Productivity efficiency analysis of the airlines in China after deregulation

Using data envelopment analysis and the Malmquist productivity index, this paper studies productivity efficiency of various airlines in China after the Chinese government deregulated the aviation industry in 2005. The results show that the productivity efficiency of non-state-owned airlines improves rapidly and eventually exceeds state-owned airlines after the deregulation policy. Among the state-owned airlines, the productivity of the local airlines and their technical changes are better than those of the central airlines.     

A comparative efficiency analysis of Spanish and Turkish airports

AENA in Spain and DHMI in Turkey operate a large majority of the airports in their respective countries. These two airport operators share some similar characteristics, but also present many differences with respect to their management strategies. For instance, the Turkish DHMI introduced a Build-Operate-Transfer (BOT) model and concession agreements, which enables active private participation in airport management. In contrast, management and operation responsibilities at all airports in Spain –with a few exceptions-have remained with AENA. This paper utilizes a data envelopment analysis (DEA) to compare the relative efficiency of airports within AENA and DHMI for the years between 2009 and 2011. Based on the efficiency scores, it further identifies the sources of inefficiencies resulting from various management strategies and other external factors. The results indicate higher average efficiency levels at Spanish airports, but private involvement enhances efficiency at Turkish airports. The majority of the airports in Spain and Turkey operate under increasing returns to scale. Certain policy options, including a higher private involvement and improvement of the airport network by closing some inefficient airports, should be considered in order to increase the airport efficiency in both countries.     

Internet of Things and Big Data as enablers for business digitalization strategies

Digitization blurs the lines between technology and management, facilitating new business models built upon the concepts, methods and tools of the digital environment. The purpose of this study is to investigate the role of the Internet of Things (IoT) and Big Data in terms of how businesses manage their digital transformation. The paper argues that the outbreak of IoT and Big Data has resulted in a mass of disorganized knowledge. In order to make sense of the noise, a literature review was carried out to examine the studies, published in the last decade (2008–2019), that analyzed both the Internet of Things and Big Data. The results show that IoT and Big Data are predominantly reengineering factors for business processes, products and services; however, a lack of widespread knowledge and adoption has led research to evolve into multiple, yet inconsistent paths. The study offers interesting implications for managers and marketers, highlighting how the digital transformation enabled by IoT and Big Data can positively impact many facets of business. By treating IoT and Big Data as faces of the same coin, this study also sheds light on current challenges and opportunities, with the hope of informing future research and practice.     

Efficiency,total factor productivity and returns to scale in a sustainable perspective: An analysis in the European Union at farm and regional level

For global sustainability it is imperative to find a balance across the three main components of sustainable development which are the economic, social and environmental aspects. However, it is not a simple task to make these contexts compatible, usually because of economic pressures which transform them into opposed objectives. This framework occurs across several dimensions within society and the economy, where the agricultural sector is not an exception. The objective of this study is to analyse the efficiency, total factor productivity and returns to scale in an economic, social and environmental perspective in farms of the European Union (EU) regions through Data Envelopment Analysis (DEA) approaches. The research concerning the returns to scale will be complemented by the Keynesian models. Data obtained from the European Union Farm Accountancy Data Network (FADN) was considered. The results show that in maintaining or improving the levels of production in farms, it is often possible to greatly reduce, in some cases, the consumption of fertilizers and crop protection products. On the other hand, from a social perspective, some European Union regions are more generous in the salaries paid to farming workers and absorb more labour, which in a European context of unemployment, may be an interesting way to realistically look at and be engaged in the agricultural planning in a sustainable way, founding a balanced trade-off among the economic, social and environmental dimensions.     

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.     

大数据时代下电子商务平台的探索和研究——以淘宝网为例

随着网络用户的日益增长,互联网记录着大量的用户个人信息和由智能终端产生的图片和信息,这些信息爆炸性的增长,并不断涌入网络海洋,产生海量的数据信息。在电子商务环境中大数据处理将会发展出更多强大和多元的功能。本文以淘宝网为例,分析研究如何合理利用数据,为企业的经营模式做出相应调整,并且在变化中不断进行企业新技术、新方法和新思路的探索。同时指出数据泄露的隐患,并给出相应建议。     

The Post-entry Performance of Cohorts of Export Starters in German Manufacturing Industries☆

This paper investigates four cohorts of firms from German manufacturing industries that started to export between 1998 and 2002, and follows them for five years after the start. Export starters are a rare species and small in Germany. Around 30–40% of those starters studied became continuous exporters. The share of total exports contributed by export starters of a cohort is tiny in the start year, and it remains so over the years that follow. Contrary to the market selection hypothesis, there is no evidence that productivity in the start year is systematically related to survival in the export market. There is no evidence of a negative impact of a smaller firm size in the start year on the chance of surviving in the export market. Starting with a higher share of exports in total sales, however, tends to increase the probability of continuing to export.