Search results

Using Social Network Analysis (SNA), this paper examines the inter-provincial logistics relationships in China. Based on the annual data of inter-provincial railway logistics quantity during the period 1998-2009, the degree of interconnection between regions could imply intensified trends of regional economic integration.

The main results of the logistic relationships in China are as follows: the regional logistic interconnection, especially between western and eastern China has increased continuously, which would imply a rising national economic integration. However, the increased centralization index and the average Degree Centrality level imply that a logistics bottleneck has intensified in several hub provinces.

Secondly, logistic center provinces evaluated by the Degree Centrality have changed. In 2009, Hebei, Liaoning, Jiangsu, Shandong, Henan and Sichuan provinces revealed the highest inward Degree Centrality. Sichuan Province is the region that most surprisingly increased its centrality.

Thirdly, the number of logistic hub provinces, evaluated by the Degree Betweenness Centrality, has increased. In 2008, Henan province was only a focal hub but in 2009, Shandong, Hubei, Sichuan provinces became logistic hubs.

Lastly, the Community Modularity which analyzed grouping structures shows that there are three time-consistent communities. This means that even though there is enhanced between-region integration, the inter-regional inter-connection is more important in explaining the regional logistic relationship.

The purpose of this paper is to elaborate value-added service of cold chain logistics between China and Korea. The covering strategy for developing the cold chain value-added service between the two countries is proposed.

The author expounds the driving power for developing cold chain logistics between the two countries basing on the trade data of agricultural exports and imports, the tariff liberalization agreement of China–Korea FTA and the short distance between the two countries. It analyzes the value-added service of cold chain logistics with exemplary cases from four aspects (customized service, integrated service, consultation/solution and strategic alliance service), and its value-added mechanism for the enhancement of core competences of the entire cold chain. Then, by considering the drawbacks of the current cold chain logistics practices, between the two countries, the author proposes certain measures for fostering the cold chain value-added services between them.

There are apparent mutual benefits in developing cold chain value-added service between the two countries, but there exists some shortcomings which impede the sound development of the cold chain logistics, such as low circulation rate and insufficient cold chain facility in China, shortage of integrated and compatible information platform between the two countries, few integrated cold chain service and strategic alliance service and occurrence of some trade frictions.

The enforcement of China–Korea FTA will greatly reduce the tariff and increase the import and export volumes between the two countries; with the proximity between them, development of cold chain logistics between the two countries holds tremendous potential. This paper thoroughly discusses the mechanism of the value-added service of the cold chain logistics, and brings into focus the development of the value-added service in the two countries.

The purpose of this paper is to attempt to expand the traditional economic effect analysis of export subsidy, which has previously ignored the incentive of export subsidies in terms of competition from re‐importation.

The paper performs a comparative static analysis based on the traditional welfare analysis of export subsidies by introducing different transportation costs and using small country model and large country model, respectively.

Compared with the traditional analysis, exporting countries that implement export subsidies suffer less welfare loss and induce intra‐industry trade of homogeneous products. Due to export subsidy policy incentives, transportation costs heavily influence trade patterns, trade volumes and welfare. Trade patterns evolve from unidirectional export to intra‐industry trade as transportation costs are reduced, with the main source of welfare loss coming from transportation costs. The distribution of export subsidies is biased when domestic transportation costs are high. Under low domestic transportation costs, inefficient intra‐industry trade would emerge as a result of export subsidy incentive.

The findings could be helpful to understand the impact of export subsidy policy on trade pattern, trade volumes and welfare when considering international and domestic transportation cost.

The paper emphasizes the incentive of export subsidy on re‐importation, and links it with transportation costs, which expand the traditional export subsidy analysis.

External stakeholder risks (ESRs) caused by unfavorable behaviors hinder the success of project portfolios (PPs). However, due to complex project dependency and numerous risk causality in PPs, assessing ESRs is difficult. This research aims to solve this problem by developing an ESR-PP two-layer fuzzy Bayesian network (FBN) model.

A two-layer FBN model for evaluating ESRs with risk causality and project dependency is proposed. The directed acyclic graph (DAG) of an ESR-PP network is first constructed, and the conditional probability tables (CPTs) of the two-layer network are further presented. Next, based on the fuzzy Bayesian network, key variables and the impact of ESRs are assessed and analyzed by using GeNIe2.3. Finally, a numerical example is used to demonstrate and verify the application of the proposed model.

The proposed model is a useable and effective approach for ESR assessment while considering risk causality and project dependency in PPs. The impact of ESRs on PP can be calculated to determine whether to control risk, and the most critical and heavily contributing risks and project(s) in the developed model are identified based on this.

This study extends prior research on PP risk in terms of stakeholders. ESRs that have received limited attention in the past are explored from an interaction perspective in the PP domain. A new two-layer FBN model considering risk causality and project dependency is proposed, which can synthesize different dependencies between projects.

Comprehensive project portfolio risk (PPR) analysis is essential for the success and sustainable development of project portfolios (PPs). However, project interdependency creates complexity for PPR analysis. In this study, considering the interdependency effect among projects, the authors develop a quantitative evaluation model to analyze PPR based on a fuzzy Bayesian network.

In this paper, the primary purpose is to comprehensively evaluate project portfolio risk considering the interdependency effect using a systematical model. Accordingly, a fuzzy Bayesian network (FBN) is developed based on the existing studies. Specifically, first, the risks in project portfolios are identified from the project interdependencies perspective. Second, a fuzzy Bayesian network is adopted to model and quantify the interaction relationships among risks. Finally, the model is implemented to analyze the occurrence situation and characteristics of risks.

The interdependency effect can lead to high-stake risks, including weak financial liquidity, a lack of cross-project members and project priority imbalance. Furthermore, project schedule risks and inconsistency between product supply and market demand are relatively sensitive and should also be prioritized. Also, the validity of this risk evaluation model has been proved.

The findings identify the most sensitive risks for guaranteeing portfolio implementation and reveal interdependency effect can trigger some specific risks more often. This study proposes for the first time to measure and analyze project portfolio risk by a systematical model. It can help systematically assess and manage the complicated and interdependent risks associated with project portfolios.

Research and development (R&D) projects are often pursued through a project portfolio (PP). R&D PPs involve many stakeholders, and without proactive management, their interactions may lead to conflict risks. These conflict risks change dynamically with different stages of the PP life cycle, increasing the challenge of PP risk management. Existing conflict risk research mainly focuses on source identification but lacks risk assessment work. To better manage the stakeholder conflict risks (SCRs) of R&D PPs, this study employs the dynamic Bayesian network (DBN) to construct its dynamic assessment model.

This study constructs a DBN model to assess the SCRs in R&D PP. First, an indicator system of SCRs is constructed from the life cycle perspective. Then, the risk relationships within each R&D PPs life cycle stage are identified via interpretative structural modeling (ISM). The prior and conditional probabilities of risks are obtained by expert judgment and Monte Carlo simulation (MCS). Finally, crucial SCRs at each stage are identified utilizing propagation analysis, and the corresponding risk responses are proposed.

The results of the study identify the crucial risks at each stage. Also, for the crucial risks, this study suggests appropriate risk response strategies to help managers better perform risk response activities.

This study dynamically assesses the stakeholder conflict risks in R&D PPs from a life-cycle perspective, extending the stakeholder risk management research. Meanwhile, the crucial risks are identified at each stage accordingly, providing managerial insights for R&D PPs.

The purpose of this paper is to design several methods for enforcing developed countries’ responsibilities under the Green Climate Fund (GCF). The GCF has been one of the core subjects of the world climate summits held under the United Nations Framework Convention on Climate Change. However, the development of the GCF has not progressed smoothly, and many concerns remain unresolved.

This paper illustrates three approaches for financing the GCF that vary in terms of the relative weights accorded to environmental responsibility and economic capacity. These three methods include the historical responsibility (HR) principle, the ability to pay (AP) principle and the preference score compromises (PSC) approach (which is a combination of the HR and the AP principles).

The empirical analysis demonstrates that the USA is the largest contributor to the GCF under the HR principle due to the volume of its historical emissions, whereas the European Union bears the greatest financial responsibility under the AP principle, based on its gross domestic product. Under the PSC approach, the European Union and the USA each undertakes a financial burden that approximates 40 per cent of the total financing for the GCF. These nations are followed by Japan, which has a share of almost 9 per cent.

This study is the first attempt to introduce the PSC concept into discussions regarding GCF financing. A scheme of burden sharing that combines environmental responsibility and economic capacity factors is developed and introduced. The respective weights assigned to the two factors are determined based on the Borda rule in voting theory, which avoids the arbitrary allocation of weights between the HR and the AP. These findings will be useful for mobilising the GCF in the Post-Kyoto era.

The purpose of this paper is to improve forecasting accuracy for short‐term load series.

A forecasting method based on chaotic time series and optimal diagonal recurrent neural networks (DRNN) is presented. The input of the DRNN is determined by the embedding dimension of the reconstructed phase space, and adaptive dynamic back propagation (DBP) algorithm is used to train the network. The connection weights of the DRNN are optimized via modified genetic algorithms, and the best results of optimization are regarded as initial weights for the network. The new method is applied to predict the actual short‐term load according to its chaotic characteristics, and the forecasting results also validate the feasibility.

For the chaos time series, the hybrid neural genetic method based on phase space reconstruction can carry out the short‐term prediction with the higher accuracy.

The proposed method is not suited to medium and long‐term load forecasting.

The accuracy of the load forecasting is important to the economic and secure operation of power systems; also, the neural genetic method can improve forecasting accuracy.

This paper will help overcome the defects of traditional neural network and make short‐term load forecasting more accurate and fast.

The purpose of this study is to establish a thermal contact conductance model of rough surfaces with inclination based on three-dimensional fractal theory.

The effects of contact load, inclination angle, fractal dimensional and fractal roughness on thermal contact conductance of rough surfaces were studied using numerical simulation.

The results show that the thermal contact conductance of the rough surface increases with the increase of contact load and fractal dimension and decreases with the increase of fractal roughness and inclination angle. The inclination angle of the rough surface has an important influence on the thermal contact conductance of the rough, and it is a factor that cannot be ignored in the study of the thermal contact conductance of rough surfaces.

A thermal contact conductance model of rough surfaces with inclination based on three-dimensional fractal theory was established in this study. The achievements of this study provide some theoretical basis for the investigation of the thermal contact conductance of rough surfaces.

Video streaming consumption has become a social and economic phenomenon in an age where the share of online entertainment has become larger and larger in the total entertainment pie. Besides the advancement of technology, the pandemic further contributed to its development. The aim of this study is to analyze the role of price consciousness, anti-socializing behavior and choice of content in shaping the video streaming consumption. Furthermore, we look at the moderating role of gender and marital status on the hypothesized relationships.

The data were collected from a sample of 533 Romanian respondents, and it was processed using Partial Least Square structural equation modeling. In addition, Necessary Condition Analysis was used to assess the necessity effect size of an independent variable for a specific outcome of a dependent variable.

We find that the main hypotheses are confirmed, and that gender as a moderating variable negatively influences the relationship between anti-socializing behavior and video streaming consumption.

Besides the main findings pertaining to video streaming consumption determinants, psychology scholars concerned about the relationship between anti-socializing behavior and video streaming consumption as a part of the total time spent online could also benefit from the current findings.

The results of this study will be helpful to industry players in designing their video streaming offer after taking into consideration the influence of the discussed variables on the consumption behavior.

While anti-socializing behavior drives consumers to a digital environment for which they control the settings, the content being streamed is a cultural product, influenced by society and its consumption immerses the viewer in the cultural mores of the time and place the content was created in or depicts; hence, anti-socializing behavior does not mean a complete break from society, but rather a different instantiation of people’s need to connect with their social environment.

The novelty of this research stands in the integration of anti-socializing behavior as an explanatory factor of video streaming consumption, which has not yet been used in models explaining online consumption so far.