Xiaoning Li, Xinbo Liao, Qingwen Zhong, Kai Zheng, Shaoxing Chen, Xiao-Jun Chen, Jin-Xiu Zhu and Hongyuan Yang
The purpose of this paper is to analyze the influencing factors of patients’ financial burden through a case study of hospital on public‒private partnerships (PPP) model (Chaonan…
Abstract
Purpose
The purpose of this paper is to analyze the influencing factors of patients’ financial burden through a case study of hospital on public‒private partnerships (PPP) model (Chaonan Minsheng Hospital of Guangdong Province) and provide some useful information to policymakers for better development of hospitals on PPP model.
Design/methodology/approach
There are total six indicators that are defined as patients’ financial burden, basing on the policy of “indicators of medical quality management and control on the third level large general hospital (2011 edition),” issued by Chinese Government. In total, 23 potentially influencing factors of patients’ financial burden for hospital on PPP model were chosen from the above policy. The five-year (2007‒2011) data for the above 29 indicators come from statistic department of hospital on PPP model. Grey relational analysis (GRA) was applied to analyze the influencing factors of patients’ financial burden for hospital on PPP model.
Findings
A clear rank of influencing factors of patients’ financial burden is obtained and suggestions are provided from results of GRA, which provide reference for policymakers of hospital on PPP model. The five main influencing factors of patients’ financial burden for hospital on PPP model, in sequence, are rescuing critical ill patients on emergency, rescuing critical ill inpatients, inpatient bed occupancy rate, working days per bed and medical building area.
Originality/value
The study on the influencing factors of patients’ financial burden for hospital on PPP model not only provides decision-making for policymaker of hospital and controlling of medical expenditure but also contributes to release patients’ financial burden for hospitals on PPP model.
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Xiaoning Li, Xinbo Liao, Xuerui Tan and Haijing Wang
The purpose of this paper is to evaluate resource configuration and service ability in hospital on public private partnership (PPP) model (Chaonan Minsheng Hospital of Guangdong…
Abstract
Purpose
The purpose of this paper is to evaluate resource configuration and service ability in hospital on public private partnership (PPP) model (Chaonan Minsheng Hospital of Guangdong Province), supplying decision-making reference for participants of hospital on PPP model.
Design/methodology/approach
Four model of grey relational analysis (GRA) (Deng's correlation degree, grey absolute correlation degree, grey relative correlation degree and grey comprehensive correlation degree) are applied to evaluate resource configuration and service ability, a total of 11 indicators of hospital on PPP model public hospital and private hospital from 2007 to 2011.
Findings
The paper finds that different GRA models have different results when the paper applied them to evaluate resource configuration and service ability in hospital on PPP model. More than 60 per cent indicators of resource configuration (total six indicators) and service ability (total six indicators) are assessed as “hospital on PPP model ≻ public hospital” or “hospital on PPP model≻ private hospital” from three models of Deng's correlation degree, grey absolute correlation degree and grey comprehensive correlation degree.
Practical implications
Evaluation of resource configuration and service ability for hospital on PPP model with GRA makes results quantified objective and provides reference for decision making and management. GRA makes the comparison of resource configuration and service ability between hospital on PPP model and other model hospitals becoming possible.
Originality/value
The shortcoming for data analysis method of “large sample” is overcome and data analysis method of “small sample” is realized by using GRA, which broaden the method of evaluating hospital on PPP model.
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Zhihui Men, Chaoqun Hu, Yong-Hua Li and Xiaoning Bai
This paper proposes an intelligent fault diagnosis method, which aims to obtain the outstanding fault diagnosis results of the gearbox.
Abstract
Purpose
This paper proposes an intelligent fault diagnosis method, which aims to obtain the outstanding fault diagnosis results of the gearbox.
Design/methodology/approach
An intelligent fault diagnosis method based on energy entropy-weighted complementary ensemble empirical mode decomposition (EWCEEMD) and support vector machine (SVM) optimized by whale optimization algorithm (WOA) is proposed. The raw signal is first denoised by the wavelet noise reduction method. Then, complementary ensemble empirical mode decomposition (CEEMD) is used to generate several intrinsic mode functions (IMFs). Next, energy entropy is used as an indicator to measure the sensibility of the IMF and converted into a weight coefficient by function. After that, IMFs are linearly weighted to form the reconstruction signal, and several features are extracted from the new signal. Finally, the support vector machine optimized by the whale optimization algorithm (WOA-SVM) model is used for gearbox fault classification using feature vectors.
Findings
The fault features extracted by this method have a better clustering effect and clear boundaries under each fault mode than the unimproved method. At the same time, the accuracy of fault diagnosis is greatly improved.
Originality/value
In most studies of fault diagnosis, the sensitivity of IMF has not been appreciated. In this paper, energy entropy is chosen to quantify sensitivity. In addition, high classification accuracy can be achieved by applying WOA-SVM as the final classification model, improving the efficiency of fault diagnosis as well.
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Qin Lian, Xiao Li, Dichen Li, Heng Gu, Weiguo Bian and Xiaoning He
Path planning is an important part of three-dimensional (3D) printing data processing technology. This study aims to propose a new path planning method based on a discontinuous…
Abstract
Purpose
Path planning is an important part of three-dimensional (3D) printing data processing technology. This study aims to propose a new path planning method based on a discontinuous grid partition algorithm of point cloud for in situ printing.
Design/methodology/approach
Three types of parameters (i.e. structural, process and path interruption parameters) were designed to establish the algorithm model with the path error and the computation amount as the dependent variables. The path error (i.e. boundary error and internal error) was further studied and the influence of each parameter on the path point density was analyzed quantitatively. The feasibility of this method was verified by skin in situ printing experiments.
Findings
Path point density was positively correlated with Grid_size and negatively related to other parameters. Point_space, Sparsity and Line_space had greater influence on path point density than Indentation and Grid_size. In skin in situ printing experiment, two layers of orthogonal printing path were generated, and the material was printed accurately in the defect, which proved the feasibility of this method.
Originality/value
This study proposed a new path planning method that converted 3D point cloud data to a printing path directly, providing a new path planning solution for in situ printing. The discontinuous grid partition algorithm achieved controllability of the path planning accuracy and computation amount that could be applied to different processes.
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Xiaodong Zhang, Ping Li, Xiaoning Ma and Yanjun Liu
The operating wagon records were produced from distinct railway information systems, which resulted in the wagon routing record with the same oriental destination (OD) was…
Abstract
Purpose
The operating wagon records were produced from distinct railway information systems, which resulted in the wagon routing record with the same oriental destination (OD) was different. This phenomenon has brought considerable difficulties to the railway wagon flow forecast. Some were because of poor data quality, which misled the actual prediction, while others were because of the existence of another actual wagon routings. This paper aims at finding all the wagon routing locus patterns from the history records, and thus puts forward an intelligent recognition method for the actual routing locus pattern of railway wagon flow based on SST algorithm.
Design/methodology/approach
Based on the big data of railway wagon flow records, the routing metadata model is constructed, and the historical data and real-time data are fused to improve the reliability of the path forecast results in the work of railway wagon flow forecast. Based on the division of spatial characteristics and the reduction of dimension in the distributary station, the improved Simhash algorithm is used to calculate the routing fingerprint. Combined with Squared Error Adjacency Matrix Clustering algorithm and Tarjan algorithm, the fingerprint similarity is calculated, the spatial characteristics are clustering and identified, the routing locus mode is formed and then the intelligent recognition of the actual wagon flow routing locus is realized.
Findings
This paper puts forward a more realistic method of railway wagon routing pattern recognition algorithm. The problem of traditional railway wagon routing planning is converted into the routing locus pattern recognition problem, and the wagon routing pattern of all OD streams is excavated from the historical data results. The analysis is carried out from three aspects: routing metadata, routing locus fingerprint and routing locus pattern. Then, the intelligent recognition SST-based algorithm of railway wagon routing locus pattern is proposed, which combines the history data and instant data to improve the reliability of the wagon routing selection result. Finally, railway wagon routing locus could be found out accurately, and the case study tests the validity of the algorithm.
Practical implications
Before the forecasting work of railway wagon flow, it needs to know how many kinds of wagon routing locus exist in a certain OD. Mining all the OD routing locus patterns from the railway wagon operating records is helpful to forecast the future routing combined with the wagon characteristics. The work of this paper is the basis of the railway wagon routing forecast.
Originality/value
As the basis of the railway wagon routing forecast, this research not only improves the accuracy and efficiency for the railway wagon routing forecast but also provides the further support of decision-making for the railway freight transportation organization.
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Yong-Hua Li, Chi Zhang, Hao Yin, Yang Cao and Xiaoning Bai
This paper proposes an improved fatigue life analysis method for optimal design of electric multiple units (EMU) gear, which aims at defects of traditional Miner fatigue…
Abstract
Purpose
This paper proposes an improved fatigue life analysis method for optimal design of electric multiple units (EMU) gear, which aims at defects of traditional Miner fatigue cumulative damage theory.
Design/methodology/approach
A fatigue life analysis method by modifying S–N curve and considering material difference is presented, which improves the fatigue life of EMU gear based on shape modification optimization. A corrected method for stress amplitude, average stress and S–N curve is proposed, which considers low stress cycle, material difference and other factors. The fatigue life prediction of EMU gear is carried out by corrected S–N curve and transient dynamic analysis. Moreover, the gear modification technology combined with intelligent optimization method is adopted to investigate the approach of fatigue life analysis and improvement.
Findings
The results show that it is more corresponded to engineering practice by using the improved fatigue life analysis method than the traditional method. The function of stress and modification amount established by response surface method meets the requirement of precision. The fatigue life of EMU gear based on the intelligent algorithm for seeking the optimal modification amount is significantly improved compared with that before the modification.
Originality/value
The traditional fatigue life analysis method does not consider the influence of working condition and material. The life prediction results by using the method proposed in this paper are more accurate and ensure the safety of the people in the EMU. At the same time, the combination of intelligent algorithm and gear modification can improve the fatigue life of gear on the basis of accurate prediction, which is of great significance to the portability of EMU maintenance.
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Xiaoning Song, Jiangyan Li and Xue Xia
Notwithstanding its significance as a form of strategic human capital investment, employee training has not yielded consistent conclusions among scholars regarding its impact on…
Abstract
Purpose
Notwithstanding its significance as a form of strategic human capital investment, employee training has not yielded consistent conclusions among scholars regarding its impact on organizational performance. Some studies deem it effective, while others regard it as ineffective. We contend that distinct types of training impact various facets of firm performance.
Design/methodology/approach
In this study, we categorize employee training as either exploitative or explorative. Specifically, we examine their impact on two aspects of organizational performance: short-term performance and long-term competence, using a quasi-experimental setting and a difference-in-differences (DID) method.
Findings
We find that exploitative training is more effective in improving firms’ short-term performance (e.g. firms’ sales revenue), while explorative training is more effective in enhancing firms’ long-term competence (e.g. firms’ innovation output).
Originality/value
The findings of this study illuminate concrete benefits of training for practitioners, suggesting that firms can strategically select employee training category to maximize returns on their investment in strategic human capital based on their strategic orientations.
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Yu Chen, Xiaoning Zhu, Xueli Xiong, Cen Zhang and Jiashun Huang
Corporations, as key contributors of greenhouse gas emissions, have been increasingly scrutinized by governments and stakeholders. Corporations have been asked to disclose their…
Abstract
Purpose
Corporations, as key contributors of greenhouse gas emissions, have been increasingly scrutinized by governments and stakeholders. Corporations have been asked to disclose their carbon-related information. This study investigates public corporate carbon disclosure, an imperative communication channel between firms.
Design/methodology/approach
This study uses generalized estimation equation models with a longitudinal panel data of 311 listed firms in the China A-share stock index from 2010 to 2020. This study collected firm-level data from the Carbon Disclosure Project survey, the China Stock Market and Accounting Research, and the National Economic Research Institute of China. Stata was used as the primary statistic software in empirical analyses.
Findings
This study finds that compared to state-owned enterprises (SOEs), private firms are more willing to disclose carbon information under legitimate environmental pressure, and firms in highly distorted factor-markets are reluctant to disclose carbon information. This study finds that factor-distortion markets further moderate ownership and lead private firms in highly distorted factor-markets to behave like SOEs by significantly reducing their carbon disclosures.
Originality/value
This study intends to contribute to the corporate carbon disclosure literature by adding important institutional determinants to the conversation in the context of China.
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Hao Zhang, Qingyue Lin, Chenyue Qi and Xiaoning Liang
This study aims to explore how online reviews and users’ social network centrality interact to influence idea popularity in open innovation communities (OICs).
Abstract
Purpose
This study aims to explore how online reviews and users’ social network centrality interact to influence idea popularity in open innovation communities (OICs).
Design/methodology/approach
This study used Python to obtain data from the LEGO Innovation Community. In total, 285,849 reviews across 4,475 user designs between March 2019 and March 2021 were extracted to test this study’s hypotheses.
Findings
The ordinary least square regression analysis results show that review volume, review valence, review variance and review length all positively influence idea popularity. In addition, users’ in-degree centrality positively interacts with review valence, review variance and review length to influence idea popularity, while their out-degree centrality negatively interacts with such effects.
Research limitations/implications
Drawing on the interactive marketing perspective, this study employs a large sample from the LEGO community and examines user design and idea popularity from a community member’s point of view. Moreover, this study is the first to confirm the role of online reviews and user network centrality in influencing idea popularity in OICs from a social network perspective. Furthermore, by integrating social network analysis and persuasion theories, this study confirms the interaction effects of review characteristics and users’ social network centrality on idea popularity.
Practical implications
This study’s results highlight that users should actively interact and share with reviewers their professional product design knowledge and/or the journey of their design to improve the volume of reviews on their user designs. Moreover, users could also draw more attention from other users by actively responding to heterogeneous reviews. In addition, users should be cautious with the number of people they follow and ensure that they improve their in-degree rather than out-degree centrality in their social networks.
Originality/value
This study integrates social network analysis and persuasion theories to explore the effects of online reviews and users’ centrality on idea popularity in OICs, a vital research issue that has been overlooked.
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Xin Zhao, Na Fu and Xiaoning Liang
Team leaders play a vital role in achieving superior team performance. However, their role in implementing the organizational customer orientation strategy is not well understood…
Abstract
Purpose
Team leaders play a vital role in achieving superior team performance. However, their role in implementing the organizational customer orientation strategy is not well understood. Drawing on social exchange theory, this study investigates how team leader customer orientation affects team customer orientation climate and team performance (i.e. customer satisfaction) as well as the moderating role of transformational leadership in such effect.
Design/methodology/approach
This study builds on survey data collected from matched team leaders, employees and customers nested in 81 service teams and employs hierarchical multiple regression analysis to test the hypotheses.
Findings
The findings indicate that team leader customer orientation increases team customer orientation climate, which leads to a higher level of customer satisfaction. Leaders' transformational leadership moderates the link between a leader customer orientation and team customer orientation climate in an unexpected way. When a team leader is transformational, the team customer orientation climate is enhanced, regardless of the level of team leader customer orientation. When a team leader's transformational leadership is low, the higher leader customer orientation is and the higher team customer orientation climate is.
Originality/value
This study contributes to the customer orientation, transformational leadership and service literature by unraveling team leaders' roles in boosting team customer orientation climate and team effectiveness.