The Effect of Knowledge Management Strategies and Enablers on Knowledge Creation Process and Organizational Performance by Using Partial Least Squares Regression Method

2012 ◽  
Vol 3 (4) ◽  
pp. 38-52 ◽  
Author(s):  
Cheng-Ping Shih ◽  
Hsin-Fu Chou
Keyword(s):  
Knowledge Management ◽  
Organizational Performance ◽  
Least Squares ◽  
Partial Least Squares ◽  
Knowledge Creation ◽  
Least Squares Method ◽  
Management Strategies ◽  
Integrative Model ◽  
Least Squares Regression ◽  
Creation Process

Under Knowledge-based economy, knowledge has been recognized as a form of capital for organizations and provides sustainable competitive advantages. knowledge is not only one of the few recyclable assets that continuously lends itself to new intellectual capital but also be integrated in many different ways in order to maximize its value. This paper has three research objectives. Firstly, measure the effect of Knowledge Management (KM) Strategies on KM Enablers; secondly, measure the effect of KM Enablers on the Knowledge Creation Process (KCP); thirdly, to measure the effect of KCP on the three aspects of Organizational Performance. A knowledge integrative model was built by using Partial Least Squares method, and the findings indicate that KM Strategies do have a significant effect on KM enablers, which in turn does have a significant effect on the KCP. KCP also has a significant effect on innovation, customer’s satisfaction and financial performance for Taiwan multinational company in Thailand.

KNOWLEDGE MANAGEMENT IN MALAYSIAN SMEs: AN EMPIRICAL EXAMINATION ON INFORMATION TECHNOLOGY (IT) SUPPORT AND STRATEGY AS PLAN

2011 ◽  
Vol 01 (04) ◽  
pp. 24-39
Author(s):  
Tan Thai Soon ◽  
Fakhrul Anwar Zainol
Keyword(s):  
Information Technology ◽  
Knowledge Management ◽  
Organizational Performance ◽  
Knowledge Creation ◽  
Research Study ◽  
Empirical Examination ◽  
Creation Process ◽  
Organizational Creativity ◽  
It Support

This research study demonstrates the important of the knowledge creation process. It helps to demonstrate that knowledge management enablers, including IT-support and Strategy as Plan can promote organizational creativity and thus organizational performance. Further, the findings on the new factor, Strategy as Plan, show that it is positively related to knowledge creation. Strategy as Plan can therefore be regarded as an integral part of knowledge creation. This reaffirms Glueck’s (1980, p.9) views of strategy as “a unified, comprehensive, and integrated plan…. designed to ensure that the basic objectives of the enterprise are achieved”. Therefore it can be argued that the best path for Malaysian SMEs to achieve organizational performance is through organizational creativity achieved through a knowledge creation process that involves knowledge enablers.

scholarly journals Partial Least Squares Method in the Analysis of the Intensity of Damage in Prefabricated Large-Block Building Structures

2017 ◽  
Vol 62 (2) ◽  
pp. 269-277 ◽  
Author(s):  
Karol Firek ◽  
Janusz Rusek
Keyword(s):  
Least Squares ◽  
Linear Combination ◽  
Partial Least Squares ◽  
Least Squares Method ◽  
Damage Index ◽  
Building Damage ◽  
Large Block ◽  
Building Structures ◽  
Least Squares Regression ◽  
Intensity Index

Abstract The paper presents the research methodology aimed at determining the building damage intensity index as a linear combination of indices describing the damage to its individual components. The research base comprised 129 building structures erected in the large-block technology. The study compared the results of a standardized approach to data mining - PCA (Principal Components Analysis) with the procedure of the PLSR method (Partial Least Squares Regression). As a result of the analysis, a generalized form of the building damage index was obtained, as a linear combination of the damage to its components.

Variation Sharing

2017 ◽  
Vol 8 (4) ◽  
pp. 46-68 ◽  
Author(s):  
Ned Kock ◽  
Shaun Sexton
Keyword(s):  
Measurement Error ◽  
Least Squares ◽  
Partial Least Squares ◽  
Latent Variables ◽  
Structural Equation ◽  
Least Squares Method ◽  
Fundamental Problem ◽  
Equation Modeling ◽  
Path Coefficient ◽  
Least Squares Regression

The most fundamental problem currently associated with structural equation modeling employing the partial least squares method is that it does not properly account for measurement error, which often leads to path coefficient estimates that asymptotically converge to values of lower magnitude than the true values. This attenuation phenomenon affects applications in the field of business data analytics; and is in fact a characteristic of composite-based models in general, where latent variables are modeled as exact linear combinations of their indicators. The underestimation is often of around 10% per path in models that meet generally accepted measurement quality assessment criteria. The authors propose a numeric solution to this problem, which they call the factor-based partial least squares regression (FPLSR) algorithm, whereby variation lost in composites is restored in proportion to measurement error and amount of attenuation. Six variations of the solution are developed based on different reliability measures, and contrasted in Monte Carlo simulations. The authors' solution is nonparametric and seems to perform generally well with small samples and severely non-normal data.

Use of the Partial Least Squares Method with Acoustic Vibration Spectra as a New Grading Technique for Structural Timber

Holzforschung ◽  
2003 ◽  
Vol 57 (6) ◽  
pp. 644-652 ◽  
Author(s):  
L. Brancheriau ◽  
H. Baillères
Keyword(s):  
Least Squares ◽  
Partial Least Squares ◽  
High Performance ◽  
Partial Least Squares Regression ◽  
Least Squares Method ◽  
Principal Component Regression ◽  
Principal Component ◽  
Acoustic Vibration ◽  
Modulus Of Rupture ◽  
Least Squares Regression

Summary This study develops a high performance grading process based on the analysis of acoustic vibrations in the audible frequency range. The unique feature of the method is that the spectrum is directly applied to obtain predictive variables for estimating the modulus of elasticity and modulus of rupture. A partial least squares regression was used. This powerful method represents a compromise between principal component regression and multi-linear regression. Partial least squares regression screens for factors which account for the variance in the predictor variables and achieves the best correlation between factors and predicted variable. The method is based on projections, similar to principle components regression, whereby a set of correlated variables is compressed into a smaller set of uncorrelated factors.

Parameter Identification of the Non-Linear Rolling Damping Based on PLS Regression Technique

2013 ◽  
Vol 779-780 ◽  
pp. 675-679 ◽  
Author(s):  
Li Ping Sun ◽  
Wei Ben Sun
Keyword(s):  
Least Squares ◽  
Partial Least Squares ◽  
Partial Least Squares Regression ◽  
Least Squares Method ◽  
Least Squares Regression ◽  
Marine Structures ◽  
Roll Damping ◽  
Experimental Conditions ◽  
Non Linear

The rolling motion analysis of ships or other marine structures is of paramount importance. However, one of the thorniest issues in the analysis is the determination of roll damping. The main objective of this work is to apply the Partial Least-Squares regression into the Bass Energy Method and Roberts Method, which are used for the identification of non-linear roll damping parameters. When the number of sample points decreases due to limitations of the experimental conditions and other factors, the differences between the results obtained from Partial Least-Squares Regression and from traditional Least-Squares method demonstrate the applicability of the proposed method.

Use of reflectance spectroscopy to estimate the organic carbon and CaCO3 contents of soils

2012 ◽  
Vol 61 (2) ◽  
pp. 277-290 ◽  
Author(s):  
Ádám Csorba ◽  
Vince Láng ◽  
László Fenyvesi ◽  
Erika Michéli
Keyword(s):  
Organic Carbon ◽  
Least Squares ◽  
Partial Least Squares ◽  
Partial Least Squares Regression ◽  
Mean Squared Error ◽  
Reflectance Spectroscopy ◽  
Least Squares Regression ◽  
Root Mean Squared Error ◽  
Squared Error

Napjainkban egyre nagyobb igény mutatkozik olyan technológiák és módszerek kidolgozására és alkalmazására, melyek lehetővé teszik a gyors, költséghatékony és környezetbarát talajadat-felvételezést és kiértékelést. Ezeknek az igényeknek felel meg a reflektancia spektroszkópia, mely az elektromágneses spektrum látható (VIS) és közeli infravörös (NIR) tartományában (350–2500 nm) végzett reflektancia-mérésekre épül. Figyelembe véve, hogy a talajokról felvett reflektancia spektrum információban nagyon gazdag, és a vizsgált tartományban számos talajalkotó rendelkezik karakterisztikus spektrális „ujjlenyomattal”, egyetlen görbéből lehetővé válik nagyszámú, kulcsfontosságú talajparaméter egyidejű meghatározása. Dolgozatunkban, a reflektancia spektroszkópia alapjaira helyezett, a talajok ösz-szetételének meghatározását célzó módszertani fejlesztés első lépéseit mutatjuk be. Munkánk során talajok szervesszén- és CaCO3-tartalmának megbecslését lehetővé tévő többváltozós matematikai-statisztikai módszerekre (részleges legkisebb négyzetek módszere, partial least squares regression – PLSR) épülő prediktív modellek létrehozását és tesztelését végeztük el. A létrehozott modellek tesztelése során megállapítottuk, hogy az eljárás mindkét talajparaméter esetében magas R2értéket [R2(szerves szén) = 0,815; R2(CaCO3) = 0,907] adott. A becslés pontosságát jelző közepes négyzetes eltérés (root mean squared error – RMSE) érték mindkét paraméter esetében közepesnek mondható [RMSE (szerves szén) = 0,467; RMSE (CaCO3) = 3,508], mely a reflektancia mérési előírások standardizálásával jelentősen javítható. Vizsgálataink alapján arra a következtetésre jutottunk, hogy a reflektancia spektroszkópia és a többváltozós kemometriai eljárások együttes alkalmazásával, gyors és költséghatékony adatfelvételezési és -értékelési módszerhez juthatunk.

Speech Emotion Recognition Based on Sparse Representation

2013 ◽  
Vol 38 (4) ◽  
pp. 465-470 ◽  
Author(s):  
Jingjie Yan ◽  
Xiaolan Wang ◽  
Weiyi Gu ◽  
LiLi Ma
Keyword(s):  
Dimensionality Reduction ◽  
Emotion Recognition ◽  
Least Squares ◽  
Partial Least Squares ◽  
Partial Least Squares Regression ◽  
Speech Emotion Recognition ◽  
Least Squares Regression ◽  
Computer Science Pedagogy ◽  
Reduction Methods ◽  
Analysis Computer

Abstract Speech emotion recognition is deemed to be a meaningful and intractable issue among a number of do- mains comprising sentiment analysis, computer science, pedagogy, and so on. In this study, we investigate speech emotion recognition based on sparse partial least squares regression (SPLSR) approach in depth. We make use of the sparse partial least squares regression method to implement the feature selection and dimensionality reduction on the whole acquired speech emotion features. By the means of exploiting the SPLSR method, the component parts of those redundant and meaningless speech emotion features are lessened to zero while those serviceable and informative speech emotion features are maintained and selected to the following classification step. A number of tests on Berlin database reveal that the recogni- tion rate of the SPLSR method can reach up to 79.23% and is superior to other compared dimensionality reduction methods.

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