Jiamin Peng, Liwen Chen, Xiaoyun Yang and Lishan Xie
Drawing on signaling theory and the “signal transmission–interpretation–feedback” framework, this study explores the effects of perceived distributive justice and respect from…
Abstract
Purpose
Drawing on signaling theory and the “signal transmission–interpretation–feedback” framework, this study explores the effects of perceived distributive justice and respect from managers on nurses' work meaningfulness and work effort in public hospitals in China and examines the moderating role of work self-efficacy.
Design/methodology/approach
We collected 341 paired questionnaires for nurses and managers from four public hospitals in China. The data were analyzed by structural equation modeling and hierarchical regression analysis.
Findings
Distributive justice and managers' respect for employees are positively related to work meaningfulness. Additionally, work self-efficacy negatively moderates this relationship. Work meaningfulness is positively related to work effort and fully mediates the relationships between perceived distributive justice and respect from the manager and work effort.
Practical implications
This study provides useful insights for healthcare organizations to improve nurses' work meaningfulness from the perspectives of their material and emotional needs, according to their work self-efficacy characteristics, thus promoting their work effort. The findings offer important guidance for improving the effectiveness of grass-roots human resources to cope with unpredictable situations such as the COVID-19 pandemic.
Originality/value
This study focuses on the organization's environmental factors that affect the primary staff's work meaningfulness. Further, it analyzes the differences in signal interpretation among nurses with different work self-efficacy characteristics, thus providing new insights into work meaningfulness. Through manager–nurse pairing data, it reveals the important role of work meaningfulness in motivating work effort.
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Qiang Li, Liwen Chen and Yong Zeng
The purpose of this paper is to investigate the mechanism how the platform obtains and uses undisclosed information to determine individual borrowers’ credit score and to examine…
Abstract
Purpose
The purpose of this paper is to investigate the mechanism how the platform obtains and uses undisclosed information to determine individual borrowers’ credit score and to examine the effectiveness of credit scoring in predicting default. The motivation stems from the fact that there is little evidence about the role of P2P platform, which has been positioned as a kind of information intermediary.
Design/methodology/approach
Using a sample of 5,176 unsecured P2P loans having expired before December 31, 2015 on Renrendai.com and an approach of two-stage regression, the paper first estimates the undisclosed information embedded in credit score by regressing credit score on four types of public information about a borrower’s creditworthiness. Then, the authors use a Logit regression to examine the role of the excess information in predicting the default probability.
Findings
The certification information provided by the platform is the most important determinant for a borrower’s credit score and the undisclosed information embedded in credit score can predict the loan performance better than the public information of posted listings. Moreover, the predictive ability of the undisclosed information is better for high-risk borrowers than for low-risk ones.
Research limitations/implications
Providing a credit score for each individual is a way for P2P platforms to play an information intermediary role. More evidence about whether or how a platform plays its role are worthy to be exploited by investigating a platform’s operating policies in detail and doing cross-platform comparative studies.
Practical implications
The results about the effect of various types of information on loan performance can provide an insightful guidance for P2P platforms to optimize their mechanism on information disclosure and credit scoring.
Originality/value
The existing literature mainly focuses on the effects of information voluntarily disclosed by borrowers and the behaviors of investors on P2P lending outcomes. The paper highlights the information intermediary role played by the platform and presents empirical evidence that credit scoring for individual borrowers is a way for P2P platforms to promote the direct lending for individual.
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The purpose of this paper is twofold: first, to explore how China uses a social credit system as part of its “data-driven authoritarianism” policy; and second, to investigate how…
Abstract
Purpose
The purpose of this paper is twofold: first, to explore how China uses a social credit system as part of its “data-driven authoritarianism” policy; and second, to investigate how datafication, which is a method to legitimize data collection, and dataveillance, which is continuous surveillance through the use of data, offer the Chinese state a legitimate method of monitoring, surveilling and controlling citizens, businesses and society. Taken together, China’s social credit system is analyzed as an integrated tool for datafication, dataveillance and data-driven authoritarianism.
Design/methodology/approach
This study combines the personal narratives of 22 Chinese citizens with policy analyses, online discussions and media reports. The stories were collected using a scenario-based story completion method to understand the participants’ perceptions of the recently introduced social credit system in China.
Findings
China’s new social credit system, which turns both online and offline behaviors into a credit score through smartphone apps, creates a “new normal” way of life for Chinese citizens. This data-driven authoritarianism uses data and technology to enhance citizen surveillance. Interactions between individuals, technologies and information emerge from understanding the system as one that provides social goods, using technologies, and raising concerns of privacy, security and collectivity. An integrated critical perspective that incorporates the concepts of datafication and dataveillance enhances a general understanding of how data-driven authoritarianism develops through the social credit system.
Originality/value
This study builds upon an ongoing debate and an emerging body of literature on datafication, dataveillance and digital sociology while filling empirical gaps in the study of the global South. The Chinese social credit system has growing recognition and importance as both a governing tool and a part of everyday datafication and dataveillance processes. Thus, these phenomena necessitate discussion of its consequences for, and applications by, the Chinese state and businesses, as well as affected individuals’ efforts to adapt to the system.
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Bingzi Jin and Xiaojie Xu
Agriculture commodity price forecasts have long been important for a variety of market players. The study we conducted aims to address this difficulty by examining the weekly…
Abstract
Purpose
Agriculture commodity price forecasts have long been important for a variety of market players. The study we conducted aims to address this difficulty by examining the weekly wholesale price index of green grams in the Chinese market. The index covers a ten-year period, from January 1, 2010, to January 3, 2020, and has significant economic implications.
Design/methodology/approach
In order to address the nonlinear patterns present in the price time series, we investigate the nonlinear auto-regressive neural network as the forecast model. This modeling technique is able to combine a variety of basic nonlinear functions to approximate more complex nonlinear characteristics. Specifically, we examine prediction performance that corresponds to several configurations across data splitting ratios, hidden neuron and delay counts, and model estimation approaches.
Findings
Our model turns out to be rather simple and yields forecasts with good stability and accuracy. Relative root mean square errors throughout training, validation and testing are specifically 4.34, 4.71 and 3.98%, respectively. The results of benchmark research show that the neural network produces statistically considerably better performance when compared to other machine learning models and classic time-series econometric methods.
Originality/value
Utilizing our findings as independent technical price forecasts would be one use. Alternatively, policy research and fresh insights into price patterns might be achieved by combining them with other (basic) prediction outputs.
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Bingzi Jin, Xiaojie Xu and Yun Zhang
Predicting commodity futures trading volumes represents an important matter to policymakers and a wide spectrum of market participants. The purpose of this study is to concentrate…
Abstract
Purpose
Predicting commodity futures trading volumes represents an important matter to policymakers and a wide spectrum of market participants. The purpose of this study is to concentrate on the energy sector and explore the trading volume prediction issue for the thermal coal futures traded in Zhengzhou Commodity Exchange in China with daily data spanning January 2016–December 2020.
Design/methodology/approach
The nonlinear autoregressive neural network is adopted for this purpose and prediction performance is examined based upon a variety of settings over algorithms for model estimations, numbers of hidden neurons and delays and ratios for splitting the trading volume series into training, validation and testing phases.
Findings
A relatively simple model setting is arrived at that leads to predictions of good accuracy and stabilities and maintains small prediction errors up to the 99.273th quantile of the observed trading volume.
Originality/value
The results could, on one hand, serve as standalone technical trading volume predictions. They could, on the other hand, be combined with different (fundamental) prediction results for forming perspectives of trading trends and carrying out policy analysis.
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Abstract
Purpose
Conventional machining methods for fabricating piezoelectric components such as ultrasound transducer arrays are time-consuming and limited to relatively simple geometries. The purpose of this paper is to develop an additive manufacturing process based on the projection-based stereolithography process for the fabrication of functional piezoelectric devices including ultrasound transducers.
Design/methodology/approach
To overcome the challenges in fabricating viscous and low-photosensitive piezocomposite slurry, the authors developed a projection-based stereolithography process by integrating slurry tape-casting and a sliding motion design. Both green-part fabrication and post-processing processes were studied. A prototype system based on the new manufacturing process was developed for the fabrication of green-parts with complex shapes and small features. The challenges in the sintering process to achieve desired functionality were also discussed.
Findings
The presented additive manufacturing process can achieve relatively dense piezoelectric components (approximately 95 per cent). The related property testing results, including X-ray diffraction, scanning electron microscope, dielectric and ferroelectric properties as well as pulse-echo testing, show that the fabricated piezo-components have good potentials to be used in ultrasound transducers and other sensors/actuators.
Originality/value
A novel bottom-up projection system integrated with tape casting is presented to address the challenges in the piezo-composite fabrication, including small curing depth and viscous ceramic slurry recoating. Compared with other additive manufacturing processes, this method can achieve a thin recoating layer (as small as 10 μm) of piezo-composite slurry and can fabricate green parts using slurries with significantly higher solid loadings. After post processing, the fabricated piezoelectric components become dense and functional.
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Liwen Guan and Lu Chen
This paper aims to present a new trajectory optimization approach targeting spray painting applications that satisfies the paint thickness requirements of complex-free surfaces.
Abstract
Purpose
This paper aims to present a new trajectory optimization approach targeting spray painting applications that satisfies the paint thickness requirements of complex-free surfaces.
Design/methodology/approach
In this paper, a new trajectory generation approach is developed to optimize the transitional segments at the junction of adjacent patches for straight line, convex arc and concave arc combinations based on different angles between normal vectors of patches. In addition, the paint parameters including the paint gun velocity, spray height and the distance between adjacent trajectories have been determined in the generation approach. Then a thickness distribution model is established to simulate the effectiveness of trajectory planning.
Findings
The developed approach was applied to a complex-free surface of various curvatures, and the analysis results of the trajectory optimization show that adopting different transitional segment according to the angle between normal vectors can obtain the optimal trajectory. Based on the simulation and experimental validation results, the proposed approach is effective at improving paint thickness uniformity, and the obtained results are consistent with the simulation results, meaning that the simulation model can be used to predict the actual paint performance.
Originality/value
This paper discusses a new trajectory generation approach to decrease the thickness error values to satisfy spray paint requirements. According to the successfully performed simulation and experimental results, the approach is useful and practical in overcoming the challenge of improving the paint thickness quality on complex-free surface.
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Lufei Huang, Liwen Murong and Wencheng Wang
Environmental issues have become an important concern in modern supply chain management. The structure of closed-loop supply chain (CLSC) networks, which considers both forward…
Abstract
Purpose
Environmental issues have become an important concern in modern supply chain management. The structure of closed-loop supply chain (CLSC) networks, which considers both forward and reverse logistics, can greatly improve the utilization of materials and enhance the performance of the supply chain in coping with environmental impacts and cost control.
Design/methodology/approach
A biobjective mixed-integer programming model is developed to achieve the balance between environmental impact control and operational cost reduction. Various factors regarding the capacity level and the environmental level of facilities are incorporated in this study. The scenario-based method and the Epsilon method are employed to solve the stochastic programming model under uncertain demand.
Findings
The proposed stochastic mixed-integer programming (MIP) model is an effective way of formulating and solving the CLSC network design problem. The reliability and precision of the Epsilon method are verified based on the numerical experiments. Conversion efficiency calculation can achieve the trade-off between cost control and CO2 emissions. Managers should pay more attention to activities about facility operation. These nodes might be the main factors of costs and environmental impacts in the CLSC network. Both costs and CO2 emissions are influenced by return rate especially costs. Managers should be discreet in coping with cost control for CO2 emissions barely affected by return rate. It is advisable to convert the double target into a single target by the idea of “Efficiency of CO2 Emissions Control Reduction.” It can provide managers with a way to double-target conversion.
Originality/value
We proposed a biobjective optimization problem in the CLSC network considering environmental impact control and operational cost reduction. The scenario-based method and the Epsilon method are employed to solve the mixed-integer programming model under uncertain demand.
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Liwen Wang, Jason Lu Jin and Defeng Yang
Contracts and trust are two prominent governance mechanisms in buyer–supplier exchanges, yet controversy persists regarding the interplay between contracts and trust. This study…
Abstract
Purpose
Contracts and trust are two prominent governance mechanisms in buyer–supplier exchanges, yet controversy persists regarding the interplay between contracts and trust. This study aims to provide a new perspective to understand the debate by differentiating between- from within-dyad effects of contracts–trust relationships.
Design/methodology/approach
Based on survey data of 250 Chinese buyer–supplier relationships collected over two time periods, this study used two-level hierarchical linear modeling (HLM) with repeated measures to test the influence of contracts (trust) on trust (contracts) over time.
Findings
The authors find that for major buyer–supplier exchanges, contracts and trust tend to complement each other when comparing across dyads, but they likely substitute for each other in within-dyad settings.
Research limitations/implications
First, to illustrate the dynamic interactions between contracts and trust, this study collected data at two time periods and assumed continuous linear relationships of time with both contracts and trust. Further research should collect multiple waves of data to explore the complex, varying changes that arise over time. Second, this study’s findings are based on buyer–supplier relationships in China, whose unique cultural features may limit the generalizability of the results to other settings.
Practical implications
Channel managers can structure exchanges by devising detailed contracts that align incentives and demonstrate commitment, which helps build trust in a relationship. Channel managers should also pay special attention to the contingency effects of their transactional and relational features.
Originality/value
This study offers the first explicit test of the dynamic contracts–trust relationship, thereby establishing a more refined understanding of interplay between contracts and trust.
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Liwen Feng, Xiangyan Ding, Yinghui Zhang, Ning Hu and Xiaoyang Bi
The study delves into the influence of wear cycles on these parameters. The purpose of this paper is to identify characteristic patterns of σRS and εPEEQ that discern varying wear…
Abstract
Purpose
The study delves into the influence of wear cycles on these parameters. The purpose of this paper is to identify characteristic patterns of σRS and εPEEQ that discern varying wear situations, thereby contributing to the enrichment of wear theory. Furthermore, the findings serve as a foundational basis for nondestructive and in situ wear detection methodologies, such as nonlinear ultrasonic detection, known for its sensitivity to σRS and εPEEQ.
Design/methodology/approach
This paper elucidates the wear mechanism through the lens of residual stress (σRS) and plastic deformation within distinct fretting regimes, using a two-dimensional cylindrical/flat contact model. It specifically explores the impact of the displacement amplitude and cycles on the distribution of residual stress and equivalent plastic strain (εPEEQ) in both gross slip regime and partial slip regimes.
Findings
Therefore, when surface observation of wear is challenging, detecting the σRS trend at the center/edge, region width and εPEEQ distribution, as well as the maximum σRS distribution along the depth, proves effective in distinguishing wear situations (partial or gross slip regimes). However, discerning wear situations based on εPEEQ along the depth direction remains challenging. Moreover, in the gross slip regime, using σRS distribution or εPEEQ along the width direction rather than the depth direction can effectively provide feedback on cycles and wear range.
Originality/value
This work introduces a novel perspective for investigating wear theory through the distribution of residual stress (σRS) and equivalent plastic strain (εPEEQ). It presents a feasible detection theory for wear situations using nondestructive and in situ methods, such as nonlinear ultrasonic detection, which is sensitive to σRS and εPEEQ.
Peer review
The peer review history for this article is available at: https://publons.com/publon/10.1108/ILT-01-2024-0005/