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.     

Interactive visualization for research contextualization in international business

We respond to calls for advances in the contextualization of international business (IB) research by introducing interactive visualization as a methodology for generating contextual insights during the exploratory phases of IB research projects. We suggest that applying interactive visualization early on improves contextualization by means of simultaneous dynamic representations of various phenomena and their respective properties and relationships, even for phenomena that have been widely researched before, like in the cases of international joint ventures and MNE foreign direct investment. The goal of this introduction is to make interactive visualization more accessible to IB scholars.     

Does the spatial representation affect criteria weights in environmental decision-making? Insights from a behavioral experiment

The need and interest to consider cognitive and motivational biases has been recognized in different disciplines (e.g. economics, decision theory, risk analysis) and has recently reached environmental decision-making. Within this domain, the intrinsic presence of a spatial dimension of both alternatives and criteria calls for the use of maps throughout the decision-making process to properly represent the spatial distribution of the features under analysis. This makes spatial Multi Criteria Decision Analysis (MCDA) a particularly interesting domain to explore new dimensions of cognitive biases. This study proposes a behavioral experiment aimed at discovering to what extent the spatial visualization (i.e. maps) of criteria versus the non-spatial one (i.e. tables) can bias the weight elicitation phase of a spatial MCDA process. The experiment simulates a very common analysis in environmental and land use planning: land suitability analysis. Our findings show that there are significant consequences on how important we perceive a certain criterion to be, depending on whether it is represented as a map or as a table among a mix of maps and tables. Indeed, the map representation of the same criterion leads to higher weights attributed to that criterion compared to the table representation. Visualizing the same information as a map or as a table, although technically equivalent, is thus not psychologically equivalent for Decision Makers. The results of this experiment are expected to have implications for spatial decision-making processes, by generating better awareness on the impacts of map-mediated land suitability analysis.     

Making sense of complex data using interactive data visualization

An important role for accountants today is to provide decision support to senior management by assisting them in the analysis of large, complex data sets. Interactive data visualization (IDV) facilitates this process by allowing users to navigate, select, and display data via an easy-to-use interface often used as a component of data analytics. Given the increasing popularity of IDV as a tool for making sense of complex data, it is important that accountants become familiar with and learn how to use this technology. This case provides a hands-on opportunity to organize complex accounting data to create IDVs for decision makers to use. Further, the case enables students to understand the potential impact of IDVs on preparers and users of accounting information. Students will assume the role of a division controller in a hypothetical company and create an IDV to assist the chief executive officer (CEO) in decision making.     

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.