constructivist focus on the functions and processes that minimize inevitable tensions between domains, this conceptual framework has become well established in fields where large complex issues have significant economic and political consequences, including environmental management, biodiversity, sustainable development, climate change and public health. To date, however, there has been little application of this framework in the disaster risk reduction field. In this doctoral project the boundary management framework informs an analysis of the research response to the 2010-2011 Canterbury Earthquake Sequence, focusing on the coordination role of New Zealand’s national Natural Hazards Research Platform. The project has two aims. It uses this framework to tell the nuanced story of the way this research coordination role evolved in response to both the complexity of the unfolding post-disaster environment, and to national policy and research developments. Lessons are drawn from this analysis for those planning and implementing arrangements across the science-policy boundary to manage research support for disaster risk reduction decision-making, particularly after disasters. The second aim is to use this case study to test the utility of the boundary management framework in the disaster risk reduction context. This requires that terminology and concepts are explained and translated in terms that make this analysis as accessible as possible across the disciplines, domains and sectors involved in disaster risk reduction. Key findings are that the focus on balance, both within organisations, and between organisations and domains, and the emphasis on systemic effects, patterns and trends, offer an effective and productive alternative to the more traditional focus on individual or organisational performance. Lessons are drawn concerning the application of this framework when planning and implementing boundary organisations in the hazard and disaster risk management context.
When disasters and crises, both man-made and natural, occur, resilient higher education institutions adapt in order to continue teaching and research. This may necessitate the closure of the whole institution, a building and/or other essential infrastructure. In disasters of large scale the impact can be felt for many years. There is an increasing recognition of the need for disaster planning to restructure educational institutions so that they become more resilient to challenges including natural disasters (Seville, Hawker, & Lyttle, 2012).The University of Canterbury (UC) was affected by seismic events that resulted in the closure of the University in September 2010 for 10 days and two weeks at the start of the 2011 academic year This case study research describes ways in which e-learning was deployed and developed by the University to continue and even to improve learning and teaching in the aftermath of a series of earthquakes in 2010 and 2011. A qualitative intrinsic embedded/nested single case study design was chosen for the study. The population was the management, support staff and educators at the University of Canterbury. Participants were recruited with purposive sampling using a snowball strategy where the early key participants were encouraged to recommend further participants. Four sources of data were identified: (1) documents such as policy, reports and guidelines; (2) emails from leaders of the colleges and academics; (3) communications from senior management team posted on the university website during and after the seismic activity of 2010 and 2011; and (4) semi-structured interviews of academics, support staff and members of senior management team. A series of inductive descriptive content analyses identified a number of themes in the data. The Technology Acceptance Model 2 (Venkatesh & Davis, 2000) and the Indicator of Resilience Model (Resilient Organisations, 2012) were used for additional analyses of each of the three cases. Within the University case, the cases of two contrasting Colleges were embedded to produce a total of three case studies describing e-learning from 2000 - 2014. One contrast was the extent of e-learning deployment at the colleges: The College of Education was a leader in the field, while the College of Business and Law had relatively little e-learning at the time of the first earthquake in September 2010. The following six themes emerged from the analyses: Communication about crises, IT infrastructure, Availability of e-learning technologies, Support in the use of e-learning technologies, Timing of crises in academic year and Strategic planning for e-learning. One of the findings confirmed earlier research that communication to members of an organisation and the general public about crises and the recovery from crises is important. The use of communication channels, which students were familiar with and already using, aided the dissemination of the information that UC would be using e-learning as one of the options to complete the academic year. It was also found that e-learning tools were invaluable during the crises and facilitated teaching and learning whilst freeing limited campus space for essential activities and that IT infrastructure was essential to e-learning. The range of e-learning tools and their deployment evolved over the years influenced by repeated crises and facilitated by the availability of centrally located support from the e-Learning support team for a limited set of tools, as well as more localised support and collaboration with colleagues. Furthermore, the reasons and/or rate of e-learning adoption in an educational institution during crises varied with the time of the academic year and the needs of the institution at the time. The duration of the crises also affected the adoption of e-learning. Finally, UC’s lack of an explicit e-learning strategy influenced the two colleges to develop college-specific e-learning plans and those College plans complemented the incorporation of e-learning for the first time in the University’s teaching and learning strategy in 2013. Twelve out of the 13 indicators of the Indicators of Resilience Model were found in the data collected for the study and could be explained using the model; it revealed that UC has become more resilient with e-learning in the aftermath of the seismic activities in 2010 and 2011. The interpretation of the results using TAM2 demonstrated that the adoption of technologies during crises aided in overcoming barriers to learning at the time of the crisis. The recommendations from this study are that in times of crises, educational institutions take advantage of Cloud computing to communicate with members of the institution and stakeholders. Also, that the architecture of a university’s IT infrastructure be made more resilient by increasing redundancy, backup and security, centralisation and Cloud computing. In addition, when under stress it is recommended that new tools are only introduced when they are essential.
