Xi Zhao, Haitao Qu and Yimin Huang
The purpose of this paper is to analyze the key influencing factors which affect the performance of M & A and the key influencing factors by establishing grey relational…
Abstract
Purpose
The purpose of this paper is to analyze the key influencing factors which affect the performance of M & A and the key influencing factors by establishing grey relational sequence model. The results can not only perfect the current existing theoretical system, but also provide theoretical guide for M & A practice.
Design/methodology/approach
First of all, this paper analyzes the M & A performance through the literature reviews, and then establishes the grey model about M & A performance from three influencing factors: profitability, operational and development capacity to analyze its key influencing factors which affect M & A performance. Furthermore, the key factors influencing of M & A performance are analyzed, the results can be obtained through the analysis of the grey relative degree based on six years’ data of state-owned enterprises.
Findings
In this paper, some results can be found that ownership restriction and executive compensation can significantly affect corporate profitability and operation ability. M & A and institutional investors can be affected greater from the perspective of development ability and profitability. In general, enterprise scale has little affect on the performance after M & A.
Practical implications
With the sustainable development of economic globalization, M & A has become an important strategy for the rapid growth of the enterprise, enlargement market and brand effect. But the performance after M & A in most enterprises does not improve. As the pillar of the national economy, state-owned enterprises have an important impact on China’s economic development and urgently need to be reformed.
Originality/value
This paper analyzes the key factors through establishing grey relative sequence model to analyze the correlation of M & A performance and influencing factors, and finds key influencing factors which affect the performance of M & A. The conclusion can provide policy guidance for the improvement of M & A performance in the future.
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Meng Li, Li Yuan Liu, Rui Zhou, Jun Yang, Qiong Qu and Haitao Song
Considering the industrial site environment and installation requirements, the straight-beam ultrasound probe with easy installation and good coupling agent adaptability is…
Abstract
Purpose
Considering the industrial site environment and installation requirements, the straight-beam ultrasound probe with easy installation and good coupling agent adaptability is adopted to replace the traditional water immersion focusing probe for film thickness measurement in cylindrical roller bearings. The straight-beam probe has a large echo receiving range, which will result in measurement regions overlapping and bring about large measurement errors. In this paper, an improved measurement method for film thickness of cylindrical roller bearing with the straight-beam probe is developed.
Design/methodology/approach
An improved method is proposed to enhance the spatial resolution of the straight-beam probe. By introducing a correction coefficient based on the percentage of the effective measurement area, the method improves the measuring accuracy successfully.
Findings
The experimental results demonstrate that the lubricant-film thickness can be measured to reasonable accuracy by this method and have a better agreement with the theoretical film thickness solutions.
Originality/value
This paper used analytical method and model that is helpful for the improvement of the spatial resolution, which has great influence on the measuring accuracy, is mainly determined by the echo reflection area size of the ultrasound transducer.
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Anyu Wang and Nuoya Chen
This case is about “Red”, a cross-border e-commerce platform developed from a community which was built to share overseas shopping experience. With sharp insights into the…
Abstract
This case is about “Red”, a cross-border e-commerce platform developed from a community which was built to share overseas shopping experience. With sharp insights into the consumption behavior of urban white-collar women and riding on its community e-commerce advantage, “Red”, a cross-border e-commerce startup, pulled in three rounds of financing within just 16 months regardless of increasingly competitive market. On the other hand, well-established platforms such as T-mall International and Joybuy also stepped in, and their involvement will also speed up the industry integration and usher in a reshuffling period. Confronted with the “price war” started by those e-commerce giants, in what ways can “Red” adjust its shopping experience and after-sales services to enhance the brand value and sharpen its edge?
Haitao Ding, Wei Li, Nan Xu and Jianwei Zhang
This study aims to propose an enhanced eco-driving strategy based on reinforcement learning (RL) to alleviate the mileage anxiety of electric vehicles (EVs) in the connected…
Abstract
Purpose
This study aims to propose an enhanced eco-driving strategy based on reinforcement learning (RL) to alleviate the mileage anxiety of electric vehicles (EVs) in the connected environment.
Design/methodology/approach
In this paper, an enhanced eco-driving control strategy based on an advanced RL algorithm in hybrid action space (EEDC-HRL) is proposed for connected EVs. The EEDC-HRL simultaneously controls longitudinal velocity and lateral lane-changing maneuvers to achieve more potential eco-driving. Moreover, this study redesigns an all-purpose and efficient-training reward function with the aim to achieve energy-saving on the premise of ensuring other driving performance.
Findings
To illustrate the performance for the EEDC-HRL, the controlled EV was trained and tested in various traffic flow states. The experimental results demonstrate that the proposed technique can effectively improve energy efficiency, without sacrificing travel efficiency, comfort, safety and lane-changing performance in different traffic flow states.
Originality/value
In light of the aforementioned discussion, the contributions of this paper are two-fold. An enhanced eco-driving strategy based an advanced RL algorithm in hybrid action space (EEDC-HRL) is proposed to jointly optimize longitudinal velocity and lateral lane-changing for connected EVs. A full-scale reward function consisting of multiple sub-rewards with a safety control constraint is redesigned to achieve eco-driving while ensuring other driving performance.
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Soumita Ghosh, Abhishek Chakraborty and Alok Raj
This study aims to examine how fairness concerns and power structure in dyadic green supply chains impact retail price, supply chain profits and greening level decisions.
Abstract
Purpose
This study aims to examine how fairness concerns and power structure in dyadic green supply chains impact retail price, supply chain profits and greening level decisions.
Design/methodology/approach
This study develops game-theoretic models considering fairness concerns and asymmetric power structures under an iso-elastic demand setting. The research paper employs the Stackelberg game approach, taking into consideration the fairness concern of the channel leader.
Findings
The findings indicate that under fairness, there is an increase in both wholesale and retail prices, as well as greening expenditures. Notably, when comparing the two models (manufacturer Stackelberg and retailer Stackelberg), double marginalization is more pronounced in the retailer Stackelberg setup than in the manufacturer Stackelberg setup. In a traditional supply chain with iso-elastic demand, the follower typically extracts higher profit compared to the leader; however, our results show that, under fairness conditions, the leader achieves higher profit than the follower. Additionally, our study suggests that supply chain coordination is unattainable in a fairness setup. This paper provides insights for managers on the optimal supply chain structure and the level of fairness to maximize profit.
Originality/value
This paper investigates the impact of a leader's fairness on the optimal decisions within a green supply chain, an area that has received limited attention previously. Additionally, the study investigates how fairness concerns manifest in distinct power dynamics, specifically, in the contexts of manufacturer Stackelberg and retailer Stackelberg.
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Hongxia Wang, Hua Zhou, Haitao Niu, Chen Huang, Amir Abbas, Jian Fang and Tong Lin
In this study, superhydrophobic fabric is prepared with a wet-chemical coating technique that uses a coating solution synthesized by the co-hydrolysis and co-condensation of…
Abstract
In this study, superhydrophobic fabric is prepared with a wet-chemical coating technique that uses a coating solution synthesized by the co-hydrolysis and co-condensation of tetraethyl orthosilicate and fluoroalkyl silane (tridecafluorooctyl triethoxysilane) under an alkaline condition. The treated fabric shows stable superhydrophobicity with a water contact angle as high as 171°, and a sliding angle as low as 2°. The coated fabric has higher repellency to saline water, and its repellency increases with increases in the salt content in the solution. The contact angle is reduced with increases in liquid temperature. When the water temperature is 90°C, the contact angle on the superhydrophobic fabric is 153°. The superhydrophobic treatment slightly reduces the air permeability, but increases the water vapor permeability of the fabric. The treatment considerably increases the liquid breakthrough pressure, but has little effect on fabric pore size and thermal conductivity. The air gap membrane distillation process is used to evaluate the desalination performance of the superhydrophobic fabric. When the feed and the condenser are kept at 90°C and 20°C, respectively, the membrane distillation (MD) system with the superhydrophobic fabric yields a permeate flux of water up to 13.8 kg m-2 hour-1, which is slightly higher than that with the use of polymer and inorganic MD membranes reported. Superhydrophobic fabrics may thus be considered as effective MD membranes for water desalination applications.
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Haitao Wu, Wenyan Zhong, Botao Zhong, Heng Li, Jiadong Guo and Imran Mehmood
Blockchain has the potential to facilitate a paradigm shift in the construction industry toward effectiveness, transparency and collaboration. However, there is currently a…
Abstract
Purpose
Blockchain has the potential to facilitate a paradigm shift in the construction industry toward effectiveness, transparency and collaboration. However, there is currently a paucity of empirical evidence from real-world construction projects. This study aims to systematically review blockchain adoption barriers, investigate critical ones and propose corresponding solutions.
Design/methodology/approach
An integrated method was adopted in this research based on the technology–organization–environment (TOE) theory and fuzzy decision-making trial and evaluation laboratory (DEMATEL) approach. Blockchain adoption barriers were first presented using the TOE framework. Then, key barriers were identified based on the importance and causality analysis in the fuzzy DEMATEL. Several suggestions were proposed to facilitate blockchain diffusion from the standpoints of the government, the industry and construction organizations.
Findings
The results highlighted seven key barriers. Specifically, the construction industry is more concerned with environmental barriers, such as policy uncertainties (E2) and technology maturity (E3), while most technical barriers are causal factors, such as “interoperability (T4)” and “smart contracts' security (T2)”.
Practical implications
This study contributes to a better understanding of the problem associated with blockchain implementation and provides policymakers with recommendations.
Originality/value
Identified TOE barriers lay the groundwork for theoretical observations to comprehend the blockchain adoption problem. This research also applied the fuzzy method to blockchain adoption barrier analysis, which can reduce the uncertainty and subjectivity in expert evaluations with a small sample.
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Pengkun Cheng, Juliang Xiao, Wei Zhao, Yangyang Zhang, Haitao Liu and Xianlei Shan
This paper aims to enhance the machining accuracy of hybrid robots by treating the moving platform as the first joint of a serial robot for direct position measurement and…
Abstract
Purpose
This paper aims to enhance the machining accuracy of hybrid robots by treating the moving platform as the first joint of a serial robot for direct position measurement and integrating external grating sensors with motor encoders for real-time error compensation.
Design/methodology/approach
Initially, a spherical coordinate system is established using one linear and two circular grating sensors. This system enables direct acquisition of the moving platform’s position in the hybrid robot. Subsequently, during the coarse interpolation stage, the motor command for the next interpolation point is dynamically updated using error data from external grating sensors and motor encoders. Finally, fuzzy proportional integral derivative (PID) control is applied to maintain robot stability post-compensation.
Findings
Experiments were conducted on the TriMule-600 hybrid robot. The results indicate that the following errors of the five grating sensors are reduced by 94%, 93%, 80%, 75% and 88% respectively, after compensation. Using the fourth drive joint as an example, it was verified that fuzzy adaptive PID control performs better than traditional PID control.
Practical implications
The proposed online error compensation strategy significantly enhances the positional accuracy of the robot end, thereby improving the actual processing quality of the workpiece.
Social implications
This method presents a technique for achieving online error compensation in hybrid robots, which promotes the advancement of the manufacturing industry.
Originality/value
This paper proposes a cost-effective and practical method for online error compensation in hybrid robots using grating sensors, which contributes to the advancement of hybrid robot technology.
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Ping Huang, Haitao Ding, Hong Chen, Jianwei Zhang and Zhenjia Sun
The growing availability of naturalistic driving datasets (NDDs) presents a valuable opportunity to develop various models for autonomous driving. However, while current NDDs…
Abstract
Purpose
The growing availability of naturalistic driving datasets (NDDs) presents a valuable opportunity to develop various models for autonomous driving. However, while current NDDs include data on vehicles with and without intended driving behavior changes, they do not explicitly demonstrate a type of data on vehicles that intend to change their driving behavior but do not execute the behaviors because of safety, efficiency, or other factors. This missing data is essential for autonomous driving decisions. This study aims to extract the driving data with implicit intentions to support the development of decision-making models.
Design/methodology/approach
According to Bayesian inference, drivers who have the same intended changes likely share similar influencing factors and states. Building on this principle, this study proposes an approach to extract data on vehicles that intended to execute specific behaviors but failed to do so. This is achieved by computing driving similarities between the candidate vehicles and benchmark vehicles with incorporation of the standard similarity metrics, which takes into account information on the surrounding vehicles' location topology and individual vehicle motion states. By doing so, the method enables a more comprehensive analysis of driving behavior and intention.
Findings
The proposed method is verified on the Next Generation SIMulation dataset (NGSim), which confirms its ability to reveal similarities between vehicles executing similar behaviors during the decision-making process in nature. The approach is also validated using simulated data, achieving an accuracy of 96.3 per cent in recognizing vehicles with specific driving behavior intentions that are not executed.
Originality/value
This study provides an innovative approach to extract driving data with implicit intentions and offers strong support to develop data-driven decision-making models for autonomous driving. With the support of this approach, the development of autonomous vehicles can capture more real driving experience from human drivers moving towards a safer and more efficient future.
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Dang Luo, Haitao Li and Qicun Qian
The purpose of this paper is to construct a key factors selection approach for a class of small-sample multi-factor cross-sectional data analysis (SMCDA) problem, which is very…
Abstract
Purpose
The purpose of this paper is to construct a key factors selection approach for a class of small-sample multi-factor cross-sectional data analysis (SMCDA) problem, which is very common in productive practice and scientific research, such as coal-bed methane (CBM) content analysis, civil aircraft cost analysis, etc. Key factors selection is an important basic work for SMCDA problem; the proposed method is constructed to improve the accuracy and explanatory of the selected key factors.
Design/methodology/approach
Using grey system theory to solve SMCDA problem is more reasonable under few data and poor information. Therefore, this paper constructs a grey incidence analysis (GIA) model with rate of change to select the key factors of an SMCDA problem. The basic idea of the proposed method is to simulate time series by randomly sorting the selected samples, and to calculate the degree of grey incidence with rate of change by loop iterative algorithm, then to construct the degree matrix of grey incidence with rate of change, and finally by which, to utilise quantitative and qualitative analysis methods to select the key factors.
Findings
The experimental analysis of application cases demonstrates that the key factors of system’s characteristic can be successfully screened out by the proposed method, the results are consistent with actual conditions, and they have a clearer meaning and a better interpretability.
Practical implications
The method proposed in this paper could be utilised to select key factors for such a class of SMCDA problem, which has fewer observation samples (small-sample), which is influenced by a number of factors (multi-factor) and whose observation samples are placed randomly rather than by time (cross-sectional data). Taking the key influence factors of CBM content and the key driving factors of the vulnerability of agricultural drought in Henan as examples, the results proved the feasibility and superiority of this proposed method.
Originality/value
Most of the existing GIA models mainly focus on these classes of issues with time series data or panel data. However, few GIA models take SMCDA problem as the research object. In this paper, the authors develop the GIA model with rate of change according to the characteristics of SMCDA problem, and present some properties and application suggestions of the proposed method.