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Research papers, University of Canterbury Library

Based on a qualitative study of four organisations involving 47 respondents following the extensive 2010 – 2011 earthquakes in Christchurch, New Zealand, this paper presents some guidance for human resource practitioners dealing with post-disaster recovery. A key issue is the need for the human resource function to reframe its practices in a post-disaster context, developing a specific focus on understanding and addressing changing employee needs, and monitoring the leadership behaviour of supervisors. This article highlights the importance of flexible organisational responses based around a set of key principles concerning communication and employee perceptions of company support.

Research papers, University of Canterbury Library

Disasters are rare events with major consequences; yet comparatively little is known about managing employee needs in disaster situations. Based on case studies of four organisations following the devastating earthquakes of 2010 - 2011 in Christchurch, New Zealand, this paper presents a framework using redefined notions of employee needs and expectations, and charting the ways in which these influence organisational recovery and performance. Analysis of in-depth interview data from 47 respondents in four organisations highlighted the evolving nature of employee needs and the crucial role of middle management leadership in mitigating the effects of disasters. The findings have counterintuitive implications for human resource functions in a disaster, suggesting that organisational justice forms a central framework for managing organisational responses to support and engage employees for promoting business recovery.

Research papers, The University of Auckland Library

This paper shows an understanding of the availability of resources in post-disaster reconstruction and recovery in Christchurch, New Zealand following its September 4, 2010 and February 22, 2011 earthquakes. Overseas experience in recovery demonstrates how delays and additional costs may incur if the availability of resources is not aligned with the reconstruction needs. In the case of reconstruction following Christchurch earthquakes, access to normal resource levels will be insufficient. An on-line questionnaire survey, combined with in-depth interviews was used to collect data from the construction professionals that had been participated in the post-earthquake reconstruction. The study identified the resources that are subject to short supply and resourcing challenges that are currently faced by the construction industry. There was a varied degree of impacts felt by the surveyed organisations from resource shortages. Resource pressures were primarily concentrated on human resources associated with structural, architectural and land issues. The challenges that may continue playing out in the longer-term reconstruction of Christchurch include limited capacity of the construction industry, competition for skills among residential, infrastructure and commercial sectors, and uncertainties with respect to decision making. Findings provide implications informing the ongoing recovery and rebuild in New Zealand. http://www.iiirr.ucalgary.ca/Conference-2012

Research papers, The University of Auckland Library

The rapid classification of building damage states or placards after an earthquake is vital for enabling an efficient emergency response and informed decision-making for rehabilitation and recovery purposes. Traditional methods rely heavily on inspector-led on-site surveys, which are often time-consuming, resource-intensive, and susceptible to human error. This study introduces a machine learning-supported surrogate model designed to streamline the assessment of building damage, focusing on the automated assignment of damage placards within the context of New Zealand's post-earthquake evaluation frameworks. The study evaluates two key safety evaluation protocols—Rapid Building Assessment (RBA) and Detailed Damage Evaluation (DDE)—and integrates corresponding databases derived from the 2010–2011 Canterbury Earthquake Sequence (CES) in Christchurch. Six ML classifiers—Multilayer Perceptron (MLP), Random Forest (RF), Support Vector Machine (SVM), K-Nearest Neighbours (KNN), Gradient Boosting Classifier (GBC), and Gradient Bagging (GBag)—were rigorously tested across both databases. The results indicate that the RF-based surrogate model outperforms the other classifiers across both RBA and DDE protocols. Two distinct sets of critical predictors have been further identified for each protocol, allowing for the rapid retrieval of essential data for future on-site surveys, while retaining the RF model's predictive accuracy. The developed surrogate model provides a pragmatic tool for practising engineers to rapidly assign placards to damaged structures and for policymakers and building owners to make informed recovery decisions for earthquake-affected buildings.