Lijun Liu and Zuhua Jiang
The purpose of this paper is to shed light on how technological innovation capabilities (TICs) influence the product competitiveness of Chinese manufacturing enterprises and…
Abstract
Purpose
The purpose of this paper is to shed light on how technological innovation capabilities (TICs) influence the product competitiveness of Chinese manufacturing enterprises and identify the key technological innovation components.
Design/methodology/approach
Quantitative research setting was applied in Chinese Yangtze River Delta. Survey was carried out with 166 responses.
Findings
The study reveals that the firm’s strategies capabilities, knowledge resources, fundamental research, application R & D, and manufacturing capabilities have significant influence on the new product development performance and product competitiveness of Chinese manufacturing enterprises. Interestingly, firm’s organizational capabilities and human, finance, and material resource have no significant correlation with the product competitiveness.
Practical implications
From a practical perspective, the relationships among TICs enablers, processes, and product competitiveness may provide a clue regarding how firms can promote technological innovation to sustain their competitive advantage. Moreover, the key factors of TICs found in the study are useful for policy makers and managers of Chinese firms to make decision.
Originality/value
This study is one of the first studies to apply the structure equation model method to measure the relationship between TICs and product competitiveness under the background of Chinese manufacturing. The results provide a new framework on the how technological innovation capability influence product competitiveness of Chinese manufacturing firms. From a managerial perspective, this study identifies several crucial TICs factors to support product competitiveness, and discusses the implications of these factors for developing organizational strategies that encourage technological innovation.
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Sreelakshmi D. and Syed Inthiyaz
Pervasive health-care computing applications in medical field provide better diagnosis of various organs such as brain, spinal card, heart, lungs and so on. The purpose of this…
Abstract
Purpose
Pervasive health-care computing applications in medical field provide better diagnosis of various organs such as brain, spinal card, heart, lungs and so on. The purpose of this study is to find brain tumor diagnosis using Machine learning (ML) and Deep Learning(DL) techniques. The brain diagnosis process is an important task to medical research which is the most prominent step for providing the treatment to patient. Therefore, it is important to have high accuracy of diagnosis rate so that patients easily get treatment from medical consult. There are many earlier investigations on this research work to diagnose brain diseases. Moreover, it is necessary to improve the performance measures using deep and ML approaches.
Design/methodology/approach
In this paper, various brain disorders diagnosis applications are differentiated through following implemented techniques. These techniques are computed through segment and classify the brain magnetic resonance imaging or computerized tomography images clearly. The adaptive median, convolution neural network, gradient boosting machine learning (GBML) and improved support vector machine health-care applications are the advance methods used to extract the hidden features and providing the medical information for diagnosis. The proposed design is implemented on Python 3.7.8 software for simulation analysis.
Findings
This research is getting more help for investigators, diagnosis centers and doctors. In each and every model, performance measures are to be taken for estimating the application performance. The measures such as accuracy, sensitivity, recall, F1 score, peak-to-signal noise ratio and correlation coefficient have been estimated using proposed methodology. moreover these metrics are providing high improvement compared to earlier models.
Originality/value
The implemented deep and ML designs get outperformance the methodologies and proving good application successive score.
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Zhining Wang, Shaohan Cai, Mengli Liu, Dandan liu and Lijun Meng
The aim of this paper is to develop a tool measuring individual intellectual capital (IIC) and investigate the relationship between self-reflection and IIC.
Abstract
Purpose
The aim of this paper is to develop a tool measuring individual intellectual capital (IIC) and investigate the relationship between self-reflection and IIC.
Design/methodology/approach
This study developed a theoretical model based on social cognitive theory and the literature of self-reflection and intellectual capital (IC). This research collected responses from 502 dyads of employees and their direct supervisors in 150 firms in China, and the study tested the research model using structural equation modeling (SEM).
Findings
The results indicate that three components of self-reflection, namely, need for self-reflection, engagement in self-reflection and insight, significantly contribute to all the three components of IIC, such as individual human capital, individual structural capital and individual relational capital. The findings suggest that need for self-reflection is the weakest component to impact individual human capital and individual relationship capital, while insight is the one that mostly enhances individual structural capital.
Practical implications
This paper suggests that managers can enhance employees' IIC by facilitating their self-reflection. Managers can develop appropriate strategies based on findings of this study, to achieve their specific goals.
Originality/value
First, this study develops a tool for measuring IIC. Second, this study provides an enriched theoretical explanation on the relationship between self-reflection and IIC – by showing that the three subdimensions of self-reflection, such as need, engagement and insight, influence the three subdimensions of IIC, such as individual human capital, individual structural capital and individual relational capital.
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Huifang Jiao, Wenzhi Tang, Tianzhuo Liu, Xuan Wang and Lijun Ma
Social media technology has changed donors' decision-making process in online philanthropy compared with traditional charity. How do IT affordances support donor perceptions and…
Abstract
Purpose
Social media technology has changed donors' decision-making process in online philanthropy compared with traditional charity. How do IT affordances support donor perceptions and motivations in charitable crowdfunding? The purpose of this study is to explore how the five sub-dimensions of charitable crowdfunding IT affordances (i.e. visibility, association, meta-voicing, trading and security) afford initiators and platforms in motivating donors to support charitable crowdfunding projects.
Design/methodology/approach
This paper uses a quantitative research approach. An online survey was conducted to collect research data from WeChat users who had experienced charitable crowdfunding. A sample of 344 valid responses were received and analyzed.
Findings
The results show that four of the five IT affordances facilitate donors' perceptions (perceived emotions and trust) and motivations (intrinsic motivations and extrinsic motivations), and thereby increase behavioral intention on supporting charitable crowdfunding projects.
Originality/value
This study advances the affordances and online charity literature by examining the antecedents and outcome of perceptions and motivations that determining behavioral intention in more detail. The authors’ findings not only benefit researchers in explaining how technology helps donors perceiving projects and motivating them to donate online, but also assists practitioners in developing better charitable crowdfunding management strategy.
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Lijun Chao, Zhi Xiong, Jianye Liu, Chuang Yang and Yudi Chen
To solve problems of low intelligence and poor robustness of traditional navigation systems, the purpose of this paper is to propose a brain-inspired localization method of the…
Abstract
Purpose
To solve problems of low intelligence and poor robustness of traditional navigation systems, the purpose of this paper is to propose a brain-inspired localization method of the unmanned aerial vehicle (UAV).
Design/methodology/approach
First, the yaw angle of the UAV is obtained by modeling head direction cells with one-dimension continuous attractor neural network (1 D-CANN) and then inputs into 3D grid cells. After that, the motion information of the UAV is encoded as the firing of 3 D grid cells using 3 D-CANN. Finally, the current position of the UAV can be decoded from the neuron firing through the period-adic method.
Findings
Simulation results suggest that continuous yaw and position information can be generated from the conjunctive model of head direction cells and grid cells.
Originality/value
The proposed period-adic cell decoding method can provide a UAV with the 3 D position, which is more intelligent and robust than traditional navigation methods.
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Bingbing Qi, Lijun Xu and Xiaogang Liu
The purpose of this paper is to exploit the multiple-Toeplitz matrices reconstruction method combined with quadratic spatial smoothing processing to improve the…
Abstract
Purpose
The purpose of this paper is to exploit the multiple-Toeplitz matrices reconstruction method combined with quadratic spatial smoothing processing to improve the direction-of-arrival (DOA) estimation performance of coherent signals at low signal-to-noise ratio (SNRs).
Design/methodology/approach
An improved multiple-Toeplitz matrices reconstruction method is proposed via quadratic spatial smoothing processing. Our proposed method takes advantage of the available information contained in the auto-covariance matrices of individual Toeplitz matrices and the cross-covariance matrices of different Toeplitz matrices, which results in a higher noise suppression ability.
Findings
Theoretical analysis and simulation results show that, compared with the existing Toeplitz matrix processing methods, the proposed method improves the DOA estimation performance in cases with a low SNR. Especially for the cases with a low SNR and small snapshot number as well as with closely spaced sources, the proposed method can achieve much better performance on estimation accuracy and resolution probability.
Research limitations/implications
The study investigates the possibility of reusing pre-existing designs for the DOA estimation of the coherent signals. The proposed technique enables achieve good estimation performance at low SNRs.
Practical implications
The paper includes implications for the DOA problem at low SNRs in communication systems.
Originality/value
The proposed method proved to be useful for the DOA estimation at low SNR.
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Zhang Jian, Deng Lijun, Hao Guannan and Liu Shiying
With the implementation of new emission standards, the thermal–mechanical coupling load of engine pistons becomes more important. In this case, forged steel material with higher…
Abstract
Purpose
With the implementation of new emission standards, the thermal–mechanical coupling load of engine pistons becomes more important. In this case, forged steel material with higher fatigue limit and impact resistance has been applied gradually in piston manufacturing. However, new failure problems emerge, and the wear of skirt under boundary lubrication conditions is an essential problem which needs to be solved urgently.
Design/methodology/approach
In this research, the abrasion testing machine was used to simulate the wear behavior under different conditions of normal pressure, relative velocity and surface roughness. Besides, the wear morphology was observed by scanning electron microscope. Then, the wear model was established by using test results fitting method, offering a way to conduct qualitative analysis for the wear problem under the same conditions.
Findings
The results show that mainly the wear mechanism of the piston skirt under boundary lubricated conditions is adhesive wear and abrasive wear. In addition, the coefficient and wear rate will increase with the increase in the normal load and surface roughness and decrease with the increase in the relative speed. In the wear model, the wear loss is mainly influenced by the normal load, the relative sliding speed and the wear time.
Originality/value
The wear degree of piston skirt was qualitatively obtained in this investigation by factors such as pressure, velocity and so on, and the wear mechanism of forged steel piston skirt under boundary lubrication conditions was also determined. These could provide theoretical support for further optimization of cylinder motion and oil supply system, reduction of friction loss and power loss.
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Yushuai Chen, Neal M. Ashkanasy, Xin Liu, Lijun Wu and An Yang
Studies of the antecedents of daily abusive supervision have mainly focused on work stressors and family stressors, ignoring the potential importance of commuting stressors that…
Abstract
Purpose
Studies of the antecedents of daily abusive supervision have mainly focused on work stressors and family stressors, ignoring the potential importance of commuting stressors that are encountered enroute to work. Based in affective events theory, the authors propose a daily, within-person model to examine how the commuting stressors faced by supervisors affect their propensity to engage in abusive supervision behavior and the mechanisms underlying this effect.
Design/methodology/approach
Using experience-sampling methodology, the authors collected data from 49 supervisors in China who responded to two daily surveys for 10 working days.
Findings
The authors found that daily morning commuting anger mediates the link between daily morning commuting stressors and subsequent abusive supervision. The authors also found that trait-displaced aggression moderates this relationship, such that the mediating effect occurs only when supervisors' trait-displaced aggression is high rather than low.
Originality/value
This study enriches the antecedents of daily abusive supervision and extends the commuting literature to the leadership context.
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Lijun Wu, Maolin Ye, Doudou Liu and Yushuai Chen
Previous studies have mainly focused on the negative impact of illegitimate tasks on recipients but ignored its impact on observers. Drawing on deservingness theory, this research…
Abstract
Purpose
Previous studies have mainly focused on the negative impact of illegitimate tasks on recipients but ignored its impact on observers. Drawing on deservingness theory, this research developed a moderated serial mediation model to examine the underlying mechanism in the relationship between observed illegitimate tasks and observers’ helping behaviour.
Design/methodology/approach
This research tested the model using a sample of 218 employees in China through a two-wave field study. A path analysis was conducted with the bootstrapping procedure to test the hypothesized model.
Findings
The results showed that the effect of observing illegitimate tasks was contingent upon the degree of the observer’s interpersonal liking towards the recipient. Specifically, when the observer had a high level of interpersonal liking for a coworker, the observer may perceive that the coworker was undeserving of being assigned illegitimate tasks, which increased sympathetic emotion and the sequent helping behaviour.
Originality/value
This study shifted the perspective of the illegitimate task literature from the recipient to the third parties. Additionally, it provided a finer-grained understanding of the mechanism between observed workplace disrespectful treatment and the observer’s helping behaviour.