Yuchen Xi, Qinying Wang, Xinyu Tan, Xingshou Zhang, Lijin Dong, Yuhui Song, Liyang Liu and Dezhi Zeng
The purpose of this work is to design the wire beam electrode (WBE) of P110 steel and study its corrosion behavior and mechanism under high temperature and pressure.
Abstract
Purpose
The purpose of this work is to design the wire beam electrode (WBE) of P110 steel and study its corrosion behavior and mechanism under high temperature and pressure.
Design/methodology/approach
Packaging materials of the new type P110 steel WBE and high pressure stable WBE structure were designed. A metallurgical microscope (XJP-3C) and scanning electron microscopy (EV0 MA15 Zeiss) with an energy dispersive spectrometer were used to analyze the microstructure and composition of the P110 steel. The electrochemical workstation (CS310, CorrTest Instrument Co., Ltd) with a WBE potential and current scanner was used to analyze the corrosion mechanism of P110 steel.
Findings
According to the analysis of Nyquist plots at different temperatures, the corrosion resistance of P110 steel decreases with the increase of temperature under atmospheric pressure. In addition, Rp of P110 steel under high pressure is maintained in the range of 200 ∼ 375 Ωcm2, while that under atmospheric pressure is maintained in the range of 20 ∼ 160 Ωcm2, indicating that the corrosion products on P110 steel under high pressure is denser, which improves the corrosion resistance of P110 steel to a certain extent.
Originality/value
The WBE applied in high temperature and pressure environment is in blank. This work designed and prepared a WBE of P110 steel for high temperature and pressure environment, and the corrosion mechanism of P110 steel was revealed by using the designed WBE.
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Su Chen, Xinyu Tan, Wenbin Shen, Rongzhi Liu and Yangui Chen
This paper examines the pre-factors of college students’ entrepreneurial behaviors and how their background characteristics affect corporate financial performance in high-tech…
Abstract
Purpose
This paper examines the pre-factors of college students’ entrepreneurial behaviors and how their background characteristics affect corporate financial performance in high-tech businesses.
Design/methodology/approach
About 67 high-tech businesses in China focusing on technical innovation from the Guotai’an database are selected to carry out empirical analysis.
Findings
It is observed that the age, educational and professional backgrounds of college entrepreneurs profoundly influence their ventures geared toward high-tech innovation. Moreover, the transformation abilities, managerial proficiency and growth capabilities, which characterize these ventures, notably affect business performance. They further serve as a moderator in the relationship between the entrepreneurial backgrounds of college students and the overall business performance of their enterprises.
Originality/value
It insinuates novel strategic avenues for collegiate entrepreneurs’ entrepreneurial mindset and industrial positioning. Moreover, our findings will not only augment the practical research in the realm of collegiate entrepreneurship but also enhance the study of technological innovation theories, thereby offering further insight and guidance for collegiate entrepreneurs’ innovative endeavors and entrepreneurial pursuits.
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Miaomiao Wang, Xinyu Chen, Yuqing Tan and Xiaoxi Zhu
To explore how the blockchain affects the pricing and financing decisions in a low-carbon platform supply chain.
Abstract
Purpose
To explore how the blockchain affects the pricing and financing decisions in a low-carbon platform supply chain.
Design/methodology/approach
Considering the dual roles of the e-commerce platform as a seller and an initiator, a typical game-theoretical method is applied to analyze the behavior of supply chain decision-makers and the impact of key variables on equilibriums.
Findings
When loan interest rates are symmetric, whether blockchain is used or not, the e-commerce platform financing mode will always produce higher wholesale price and unit carbon emission reduction, while the retail price is the opposite. Higher unit additional income brought by the blockchain can bring higher economic and environmental performances, and the e-commerce platform financing mode is superior to bank financing mode. The application of blockchain may cause the manufacturer to change his/her financing choice. For bank financing, with the increase of loan interest rates, the advantages brought by blockchain will gradually disappear, but this situation will not occur under e-commerce platform financing.
Originality/value
Blockchain is known for its information transparency properties and its ability to enhance user trust. However, the impacts of applying blockchain in a low-carbon platform supply chain with different financing options are not clear. The authors examine the manufacturer's strategic choices for platform financing and bank financing, whether to adopt blockchain, and the impact of these decisions on carbon emissions reduction, consumer surplus and social welfare. The research conclusion can provide reference for the operation and financing decisions of platform supply chain under the carbon reduction target in the digital economy era.
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Lijun Meng, Xinyu Li and Xin Tan
A fiber Bragg grating (FBG) sensor was designed to measure the door gap of automobile bodies.
Abstract
Purpose
A fiber Bragg grating (FBG) sensor was designed to measure the door gap of automobile bodies.
Design/methodology/approach
The gap sensor was designed through a combination of the sliding wedge and cantilever beam, involving a magnetic force installation and arc structure of the force transmission point. Moreover, the sliding block adopted an anti-magnetic and wear-resistant material and the temperature compensation of the two FBGs was conducted. The magnetic force and contact stress of the sensor were examined to ensure that the sensor exhibited a certain magnetic attraction force and fatigue life. The performance of the gap sensor was examined experimentally.
Findings
The sensor could measure gaps with dimensions of 5 mm to 11 mm, with a sensitivity and measurement accuracy of 150.9 pm/mm and 0.0063% F.S., respectively. Moreover, the sensor exhibited a small gap sensitivity, with a repeatability error of 0.15%, anti-creep properties and magnetic interference abilities.
Originality/value
The sensor is compact and easy to install, as well as use for multiple sensor locations, with a maximum size of 43 mm, a mass of 26 g and installation type of magnetic suction. It can be used for high-precision static and dynamic measurements of the door inner clearance with a resolution of 0.013 mm to improve the efficiency of internal clearance on-line analysis and assembly quality inspection.
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Xinyu Chen, Wenjun Wang, Shuaijie Chen and Yubing Dong
This paper aims to study the effect of microcrystalline cellulose (MCC) on the mechanical property and shape memory property of water-borne epoxy (WEP).
Abstract
Purpose
This paper aims to study the effect of microcrystalline cellulose (MCC) on the mechanical property and shape memory property of water-borne epoxy (WEP).
Design/methodology/approach
In the present study, the MCC/WEP composites were successfully prepared by melt-blending, freeze-drying and hot-pressing. The mechanical property tests were performed using a tensile test instrument (Instron Corp, Norwood, Massachusetts, USA). dynamic mechanical analysis Q800 was performed to analyze the sample’s dynamic mechanics. The thermal–mechanical cycle tests performed on a thermal mechanical analysis (TMA) Q400 in dynamic TMA mode enabled to analysis of the shape memory properties of the MCC/WEP composites.
Findings
The results showed that the inclusion of 2 wt.% MCC led to significant improvements in tensile strength and modulus of the composites, with tensile strength increasing by 33.2% and modulus expanding by 65.0%. Although the inclusion of the MCC into WEP enhanced the shape memory property, the MCC/WEP composites still maintained good shape memory fixity and shape memory recovery ratio of more than 95.0%.
Originality/value
This study has a significant reference value for improving the mechanical properties of WEP and other water-borne shape memory polymers.
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Xiaodi Xu, Shanchao Sun, Yang Fei, Liubin Niu, Xinyu Tian, Zaitian Ke, Peng Dai and Zhiming Liang
This article aims to predict the rapid track geometry change in the short term with a higher detection frequency, and realize the monitoring and maintenance of the railway state.
Abstract
Purpose
This article aims to predict the rapid track geometry change in the short term with a higher detection frequency, and realize the monitoring and maintenance of the railway state.
Design/methodology/approach
Firstly, the ABA data needs to be filtered to remove the DC component to reduce the drift due to integration. Secondly, the quadratic integration in frequency domain for concern components of the vertical and lateral ABA needs to be done. Thirdly, the displacement in lateral of the wheelset to rail needs to be calculated. Then the track alignment irregularity needs to be calculated by the integration of lateral ABA and the lateral displacement of the wheelset to rail.
Findings
By comparing with a commercial track geometry measurement system, the high-speed railway application results in different conditions, after removal of the influence of LDWR, identified that the proposed method can produce a satisfactory result.
Originality/value
This article helps realize detection of track irregularity on operating vehicle, reduce equipment production, installation and maintenance costs and improve detection density.
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Ming K. Lim, Yan Li and Xinyu Song
With the fierce competition in the cold chain logistics market, achieving and maintaining excellent customer satisfaction is the key to an enterprise's ability to stand out. This…
Abstract
Purpose
With the fierce competition in the cold chain logistics market, achieving and maintaining excellent customer satisfaction is the key to an enterprise's ability to stand out. This research aims to determine the factors that affect customer satisfaction in cold chain logistics, which helps cold chain logistics enterprises identify the main aspects of the problem. Further, the suggestions are provided for cold chain logistics enterprises to improve customer satisfaction.
Design/methodology/approach
This research uses the text mining approach, including topic modeling and sentiment analysis, to analyze the information implicit in customer-generated reviews. First, latent Dirichlet allocation (LDA) model is used to identify the topics that customers focus on. Furthermore, to explore the sentiment polarity of different topics, bi-directional long short-term memory (Bi-LSTM), a type of deep learning model, is adopted to quantify the sentiment score. Last, regression analysis is performed to identify the significant factors that affect positive, neutral and negative sentiment.
Findings
The results show that eight topics that customer focus are determined, namely, speed, price, cold chain transportation, package, quality, error handling, service staff and logistics information. Among them, speed, price, transportation and product quality significantly affect customer positive sentiment, and error handling and service staff are significant factors affecting customer neutral and negative sentiment, respectively.
Research limitations/implications
The data of the customer-generated reviews in this research are in Chinese. In the future, multi-lingual research can be conducted to obtain more comprehensive insights.
Originality/value
Prior studies on customer satisfaction in cold chain logistics predominantly used questionnaire method, and the disadvantage of which is that interviewees may fill out the questionnaire arbitrarily, which leads to inaccurate data. For this reason, it is more scientific to discover customer satisfaction from real behavioral data. In response, customer-generated reviews that reflect true emotions are used as the data source for this research.
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Xinyu Ma, Eugene Cheng-Xi Aw and Raffaele Filieri
The recent livestreaming commerce has magnified the role of influencer marketing, where the influencers are partnering with brands for product promotion. This study examines the…
Abstract
Purpose
The recent livestreaming commerce has magnified the role of influencer marketing, where the influencers are partnering with brands for product promotion. This study examines the impact of influencer attributes, interaction strategies and parasocial relationships on impulsive buying in livestreaming commerce.
Design/methodology/approach
A survey with 368 livestreaming commerce users was analyzed using the symmetric-thinking approach – partial least squares structural equation modeling (PLS-SEM) and asymmetric thinking approach – fuzzy set qualitative comparative analysis (fsQCA).
Findings
The results of PLS-SEM indicate that influencer trustworthiness, influencer interactivity and self-disclosure determine parasocial relationships, which in turn influence impulsive buying. The fsQCA finding returned three configurations with various combinations of the causal conditions (i.e. influencer attributes, interaction strategies, parasocial relationships, perceived fit uncertainty and perceived quality uncertainty) explaining the formation of impulsive buying.
Originality/value
These findings provide unique linear and nonlinear insights to explain the combinatory effects of influencer attributes, interaction strategies, parasocial relationships, perceived fit uncertainty and perceived quality uncertainty on impulsive buying in livestreaming commerce.
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Liang Xiao, Jiawei Wang and Xinyu Wei
Value co-creation (VCC) helps platforms establish competitive advantages. Unlike their traditional counterparts, social attribute is a key concept of social e-commerce platforms…
Abstract
Purpose
Value co-creation (VCC) helps platforms establish competitive advantages. Unlike their traditional counterparts, social attribute is a key concept of social e-commerce platforms. This study integrates VCC and social network theories, introduces relational embeddedness and divides this variable into economic and social relational embeddedness to explore its impact on VCC intention. This study also explores the mediating and moderating roles of customers' psychological ownership (CPO) and regulatory focus, respectively.
Design/methodology/approach
A questionnaire survey was conducted among users of mainstream social e-commerce platforms in China, and the relationship among the variables was revealed through a structural equation modeling of 464 valid responses.
Findings
The dimensions of relational embeddedness positively affect CPO and VCC intention, with social relational embeddedness exerting the strongest effect. CPO positively affects VCC intention and partially mediates the relationship between relational embeddedness and VCC intention. Promotion and prevention focus positively and negatively moderate the relationship between CPO and VCC intention, respectively.
Originality/value
This study expands the VCC research perspective and links the VCC concepts to social network dynamics. From the relational embeddedness perspective, this study identifies the type and intensity of relational embeddedness that promotes users' VCC intention and contributes to theoretical research on VCC and relational embeddedness. This study also introduces CPO as an intermediary variable, thus opening the black box of this mechanism, and confirms the moderating role of regulatory focus as the key psychological factor motivating users' VCC intention.
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Qiang Zhang, Xinyu Zhu, J. Leon Zhao and Liang Liang
Digital platforms have grown significantly in recent years. Although high platform failure risks (PFR) have plagued the industry, the literature has only given this issue scant…
Abstract
Purpose
Digital platforms have grown significantly in recent years. Although high platform failure risks (PFR) have plagued the industry, the literature has only given this issue scant treatment. Customer sentiments are crucial for platforms and have a growing body of knowledge on its analysis. However, previous studies have overlooked rich contextual information emb`edded in user-generated content (UGC). Confronting the research gap of digital platform failure and drawbacks of customer sentiment analysis, we aim to detect signals of PFR based on our advanced customer sentiment analysis approach for UGC and to illustrate how customer sentiments could predict PFR.
Design/methodology/approach
We develop a deep-learning based approach to improve the accuracy of customer sentiment analysis for further predicting PFR. We leverage a unique dataset of online P2P lending, i.e., a typical setting of transactional digital platforms, including 97,876 pieces of UGC for 2,467 platforms from 2011 to 2018.
Findings
Our results show that the proposed approach can improve the accuracy of measuring customer sentiment by integrating word embedding technique and bidirectional long short-term memory (Bi-LSTM). On top of that, we show that customer sentiment can improve the accuracy for predicting PFR by 10.96%. Additionally, we do not only focus on a single type of customer sentiment in a static view. We discuss how the predictive power varies across positive, neutral, negative customer sentiments, and during different time periods.
Originality/value
Our research results contribute to the literature stream on digital platform failure with online information processing and offer implications for digital platform risk management with advanced customer sentiment analysis.