The impacts of risk-taking behaviour and competition on technical efficiency: Evidence from the Chinese banking industry

This paper tests the impact of risk and competition on efficiency in the Chinese banking industry over the period 2003–2013. Comprehensive types of risk-taking behaviour are considered including credit risk, liquidity risk, capital risk, and insolvency risk. Competition is measured by the Lerner index. The results are cross-checked using an alternative econometric technique as well as an alternative competition indicator. The findings show that the technical and pure technical efficiencies of Chinese commercial banks are significantly and negatively affected by liquidity risk. They further show that greater competition precedes declines in technical and pure technical efficiencies of Chinese commercial banks. The results suggest that Chinese bank efficiency is significantly affected by bank diversification, banking sector development, stock market development, inflation and GDP growth rate. The findings also indicate that, compared to state-owned commercial banks, joint-stock commercial banks and city commercial banks have lower technical and pure technical efficiencies.     

Why you should care about the Target data breach

Data breaches are becoming more frequent and more damaging to the bottom line of many businesses. The Target data breach marked the beginning of increased scrutiny of cybersecurity practices. In the past, data breaches were seen as a cost of doing business, but Target's negligence and the scale of the data loss forced businesses and the courts to reevaluate current practices and regulatory frameworks. Businesses must make strategic use of their chief information officers, adopt cybersecurity best practices, and effectively train their employees to respond to growing security threats. They must also shape the cybersecurity narrative to influence regulatory responses to these threats.     

大数据时代进行服务创新的思考

在大数据时代,服务创新必须与超级计算技术密切结合,同时要注意一些应该充分体现的原则。可以开展服务创新的领域是极其广泛的,在经济管理领域,主要可以在企业经营决策、金融理财、商业模式、选择业务外包等诸多领域提供创新的服务。     

基于特征增量的新类识别方法研究

在分类应用的过程中,经常会出现新的类别,导致数据分布发生显著变化,使得原分类模型不再适用。如何识别新的类别使分类模型能适应其出现已经成为一个亟需解决的问题。本文提出基于特征增量的SVDD(支持向量数据描述)新类识别方法。该方法在SVDD算法的基础上,通过增加新特征,扩大特征空间维度从而提高模型对于新类的识别能力。在多个数据集上的实验结果表明,该方法能有效识别新类,使更新后的模型具有更高的准确度。     

An estimation of returns to scale of airport airsides under multiple optimal solutions in DEA

The returns to scale (RTS) nature of 37 Chinese airport airsides are investigated in this paper. Multiple optimal solutions in DEA (Data Envelopment Analysis) models may lead to error RTS estimation. To address this problem, we use the (Zhu and Shen, 1995) RTS method. The empirical study shows that all those airsides with two runways operate under decreasing RTS and those airsides with only one runway either operate in the area of increasing RTS or in the area of constant RTS.     

Urban Sensing by Crowdsourcing: Analysing Urban Trip behaviour in Zurich

The research on which this essay is based sought to provide a deeper understanding of live urban activities. The main idea was to obtain in‐depth trip information by integrating sensing technology with crowdsourcing‐based methods. Location and acceleration sensors embedded in smartphones can deliver useful trip information. Research had previously been conducted to find an accurate data analysis algorithm for high‐level data mining, an efficient sensing method for power saving. As a proof of concept, this essay presents a case study of Zurich that successfully implemented previous research achievements in the real environment. It established that urban travel behaviour can, in fact, be collected by sensors embedded in mobile devices, and that the data thus obtained can be used to measure the characteristics of trip behaviour in cities by means of an advanced classifying algorithm and analysis. The strength of this research is its pioneering role in clearing the ground for future urban data collection methods and planning strategies. Specifically, this study could provide answers to the following key questions: How can crowdsourcing be applied to the collection of urban transportation data? What kinds of information can be extracted from the crowdsourced mobile sensing platform? What kinds of knowledge, within the transportation domain, can be derived from the above?     

Comparing efficiency of holding business model and individual management model of airports

This research compares the efficiency of holding business model to individual management model of airports, employing some robust non-parametric partial frontier-based methods to compare the statistical distributions of efficiency, under different scenarios, to find out which group of airports yields better global performance. The comparison between groups will follow a Malmquist index decomposition, which seems to be the most appropriate tool for within- and inter-group performance comparison. For this purpose, a sample of 145 airports from three continents is utilized. The results provide evidence that European airports are the most productive ones, and within this cluster, the individual management model presented a significant frontier shift with respect the holding cluster frontier, meaning that the former is much more productive than the latter.     

Data mining framework based on rough set theory to improve location selection decisions: A case study of a restaurant chain

Location selection plays a crucial role in the retail and service industries. A comprehensive location selection model and appropriate analytical technique can improve the quality of location decisions, attracting more customers and substantially impacting market share and profitability. This study developed a data mining framework based on rough set theory (RST) to support location selection decisions. The proposed framework consists of four stages: (1) problem definition and data collection; (2) RST analysis; (3) rule validation; and (4) knowledge extraction and usage. An empirical study focused on a restaurant chain to demonstrate the validity of the proposed approach. Twenty location variables relevant to five location aspects were examined, and the results indicated that latent knowledge can be identified to support location selection decisions.     

基于DEA的绿色经济增长核算:以中国地区工业为例

本文构建基于DEA的绿色经济增长核算模型,将考虑非期望产出的劳动生产率变化分解为技术效率变化、技术进步和资本深化。中国地区工业的经验分析表明,与发展水平无关且省际差异大的技术无效率普遍存在;各省工业增长的首要源泉是资本深化,其次是技术进步主导的全要素生产率进步;各省工业劳动生产率增长差异主要源于资本深化差异,其次是技术效率变化差异主导的全要素生产率进步差异;如不考虑非期望产出,会低估资本深化对经济增长的贡献,高估技术进步的贡献,而技术效率变化的贡献可能被高估、低估或无影响。