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1 – 7 of 7Xiwen Zhang, Zhen Zhang, Wenhao Sun, Jilei Hu, Liangliang Zhang and Weidong Zhu
Under the repeated action of the construction load, opening deformation and disturbed deformation occurred at the precast box culvert joints of the shield tunnel. The objective of…
Abstract
Purpose
Under the repeated action of the construction load, opening deformation and disturbed deformation occurred at the precast box culvert joints of the shield tunnel. The objective of this paper is to investigate the effect of construction vehicle loading on the mechanical deformation characteristics of the internal structure of a large-diameter shield tunnel during the entire construction period.
Design/methodology/approach
The structural response of the prefabricated internal structure under heavy construction vehicle loads at four different construction stages (prefabricated box culvert installation, curved lining cast-in-place, lane slab installation and pavement structure casting) was analyzed through field tests and ABAQUS (finite element analysis software) numerical simulation.
Findings
Heavy construction vehicles can cause significant mechanical impacts on the internal structure, as the construction phase progresses, the integrity of the internal structure with the tunnel section increases. The vertical and horizontal deformation of the internal structure is significantly reduced, and the overall stress level of the internal structure is reduced. The bolts connecting the precast box culvert have the maximum stress at the initial stage of construction, as the construction proceeds the stress distribution among the bolts gradually becomes uniform.
Originality/value
This study can provide a reference for the design model, theoretical analysis and construction technology of the internal structure during the construction of large-diameter tunnel projects.
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Wenhao Zhou, Hailin Li, Hufeng Li, Liping Zhang and Weibin Lin
Given the regional heterogeneity of economic development, electricity consumption in various regions exhibits a discrepant growth pattern. The purpose of this study is to…
Abstract
Purpose
Given the regional heterogeneity of economic development, electricity consumption in various regions exhibits a discrepant growth pattern. The purpose of this study is to construct a grey system forecasting model with intelligent parameters for predicting provincial electricity consumption in China.
Design/methodology/approach
First, parameter optimization and structural expansion are simultaneously integrated into a unified grey system prediction framework, enhancing its adaptive capabilities. Second, by setting the minimum simulation percentage error as the optimization goal, the authors apply the particle swarm optimization (PSO) algorithm to search for the optimal grey generation order and background value coefficient. Third, to assess the performance across diverse power consumption systems, the authors use two electricity consumption cases and select eight other benchmark models to analyze the simulation and prediction errors. Further, the authors conduct simulations and trend predictions using data from all 31 provinces in China, analyzing and predicting the development trends in electricity consumption for each province from 2021 to 2026.
Findings
The study identifies significant heterogeneity in the development trends of electricity consumption systems among diverse provinces in China. The grey prediction model, optimized with multiple intelligent parameters, demonstrates superior adaptability and dynamic adjustment capabilities compared to traditional fixed-parameter models. Outperforming benchmark models across various evaluation indicators such as root mean square error (RMSE), average percentage error and Theil’s index, the new model establishes its robustness in predicting electricity system behavior.
Originality/value
Acknowledging the limitations of traditional grey prediction models in capturing diverse growth patterns under fixed-generation orders, single structures and unadjustable background values, this study proposes a fractional grey intelligent prediction model with multiple parameter optimization. By incorporating multiple parameter optimizations and structure expansion, it substantiates the model’s superiority in forecasting provincial electricity consumption.
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Wenhao Zhou, Hailin Li, Liping Zhang, Huimin Tian and Meng Fu
The purpose of this work is to construct a grey entropy comprehensive evaluation model to measure the regional green innovation vitality (GIV) of 31 provinces in China.
Abstract
Purpose
The purpose of this work is to construct a grey entropy comprehensive evaluation model to measure the regional green innovation vitality (GIV) of 31 provinces in China.
Design/methodology/approach
The traditional grey relational proximity and grey relational similarity degree are integrated into the novel comprehensive grey evaluation framework. The evaluation system of regional green innovation vitality is constructed from three dimensions: economic development vitality, innovative transformation power and environmental protection efficacy. The weights of each indicator are obtained by the entropy weight method. The GIV of 31 provinces in China is measured based on provincial panel data from 2016 to 2020. The ward clustering and K-nearest-neighbor (KNN) algorithms are utilized to explore the regional green innovation discrepancies and promotion paths.
Findings
The novel grey evaluation method exhibits stronger ability to capture intrinsic patterns compared with two separate traditional grey relational models. Green innovation vitality shows obvious regional discrepancies. The Matthew effect of China's regional GIV is obvious, showing a basic trend of strong in the eastern but weak in the western areas. The comprehensive innovation vitality of economically developed provinces exhibits steady increasing trend year by year, while the innovation vitality of less developed regions shows an overall steady state of no fluctuation.
Practical implications
The grey entropy comprehensive relational model in this study is applied for the measurement and evaluation of regional GIV, which improves the one-sidedness of traditional grey relational analysis on the proximity or similarity among sequences. In addition, a three-dimensional evaluation system of regional GIV is constructed, which provides the practical guidance for the research of regional development strategic planning as well as promotion paths.
Originality/value
A comprehensive grey entropy relational model based on traditional grey incidence analysis (GIA) in terms of proximity and similarity is proposed. The three-dimensional evaluation system of China's regional GIV is constructed, which provides a new research perspective for regional innovation evaluation and expands the application scope of grey system theory.
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Wenhao Zhou and Hailin Li
This study aims to propose a combined effect framework to explore the relationship between research and development (R&D) team networks, knowledge diversity and breakthrough…
Abstract
Purpose
This study aims to propose a combined effect framework to explore the relationship between research and development (R&D) team networks, knowledge diversity and breakthrough technological innovation. In contrast to conventional linear net effects, the article explores three possible types of team configuration within enterprises and their breakthrough innovation-driving mechanisms based on machine learning methods.
Design/methodology/approach
Based on the patent application data of 2,337 Chinese companies in the biopharmaceutical manufacturing industry to construct the R&D team network, the study uses the K-Means method to explore the configuration types of R&D teams with the principle of greatest intergroup differences. Further, a decision tree model (DT) is utilized to excavate the conditional combined relationships between diverse team network configuration factors, knowledge diversity and breakthrough innovation. The network driving mechanism of corporate breakthrough innovation is analyzed from the perspective of team configurations.
Findings
It has been discerned that in the biopharmaceutical manufacturing industry, there exist three main types of enterprise R&D team configurations: tight collaboration, knowledge expansion and scale orientation, which reflect the three resource investment preferences of enterprises in technological innovation, network relationships, knowledge resources and human capital. The results highlight both the crowding-out effects and complementary effects between knowledge diversity and team network characteristics in tight collaborative teams. Low knowledge diversity and high team structure holes (SHs) are found to be the optimal team configuration conditions for breakthrough innovation in knowledge-expanding and scale-oriented teams.
Originality/value
Previous studies have mainly focused on the relationship between the external collaboration network and corporate innovation. Moreover, traditional regression methods mainly describe the linear net effects between variables, neglecting that technological breakthroughs are a comprehensive concept that requires the combined action of multiple factors. To address the gap, this article proposes a combination effect framework between R&D teams and enterprise breakthrough innovation, further improving social network theory and expanding the applicability of data mining methods in the field of innovation management.
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Qi He, Jingtao Fu, Wenhao Wu and Siqi Feng
Based on achievement motivation theory and two-factor theory, this research aimed to synergize cooperative goal interdependence (refer to possessing incentive factors) and…
Abstract
Purpose
Based on achievement motivation theory and two-factor theory, this research aimed to synergize cooperative goal interdependence (refer to possessing incentive factors) and illegitimate tasks (refer to the absence of security factors) and build a triple interaction model in the process of performance pressure affecting employees’ thriving at work.
Design/methodology/approach
This research collected 291 valid data through a two-point time-lagged method to test the direct effect of performance pressure on employees’ thriving at work and its moderating mechanism.
Findings
Performance pressure has a significant positive effect on employees’ thriving at work. Cooperative goal interdependence imposes an enhanced moderating effect between performance pressure and employees’ thriving at work. Illegitimate task imposes an interfering moderating effect between performance pressure and employees’ thriving at work and further interferes the enhanced moderating effect of cooperative goal interdependence.
Practical implications
Under the premise of advocating for employees to internalize performance pressure originating from the organizational performance management system into their own achievement motivation, leaders should establish incentive systems and security systems for employees to realize self-achievement through the process of goal management and task management.
Originality/value
This research confirmed the joint determination of incentive effect and insecurity effect on employees’ achievement motivation by cooperative goal interdependence and illegitimate task and revealed the boundary conditions of employees’ choice of thriving at work.
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Xiaojun Zhan, Wei Yang, Yirong Guo and Wenhao Luo
Nurses' work engagement is critical for the service quality of the hospital. Thus, investigation on the influencing factors of nurses' work engagement has become an important…
Abstract
Purpose
Nurses' work engagement is critical for the service quality of the hospital. Thus, investigation on the influencing factors of nurses' work engagement has become an important issue. This study addresses this issue by exploring the effect of daily family-to-work conflict (FWC) on next-day work engagement among Chinese nurses.
Design/methodology/approach
The theoretical model was tested using 555 experience sampling data from 61 nurses collected for 10 workdays in China.
Findings
Nurses' daily FWC is associated with their next-day ego depletion. Moreover, increased ego depletion ultimately reduces their next-day work engagement. In addition, a between-individual factor of frequency of perceived patient gratitude mitigates the effect of FWC on ego depletion and the indirect effect on work engagement via ego depletion.
Originality/value
This study is important to the management of health-care organizations as it carries significant implications for theory and practice toward understanding the influence of FWC among nurses. On the one hand, the authors apply the job demands-resources (JD-R) model as the overarching theoretical framework, which contributes to the authors’ understanding of how FWC impairs work engagement. On the other hand, the authors extend extant theoretical models of FWC by identifying the frequency of perceived patient gratitude as an important contextual factor that counteracts the negative effects of FWC among nurses. Moreover, organizations could encourage patients to express their gratitude to nurses by providing more channels, such as thank-you notes, to offer nurses some support for overcoming the destructive effect of FWC.
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Anaile Rabelo, Marcos W. Rodrigues, Cristiane Nobre, Seiji Isotani and Luis Zárate
The purpose of this study is to identify the main perspectives and trends in educational data mining (EDM) in the e-learning environment from a managerial perspective.
Abstract
Purpose
The purpose of this study is to identify the main perspectives and trends in educational data mining (EDM) in the e-learning environment from a managerial perspective.
Design/methodology/approach
This paper proposes a systematic literature review to identify the main perspectives and trends in EDM in the e-learning environment from a managerial perspective. The study domain of this review is restricted by the educational concepts of e-learning and management. The search for bibliographic material considered articles published in journals and papers published in conferences from 1994 to 2023, totaling 30 years of research in EDM.
Findings
From this review, it was observed that managers have been concerned about the effectiveness of the platform used by students as it contains the entire learning process and all the interactions performed, which enable the generation of information. From the data collected on these platforms, there are improvements and inferences that can be made about the actions of educators and human tutors (or automatic tutoring systems), curricular optimization or changes related to course content, proposal of evaluation criteria and also increase the understanding of different learning styles.
Originality/value
This review was conducted from the perspective of the manager, who is responsible for the direction of an institution of higher education, to assist the administration in creating strategies for the use of data mining to improve the learning process. To the best of the authors’ knowledge, this review is original because other contributions do not focus on the manager.
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