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Article
Publication date: 16 May 2016

Po-Yen Lee, Meng-Ling Wu, Cheng-Chung Kuo and Chun-Sheng Joseph Li

The purpose of this paper is to provide a more robust understanding of how to deploy multiunit organizations’ dynamic capabilities (DCs) by examining the roles of embedded social…

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Abstract

Purpose

The purpose of this paper is to provide a more robust understanding of how to deploy multiunit organizations’ dynamic capabilities (DCs) by examining the roles of embedded social (structural and relational) capital and knowledge archetype (exploitative and exploratory) learning.

Design/methodology/approach

This study uses 315 multiunit samples and structural equation modeling to determine the relationships among the variables.

Findings

The analysis reveals that, while embedded structural social capital exerts a positive influence on exploratory knowledge learning in multiunit organizations, embedded relational social capital exerts a positive influence on knowledge archetype (exploitative and exploratory) learning. Knowledge archetype (exploitative and exploratory) learning also positively influences DC deployment in multiunit organizations.

Research limitations/implications

Few DCs studies have empirically examined the roles of embedded social (structural and relational) capital and knowledge archetype (exploitative and exploratory) learning in multiunit organizations. The results of this study address the failure of past theoretical perspectives on DCs to fully specify and verify the links between the roles of embedded social (structural and relational) capital and knowledge archetype (exploitative and exploratory) learning.

Originality/value

This paper offers one practical trajectory for DC deployment in modern multiunit organizations and offers two contributions to the theoretical perspectives on DCs. First, it identifies the critical role of embedded social capital in enabling knowledge archetype learning and DC deployment, which had never been fully specified or verified in the DCs literature. Second, it identifies the importance of DCs’ deployment trajectory in multiunit organizations’ routine processes.

Details

Management Decision, vol. 54 no. 4
Type: Research Article
ISSN: 0025-1747

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Article
Publication date: 28 October 2014

Shihping Kevin Huang and Chih-Lung Yang

The objective of this article is to explore the relationship between corporate social performance (CSP) and corporate financial performance (CFP) of firms in Taiwan, as the…

691

Abstract

Purpose

The objective of this article is to explore the relationship between corporate social performance (CSP) and corporate financial performance (CFP) of firms in Taiwan, as the empirical evidence of Taiwan firms is scarce.

Design/methodology/approach

This paper studies the empirical relation between CSP and CFP using a sample of 71 Taiwan-based companies during 2005-2011. CSP data are a composite of two Taiwan’s CSP ratings, and CFP data are retrieved from Taiwan Economic Journal database. Two control variables, R&D investment (R&D) and industry type (IND), are included in our models. The multiple regression is used as a statistical analysis tool.

Findings

Our findings indicate a significantly positive CSP–CFP relationship of firms in Taiwan. Furthermore, our study reveals that the CSP in the non-manufacturing sector is more highly related with CFP than the case in the manufacturing sector in Taiwan.

Originality/value

First, Our findings are consistent with the majority of recent research and are supported by the stakeholder theory. The paper argues that Taiwan firms should incorporate CSP into their business strategies for improving their competitive advantages. Second, our findings argue that Taiwan firms in the manufacturing sector should learn the best CSP practices from firms in the non-manufacturing sector to maintain and enhance their sustainability. Third, this paper extends the subject study of Taiwan scenario, and it is the first paper combining two CSP local ratings as the proxy for the CSP measure.

Details

Chinese Management Studies, vol. 8 no. 4
Type: Research Article
ISSN: 1750-614X

Keywords

Available. Open Access. Open Access
Article
Publication date: 19 January 2024

Fuzhao Chen, Zhilei Chen, Qian Chen, Tianyang Gao, Mingyan Dai, Xiang Zhang and Lin Sun

The electromechanical brake system is leading the latest development trend in railway braking technology. The tolerance stack-up generated during the assembly and production…

477

Abstract

Purpose

The electromechanical brake system is leading the latest development trend in railway braking technology. The tolerance stack-up generated during the assembly and production process catalyzes the slight geometric dimensioning and tolerancing between the motor stator and rotor inside the electromechanical cylinder. The tolerance leads to imprecise brake control, so it is necessary to diagnose the fault of the motor in the fully assembled electromechanical brake system. This paper aims to present improved variational mode decomposition (VMD) algorithm, which endeavors to elucidate and push the boundaries of mechanical synchronicity problems within the realm of the electromechanical brake system.

Design/methodology/approach

The VMD algorithm plays a pivotal role in the preliminary phase, employing mode decomposition techniques to decompose the motor speed signals. Afterward, the error energy algorithm precision is utilized to extract abnormal features, leveraging the practical intrinsic mode functions, eliminating extraneous noise and enhancing the signal’s fidelity. This refined signal then becomes the basis for fault analysis. In the analytical step, the cepstrum is employed to calculate the formant and envelope of the reconstructed signal. By scrutinizing the formant and envelope, the fault point within the electromechanical brake system is precisely identified, contributing to a sophisticated and accurate fault diagnosis.

Findings

This paper innovatively uses the VMD algorithm for the modal decomposition of electromechanical brake (EMB) motor speed signals and combines it with the error energy algorithm to achieve abnormal feature extraction. The signal is reconstructed according to the effective intrinsic mode functions (IMFS) component of removing noise, and the formant and envelope are calculated by cepstrum to locate the fault point. Experiments show that the empirical mode decomposition (EMD) algorithm can effectively decompose the original speed signal. After feature extraction, signal enhancement and fault identification, the motor mechanical fault point can be accurately located. This fault diagnosis method is an effective fault diagnosis algorithm suitable for EMB systems.

Originality/value

By using this improved VMD algorithm, the electromechanical brake system can precisely identify the rotational anomaly of the motor. This method can offer an online diagnosis analysis function during operation and contribute to an automated factory inspection strategy while parts are assembled. Compared with the conventional motor diagnosis method, this improved VMD algorithm can eliminate the need for additional acceleration sensors and save hardware costs. Moreover, the accumulation of online detection functions helps improve the reliability of train electromechanical braking systems.

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