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Article
Publication date: 5 February 2025

Justyna Żywiołek, Kaliyan Mathiyazhagan, Umer Shahzad, Xin Zhao and Tarik Saikouk

This research primarily aims to investigate the impact of organizational implants on knowledge transmission, process innovation and security integration in intricate supply chains.

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Abstract

Purpose

This research primarily aims to investigate the impact of organizational implants on knowledge transmission, process innovation and security integration in intricate supply chains.

Design/methodology/approach

The research utilizes a mixed-method approach, employing a stratified sampling strategy to get a representative sample of 1,284 enterprises from various sectors within the logistics industry within the European Union. Data were gathered by computer-assisted web interviewing (CAWI) and analysed utilizing structural equation modelling (SEM) to evaluate hypotheses concerning cognitive congruence, process diffusion and security integration.

Findings

The results indicate that while task interdependence clearly improves face-to-face communication, excessive cognitive congruence can hinder process innovation, resulting in what the article terms “cognitive rigidity.” The study suggests that achieving a balance between cognitive congruence and cognitive flexibility is crucial to improving the safety diffusion and integration process.

Originality/value

This study presents an innovative conceptual framework that synthesizes cognitive congruence, cognitive flexibility and cognitive rigidity to examine their combined influence on knowledge transfer and process dissemination throughout supply chains. It presents cognitive stiffness as a boundary condition, contesting the conventional belief that more cognitive congruence is invariably advantageous.

Details

The International Journal of Logistics Management, vol. ahead-of-print no. ahead-of-print
Type: Research Article
ISSN: 0957-4093

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Article
Publication date: 19 September 2024

Xin Zhao and Zhengwei Li

Social media is booming in the digital age, and its rich availability provides many opportunities for companies to innovate across borders. In reality, how enterprises use social…

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Abstract

Purpose

Social media is booming in the digital age, and its rich availability provides many opportunities for companies to innovate across borders. In reality, how enterprises use social media to achieve cross-border innovation also faces important challenges such as breaking path dependency.

Design/methodology/approach

This paper explores how social media can facilitate cross-border innovation from the perspective of strategic capability, combined with the path dependency theory and attention-based view. Hierarchical regression analysis and bootstrap method are adopted to test the hypotheses based on survey data provided by 173 firms in China.

Findings

The findings show a positive relationship between social media strategic capability and cross-border innovation, with path dependency playing a mediating role. In addition, two internal and external contextual factors, namely customer embeddedness and competitive pressure, play moderating roles, with customer embeddedness negatively moderating the negative relationship between social media strategic capability and path dependency and competitive pressure negatively moderating the negative relationship between path dependency and cross-border innovation.

Originality/value

These findings provide not only new insights into social media and cross-border innovation but also theoretical guidance on how companies can effectively use social media in practice.

Details

Business Process Management Journal, vol. ahead-of-print no. ahead-of-print
Type: Research Article
ISSN: 1463-7154

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Article
Publication date: 20 September 2024

Ning Wang and Deqing Tan

This study examines how local governments and enterprises can implement ecological restoration of abandoned mines based on ecology-oriented development (EOD), which will be more…

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Abstract

Purpose

This study examines how local governments and enterprises can implement ecological restoration of abandoned mines based on ecology-oriented development (EOD), which will be more beneficial to local environmental protection and economic development under the central government’s policy of outcome incentives or process subsidies.

Design/methodology/approach

We construct a dynamic differential game model to simulate the interactions between local governments and enterprises during the ecological restoration of abandoned mines from an EOD perspective.

Findings

The findings suggest that under the central government’s outcome incentive policy, cooperation between local governments and enterprises is an optimal strategy. Under the process subsidy policy, while neither cooperative nor non-cooperative models significantly affect the investment levels of local governments and enterprises, a cooperative approach ensures optimal investments from both without solely relying on the process subsidy. Additionally, incorporating altruistic preferences can lead to Pareto improvements in economic and environmental results under central government outcome incentives.

Practical implications

This research offers a policy foundation for governments to encourage the EOD model in the ecological restoration of abandoned mines. It provides theoretical support for achieving environmental sustainability and high-quality economic development, and is particularly significant for resource-depleted cities seeking to transform their development strategies.

Originality/value

Through a dynamic differential game model involving government agencies and enterprises to simulate decision-making in the ecological restoration of abandoned mines, incorporating altruistic preferences into this restoration process, and identifying optimal strategies and policies for ecological restoration.

Details

Kybernetes, vol. ahead-of-print no. ahead-of-print
Type: Research Article
ISSN: 0368-492X

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Article
Publication date: 19 July 2024

Xinran Yang, Junhui Du, Hongshuo Chen, Chuanjin Cui, Haibin Liu and Xuechao Zhang

Field-effect transistor (FET) has excellent electronic properties and inherent signal amplification, and with the development of nanomaterials technology, FET biosensors with…

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Abstract

Purpose

Field-effect transistor (FET) has excellent electronic properties and inherent signal amplification, and with the development of nanomaterials technology, FET biosensors with nanomaterials as channels play an important role in the field of heavy metal ion detection. This paper aims to review the research progress of silicon nanowire, graphene and carbon nanotube field-effect tube biosensors for heavy metal ion detection, so as to provide technical support and practical experience for the application and promotion of FET.

Design/methodology/approach

The article introduces the structure and principle of three kinds of FET with three kinds of nanomaterials, namely, silicon nanowires, graphene and carbon nanotubes, as the channels, and lists examples of the detection of common heavy metal ions by the three kinds of FET sensors in recent years. The article focuses on the advantages and disadvantages of the three sensors, puts forward measures to improve the performance of the FET and looks forward to its future development direction.

Findings

Compared with conventional instrumental analytical methods, FETs prepared using nanomaterials as channels have the advantages of fast response speed, high sensitivity and good selectivity, among which the diversified processing methods of graphene, the multi-heavy metal ions detection of silicon nanowires and the very low detection limit and wider detection range of carbon nanotubes have made them one of the most promising detection tools in the field of heavy metal ions detection. Of course, through in-depth analysis, this type of sensor has certain limitations, such as high cost and strict process requirements, which are yet to be solved.

Originality/value

This paper elaborates on the detection principle and classification of field-effect tube, investigates and researches the application status of three kinds of FET biosensors in the detection of common heavy metal ions. By comparing the advantages and disadvantages of each of the three sensors in practical applications, the paper focuses on the feasibility of improvement measures, looks forward to the development trend in the field of heavy metal detection and ultimately promotes the application of field-effect tube development technology to continue to progress, so that its performance continues to improve and the application field is constantly expanding.

Details

Sensor Review, vol. 44 no. 5
Type: Research Article
ISSN: 0260-2288

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Article
Publication date: 17 December 2024

Libiao Bai, Xinru Zhang, Chaopeng Song and Jiaqi Wei

Effectively predicting research and development project portfolio benefit (R&D PPB) could assist organizations in monitoring the execution of research and development project…

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Abstract

Purpose

Effectively predicting research and development project portfolio benefit (R&D PPB) could assist organizations in monitoring the execution of research and development project portfolio (R&D PP). However, due to the uncertainty and complexity of R&D PPB, current research remains lacking a valid R&D PPB prediction tool. Therefore, an R&D PPB prediction model is proposed via a backpropagation neural network (BPNN).

Design/methodology/approach

The R&D PPB prediction model is constructed via a refined immune genetic algorithm coupling backpropagation neural network (RIGA-BPNN). Firstly, considering the characteristics of R&D PP, benefit evaluation criteria are identified. Secondly, the benefit criteria values are derived as input variables to the model via trapezoidal fuzzy numbers, and then the R&D PPB value is determined as the output variable through the CRITIC method. Thirdly, a refined immune genetic algorithm (RIGA) is designed to optimize BPNN by enhancing polyfitness, crossover and mutation probabilities. Lastly, the R&D PPB prediction model is constructed via the RIGA-BPNN, followed by training and testing.

Findings

The accuracy of the R&D PPB prediction model stands at 99.26%. In addition, the comparative experiment results indicate that the proposed model surpasses BPNN and the immune genetic algorithm coupling backpropagation neural network (IGA-BPNN) in both convergence speed and accuracy, showcasing superior performance in R&D PPB prediction. This study enriches the R&D PPB predicting methodology by providing managers with an effective benefits management tool.

Research limitations/implications

The research implications of this study encompass three aspects. First, this study provides a profound insight into R&D PPB prediction and enriches the research in PP fields. Secondly, during the construction of the R&D PPB prediction model, the utilization of the composite system synergy model for quantifying synergy contributes to a comprehensive understanding of intricate interactions among benefits. Lastly, in this research, a RIGA is proposed for optimizing the BPNN to efficiently predict R&D PPB.

Practical implications

This study carries threefold implications for the practice of R&D PPM. To begin with, the approach proposed serves as an effective tool for managers to predict R&D PPB. Then, the model excels in efficiency and flexibility. Furthermore, the proposed model could be used to tackle additional challenges in R&D PPM, such as gauging the potential risk level of R&D PP.

Social implications

Effective predicting of R&D PPB enables organizations to allocate their limited resources more strategically, ensuring optimal use of capital, manpower and time. By accurately predicting benefit, an organization can prioritize high-potential initiatives, thereby improving innovation efficiency and reducing the risk of failed investments. This approach not only strengthens market competitiveness but also positions organizations to adapt more effectively to changing market conditions, fostering long-term growth and sustainability in a competitive business environment.

Originality/value

Incorporating the characteristics of R&D PP and quantifying the synergy between benefits, this study facilitates a more insightful R&D PPB prediction. Additionally, improvements to the polyfitness, crossover and mutation probabilities of IGA are made, and the aforementioned RIGA is applied to optimize the BPNN. It significantly enhances the prediction accuracy and convergence speed of the neural network, improving the effectiveness of the R&D PPB prediction model.

Details

Kybernetes, vol. ahead-of-print no. ahead-of-print
Type: Research Article
ISSN: 0368-492X

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Article
Publication date: 25 November 2024

Gurmeet Singh

The process of conveyance of solid–liquid mixtures poses a significant challenge due to the considerable wear and tear experienced by critical components. This issue not only…

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Abstract

Purpose

The process of conveyance of solid–liquid mixtures poses a significant challenge due to the considerable wear and tear experienced by critical components. This issue not only affects the lifespan of the system but also jeopardizes its safe operation. The purpose of this study is to numerically and experimentally investigate the erosion wear behavior of impeller steels (SS-410 and S-317) using Computational Fluid Dynamics (CFD) and Design of Experiments (DOE) techniques, aiming to address the significant challenges posed by wear in slurry transportation systems.

Design/methodology/approach

In this study, a robust two-phase solid-liquid model combining CFD with Discrete Phase Modeling (DPM) was applied to simulate the effects of coal-ash slurries on impeller steel. Additionally, an experimental evaluation was conducted using the DOE approach to analyze the impact of various parameters on impeller steel. This integrated methodology enabled a comprehensive analysis of erosion wear behavior and the influence of multiple factors on impeller durability by leveraging CFD for fluid flow dynamics and DPM to model particle interactions with the steel surface.

Findings

Simulation results highlight a strong link between particle size and the wear life of impeller steel. Through simulations and experiments on SS-410 and SS-317 under varied conditions, it’s evident that SS-410 outperforms SS-317 due to its higher hardness and density. This is supported by Taguchi’s method, with SS-410 showing a higher Signal-to-Noise ratio. Notably, particle size emerges as the most influential parameter compared to others.

Originality/value

Current research primarily focuses on either CFD or experimentation to predict pump impeller steel erosion wear, lacking relevant erosion mechanism insights and experimental data. This study bridges this gap by employing both CFD and DPM methods to comprehensively investigate particle effects on pump impeller steel and elucidate erosion mechanisms.

Details

Industrial Lubrication and Tribology, vol. 77 no. 2
Type: Research Article
ISSN: 0036-8792

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Article
Publication date: 2 September 2024

Ling Wang, Jianqiu Gao, Changjun Chen, Congli Mei and Yanfeng Gao

Harmonic drives are used widely in aviation, robotics and instrumentation due to their benefits including high transmission ratio, compact structure and zero backlash. One of the…

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Abstract

Purpose

Harmonic drives are used widely in aviation, robotics and instrumentation due to their benefits including high transmission ratio, compact structure and zero backlash. One of the common faults of a harmonic drive is the axial movement of the input shaft. In such a case, its input shaft moves in the axial direction relative to the body of the harmonic drive. The purpose of this study is to propose two fault diagnosis methods based on the current signal of the driving servomotor for the axial movement failure in terms of input shafts of harmonic drives.

Design/methodology/approach

In the two proposed fault diagnosis methods, the wavelet threshold algorithm is firstly used for filtering noises of the motor current signal. Then, the feature of the denoised current signal is extracted by the empirical mode decomposition (EMD) method and the wavelet packet energy-entropy (WPEE) theory, respectively, obtaining two kinds of feature sets. After a deep learning model based on the deep belief network (DBN) is constructed and trained by using these feature sets, we finally identify the normal harmonic drives and the ones with the axial movement fault.

Findings

In contrast to the traditional back propagation (BP) neural network model and support vector machine (SVM) model, the fault diagnosis methods based on the combination of the EMD (as well as the WPEE) and the DBN model can obtain higher accuracy rates of fault diagnosis for axial movement of harmonic drives, which can be greater than or equal to 97% based on the data of the performed experiment.

Originality/value

The authors propose two fault diagnosis methods based on the current signal of the driving servomotor for the axial movement failure in terms of input shafts of harmonic drives, which are verified by the experiment. The presented study may be beneficial for the development of self-diagnosis and self-repair systems of different robots and precision machines using harmonic drives.

Details

Journal of Quality in Maintenance Engineering, vol. 30 no. 4
Type: Research Article
ISSN: 1355-2511

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Article
Publication date: 16 December 2024

Yuqi Zhang, Xue Chen and Chunping Tan

This paper aims to understand how quantum leaders influence employee work behavior through effective tasks.

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Abstract

Purpose

This paper aims to understand how quantum leaders influence employee work behavior through effective tasks.

Design/methodology/approach

In this study, 516 questionnaires were collected using the interval data method to explore the triggering mechanisms and paths of emerging quantum leadership on constructive deviance.

Findings

The findings indicate that quantum leadership promotes constructive deviance through facilitating recovery experience (affective path), job crafting (task path) and the chained mediation path between the two. Additionally, the moderating effect of openness to experience strengthens the pathways between quantum leadership and recovery experience, and between quantum leadership and job crafting.

Research limitations/implications

This study focuses closely on the mechanism of leadership behavior on employees, neglecting the psychological state and behavior of the leader as a key resource element in the work environment. Quantum leadership emphasizes value-bound characteristics, so the role played by quantum leaders may vary in different cultures and values.

Practical implications

First, this study calls for the organizational management focusing on the advantages of quantum leadership thinking and its positive effects in practice. Second, the mediating mechanisms of recovery experience and job crafting provide insights into how quantum leadership can be used to enhance constructive deviance. Third, this study elucidates how individual responses to organizational environment and leadership style vary in management practices. Our study helps managers better understand how individual characteristics, such as openness to experience, influence managerial behavior.

Social implications

This study enriches the qualitative research on emerging “quantum” perspectives of leadership, expands the mechanism of employee constructive deviance and highlights the need for organizations to take measures that encourage constructive deviance by their employees, as this can lead to high-quality and long-term growth.

Originality/value

Based on conservation of resources theory, authors revealed the mechanisms by which quantum leadership influences employees’ constructive deviance, confirming the mediating role of recovery experience and job crafting as well as the moderating role of openness to employee experience. We explored the moderating mechanisms of the individual trait of openness to experience in the quantum leadership-to-job crafting and the recovery experience-to-job crafting.

Details

Chinese Management Studies, vol. ahead-of-print no. ahead-of-print
Type: Research Article
ISSN: 1750-614X

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Article
Publication date: 30 January 2025

Guangchao Lv, Qi Gao and Quanzhao Wang

To improve the surface quality of Mg2Si/Al composites after solution treatment, the formation mechanism of surface defects under milling machining conditions is investigated to…

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Abstract

Purpose

To improve the surface quality of Mg2Si/Al composites after solution treatment, the formation mechanism of surface defects under milling machining conditions is investigated to reduce the surface roughness.

Design/methodology/approach

This paper analyzes the formation mechanism of surface defects on Mg2Si/Al composites under micro-milling conditions by establishing a two-dimensional finite element simulation model. Response surface (Box–Behnken) experiments are designed to establish a prediction model for surface roughness, and an analysis of extreme variance is used to investigate the effects of milling depth (ap), spindle speed (vs) and feed rate (vf) on surface quality. NSGA-II multi-objective optimization algorithm is used to optimize the process parameters by considering surface roughness and milling efficiency. Experiments are also applied to verify the relationship between surface defects and particle damage. The effect of depth of cut on surface defects is also investigated.

Findings

There are few studies on solid solution treated Mg2Si/Al composites. Solid solution treated Mg2Si/Al composites have excellent material properties without changing the original shape of the material, and they are indispensable and critical materials in the fields of aerospace, energy, electronic information and energy transportation.

Originality/value

This study elucidates the formation mechanism of surface damage in Mg2Si/Al composites, optimizes reasonable process parameters and provides technical guidance for its milling processing.

Peer review

The peer review history for this article is available at: https://publons.com/publon/10.1108/ILT-08-2024-0309/

Details

Industrial Lubrication and Tribology, vol. ahead-of-print no. ahead-of-print
Type: Research Article
ISSN: 0036-8792

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Article
Publication date: 27 August 2024

Jingyi Zhao and Mingjun Xin

The purpose of this paper is to present a method that addresses the data sparsity problem in points of interest (POI) recommendation by introducing spatiotemporal context features…

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Abstract

Purpose

The purpose of this paper is to present a method that addresses the data sparsity problem in points of interest (POI) recommendation by introducing spatiotemporal context features based on location-based social network (LBSN) data. The objective is to improve the accuracy and effectiveness of POI recommendations by considering both spatial and temporal aspects.

Design/methodology/approach

To achieve this, the paper introduces a model that integrates the spatiotemporal context of POI records and spatiotemporal transition learning. The model uses graph convolutional embedding to embed spatiotemporal context information into feature vectors. Additionally, a recurrent neural network is used to represent the transitions of spatiotemporal context, effectively capturing the user’s spatiotemporal context and its changing trends. The proposed method combines long-term user preferences modeling with spatiotemporal context modeling to achieve POI recommendations based on a joint representation and transition of spatiotemporal context.

Findings

Experimental results demonstrate that the proposed method outperforms existing methods. By incorporating spatiotemporal context features, the approach addresses the issue of incomplete modeling of spatiotemporal context features in POI recommendations. This leads to improved recommendation accuracy and alleviation of the data sparsity problem.

Practical implications

The research has practical implications for enhancing the recommendation systems used in various location-based applications. By incorporating spatiotemporal context, the proposed method can provide more relevant and personalized recommendations, improving the user experience and satisfaction.

Originality/value

The paper’s contribution lies in the incorporation of spatiotemporal context features into POI records, considering the joint representation and transition of spatiotemporal context. This novel approach fills the gap left by existing methods that typically separate spatial and temporal modeling. The research provides valuable insights into improving the effectiveness of POI recommendation systems by leveraging spatiotemporal information.

Details

International Journal of Web Information Systems, vol. 20 no. 5
Type: Research Article
ISSN: 1744-0084

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