Assessing the Logistics Efficiency of European Countries by Using the Dea-Pc Methodology

Markovits-Somogyi, Rita; Bokor, Zoltán

doi:10.3846/16484142.2014.928787

Cited by 56 publications

(34 citation statements)

References 26 publications

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“…Sun et al established the input-output index system and estimated the logistics efficiency of the three northeastern provinces of China by three-stage DEA [14]. Markovits-Somogyi used the method of DEA-PC (pairwise comparison) to evaluate the efficiency of the logistics industry in 29 European countries and proved the superiority of this method [15]. Chen et al used the method of super-efficiency data envelopment analysis (SE-DEA) to measure the comprehensive technical efficiency and pure technical efficiency of the logistics industry in 31 provinces of China, decomposed the scale efficiency, and analyzed the difference of its efficiency value [16].…”

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confidence: 99%

Research on Spatial Correlations and Influencing Factors of Logistics Industry Development Level

Tian

Zhang

2019

Sustainability

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The logistics industry plays a greater role in the sustainable development of regional economies. The development of the logistics industry between regions is not independent, and there is a spatial correlation due to the existence of spatial spillover effect or spatial expansion among regions. This paper uses the method of entropy weight to evaluate the development level of the logistics industry in 31 provinces in China. On this basis, Moran’s index (Moran’s I), Moran’s I scatter plot, and local indicators of spatial association (LISA) agglomeration plot are used to analyze the overall and local spatial agglomeration characteristics of the logistics industry. Four main factors affecting the spatial relationship of the logistics industry are analyzed by choosing the fixed effect of the spatial error model. We find that: (i) There is spatial agglomeration effect in the development level of the logistics industry from the overall perspective; (ii) regional differentiation of the spatial agglomeration effect of logistics industry development level is obvious from the local perspective; and (iii) the influence of human resource factors on the spatial relationship of logistics development level is declining.

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confidence: 99%

Research on Spatial Correlations and Influencing Factors of Logistics Industry Development Level

Tian

Zhang

2019

Sustainability

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“…This method is often described as nonparametric for that its exact sampling distributions could be acquired without knowing joint probability distributions of two variables. Therefore, it is often used in condition that distribution of data is uncertain (Markovits-Somogyi & Bokor, 2014). This method has been used to indicate whether there is a positive correlation between two sets of calculated DEA efficiencies (Hatefi & Torabi, 2010).…”

Section: Relationship Between Manufacturing Development and Innovatmentioning

confidence: 99%

Improving innovation efficiency of emerging economies: The role of manufacturing

Wang

Hung

2019

Manage Decis Econ

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These studies have neglected that innovation activities in a country are generated by interactions among different sectors, especially manufacturing. The existing literature focusing on the influence of manufacturing on innovation indicated that an integrated pipeline of information technology, materials, and production process in manufacturing operation will provide manufacturers with the capability to bring renewed dynamism to this sector (Jagoda, Kiridena, & Lin, 2016). Manufacturing plays a critical role in innovative production by providing precision instruments and integrated systems that can efficiently transform investments in innovation into products (Cozzarin, 2016). However, studies rarely investigate the influence of manufacturing on the innovation process for emerging economies.This study provides an alternative model by characterizing the process of innovation via a three-stage model that emphasizes manufacturing effects. This model consists of human capital cultivation (Stage 1), manufacturing development (Stage 2), and innovation production (Stage 3). The dynamic network slacks-based measure (SBM), which can deal with multiple input/output variables of multidivisions in a long-term optimization, is employed to evaluate the performance of this three-stage model. This study also utilizes a truncated regression method to explore external factors, including enrollment rate in higher education, market size, intellectual property protection, and knowledge transfer (KT) that affect innovation efficiency. The empirical results may shed light on the importance of manufacturing to emerging economies in terms of improvement in the production of innovations. The structure of this study is presented as follows. Section 2 provides the literature review. Section 3 introduces the innovation model, data collection procedure, and research methodology. Section 4 addresses the experimental results. Section 5 provides managerial implications for innovation in emerging economies. Section 6 offers conclusions of this study.

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“…An extended DEA model [4] on different input constraints was defined, and then the efficiency of energy utilization for logistics enterprises in Hefei was evaluated. Markovits et al [5] combined DEA with analytic hierarchy process and studied the efficiency of logistics enterprises in 29 European countries. Yang et al [6] provided the DEA and IAHP method, where the entropy method is used to determine the weights.…”

Section: Literature Reviewmentioning

confidence: 99%

“…But it is the common phenomenon of sacrificing the environment for rapid economic growth. Some works [2][3][4][5][6][7][8] focus on socioeconomic contributions and ignore environmental effects. In fact, they are both the key contents of sustainable development.…”

Section: Introductionmentioning

confidence: 99%

Measuring Sustainable Development Efficiency of Urban Logistics Industry

Cao

2018

Mathematical Problems in Engineering

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Logistics plays a basic supporting role in the growth of national economy. However, tail gas, noise, and traffic congestion caused by logistics have a negative impact on the environment. An effective evaluation mechanism for sustainable development of urban logistics industry is necessary. Data envelopment analysis (DEA) is a common tool for efficiency evaluation. But, DEA has a limited effect on resource allocation in advance because it is ex-post evaluation. It requires input-output indications and the output is after-the-fact data. This defect is particularly prominent in the evaluation of ecological logistics because pollution indicators belong to ex-post output data that threaten the human environment. First prediction and then evaluation is a possible idea. In addition, DEA efficiency ranking does not have a good discrimination due to its coarse granularity. To solve the issues, combining DEA with the Bayes method, we propose an efficiency evaluation model without after-the-fact data, where an efficiency level is predicted and an evaluation value is calculated according to different investment combinations. Then, it is applied to logistics industries of Jiangsu province in China. The results show that our DEA-Bayes method has good discrimination and is easy to operate; a city with geographical advantage and environmental awareness generally gets a higher efficiency score. So the method can help decision makers to allocate resources rationally and further promote the coordinated development of logistics industry.

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Assessing the Logistics Efficiency of European Countries by Using the Dea-Pc Methodology

Cited by 56 publications

References 26 publications

Research on Spatial Correlations and Influencing Factors of Logistics Industry Development Level

Research on Spatial Correlations and Influencing Factors of Logistics Industry Development Level

Improving innovation efficiency of emerging economies: The role of manufacturing

Measuring Sustainable Development Efficiency of Urban Logistics Industry

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