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1 – 10 of 100Xiaoling Li, Zongshu Wu, Qing Huang and Juanyi Liu
This study develops an empirical framework to address how large third-party sellers (TPSs) can apply customer acquisition strategies to improve their performance in consumers’…
Abstract
Purpose
This study develops an empirical framework to address how large third-party sellers (TPSs) can apply customer acquisition strategies to improve their performance in consumers’ person-goods matching process and how the platform firm’s similar strategies moderate the effects of TPSs’ strategies.
Design/methodology/approach
Using data collected from the top ten TPSs from a Chinese e-commerce platform, the fixed effect model is used to validate the conceptual model and hypotheses.
Findings
The study results show that both market detection strategy and matching optimization strategy can help large TPSs improve their sales performance. Moreover, the similar market detection strategy applied by the platform firm weakens the effect of large TPSs’ customer acquisition strategies, while the similar matching optimization strategy applied by the platform firm strengthens the effect of large TPSs’ customer acquisition strategies.
Originality/value
This study provides firsthand evidence on the performance of large TPSs’ and the platform firm’s strategies. It demonstrates the effectiveness of large TPSs’ market detection strategy and matching optimization strategy, which can be adopted to meet consumers’ search and evaluation motivations in their person-goods matching process respectively. Moreover, it identifies the role of platform firms by showing the moderating effect of similar strategies adopted by the platform firm on the effect of large TPSs’ customer acquisition strategies.
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In order to improve the weak recognition accuracy and robustness of the classification algorithm for brain-computer interface (BCI), this paper proposed a novel classification…
Abstract
Purpose
In order to improve the weak recognition accuracy and robustness of the classification algorithm for brain-computer interface (BCI), this paper proposed a novel classification algorithm for motor imagery based on temporal and spatial characteristics extracted by using convolutional neural networks (TS-CNN) model.
Design/methodology/approach
According to the proposed algorithm, a five-layer neural network model was constructed to classify the electroencephalogram (EEG) signals. Firstly, the author designed a motor imagery-based BCI experiment, and four subjects were recruited to participate in the experiment for the recording of EEG signals. Then, after the EEG signals were preprocessed, the temporal and spatial characteristics of EEG signals were extracted by longitudinal convolutional kernel and transverse convolutional kernels, respectively. Finally, the classification of motor imagery was completed by using two fully connected layers.
Findings
To validate the classification performance and efficiency of the proposed algorithm, the comparative experiments with the state-of-the-arts algorithms are applied to validate the proposed algorithm. Experimental results have shown that the proposed TS-CNN model has the best performance and efficiency in the classification of motor imagery, reflecting on the introduced accuracy, precision, recall, ROC curve and F-score indexes.
Originality/value
The proposed TS-CNN model accurately recognized the EEG signals for different tasks of motor imagery, and provided theoretical basis and technical support for the application of BCI control system in the field of rehabilitation exoskeleton.
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Shuangshuang Liu and Xiaoling Li
Conventional image super-resolution reconstruction by the conventional deep learning architectures suffers from the problems of hard training and gradient disappearing. In order…
Abstract
Purpose
Conventional image super-resolution reconstruction by the conventional deep learning architectures suffers from the problems of hard training and gradient disappearing. In order to solve such problems, the purpose of this paper is to propose a novel image super-resolution algorithm based on improved generative adversarial networks (GANs) with Wasserstein distance and gradient penalty.
Design/methodology/approach
The proposed algorithm first introduces the conventional GANs architecture, the Wasserstein distance and the gradient penalty for the task of image super-resolution reconstruction (SRWGANs-GP). In addition, a novel perceptual loss function is designed for the SRWGANs-GP to meet the task of image super-resolution reconstruction. The content loss is extracted from the deep model’s feature maps, and such features are introduced to calculate mean square error (MSE) for the loss calculation of generators.
Findings
To validate the effectiveness and feasibility of the proposed algorithm, a lot of compared experiments are applied on three common data sets, i.e. Set5, Set14 and BSD100. Experimental results have shown that the proposed SRWGANs-GP architecture has a stable error gradient and iteratively convergence. Compared with the baseline deep models, the proposed GANs models have a significant improvement on performance and efficiency for image super-resolution reconstruction. The MSE calculated by the deep model’s feature maps gives more advantages for constructing contour and texture.
Originality/value
Compared with the state-of-the-art algorithms, the proposed algorithm obtains a better performance on image super-resolution and better reconstruction results on contour and texture.
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Keywords
Xiaoling Li and Shuang shuang Liu
For the large-scale power grid monitoring system equipment, its working environment is increasingly complex and the probability of fault or failure of the monitoring system is…
Abstract
Purpose
For the large-scale power grid monitoring system equipment, its working environment is increasingly complex and the probability of fault or failure of the monitoring system is gradually increasing. This paper proposes a fault classification algorithm based on Gaussian mixture model (GMM), which can complete the automatic classification of fault and the elimination of fault sources in the monitoring system.
Design/methodology/approach
The algorithm first defines the GMM and obtains the detection value of the fault classification through a method based on the causal Mason Young Tracy (MYT) decomposition under each normal distribution in the GMM. Then, the weight value of GMM is used to calculate weighted classification value of fault detection and separation, and by comparing the actual control limits with the classification result of GMM, the fault classification results are obtained.
Findings
The experiment on the defined non-thermostatic continuous stirred-tank reactor model shows that the algorithm proposed in this paper is superior to the traditional algorithm based on the causal MYT decomposition in fault detection and fault separation.
Originality/value
The proposed algorithm fundamentally solves the problem of fault detection and fault separation in large-scale systems and provides support for troubleshooting and identifying fault sources.
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recent years, facial expression recognition has been widely used in human machine interaction, clinical medicine and safe driving. However, there is a limitation that conventional…
Abstract
Purpose
recent years, facial expression recognition has been widely used in human machine interaction, clinical medicine and safe driving. However, there is a limitation that conventional recurrent neural networks can only learn the time-series characteristics of expressions based on one-way propagation information.
Design/methodology/approach
To solve such limitation, this paper proposes a novel model based on bidirectional gated recurrent unit networks (Bi-GRUs) with two-way propagations, and the theory of identity mapping residuals is adopted to effectively prevent the problem of gradient disappearance caused by the depth of the introduced network. Since the Inception-V3 network model for spatial feature extraction has too many parameters, it is prone to overfitting during training. This paper proposes a novel facial expression recognition model to add two reduction modules to reduce parameters, so as to obtain an Inception-W network with better generalization.
Findings
Finally, the proposed model is pretrained to determine the best settings and selections. Then, the pretrained model is experimented on two facial expression data sets of CK+ and Oulu- CASIA, and the recognition performance and efficiency are compared with the existing methods. The highest recognition rate is 99.6%, which shows that the method has good recognition accuracy in a certain range.
Originality/value
By using the proposed model for the applications of facial expression, the high recognition accuracy and robust recognition results with lower time consumption will help to build more sophisticated applications in real world.
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Xiaoling Li, Yufang Wang, Liping Fu and Meirong Xu
The purpose of this paper is to explore why the university library should become the incubation center of research innovation literacy (RIL) competency. It states how the library…
Abstract
Purpose
The purpose of this paper is to explore why the university library should become the incubation center of research innovation literacy (RIL) competency. It states how the library develops RIL instruction and how the library impels university students to improve their independent knowledge innovation competence.
Design/methodology/approach
This paper reviews some of the research papers on information literacy (IL) instruction. Based on background analysis, it enumerates and states the methods of the effective practice of the library in RIL competency instruction.
Findings
The university library possesses the information resources and advanced web technology, owns the experience in IL instruction, and has an advantage in terms of information instruction and research environment. The university library has the ability and responsibility to practice RIL instruction.
Originality/value
This paper provides a point of view that the university library can develop the RIL instruction based on IL. The university library can play not only an important role in general courses of the university, but can also sufficiently expand the function of librarians.
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Xiaoling Li, Xingyao Ren and Xu Zheng
This paper aimed to analyze the short- and long-term effects of the breadth and depth of seller competition on the performance of platform companies, and investigated the…
Abstract
Purpose
This paper aimed to analyze the short- and long-term effects of the breadth and depth of seller competition on the performance of platform companies, and investigated the underlying mechanisms of customers’ two-sided marketing tactics on the structure of the competition between sellers.
Design/methodology/approach
A longitudinal research design was adopted by gathering daily market objective data on e-commerce platforms for 250 days, and the dynamic evolution effects was analyzed by using a vector autoregression model which compared the differences between the short- and long-term effectiveness of different customer relationship management (CRM) strategies.
Findings
The breadth of competition amongst sellers improves the performance of platforms, whilst the depth of competition among sellers has a positive effect on the short-term performance. However, it has a negative effect on the long-term performance of their platforms. In both the short and long terms, advertising tactics that attract new buyers contribute more to increases in the breadth of seller competition than those that attract existing buyers do. Subsidies for new sellers decrease the depth of seller competition more than those for old sellers.
Research limitations/implications
Further research could be undertaken to investigate the validity of marketing tactics other than advertising tactics, and thus expand the time windows of the available data.
Practical implications
It is imperative for platform companies to implement effective control over seller competition to balance the interests of the sellers and of themselves.
Originality/value
The dyadic paradigm of CRM research has been extended by considering the perspective of the electronic platform company, how the tactics of exploitation and exploration of two-sided customers impact upon seller competitive structures have been delved into and why new customers have a unique value to platform companies has been identified.
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Zhao-Peng Li, Li Yang, Si-Rui Li and Xiaoling Yuan
China’s national carbon market will be officially launched in 2020, when it will become the world’s largest carbon market. However, China’s carbon market is faced with various…
Abstract
Purpose
China’s national carbon market will be officially launched in 2020, when it will become the world’s largest carbon market. However, China’s carbon market is faced with various major challenges. One of the most important challenges is its impact on the social and economic development of arid and semi-arid regions. By simulating the carbon price trends under different economic development and energy consumption levels, this study aims to help the government can plan ahead to formulate various countermeasures to promote the integration of arid and semi-arid regions into the national carbon market.
Design/methodology/approach
To achieve this goal, this paper builds a back propagation neural network model, takes the third phase of the European Union Emissions Trading System (EU ETS) as the research object and uses the mean impact value method to screen out the important driving variables of European Union Allowance (EUA) price, including economic development (Stoxx600, Stoxx50, FTSE, CAC40 and DAX), black energy (coal and Brent), clean energy (gas, PV Crystalox Solar and Nordex) and carbon price alternatives Certification Emission Reduction (CER). Finally, this paper sets up six scenarios by combining the above variables to simulate the impact of different economic development and energy consumption levels on carbon price trends.
Findings
Under the control of the unchanged CER price level, economic development, black energy and clean energy development will all have a certain impact on the EUA price trends. When economic development, black energy consumption and clean energy development are on the rise, the EUA price level will increase. When the three types of variables show a downward trend, except for the sluggish development of clean energy, which will cause the EUA price to rise sharply, the EUA price trend will also decline accordingly in the remaining scenarios.
Originality/value
On the one hand, this paper incorporates driving factors of carbon price into the construction of carbon price prediction system, which not only has higher prediction accuracy but also can simulate the long-term price trend. On the other hand, this paper uses scenario simulation to show the size, direction and duration of the impact of economic development, black energy consumption and clean energy development on carbon prices in a more intuitive way.
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Dwi Suhartanto, Marwansyah Marwansyah, Muhammad Muflih, Moh Farid Najib and Irgiana Faturohman
The purpose of this paper is to integrate the Quality–Loyalty Model and the Religiosity–Loyalty Model to assess loyalty formation toward Halal food.
Abstract
Purpose
The purpose of this paper is to integrate the Quality–Loyalty Model and the Religiosity–Loyalty Model to assess loyalty formation toward Halal food.
Design/methodology/approach
Data from 320 respondents were collected in Bandung Indonesia through a survey. A partial least-square modeling was employed to evaluate the association between food quality, religiosity, perceived value, satisfaction and loyalty.
Findings
This study discloses that the two integrated loyalty models are fit, indicating that incorporating these loyalty models provides a better comprehension of loyalty toward Halal food. Further, this study confirms the importance of both food quality and religiosity in determining loyalty.
Practical implications
This research offers an important finding for Halal food managers to develop customer loyalty through food quality and religiosity. This research recommends that Halal food managers, besides obtaining Halal certification, need to constantly innovate and adopt world food-quality standards to deal with customers’ constantly changing demands.
Originality/value
This research is the first that integrates the Quality–Loyalty Model and the Religiosity–Loyalty Model to get a better understanding of loyalty formation toward Halal food.
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Xiaoling Wang, Yingrui Hou, Guoqing Wang, Mudong Hao and Hao Li
The purpose of this paper is to illustrate the dependence of flexible-electronics properties on the metal conductor parameters, such as the width, thickness, connection length and…
Abstract
Purpose
The purpose of this paper is to illustrate the dependence of flexible-electronics properties on the metal conductor parameters, such as the width, thickness, connection length and inner meander radius of the conductor.
Design/methodology/approach
This paper uses the finite element method to simulate flexible electronics with a copper conductor attached to polyimide substrate under tension, by using different parameters of the conductor.
Findings
By careful variation of copper conductor parameters, the authors obtain an optimized structure that can undergo large deformations with small stress concentrations, lending convenience for packaging.
Originality/value
The authors have developed an optimization method for selecting metal conductor parameters in flexible electronics.
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