The main purpose of this paper is to provide an introduction to artificial neural networks (ANNs) and to review their applications in efficiency analysis. Finally, a comparison of efficiency techniques in a non-linear production function is carried out. The results suggest that ANNs are a promising alternative to traditional approaches, econometric models and non-parametric methods such as data envelopment analysis, to fit production functions and measure efficiency under non-linear contexts.
We conducted two bi-national experiments regarding emotional and behavioral responses to a terrorist plot against commercial flights, examining both feelings and projected action. The studies employed hypothetical scenarios in which terrorists attacked airplanes with shoulder-fired missiles as they were landing or taking off from an international airport. The scenarios were built around two factorially crossed manipulated variables, each with three levels: (1) government announcements or actions (2) social norm, expressed as variation in airline ticket sales. Each respondent read a questionnaire containing only one of the nine scenarios. Experiment 1 was conducted in Spain and California (n = 360, 50% female), Experiment 2 in Israel and California (n = 504, 50% female). In both studies, fear and flight plans were not differentially affected by governmental response or social norm. Women expressed more fear than men. Experiment 1 examined the purpose of the trip. Most respondents would not change a planned flight to attend a close friend's wedding or important job interview, but a substantial number would postpone a vacation or drive to a different location. Experiment 2 featured escalating attacks. These yielded increased fear and more canceled trips. Within both studies, responses were similar across countries despite national differences in direct experience with terrorism.
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