Efficiency Assessment of Seaport Terminal Operators Using DEA Malmquist and Epsilon-Based Measure Models

Evaluating Shipping Efficiency in Chinese Port Cities: Four-Stage Bootstrap DEA Model

This study examines shipping efficiency and its influencing factors in 19 Chinese port cities using a four-stage bootstrap DEA approach. Infrastructure, asset investment, and labor are selected as its inputs; cargo throughput and cargo turnover are selected as the outputs. First, in the three-stage DEA model, the SFA regression method is used to eliminate the impact of external environmental factors and random factors on shipping efficiency. Furthermore, the Bootstrap DEA method is applied to correct deviation to solve the problem of the traditional DEA method being sensitive to the number of variables of a chosen sample. Finally, the real shipping efficiency of the port cities is measured. The empirical results show that the shipping efficiency of each port city is affected by the factors of foreign trade, population size, economic development, consumption level, and government support. Additionally, the average efficiency values of port cities in the eastern region is higher than the general average at each stage; on the contrary, the average efficiency values in the central and western regions are lower than the general average. Finally, the study provides policy implications for the future improvement of shipping efficiency.

Promoting the efficiency of scientific and technological innovation in regional industrial enterprises: Data-driven DEA-Malmquist evaluation model

With the increasing severity of the global energy crisis and environmental pollution, there is an urgent need to change the economic development model driven by certain factors and the investment scale and pursue science- and technology-driven innovative development. This study aims to improve the efficiency of scientific and technological innovation and promote the high-quality development of regional industrial enterprises. It constructs a data-driven DEA-Malmquist evaluation model to evaluate and optimize regional industrial enterprises’ scientific and technological innovation efficiency. First, we collect the panel data of regional industrial enterprises’ scientific and technological innovation input-output indexes. Second, we use the Pearson correlation coefficient method to identify and construct the evaluation index system of regional industrial enterprises’ scientific and technological innovation efficiency. Third, we build a DEA-Malmquist evaluation model to quantitatively evaluate regional industrial enterprises’ scientific and technological innovation efficiency from static and dynamic aspects. Finally, we verify the feasibility and effectiveness of the method using statistical data on scientific and technological innovation and development of Anhui Industrial Enterprises from 2011 to 2019 and put forth targeted countermeasures and suggestions. This study provides theoretical and methodological support for the sustainable development of industrial enterprises.

A Recommendation Model for Selling Rules in the Telecom Retail Industry

The recommendation of the optimal selling rules for any product or service is challenging, owing to the complexity of the customer’s behavior and the competitiveness existing in the telecom retail industry. This study proposes a recommendation model for selling rules that utilizes a hybrid decision-making approach based on K-means and the C5.0 decision tree to analyze the historical sales information of telecom retailers. To evaluate the efficacy of the proposed recommendation model, it was used to analyze original data from a case company. The results indicated that the proposed hybrid decision-making approach resulted in sales content with a high gross profit and high agreement rates. The experimental results show each cluster that can be used to identify rules for the combination of good tariff items in different tariff ranges. Rules for the recommendation of special tariffs are also established to assist salespeople.

Joined Efficiency and Productivity Evaluation of Tunisian Commercial Seaports Using DEA-Based Approaches

Seaports are important infrastructures to support international trade. Therefore, it is vital that port efficiency and productivity are continuously evaluated and improved. In this context, the objective of this article is to evaluate both the technical efficiency and the change in productivity of the six most important Tunisian commercial seaports, Bizerte, Rades, Sousse, Sfax, Gabes, and Zarzis, over a period of twelve years from 2005 to 2016. To achieve this objective, the data envelopment analysis (DEA) method is applied. The first output-oriented DEA application is about efficiency evaluation, which, for each seaport, allows the estimation of overall technical efficiency, pure technical efficiency, and scale efficiency. The second application concerns the evolution of the productivity of Tunisian seaports during the study period using the Malmquist DEA-based productivity index. The productivity analysis is performed according to the year (period) and according to each studied seaport. The first output-oriented DEA method provides that the overall technical efficiency in the above-mentioned ports is 69.4% while the pure technical efficiency is 83.3%. Furthermore, the average scale efficiency is about 82.6%, which implies that the decreasing type of returns to scale dominates in this study. Regarding the second DEA application for productivity evolution, the obtained results from the data analysis revealed that it fell by 6.7%, mainly due to the degradation of the technological change (8.3%). The results obtained provide useful basic criteria for establishing efficiency improvement strategies for each studied seaport.

A Cross-Country European Efficiency Measurement of Maritime Transport: A Data Envelopment Analysis Approach

Maritime transport, which includes shipping and port operations, is the fundamental basis of international trade and globalization. In transportation management, efficiency is critical for verifying performance and proposing the best countermeasure to meet predetermined goals. Various efforts in this field have been made to solve this problem satisfactorily. However, the significant proportion of conventional approaches are based on long-term observations and professional expertise, with only a few exceptions based on practice-based historical data. Data Envelopment Analysis (DEA) is a non-parametric technique for analyzing various output and input variables parallelly. The efficiency of maritime transport in European countries is explored using a two-stage DEA approach based on Malmquist and Epsilon-Based Measure (EBM). First, the Malmquist model analyses countries’ total productivity growth rates and their breakdown into technical efficiency (catch-up) and technology change (frontier-shift). Second, the EBM model is used to determine the efficiency and inefficiency of the maritime transportation systems in each European country. Apart from identifying the best-performing countries in specific areas over the study period (2016–2019), the results highlight that the gap in applying the EBM method to maritime transport has been successfully closed and that the emerging paradigm, when combined with the Malmquist model, can be a sustainable and appropriate evaluation model for other research areas.

Exploring the effect of COVID-19 on airline environmental efficiency through an interval epsilon-based measure model

COVID-19 has dealt an unprecedented blow to the aviation industry since 2020. This paper applies the interval epsilon-based measure (IEBM) model to evaluate the optimal quarterly environmental efficiency of 14 global airlines of passenger and cargo subsystems during 2018-2020. Then, the time series prediction method is applied to forecast the interval data of inputs and outputs from 2021 to 2022. Finally, we can calculate the quarterly efficiency. Thus, the future development trends of airlines can be predicted. The results show that (1) COVID-19 has hit the passenger subsystem harder, while the freight subsystem has become more efficient; (2) the efficiency of the freight subsystem has inevitably declined in the post-epidemic era; and (3) therefore, the airlines will have a "√" shaped recovery curve in the next few years.

Output Targeting and Runway Utilization of Major International Airports: A Comparative Analysis Using DEA

The aviation industry is a prominent contributor to economic development. The existence of an airport hub that provides a worldwide transportation network generates economic growth, creates jobs, and facilitates international trade and tourism. This industry also helps in connecting different continents, countries, and cultures. This study utilizes the Data Envelopment Analysis models Charnes, Cooper, and Rhodes (CCR), Banker, Charnes, and Cooper (BCC), Slacks-Based Measure (SBM), and Epsilon Based Measure (EBM) in analyzing and evaluating the operational performance of the 21 major airports runway design during the years of 2016–2019 using the data of the International Civil Aviation Organization (ICAO) air transport statistics. The objective of this paper is to assess the efficiency of various airport runway configurations based on input factors such as number of runways, dimension of runways, airport area, and output factors such as annual number of flights and annual number of passengers. In the four applied models, the results indicated London Heathrow Airport (LHR) and Munich International Airport (MUC) are efficient in utilizing the runway during the considered periods. Surprisingly, airports in the Asian continent with a parallel runway design are more efficient than in North America and Europe. This study can be a valuable reference for operation managers in evaluating and benchmarking the performance of an airport with different types of runway configurations. Moreover, it can be used by decision-makers, investors, stakeholders, policymakers, private companies, and government agencies as a guideline suitable for an airport.

A Study on the Efficiency Analysis of Global Terminal Operators Based on the Operation Characteristics

Shipping and port industries are undergoing rapid environmental changes because of the reorganization of carrier alliances, enlargement of ships, and an increase in global uncertainty. Thus, the sustainable operation of container terminals requires a new assessment of port efficiency and measures to enhance efficient operation. Hence, we classified 21 global terminal operators (GTOs) into stevedore, carrier, and hybrid GTOs based on their operation characteristics and derived a sustainable container terminal operation method using data envelopment analysis efficiency and Malmquist productivity index analysis. The results showed that stevedore GTOs exhibited improved efficiency when the terminal infrastructure was expanded. However, the returns to scale and technical change factors in the productivity change trend decreased. Meanwhile, the objective of carrier GTOs is cost reduction, unlike stevedore and hybrid GTOs, which focus on generating profits. Consequently, carrier GTOs were the most inefficient with little intention to improve efficiency. A systematic efficiency improvement strategy through the acquisition of a terminal share was effective for hybrid GTOs. However, similar to stevedore GTOs, investment in technical change was insufficient for hybrid GTOs. The efficiency analysis we conducted for each operation characteristic is expected to provide useful basic data for establishing efficiency improvement strategies for every GTO.

An Advanced Decision Making Framework via Joint Utilization of Context-Dependent Data Envelopment Analysis and Sentimental Messages

Compared to widely examined topics in the related literature, such as financial crises/difficulties in accurate prediction, studies on corporate performance forecasting are quite scarce. To fill the research gap, this study introduces an advanced decision making framework that incorporates context-dependent data envelopment analysis (CD-DEA), fuzzy robust principal component analysis (FRPCA), latent Dirichlet allocation (LDA), and stochastic gradient twin support vector machine (SGTSVM) for corporate performance forecasting. Ratio analysis with the merits of easy-to-use and intuitiveness plays an essential role in performance analysis, but it typically has one input variable and one output variable, which is unable to appropriately depict the inherent status of a corporate’s operations. To combat this, we consider CD-DEA as it can handle multiple input and multiple output variables simultaneously and yields an attainable target to analyze decision making units (DMUs) when the data present great variations. To strengthen the discriminant ability of CD-DEA, we also conduct FRPCA, and because numerical messages based on historical principles normally cannot transmit future corporate messages, we execute LDA to decompose the accounting narratives into many topics and preserve those topics that are relevant to corporate operations. Sequentially, the process matches the preserved topics with a sentimental dictionary to exploit the hidden sentiments in each topic. The analyzed data are then fed into SGTSVM to construct the forecasting model. The result herein reveals that the introduced decision making framework is a promising alternative for performance forecasting.

A Decision Support Model for Measuring Technological Progress and Productivity Growth: The Case of Commercial Banks in Vietnam

The interactive relationship between the banking system and enterprise makes up the role that affects a national economy. Significantly, the relationship between banking and technology has become tighter over the past few decades. An assessment of bank performance is critical for understanding their position and provides valuable information to practitioners. In this paper, we assess the performance of the top 18 commercial banks in Vietnam during 2015–2019. The assessment utilizes two data envelopment analysis (DEA) models while involving the banks’ performance in six dimensions, including assets, deposits, operating expenses, liabilities as inputs, while treating loans and net income as outputs. Using the Malmquist measurement, the total productivity growth indexes of the banks are obtained, which are decomposed into technical and technological evolutions. Window analysis is used to compute the efficiencies of the banks in every single year in 2015–2019. From the results of Malmquist, most banks are found to decrease their Malmquist productivity indexes from 2015 to 2019, wherein both of their technical and technological indexes declined. Window analysis indicates B6-SHBank, B1-Vietinbank, and B18-PetrolimexGroup as the most efficient banks during 2015–2019, and in the interim, B16-BaoVietBank, B11-NationalCitizen, and B13-VietnamMaritime ranked on the bottom line. The managerial implications of this research help to reflect the comprehensive insights of the top Vietnamese commercial bank performance and offer a strategic guideline for decision-makers toward sustainable development in the banking industry.