Benchmarking the rescue departments of the Austrian Red Cross using data envelopment analysis and fractional regression models
本研究使用数据包络分析和分数回归模型，评估奥地利某省52个红十字会救援部门的效率，发现平均技术效率约88%，并分析了冬季天气、老年人口和医院床位等因素对绩效的影响。
We performed a radial, input-oriented variable returns-to-scale data envelopment analysis to benchmark 52 rescue departments of the Austrian Red Cross from a single province. Three inputs (working hours of employed personnel/non-employed personnel and number of vehicles) and two output variables (duration-weighted number of two service transportation categories) were selected. First, we assessed the service production process of the Red Cross to obtain an insight into the level of performance, and performance differences among the rescue departments. We found that the average technical efficiency of the rescue departments amounted to almost 88%. The theoretically derived potentials of input reductions for inefficient rescue departments were, among others, restricted by several settings, which were analysed in the second step. Here, several socio-economic, environmental and institutional factors were investigated using a second stage regression analysis based on fractional regression models to find out whether they impacted on the performance of the rescue departments. We found a negative relationship between wintry weather conditions, measured as the number of ice days, and performance, while we identified a positive impact of the number of people aged 64+ and the number of hospital beds in the catchment area on Red Cross performance.