A study on the efficiency and influencing factors of China’s four-stage ports using DEA and Tobit two-stage models

This paper adopts the DEA model to conduct in-depth research and analysis on the evaluation of the quality training of engineering English translation talents. The BIM application performance of nine engineering projects is empirically analyzed, including slack value analysis, correlation validity analysis, and BIM application performance path optimization. The nine cases have an unreasonable allocation of resources invested in BIM application in the early stage so that the performance path optimization is carried out to find out the key paths for the reallocation of resources. Further analysis of BIM application performance improvement strategies, including strengthening the training of BIM application professionals. The fuzzy two-stage DEA model with adjustable fuzzy opportunity constraint constructed for the situation where both intermediate and final processes have non-desired outputs. First, the additive efficiency decomposition model in the exact number environment is extended to the non-desired output situation. Second, the generalized fuzzy measure and opportunity constraint planning are applied to further extend the model to a non-expected output fuzzy DEA model, and a two-stage DEA model with adjustable fuzzy opportunity constraints is constructed. There are still some differences between the two. Strengthening practice is the general trend of current curriculum reform in our country. Finally, the proposed model is applied to the evaluation problem of quality training of engineering English translators. The adjustable fuzzy opportunity-constrained DEA model proposed in this paper can effectively evaluate the efficiency of real production and operation activities. The research results of this paper not only enrich the existing DEA model theoretical system but also have broad application prospects and values in practical problems. We provide a reasonable guarantee in terms of system and funding and pay attention to the follow-up communication with employers to strengthen the implementation of the influence of social forces on the quality evaluation of English teaching in higher education. To a certain extent, this study enriches the theoretical research on the quality evaluation of English translation talents’ quality training and can play a role in strengthening and improving the quality evaluation of translation talents’ quality training at the present stage.

Integrated data envelopment analysis: Global vs. local optimum

A note on two-stage network DEA model: Frontier projection and duality

Innovation resource allocation performance is an important driver of global competitiveness of nations. Due to the complexity of innovation production process, the structure of the innovation resource allocation is no longer single, but most of the existing studies on the innovation resource allocation performance are based on series cases overlooking parallel cases. We evaluate the innovation resource allocation efficiency of Chinese industrial enterprises from the research and development (R&D) stage and the commercialisation stage, where the commercialisation stage consists of two parallel sub-stages (technology commercialisation stage and product commercialisation stage). As a result, we develop a cross two-stage data envelopment analysis model (TS-DEA model) with nested parallel structure. The result shows that traditional DEA model overestimates the efficiency score due to pursuing the efficiency optimisation for decision-making unit (DMU). Compared with western China, central China was more efficient in allocating innovation resource for industrial enterprises.

With rapid development of road and traffic construction, China achieved tremendous growth in social and economic progress, yet also caused a mass of road traffic accidents and suffered great losses in people’s lives and property. Through opening the “black box” the road traffic system was decomposed into two subsystems: accident occurrence and accident loss. A serial two-stage additive DEA model was employed to analyze the performance of road traffic safety of 31 provinces in China. The results effectively show regional differences in road traffic safety, identify the key link affected the whole performance, and provide some feasible suggestions for government traffic management department

This paper measures university teacher’s R&D efficiency based on a two-stage DEA model. Firstly, this paper analyzes traditional DEA model and proposes a two-stage DEA model to overcome the disadvantage of traditional DEA, and then builds a new input-output index system to evaluate teacher’s R&D efficiency. The new model is applied to analyze the R&D efficiency of 75 teachers of a university in 2014. The result show that professors, female teachers and engineering teachers have larger efficiency.

This paper puts forward a two-stage correlative DEA model to measure airport production efficiency. The complicated production process of decision-making units is decomposed into two sub-processes. The correlative DEA model is used to evaluate the airport production efficiency by considering the co-relationship of sub-processes within the whole production process; an empirical study on 8 airports in Asia is conducted and it analyzes the factors influencing the airport inefficiency with Tobit regression mode. The results indicate two-stage correlative DEA model can give more information to improve the airport's efficiency than one-stage DEA model.

This study aims to evaluate the performance of the machine tool industry in Taiwan between 2010 and 2014 by using a dynamic network data envelopment analysis (DN-DEA) model. Taiwanese machine tool industry had an improvement in productivity from 2010 to 2014. Under the two-stage DEA model, we find that productivity in the production and market stage represent a rapid growth, and the productivity growth of the market stage is greater than that of the production stage. The overall productivity growth is mainly attributable to the market stage productivity growth and partially to the production stage productivity growth.

This article creates a conceptual model, called a network system, to represent the Brazilian banking production system, based on its internal operational processes. The first, called the intermediation process, measures a bank's efficiency in extending loans from its available resources. The second, called the revenue process, measures a bank's efficiency in earning profit, mainly from loans granted. We adopt a two-stage DEA model. In the first stage, a relational network DEA model measures both the network system efficiency scores and internal processes. This technique, associated with the Malmquist Index, assesses performance changes over time. In the second stage, these efficiency scores are considered dependent variables, such that Tobit models can determine how the Brazilian credit market's characteristics can explain the network system and internal processes' efficiency. Results show not only a growing trend toward greater efficiency in the revenue process, but also an increase in productivity accompanied by a decline in the intermediation process technology. Given the high banking spreads in Brazil, these results indicate deterioration in the quality of the credit portfolio and the prospect of future insolvency. We discuss implications of this scenario for domestic banks and collateral policy.

In numerous real-world scenarios, decision-making units (DMUs) are organized into two-stage structures, with the total outputs of the DMU set remaining fixed. The fundamental approaches to two-stage structure with fixed-sum output analysis are the centralized models, global perspective models, and non-cooperative game models. While these approaches are valuable, they face persistent challenges such as non-unique stage efficiency decomposition, difficulties in defining stage prioritization, and imprecise measurement of stage-level efficiencies. In this study, we propose a bi-objective two-stage fixed-sum output DEA model, aiming to simultaneously maximize the efficiencies of both stages. We transform the bi-objective model into a single-objective optimization problem using the Tchebycheff norm scalarization method and devise an algorithm to calculate both stage efficiency and overall efficiency. Finally, to demonstrate the applicability of our approach, we apply the proposed model to evaluate the performance of China’s provincial-level green low-carbon innovation development (GLID).

Understanding factors driving the operational efficiency of urban rail systems and providing an evaluation of relative efficiency internationally is the purpose of this paper. Two-stage DEA models explore determinants of technical, allocative, and cost efficiency in twenty international urban rail systems (2009–11). This identifies systems with superior overall efficiency (for example, Hong Kong), with others performing better in technical efficiency than allocative and cost efficiency (for example, Sydney). Diseconomies of scale are identified for some systems (for example, Sydney). The number of stations significantly influences technical efficiency, with the key determinant of allocative and cost efficiency being population density.

The impact of uncertain financial risk on the operation efficiency of banks

In the era of technological revolution, high-tech industries have gained prominence in national innovation systems. However, China’s high-tech sector faces challenges such as late development, weak foundations, and regional disparities. To address these issues, this study proposes a shared-input two-stage network DEA model. This model, based on an input-output perspective, considers resources that circulate and collaboratively function across multiple stages in the form of shared inputs. This paper analyzes data from 25 provinces (including municipalities) in China from 2011 to 2020 and divides the patent conversion process into two sub-stages: the upstream technology research and development stage and the downstream achievement transformation stage, measuring the stage efficiency values and overall efficiency values, respectively. To align with reality, this paper incorporates the intensity of the strength of intellectual property protection, strength of government financial support, and the expenditure on technology import as regional shared input variables. Meanwhile, expenditure on technological transformation is treated as a capital-type intermediate input variable. This approach unveils the “black box” of single-stage DEA, enabling more accurate efficiency measurement. Key findings reveal: (1) China’s high-tech research and development of patent technology, the achievement transformation and overall conversion efficiency show annual improvement, yet overall efficiency remains low with regional imbalances; (2) Achievement transformation efficiency exerts a greater impact on overall conversion efficiency than research and development of patent technology efficiency. Comparative analyses with single-stage and chained two-stage DEA models confirm the necessity of phased evaluation and shared-input variables, supported by input-output elasticity tests. The findings validate the applicability and interpretability of the proposed model in efficiency evaluation.

The present paper proposes a number of models for calculating the average efficiency of two-stage networks using DEA and DEA-R with fuzzy data. If the input, intermediate, and output parameters are available in a two-stage network, DEA and DEA-R models can be used to compute the efficiency. When evaluating decision-making units (DMUs) in two-stage network DEA, the respective programming models are fractional. Meanwhile, in DEA-R, the proposed programming models are linear. Although, it is necessary that the output-to-input ratios (output-orientation) or vice versa (input orientation) be defined and available. Furthermore, DEA-R models can also evaluate DMUs with a network structure when only ratio data are available. Generally, using fuzzy data is necessary for an accurate evaluation of organizations with a two-stage network structure. Therefore, in the present article, using the α-cut approach, an average efficiency model is proposed for the first and second stages of a network structure. At the end, a comparison is made between the mean efficiency scores of a number of airlines by considering fuzzy data in two-stage network DEA and DEA-R.

Governments in many developing countries are overwhelmed by the inefficiencies of their healthcare facilities. This is the main cause of increasing healthcare costs. A major challenge faced by policy makers and administrators is how to assess and identify these inefficiencies. In this paper, we discuss measuring operational efficiency of public hospitals in Algeria using a two-stage DEA and partial least squares regression model. The paper will present results of a pilot study to evaluate the technical and scale efficiency of a sample of 174 hospitals. First, the Kohonen algorithm is used for the classification of the dataset. Then, a two-stage DEA has been employed. In the first stage the model calculates an efficiency score for each hospital. This helps to identify efficient as well as inefficient hospitals in the set. The information provided includes technical and scale efficiency levels, a measure of possible input reductions and output improvements, and identification of appropriate benchmarks. In the second stage the efficiency scores are used as the dependent variable to investigate the determinants of efficiency. This is done using the partial least squares (PLS) method to analyse factors which explain efficiency in the healthcare sector.