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Article
Publication date: 25 May 2021

Seok-Young Oh and Seonhui Koo

This study aims to identify the relationship between protean career attitude (PCA) and organisational commitment (OC) in a learning organisation (LO) climate. The study…

Abstract

Purpose

This study aims to identify the relationship between protean career attitude (PCA) and organisational commitment (OC) in a learning organisation (LO) climate. The study also identified whether negative relationships exist between the structure dimension of LO (SDLO) and PCA, and between PCA and OC, and whether such relationships can be moderated by the people dimension of LO (PDLO).

Design/methodology/approach

Data collected from 305 employees of 26 firms were analysed using the PROCESS macro for SPSS.

Findings

This study found that SDLO had a negative relationship with PCA, whereas PCA was negatively associated with OC. Furthermore, this study found that PDLO moderated these relationships, in that the negative relationships were absent when PDLO activities were stronger, in contrast to when they were weaker.

Originality/value

The study is among the first to identify the negative relationships that exist between SDLO and PCA and between PCA and OC in Korean firms. An important implication for managers or OD professionals is that PDLO plays an important role in not only reducing the negative mediation effects of PCA in the relationships but also making the relationships positive.

Details

Leadership & Organization Development Journal, vol. 42 no. 6
Type: Research Article
ISSN: 0143-7739

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Article
Publication date: 11 May 2015

Mohammad Reza Taghizadeh Yazdi

The purpose of this paper is to illustrate the application of statistical tools and techniques for quantitative assessment of spiritual capital (SC) based on a…

Abstract

Purpose

The purpose of this paper is to illustrate the application of statistical tools and techniques for quantitative assessment of spiritual capital (SC) based on a questionnaire survey in the organizations which undergo large-scale organizational change projects.

Design/methodology/approach

A sample of 65 individuals from three organizations were interviewed. The paper uses the 12 principles of transformation available to spiritual intelligence (referred to as SQ characteristics) to assess SC in a two-phase integrated algorithm of principal component analysis (PCA) and fuzzy clustering.

Findings

The paper proposes a two-phase integrated algorithm. In the first phase, PCA is used to reduce the scores of items related to each of SQ characteristics and aggregate them into a single and unique measure. In the second phase, PCA is applied for total SQ quantification. For verification and validation, fuzzy clustering is employed along with PCA to cluster the people in the survey into different classes, which may possess different stocks of SC and rank them based on their level of SQ. The results of PCA are verified and validated by fuzzy clustering revealing the applicability and usefulness of PCA for SC quantification.

Research limitations/implications

The paper is based on individual judgments about their own SQ characteristics hence the results of questionnaire survey may be biased by individual personal characteristics. Future research can apply the proposed algorithm and check for its reliability using other psychometric instruments available in the field.

Originality/value

The paper contributes by filling a gap in the quantitative management tools literature, in which empirical studies on validated multivariate analysis of spirituality have been scarce until now.

Details

Journal of Organizational Change Management, vol. 28 no. 3
Type: Research Article
ISSN: 0953-4814

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Article
Publication date: 20 December 2007

Chuanfeng Lv and Qiangfu Zhao

In recent years, principal component analysis (PCA) has attracted great attention in dimension reduction. However, since a very large transformation matrix must be used…

Abstract

Purpose

In recent years, principal component analysis (PCA) has attracted great attention in dimension reduction. However, since a very large transformation matrix must be used for reconstructing the original data, PCA has not been successfully applied to image compression. To solve this problem, this paper aims to propose a new technique called kPCA.

Design/methodology/approach

Actually, kPCA is a combination of vector quantization (VQ) and PCA. The basic idea is to divide the problem space into k clusters using VQ, and then find a PCA encoder for each cluster. The point is that if the kPCA encoder is obtained using data containing enough information, it can be used as a semi‐universal encoder to compress all images in a given domain.

Findings

Although a kPCA encoder is more complex than a single PCA encoder, the compression ratio can be much higher because the transformation matrices can be excluded from the encoded data. The performance of the kPCA encoder can be improved further through learning. For this purpose, this paper‐proposes an extended LBG algorithm.

Originality/value

The effectiveness of the kPCA is demonstrated through experiments with several well‐known test images.

Details

International Journal of Pervasive Computing and Communications, vol. 3 no. 2
Type: Research Article
ISSN: 1742-7371

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Article
Publication date: 1 February 1995

Charles W. Neale

Investment project post‐completion auditing (PCA) is capable ofyielding significant benefits to firms wishing to tighten the control ofexisting projects and also to…

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2402

Abstract

Investment project post‐completion auditing (PCA) is capable of yielding significant benefits to firms wishing to tighten the control of existing projects and also to improve their decision‐making and planning procedures. An increasing proportion of large companies now operates post‐audits in pursuit of these benefits. However, adoption and implementation of PCAs is not problem‐free. Among the likely drawbacks is the possibility of deterring staff from advancing proposals for new investment. However, evidence from an empirical study shows that there appears to be no significant relationship between adoption of PCA and level of investment expenditure, in either absolute terms or when adjusted for disposals or size of firm. This suggests that some of the difficulties typically associated with PCAs may be overstated. Concludes with a survey of the issues which would‐be adopters of PCAs should address and offers a checklist of action points designed to lubricate the introduction of PCAs.

Details

Managerial Auditing Journal, vol. 10 no. 1
Type: Research Article
ISSN: 0268-6902

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Article
Publication date: 5 March 2018

Xu Kang and Dechang Pi

The purpose of this paper is to detect the occurrence of anomaly and fault in a spacecraft, investigate various tendencies of telemetry parameters and evaluate the…

Abstract

Purpose

The purpose of this paper is to detect the occurrence of anomaly and fault in a spacecraft, investigate various tendencies of telemetry parameters and evaluate the operation state of the spacecraft to monitor the health of the spacecraft.

Design/methodology/approach

This paper proposes a data-driven method (empirical mode decomposition-sample entropy-principal component analysis [EMD-SE-PCA]) for monitoring the health of the spacecraft, where EMD is used to decompose telemetry data and obtain the trend items, SE is utilised to calculate the sample entropies of trend items and extract the characteristic data and squared prediction error and statistic contribution rate are analysed using PCA to monitor the health of the spacecraft.

Findings

Experimental results indicate that the EMD-SE-PCA method could detect characteristic parameters that appear abnormally before the anomaly or fault occurring, could provide an abnormal early warning time before anomaly or fault appearing and summarise the contribution of each parameter more accurately than other fault detection methods.

Practical implications

The proposed EMD-SE-PCA method has high level of accuracy and efficiency. It can be used in monitoring the health of a spacecraft, detecting the anomaly and fault, avoiding them timely and efficiently. Also, the EMD-SE-PCA method could be further applied for monitoring the health of other equipment (e.g. attitude control and orbit control system) in spacecraft and satellites.

Originality/value

The paper provides a data-driven method EMD-SE-PCA to be applied in the field of practical health monitoring, which could discover the occurrence of anomaly or fault timely and efficiently and is very useful for spacecraft health diagnosis.

Details

Aircraft Engineering and Aerospace Technology, vol. 90 no. 2
Type: Research Article
ISSN: 1748-8842

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Article
Publication date: 5 June 2007

A. Azadeh, S.F. Ghaderi and V. Ebrahimipour

This paper seeks to present an integrated principal component analysis (PCA) data envelopment analysis (DEA) framework for assessment and ranking of manufacturing systems…

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1077

Abstract

Purpose

This paper seeks to present an integrated principal component analysis (PCA) data envelopment analysis (DEA) framework for assessment and ranking of manufacturing systems based on equipment performance indicators.

Design/methodology/approach

The integrated framework discussed in this paper is based on PCA and DEA. The validity of the integrated model is further verified and validated by numerical taxonomy (NT) methods.

Findings

The results of the integrated PCA DEA framework show the ranking of sectors and weak and strong points of each sector with regard to equipment and machinery. Moreover, a non‐parametric correlation method, namely, Spearman correlation experiment shows high level of correlation among the findings of PCA, DEA and NT. Furthermore, it identifies which indicators have major impacts on the performance of manufacturing sectors.

Practical implications

To achieve the objectives of this study, a comprehensive study was conducted to locate all economic and technical indicators which influence equipment performance. These indicators are related to equipment productivity, efficiency, effectiveness and profitability. Standard factors such as down time, time to repair, mean time between failure, operating time, value added and production value were considered as shaping factors. The manufacturing sectors are selected according to the format of International Standard for Industrial Classification.

Originality/value

The modeling approach of this paper could be used for ranking and analysis of other sectors in particular or countries in general.

Details

Engineering Computations, vol. 24 no. 4
Type: Research Article
ISSN: 0264-4401

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Article
Publication date: 9 November 2015

Maria Martins, Cristina Santos, Lino Costa and Anselmo Frizera

The purpose of this paper is to propose a gait analysis technique that aims to identify differences and similarities in gait performance between three different assistive…

Abstract

Purpose

The purpose of this paper is to propose a gait analysis technique that aims to identify differences and similarities in gait performance between three different assistive devices (ADs).

Design/methodology/approach

Two feature reduction techniques, linear principal component analysis (PCA) and nonlinear kernel-PCA (KPCA), are expanded to provide a comparison of the spatio-temporal, symmetrical indexes and postural control parameters among the three different ADs. Then, a multiclass support vector machine (MSVM) with different approaches is designed to evaluate the potential of PCA and KPCA to extract relevant gait features that can differentiate between ADs.

Findings

Results demonstrated that symmetrical indexes and postural control parameters are better suited to provide useful information about the different gait patterns that total knee arthroplasty (TKA) patients present when walking with different ADs. The combination of KPCA and MSVM with discriminant functions (MSVM DF) resulted in a noticeably improved performance. Such combination demonstrated that, with symmetric indexes and postural control parameters, it is possible to extract with high-accuracy nonlinear gait features for automatic classification of gait patterns with ADs.

Originality/value

The information obtained with the proposed technique could be used to identify benefits and limitations of ADs on the rehabilitation process and to evaluate the benefit of their use in TKA patients.

Details

International Journal of Intelligent Computing and Cybernetics, vol. 8 no. 4
Type: Research Article
ISSN: 1756-378X

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Article
Publication date: 6 November 2007

Wayne S. DeSarbo, Robert E. Hausman and Jeffrey M. Kukitz

Principal components analysis (PCA) is one of the foremost multivariate methods utilized in marketing and business research for data reduction, latent variable modeling…

Downloads
1433

Abstract

Purpose

Principal components analysis (PCA) is one of the foremost multivariate methods utilized in marketing and business research for data reduction, latent variable modeling, multicollinearity resolution, etc. However, while its optimal properties make PCA solutions unique, interpreting the results of such analyses can be problematic. A plethora of rotation methods are available for such interpretive uses, but there is no theory as to which rotation method should be applied in any given social science problem. In addition, different rotational procedures typically render different interpretive results. The paper aims to introduce restricted PCA (RPCA), which attempts to optimally derive latent components whose coefficients are integer‐constrained (e.g.: {−1,0,1}, {0,1}, etc.).

Design/methodology/approach

The paper presents two algorithms for deriving efficient solutions for RPCA: an augmented branch and bound algorithm for sequential extraction, and a combinatorial optimization procedure for simultaneous extraction of these constrained components. The paper then contrasts the traditional PCA‐derived solution with those obtained from both proposed RPCA procedures with respect to a published data set of psychographic variables collected from potential buyers of the Dodge Viper sports car.

Findings

This constraint results in solutions which are easily interpretable with no need for rotation. In addition, the proposed procedure can enhance data reduction efforts since fewer raw variables define each derived component.

Originality/value

The paper provides two algorithms for estimating RPCA solutions from empirical data.

Details

Journal of Modelling in Management, vol. 2 no. 3
Type: Research Article
ISSN: 1746-5664

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Article
Publication date: 1 March 2002

KEVIN DOWD

The pre‐commitment approach to bank capital regulation proposes that banks self‐select capital reserve requirements, facing penalties ex post for incurring losses in…

Abstract

The pre‐commitment approach to bank capital regulation proposes that banks self‐select capital reserve requirements, facing penalties ex post for incurring losses in excess of reserves, hence providing incentives for high‐ risk banks to choose higher capital requirements. In order to assess the validity of the pre‐commitment approach, this article analyzes its comparative statics within the context of a standard European option written against the bank's capital base. The author finds that this approach works when it is not needed (when banks possess unlimited capital and hence cannot fail), but not when it is.

Details

The Journal of Risk Finance, vol. 3 no. 4
Type: Research Article
ISSN: 1526-5943

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Article
Publication date: 14 September 2010

H. Omrani, A. Azadeh, S.F. Ghaderi and S. Aabdollahzadeh

The purpose of this paper is to present an integrated algorithm composed of data envelopment analysis (DEA), corrected ordinary least squares (COLS) and principal…

Abstract

Purpose

The purpose of this paper is to present an integrated algorithm composed of data envelopment analysis (DEA), corrected ordinary least squares (COLS) and principal component analysis (PCA) to estimate efficiency scores of electricity distribution units.

Design/methodology/approach

Several DEA and COLS models are prescribed and their results are verified and validated by the algorithm. To calculate efficiency scores, three standard internal consistency conditions between DEA and COLS results are checked by the algorithm. If these conditions are satisfied, DEA is chosen as the superior model because it could be used for optimization as well. Otherwise, the geometric mean of DEA and COLS model is used as the final efficiency scores.

Findings

The algorithm of this paper may be easily applied to decision‐making units because of its robustness (combined DEA‐COLS input and output) and validity gained through PCA.

Originality/value

The integrated approach has several unique features which are: verification and validation mechanism by PCA, consideration of internal consistency conditions between DEA and COLS and consolidation of DEA and COLS for improved ranking given consistency conditions are violated.

Details

International Journal of Energy Sector Management, vol. 4 no. 3
Type: Research Article
ISSN: 1750-6220

Keywords

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