THE EFFECT OF WORKLOAD AND SUPERVISION ON WORK STRES AND PERFORMANCE : A STUDY OF PRODUCTION DEPARTMENT ON LAMONGAN SHIPYARD

2019 ◽  
Vol 2 (1) ◽  
pp. 31-36
Author(s):  
Rezha Isyraqi Qastalano ◽  
Bambang Syairudin
Keyword(s):  
Least Squares ◽  
Partial Least Squares ◽  
Work Stress ◽  
Production Department ◽  
And Performance

The purpose of the study was to identify the effect of workload and supervision on work stres and performance. Sample determination used production department employees in Lamongan Shipyard, which made a total of 134 respondents. The method used partial least squares (PLS). Results of the analysis showed that workload had positively and significantly affect on work stress; workload had positively and significantly affect on performance; supervision had positively and significantly affect on work stress; supervision had positively and significantly affect on work stress; supervision had positively and not significantly affect on performance and  lastly, work stress positively and had not significantly affect on performance

Relationships Among Knowledge Management, Organisational Innovativeness and Performance: Covariance-Based Versus Partial Least-Squares Structural Equation Modelling

2018 ◽  
Vol 17 (01) ◽  
pp. 1850008 ◽  
Author(s):  
José Roberto Frega ◽  
Alex Antonio Ferraresi ◽  
Carlos Olavo Quandt ◽  
Claudimar Pereira da Veiga
Keyword(s):  
Knowledge Management ◽  
Least Squares ◽  
Partial Least Squares ◽  
Structural Equation Modelling ◽  
Structural Equation ◽  
Discriminant Validity ◽  
Model Stability ◽  
Equation Modelling ◽  
Path Modelling ◽  
And Performance

The relationships among effective knowledge management (KM), organisational innovativeness (OI), market orientation (MO) and organisational performance (OP) have been explored in the literature. These constructs are generally analysed in pairs, such as the influence of KM on OI, or KM on OP, and other combinations, but the relationships among the full set of constructs in question are not fully understood yet. In the extant literature, the relationships among them are analysed for the most part with covariance-based structural equation modelling (CB-SEM). Partial least-squares (PLS) path modelling is a component-based approach to SEM that is not as widely used as CB-SEM, but it has the potential to allow increased flexibility in handling various modelling problems in comparison with CB models, particularly for predictive and exploratory purposes. This paper aims to verify whether the PLS method could confirm or reject the results of the more restrictive covariance-based method in modelling the relationships among KM, OI, MO and OP. The results indicate that both methods yielded convergent and discriminant validity for the constructs, displaying stability across model analysis and depuration. The PLS model revealed the influence of KM on MO, OI and OP. It also shows that OI is the main driving factor for OP. KM seems to have a direct effect on OP, which is greatly magnified when mediated by OI. The sample size, although borderline adequate for the CB method, was more than adequate for PLS, yielding excellent model stability.

scholarly journals Mechanism for Green Development Behavior and Performance of Industrial Enterprises (GDBP-IE) Using Partial Least Squares Structural Equation Modeling (PLS-SEM)

2020 ◽  
Vol 17 (22) ◽  
pp. 8450
Author(s):  
Xingwei Li ◽  
Jianguo Du ◽  
Hongyu Long
Keyword(s):  
Structural Equation Modeling ◽  
Least Squares ◽  
Partial Least Squares ◽  
Structural Equation ◽  
Equation Modeling ◽  
Significant Positive Effect ◽  
Industrial Enterprises ◽  
Green Development ◽  
And Performance ◽  
Positive Effect

Although the theory of green development behavior and performance of industrial enterprises (GDBP-IE) reveals that the green development behavior (GDB) of industrial enterprises is affected by internal and external factors and produces performance, it lacks a clear mechanism. This paper aims to verify the theory of GDBP-IE and reveals the mechanism of GDBP-IE in the Chinese context. The partial least squares structural equation modeling (PLS-SEM) method was used to analyze valid samples of Chinese industrial enterprises (N = 615). The empirical conclusions are as follows. (1) Corporate tangible resources, corporate intangible resources (CIR), market environment, public supervision and policy and institutional environment (PIE) have a significant positive effect on GDB (i.e., green supply chain management practice and clean production behavior). (2) Compared with other factors, the positive effect of CIR on GDB is the strongest. (3) The level of positive effect of PIE on GDB is not as significant as other factors. (4) GDB has a significant positive effect on green development performance (i.e., corporate social performance, corporate financial performance and corporate environmental performance). This paper provides effective evidence for researchers to use other methods to further verify the theory of GDBP-IE in the Chinese context. This paper also provides an opportunity for cluster analysis of GDBP-IE in different countries or regions. In addition, this paper not only gives a targeted reference for the government to formulate guidelines concerning the green development of industrial enterprises but also encourages industrial enterprise managers to formulate green development strategies, which is a way to help industrial enterprise managers and workers to participate in and comply with GDB.

An fMRI study examining spatial navigation through a virtual environment: Partial least squares analysis

2005 ◽  
Author(s):  
Richard Mraz ◽  
Nancy J. Lobaugh ◽  
Genevieve Quintin ◽  
Konstantine K. Kakzanis ◽  
Simon J. Graham
Keyword(s):  
Least Squares ◽  
Virtual Environment ◽  
Partial Least Squares ◽  
Spatial Navigation ◽  
Partial Least Squares Analysis ◽  
Fmri Study

Controlling in der Produktentwicklung

Controlling ◽  
2020 ◽  
Vol 32 (3) ◽  
pp. 45-50
Author(s):  
Marc Janka
Keyword(s):  
Least Squares ◽  
Partial Least Squares ◽  
Finite Mixture

Gemeinhin gilt die Annahme, dass das Controlling für viele deutsche Unternehmen auch oder besonders in der Produktentwicklung von großer Bedeutung ist und vor allem unter Umfeldunsicherheit ein wesentlicher Erfolgsfaktor sein kann. Der vorliegende Beitrag zeigt unter Anwendung einer für die Controlling-Forschung neuartigen Methode zur Schätzung von Mischverteilungen mittels partieller Regressionen (englisch finite mixture partial least squares [FIMIX-PLS]), ob diese Annahme für alle Unternehmen gleichermaßen gilt.

Partial Least Squares Strukturgleichungsmodellierung

2017 ◽  
Author(s):  
Joseph F. Hair ◽  
Sven Hauff ◽  
G. Tomas M. Hult ◽  
Nicole F. Richter ◽  
Christian M. Ringle ◽  
...  
Keyword(s):  
Least Squares ◽  
Partial Least Squares

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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