Furthermore it contains modular methods for computation of bootstrap confidence intervals, model parameters and several quality indices. Various plot functions help to evaluate the model. The well known mobile phone dataset from marketing research is used to demonstrate the features of the package.
However, Model Tree MT has a good adaptability to nonlinear function, which is made up of many multiple linear segments. Based on this, a new method combining PLS and MT to analysis and predict the data is proposed, which build MT through the main ingredient and the explanatory variables the dependent variable extracted from PLS, and extract residual information constantly to build Model Tree until well-pleased accuracy condition is satisfied. Using the data of the maxingshigan decoction of the monarch drug to treat the asthma or cough and two sample sets in the UCI Machine Learning Repository, the experimental results show that, the ability of explanation and predicting get improved in the new method.
Partial Least Squares tutorial for analyzing neuroimaging data. Full Text Available Partial least squares PLS has become a respected and meaningful soft modeling analysis technique that can be applied to very large datasets where the number of factors or variables is greater than the number of observations.
Current biometric studies e. PLS eliminates the multiple linear regression issues of over-fitting data by finding a few underlying or latent variables factors that account for most of the variation in the data. In real-world applications, where linear models do not always apply, PLS can model the non-linear relationship well. Both methods provide straightforward and comprehensible techniques for determining and modeling relationships between two multivariate data blocks by finding latent variables that best describes the relationships.
In the examples, the PLSC will analyze the relationship between neuroimaging data such as Event-Related Potential ERP amplitude averages from different locations on the scalp with their corresponding behavioural data. Using the same data, the PLSR will be used to model the relationship between neuroimaging and behavioural data.
This model will be able to predict future behaviour solely from available neuroimaging data. SVD decomposes the large data block into three manageable matrices containing a diagonal set of singular values, as well as left and right singular vectors. Mathematica notebooks are provided for each PLS method with clearly labeled sections and subsections.
Brightness-normalized Partial Least Squares Regression for hyperspectral data. PLSR was primarily designed for laboratory analysis of prepared material samples. Under field conditions in vegetation remote sensing, the performance of the technique may be negatively affected by differences in brightness due to amount and orientation of plant tissues in canopies or the observing conditions.
To minimize these effects, we introduced brightness normalization to the PLSR approach and tested whether this modification improves the performance under changing canopy and observing conditions. This test was carried out using high-fidelity spectral data nm to model observed leaf chemistry. The spectral data was combined with a canopy radiative transfer model to simulate effects of varying canopy structure and viewing geometry.
Brightness normalization enhanced the performance of PLSR by dampening the effects of canopy shade, thus providing a significant improvement in predictions of leaf chemistry up to 3. Little improvement was made on effects due to variable leaf area index, while minor improvement mostly not significant was observed for effects of variable viewing geometry. In general, brightness normalization increased the stability of model fits and regression coefficients for all canopy scenarios.
Brightness-normalized PLSR is thus a promising approach for application on airborne and space-based imaging spectrometer data. Full Text Available Partial least squares PLS path modeling is a component-based structural equation modeling that has been adopted in social and psychological research due to its data-analytic capability and flexibility.
A recent methodological advance is consistent PLS PLSc, designed to produce consistent estimates of path coefficients in structural models involving common factors. Nonnegative least-squares image deblurring: The least-squares approach to image deblurring leads to an ill-posed problem. The addition of the nonnegativity constraint, when appropriate, does not provide regularization, even if, as far as we know, a thorough investigation of the ill-posedness of the resulting constrained least-squares problem has still to be done.
Iterative methods, converging to nonnegative least-squares solutions, have been proposed. Some of them have the 'semi-convergence' property, i. In this paper we consider two of these methods: Even if they work well in many instances, they are not frequently used in practice because, in general, they require a large number of iterations before providing a sensible solution. Therefore, the main purpose of this paper is to refresh these methods by increasing their efficiency. Starting from the remark that PL and ISRA require only the computation of the gradient of the functional, we propose the application to these algorithms of special acceleration techniques that have been recently developed in the area of the gradient methods.
In particular, we propose the application of efficient step-length selection rules and line-search strategies. Moreover, remarking that ISRA is a scaled gradient algorithm, we evaluate its behaviour in comparison with a recent scaled gradient projection SGP method for image deblurring. Numerical experiments demonstrate that the accelerated methods still exhibit the semi-convergence property, with a considerable gain both in the number of iterations and in the computational time; in particular, SGP appears definitely the most efficient one.
Error propagation of partial least squares for parameters optimization in NIR modeling. A novel methodology is proposed to determine the error propagation of partial least-square PLS for parameters optimization in near-infrared NIR modeling. The parameters include spectral pretreatment, latent variables and variable selection. In this paper, an open source dataset corn and a complicated dataset Gardenia were used to establish PLS models under different modeling parameters.
The results demonstrated how and what extent the different modeling parameters affect error propagation of PLS for parameters optimization in NIR modeling. The larger the error weight, the worse the model. Finally, our trials finished a powerful process in developing robust PLS models for corn and Gardenia under the optimal modeling parameters.
Furthermore, it could provide a significant guidance for the selection of modeling parameters of other multivariate calibration models. This volume presents state of the art theories, new developments, and important applications of Partial Least Square PLS methods. The text begins with the invited communications of current leaders in the field who cover the history of PLS, an overview of methodological issues, and recent advances in regression and multi-block approaches.
The rest of the volume comprises selected, reviewed contributions from the 8th International Conference on Partial Least Squares and Related Methods held in Paris, France, on May, They are organized in four coherent sections: PLS methods are very versatile methods that are now used in areas as diverse as engineering, life science, sociology, psychology, brain imaging, genomics, and business among both academics ASPLS method can be acommodation to non linear and multicollinearity case of predictor variables.
The first is to used parametric transformations of predictors by spline function; the second is to make ASPLS components mutually uncorrelated, to preserve properties of the linear PLS components. Explanatory variables, X, having multicollinearity are reduced to components which explain the great amount of covariance between explanatory and response variable.
Then multiple linear regression analysis is applied to those components to model the response variable Y. There are various PLSR algorithms. Research has been carried out to determine the feasibility of partial least-squares regression PLS modeling of infrared IR spectra of crude oils as a tool for fast sulfur speciation. The study is a continuation of a previously developed method to predict long and short residue properties of.
Estimation of active pharmaceutical ingredients content using locally weighted partial least squares and statistical wavelength selection. Development of quality estimation models using near infrared spectroscopy NIRS and multivariate analysis has been accelerated as a process analytical technology PAT tool in the pharmaceutical industry.
Although linear regression methods such as partial least squares PLS are widely used, they cannot always achieve high estimation accuracy because physical and chemical properties of a measuring object have a complex effect on NIR spectra. Use of correspondence analysis partial least squares on linear and unimodal data. Correspondence analysis partial least squares CA-PLS has been compared with PLS conceming classification and prediction of unimodal growth temperature data and an example using infrared IR spectroscopy for predicting amounts of chemicals in mixtures.
CA-PLS was very effective for ordinating Partial least square was developed to predict blood hemoglobin concentration using NIRS. The aims of this paper are i to develop predictive model for near infrared spectroscopic analysis in blood hemoglobin prediction, ii to establish relationship between blood hemoglobin and near infrared spectrum using a predictive model, iii to evaluate the predictive accuracy of a predictive model based on root mean squared error RMSE and coefficient of determination rp2.
Optimum number of latent variable LV and frame length f were 32 and 27 nm, respectively. These findings suggest that the relationship between blood hemoglobin and near infrared spectrum is strong, and the partial least square with first order SG derivative is able to predict the blood hemoglobin using near infrared spectral data. In addition, a statistical wavelength selection method which quantifies the effect of API content and other factors on NIR spectra is proposed. The results clearly show that the proposed calibration modeling technique is useful for API content estimation and is superior to the conventional one.
Quantitative structure-retention relationship studies with immobilized artificial membrane chromatography II: We aimed to establish quantitative structure-retention relationship QSRR with immobilized artificial membrane IAM chromatography using easily understood and obtained physicochemical molecular descriptors and to elucidate which descriptors are critical to affect the interaction process between solutes and immobilized phospholipid membranes.
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The retention indices logk IAM of 55 structurally diverse drugs were determined on an immobilized artificial membrane column IAM. DD2 directly or obtained by extrapolation method for highly hydrophobic compounds. Ten simple physicochemical property descriptors clogP, rings, rotatory bond, hydro-bond counting, etc. Comparison of coefficients of centered and scaled variables by PLSR demonstrated that, for the descriptors studied, clogP and TSA have the most significant positive effect but the rotatable bond has significant negative effect on drug IAM chromatographic retention.
Prediction of Placental Barrier Permeability: Full Text Available Assessing the human placental barrier permeability of drugs is very important to guarantee drug safety during pregnancy. Quantitative structure—activity relationship QSAR method was used as an effective assessing tool for the placental transfer study of drugs, while in vitro human placental perfusion is the most widely used method. In this study, the partial least squares PLS variable selection and modeling procedure was used to pick out optimal descriptors from a pool of descriptors of 65 compounds and to simultaneously develop a QSAR model between the descriptors and the placental barrier permeability expressed by the clearance indices CI.
The model was subjected to internal validation by cross-validation and y-randomization and to external validation by predicting CI values of 19 compounds. The mechanistic interpretation of the final model was given by the high variable importance in projection values of descriptors. Using PLS procedure, we can rapidly and effectively select optimal descriptors and thus construct a model with good stability and predictability.
This analysis can provide an effective tool for the high-throughput screening of the placental barrier permeability of drugs. Lameness detection challenges in automated milking systems addressed with partial least squares discriminant analysis. Lameness is prevalent in dairy herds. It causes decreased animal welfare and leads to higher production costs. This study explored data from an automatic milking system AMS to model on-farm gait scoring from a commercial farm.
A total of 88 cows were gait scored once per week, for 2 5-wk periods Two partial least squares discriminant analysis models were fitted to parity 1 and parity 2 groups, respectively, to assign the lameness class according to the predicted probability of being lame score Generalized least squares and empirical Bayes estimation in regional partial duration series index-flood modeling.
Two different Bayesian T-year event estimators are introduced: A regional estimation procedure that combines the index-flood concept with an empirical Bayes method for inferring regional information is introduced. The model is based on the partial duration series approach with generalized Pareto GP distributed exceedances.
The prior information of the model Improved variable reduction in partial least squares modelling based on predictive-property-ranked variables and adaptation of partial least squares complexity. The calibration performance of partial least squares for one response variable PLS1 can be improved by elimination of uninformative variables.
Many methods are based on so-called predictive variable properties, which are functions of various PLS-model parameters, and which may change during the variable reduction process. In these methods variable reduction is made on the variables ranked in descending order for a given variable property. The methods start with full spectrum modelling.
Iteratively, until a specified number of remaining variables is reached, the variable with the smallest property value is eliminated; a new PLS model is calculated, followed by a renewed ranking of the variables. The selective and predictive abilities of the new methods are investigated and tested, using the absolute PLS regression coefficients as predictive property.
The performance of the methods is investigated in conjunction with two data sets from near-infrared sources NIR and one simulated set. The selective and predictive performances of the variable reduction methods are compared statistically using the Wilcoxon signed rank test.
Multivariate fault isolation of batch processes via variable selection in partial least squares discriminant analysis. In recent years, multivariate statistical monitoring of batch processes has become a popular research topic, wherein multivariate fault isolation is an important step aiming at the identification of the faulty variables contributing most to the detected process abnormality.
Although contribution plots have been commonly used in statistical fault isolation, such methods suffer from the smearing effect between correlated variables. In particular, in batch process monitoring, the high autocorrelations and cross-correlations that exist in variable trajectories make the smearing effect unavoidable.
To address such a problem, a variable selection-based fault isolation method is proposed in this research, which transforms the fault isolation problem into a variable selection problem in partial least squares discriminant analysis and solves it by calculating a sparse partial least squares model.
As different from the traditional methods, the proposed method emphasizes the relative importance of each process variable. Such information may help process engineers in conducting root-cause diagnosis. Published by Elsevier Ltd. Dual stacked partial least squares for analysis of near-infrared spectra. A new ensemble learning algorithm is presented for quantitative analysis of near-infrared spectra.
First, several sub-models were generated from the whole calibration set. The inner-stack step was implemented on sub-intervals of the spectrum. Then the outer-stack step was used to combine these sub-models. In addition, a novel selective weighting rule was also involved to select a subset of all available sub-models. Experiments on two public near-infrared datasets demonstrate that the proposed DSPLS with selective weighting rule provided superior prediction performance and outperformed the conventional PLS algorithm.
Compared with the single model, the new ensemble model can provide more robust prediction result and can be considered an alternative choice for quantitative analytical applications. Partial least squares path modeling basic concepts, methodological issues and applications. This edited book presents the recent developments in partial least squares -path modeling PLS-PM and provides a comprehensive overview of the current state of the most advanced research related to PLS-PM.
The second section discusses the methodological issues that are the focus of the recent development of the PLS-PM method. Nonlinear fourth-order space state model of PMSM is selected. This model is rewritten to the linear regression form without linearization. Simulation results validate the efficacy of the proposed algorithm. Partial least squares based gene expression analysis in estrogen receptor positive and negative breast tumors.
Breast cancer is categorized into two broad groups: Exploration of the molecular difference of these two groups may help developing new therapeutic strategies, especially for ER- patients. We acquired differentially expressed genes. Four pathways were found to be enriched with differentially expressed genes, involving immune system, metabolism and genetic information processing process.
Quantification of anaesthetic effects on atrial fibrillation rate by partial least-squares.
International Review of Research in Open and Distributed Learning
The mechanism underlying atrial fibrillation AF remains poorly understood. Multiple wandering propagation wavelets drifting through both atria under hierarchical models are not understood. Some pharmacological drugs, known as antiarrhythmics, modify the cardiac ionic currents supporting the fibrillation process within the atria and may modify the AF propagation dynamics terminating the fibrillation process.
Other medications, theoretically non-antiarrhythmic, may slightly affect the fibrillation process in non-defined mechanisms. We evaluated whether the most commonly used anaesthetic agent, propofol, affects AF patterns. Partial least-squares PLS analysis was performed to reduce significant noise into the main latent variables to find the differences between groups. The final results showed an excellent discrimination between groups with slow atrial activity during the propofol infusion. Full Text Available Theories are developed to explain an observed phenomenon in an effort to understand why and how things happen.
Theories thus, use latent variables to estimate conceptual parameters. The level of abstraction depends, partly on the complexity of the theoretical model explaining the phenomenon. The conjugation of directly-measured variables leads to a formation of a first-order factor. A combination of theoretical underpinnings supporting an existence of a higher-order components, and statistical evidence pointing to such presence adds advantage for the researchers to investigate a phenomenon both at an aggregated and disjointed dimensions.
As partial least square PLS gains its tractions in theory development, behavioural accounting discipline in general should exploit the flexibility of PLS to work with the higher-order factors. However, technical guides are scarcely available. Therefore, this article presents a PLS approach to validate a higher-order factor on a statistical ground using accounting information system dataset.
Extracting information from two-dimensional electrophoresis gels by partial least squares regression. In the present study it is demonstrated how information can be extracted by multivariate data analysis. The strategy is based on partial least squares regression followed by variable selection to find proteins that individually or in combination with other proteins vary Two-dimensional gel electrophoresis 2-DE produces large amounts of data and extraction of relevant information from these data demands a cautious and time consuming process of spot pattern matching between gels.
The classical approach of data analysis is to detect protein markers that appear Such biomarkers are found by comparing the relative volumes of individual spots in the individual gels. Multivariate statistical analysis and modelling of 2-DE data for comparison and classification is an alternative approach utilising the combination Modified multiblock partial least squares path modeling algorithm with backpropagation neural networks approach.
This method uses an iterative procedure in its algorithm. The result is MBPLS-PM algorithm can be modified using the Back Propagation Neural Network approach to replace the iterative process in backward and forward step to get the matrix t and the matrix u in the algorithm. An improved partial least-squares regression method for Raman spectroscopy.
The proposed method is based on sorting the weighted regression coefficients, and then the importance of each variable of the sorted list is evaluated using root mean square errors of prediction RMSEP criterion in each iteration step. Our method was consistently better than the jack-knifing method and showed either a similar or a better performance compared to the genetic algorithm. Totally 58 samples of longissmus dorsi muscle were scanned by a HSI system - nm in reflectance mode, using 44 samples to build t he PLSR model and 14 samples to model validation.
The scores were assigned as continuous values and varied from 1. Recursive N-way partial least squares for brain-computer interface. It combines the multi-way tensors decomposition with a consecutive calculation scheme and allows blockwise treatment of tensor data arrays with huge dimensions, as well as the adaptive modeling of time-dependent processes with tensor variables. In the article the numerical study of the algorithm is undertaken.
Applied to Brain Computer Interface system calibration, the algorithm provides an efficient adjustment of the decoding model. Combining the online adaptation with easy interpretation of results, the method can be effectively applied in a variety of multi-modal neural activity flow modeling tasks. Full Text Available To tackle the sensitivity to outliers in system identification, a new robust dynamic partial least squares PLS model based on an outliers detection method is proposed in this paper.
An improved radial basis function network RBFN is adopted to construct the predictive model from inputs and outputs dataset, and a hidden Markov model HMM is applied to detect the outliers. After outliers are removed away, a more robust dynamic PLS model is obtained. In addition, an improved generalized predictive control GPC with the tuning weights under dynamic PLS framework is proposed to deal with the interaction which is caused by the model mismatch.
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The results of two simulations demonstrate the effectiveness of proposed method. The collected data were analyzed by using Partial Least Square. Attitudes are formed encourage entrepreneurship intentions to start a business significantly. Multivariate analysis of remote LIBS spectra using partial least squares , principal component analysis, and related techniques.
Quantitative analysis with LIBS traditionally employs calibration curves that are complicated by the chemical matrix effects. These chemical matrix effects influence the LIBS plasma and the ratio of elemental composition to elemental emission line intensity. Consequently, LIBS calibration typically requires a priori knowledge of the unknown, in order for a series of calibration standards similar to the unknown to be employed. Partial Least Squares PLS analysis is used to generate a calibration model from which unknown samples can be analyzed.
These MV A techniques appear to exploit the matrix effects associated with the chemistries of these 18 samples. Statistical CT noise reduction with multiscale decomposition and penalized weighted least squares in the projection domain. The suppression of noise in x-ray computed tomography CT imaging is of clinical relevance for diagnostic image quality and the potential for radiation dose saving.
Toward this purpose, statistical noise reduction methods in either the image or projection domain have been proposed, which employ a multiscale decomposition to enhance the performance of noise suppression while maintaining image sharpness. Recognizing the advantages of noise suppression in the projection domain, the authors propose a projection domain multiscale penalized weighted least squares PWLS method, in which the angular sampling rate is explicitly taken into consideration to account for the possible variation of interview sampling rate in advanced clinical or preclinical applications.
The projection domain multiscale PWLS method is derived by converting an isotropic diffusion partial differential equation in the image domain into the projection domain, wherein a multiscale decomposition is carried out. With adoption of the Markov random field or soft thresholding objective function, the projection domain multiscale PWLS method deals with noise at each scale. To compensate for the degradation in image sharpness caused by the projection domain multiscale PWLS method, an edge enhancement is carried out following the noise reduction. The performance of the proposed method is experimentally evaluated and verified using the projection data simulated by computer and acquired by a CT scanner.
The preliminary results show that the proposed projection domain multiscale PWLS method outperforms the projection domain single-scale PWLS method and the image domain multiscale anisotropic diffusion method in noise reduction. Since the interview sampling rate is taken into account in the projection domain. Unlocking interpretation in near infrared multivariate calibrations by orthogonal partial least squares.
Near infrared spectroscopy NIR was developed primarily for applications such as the quantitative determination of nutrients in the agricultural and food industries. Examples include the determination of water, protein, and fat within complex samples such as grain and milk. Because of its useful properties, NIR analysis has spread to other areas such as chemistry and pharmaceutical production. NIR spectra consist of infrared overtones and combinations thereof, making interpretation of the results complicated.
It can be very difficult to assign peaks to known constituents in the sample. Thus, multivariate analysis MVA has been crucial in translating spectral data into information, mainly for predictive purposes. The first two sets included simulated data to demonstrate the overall principles; the third set comprised a statistically replicated design of experiments DoE , to demonstrate how instrumental difference could be accurately visualized and correctly attributed to Wood's anomaly phenomena; the fourth set was chosen to challenge the MVA by using data relating to powder mixing, a crucial step in the pharmaceutical industry prior to tabletting.
It is expected that OPLS will be used mostly in applications where improved interpretation is crucial; one such area is process analytical technology PAT. PAT involves fewer independent samples, i. This work was undertaken to establish a quantitative analysis model which can rapid determinate the content of linalool, linalyl acetate of Xinjiang lavender essential oil. Thus, the PLS models was constructed by using this interval for further analysis. Through the clustering method, lavender essential oil samples were divided into calibration set samples and 52 validation set samples.
Gas chromatography mass spectrometry GC-MS was used as a tool to determine the content of linalool and linalyl acetate in lavender essential oil. In order to optimize the model, different pretreatment methods were used to preprocess the raw NIR spectral to contrast the spectral filtering effect, after analysizing the quantitative model results of linalool and linalyl acetate, the root mean square error prediction RMSEP of orthogonal signal transformation OSC was 0.
In addition, forward interval partial least squares FiPLS method was used to exclude the wavelength points which has nothing to do with determination composition or present nonlinear correlation, finally 8 spectral intervals totally wavelength points were obtained as the dataset.
Combining the data sets which have optimized by OSC-FiPLS with partial least squares PLS to establish a rapid quantitative analysis model for determining the content of linalool and linalyl acetate in Xinjiang lavender essential oil, numbers of hidden variables of two. Full Text Available Linear regression analysis is one of the parametric statistical methods which utilize the relationship between two or more quantitative variables.
In linear regression analysis, there are several assumptions that must be met that is normal distribution of errors, there is no correlation between the error and error variance is constant and homogent. There are some constraints that caused the assumption can not be met, for example, the correlation between independent variables multicollinearity, constraints on the number of data and independent variables are obtained. When the number of samples obtained less than the number of independent variables, then the data is called the microarray data.
This study uses coronary heart and stroke patients data which is a microarray data and contain multicollinearity. Prediction of ferric iron precipitation in bioleaching process using partial least squares and artificial neural network. The output of the model was ferric iron precipitation. The optimal condition of the neural network was obtained by adjusting various parameters by trial-and-error. After optimization and training of the network according to back-propagation algorithm, a neural network was generated for prediction of ferric iron precipitation.
The root mean square error for the neural network calculated ferric iron precipitation for training, prediction and validation set are Results obtained reveal the reliability and good predictivity of neural network model for the prediction of ferric iron precipitation in bioleaching process. Radioisotopic neutron transmission spectrometry: Quantitative analysis by using partial least-squares method.
Neutron spectrometry, based on the scattering of high energy fast neutrons from a radioisotope and slowing-down by the light hydrogen atoms, is a useful technique for non-destructive, quantitative measurement of hydrogen content because it has a large measuring volume, and is not affected by temperature, pressure, pH value and color.
The most common choice for radioisotope neutron source is Cf or Am-Be.
In this study, Cf with a neutron flux of 6. Advances in International Marketing, 20 , Journal of Business Market Management, 3 3 , Die Definition in der betriebswirtschaftlichen Forschung: Reflexionen und empirischer Befund. Journal of Business Research, 61 12 , Marketing ZFP, 30 4 , Das Wirtschaftsstudium, 36 12 , Analyse der Zufriedenheit von Besuchern moderner Multifunktionsarenen: Eine kausalanalytische Untersuchung und indexwertorientierte Ergebnisbeurteilung.
Marketing ZFP, 29 3 , Das Wirtschaftsstudium, 36 2 , Das Wirtschaftsstudium, 35 1 , BankArchiv, 54 3 , Erfolgsmessung und Erfolgswirkung Virtueller Unternehmungen. Wirkung einer Teilnahme an Unternehmensnetzwerken auf die strategischen Erfolgsfaktoren der Partnerunternehmen: Die Unternehmung, 59 3 , Die Gewerbliche Genossenschaft, 9 , Das Kooperationskonzept der Virtuellen Unternehmung. Der Betriebswirt 46 1 , Ameisen-Algorithmen und das Hochzeitsproblem. Erfolgswirkung einer Partizipation an Virtuellen Unternehmungen. Das Kooperationskonzept des Virtuellen Unternehmens.
Das Wirtschaftsstudium, 33 10 , Der Betriebswirt, 45 2 , Der genossenschaftliche Bankensektor in Japan. Kooperation in Virtuellen Unternehmungen: Auswirkungen auf die strategischen Erfolgsfaktoren der Partnerunternehmen. Mit Virtuellen Unternehmen zum Erfolg: Recent Advances in Banking and Finance pp. Basic Concepts, Methodological Issues and Applications pp. What Drives Customer Loyalty? Drawing on the Past to Shape the Future of Marketing pp. Basic Concepts and Recent Issues.
Methodology and Numerical Examples. Zum Einsatz quantitativer Methoden in der betriebswirtschaftlichen Forschung: Aktuelle Entwicklungen des Industriellen Managements pp.
Erfolgswirkung strategischer Allianzen aus Sicht der Kooperationspartner. Methode, Anwendung, Praxisbeispiele pp. Enterprise-Networks and Strategic Success: Determinanten einer erfolgreichen Kooperation in Netzwerken. Netzwerkmanagement in Vertrieb, Handel und Dienstleistung pp. Erfolgswirkungen von Kooperationen kleiner und mittlerer Unternehmen - eine vergleichende Analyse. Recent Advances in Banking and Finance. Digitalization in Maritime and Sustainable Logistics. Digitalization in Supply Chain Management and Logistics.
Operational Excellence in Logistics and Supply Chains. Innovations and Strategies for Logistics and Supply Chains. Sustainability in Logistics and Supply Chain Management. Next Generation Supply Chains. Sustainability and Collaboration in Supply Chain Management. Pioneering Supply Chain Design: Managing the Future Supply Chain: Current Concepts and Solutions for Reliability and Robustness.
Pre-Conference Workshop at the Publish Research with Impact. Presentation at Research Seminar Series: Massey University, Auckland New Zealand. Presentation at the Research Seminar Series: University of Waikato, Hamilton New Zealand. PLS for Research Workshop. Presentation at the PhD Course: University of Canterbury, Christchurch, New Zealand. Presentation at the Doctoral Training: Molde University College, Molde, Norway.
University of Seville, Seville, Spain. Hochschule Reultingen, Reultingen, Germany. Quantitative Research Methods Workshop. Presentation at the Fachhochschule Kiel, Kiel, Germany. Neue Methoden zur Messung von Patientenzufriedenheit. Presentation at the PhD-Seminar: Workshop and Board Meeting. New Insights into the Role of Customer Heterogeneity. Also, given that strengths use may facilitate individuals feeling good about themselves, which in turn contributes to increased levels of self-esteem, it would not be surprising to find self-esteem moderating the relationship of strengths use to well-being.
Moreover, how experienced a person is in drawing on and applying strengths may also influence the magnitude of the strengths use to well-being relationship. Thus, whether experience in applying strengths alters the strengths use to well-being relationship would represent a worthy question to address. According to these results, further investigations due to strengths use and related constructs need to consider mediator or moderator effects more carefully. Additionally, given that strengths use is suggested to be more intrinsically motivated e.
The extant body of self-determination theory research could prove insightful when exploring the relevance of motivations. In addition, given that the incidence of mental illness in society is increasing Seligman et al. Interventions promoting strengths use in education, work and private life may be a way to foster long-term individual resilience and optimal functioning with a favorable cost-value ratio [e. Can one use an individual strength like one uses a bicycle?
Personality characteristics can be seen as constructs, a summary of behaviors and they are not an existing entity. However, the questions is whether having a certain personality characteristic automatically implies using the associated behavior, or whether using the associated behavior without having the associated personality characteristic is also related to the outcome of interest? However, these questions ignore the environment and circumstances completely in which behavior of a person usually takes place e.
The results presented here make several useful contributions to the science how to potentially increase well-being of societies. First, the study confirmed the single-factor structure of the German version of the SUS and second, hypothesized relationships of strengths use and well-being indicators positive inter-correlations with positive affect, self-esteem and vitality, negative inter-correlations with perceived stress and negative affect.
Author Impact
Replications in social science research are of utmost importance to contribute to the establishment of evidence. Therefore, the German version of the SUS is psychometrically sound and the scale can be recommended for further research studies whether applying individual strengths can lead to, e.
Future research should address for example, 1 if and how the promotion of applying individual strengths during education, can result in higher levels of well-being and healthiness in future lives, and 2 how the implementation of strength use in job-design guidelines or working conditions can contribute to higher levels of well-being.
The German version of the SUS was introduced and accordingly found to be well suited for studies with German-speaking adults. The revelation that using individual strengths is positively associated with well-being was reinforced. Clearly, there remains much to be done to scientifically explore the impact of individual strengths use in other countries and cultures. All other personal data used for the statistical analyses are presented sufficiently within the paper.
University of Innsbruck, vice director for research: It is hereby certified that this project is in correspondence with all requirements of the ethical principles and the guidelines of good scientific practice of the University of Innsbruck. The study was designed by AH and SH and carried out by them as well. Data were analyzed and interpreted by AH and DW. All authors contributed to the manuscript essentially drafting, revising. All authors read and approved the final manuscript. The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
We express our gratitude to Dr. Astrid Oberkleiner and MMag. Kristin Kleon for their contribution to the translation process as well. We further wish to acknowledge Ms. Resch BSc for assisting in the process of data collection. The Supplementary Material for this article can be found online at: The moderator-mediator variable distinction in social psychological research: Personality strengths in adolescence and decreased risk of developing mental health problems in early adulthood.
Now, Discover Your Strengths: German PSS — Translation. Oskamp Newbury Park, CA: Hedonia, eudaimonia, and well-being: Strengths use and life satisfaction: Calling and career adaptability among undergraduate students. From strengths use to work performance: Counting blessings versus burdens: Toward a structure- and process-integrated view of personality: Evaluating structural equation models with unobservable variables and measurement error. What good are positive emotions in crises? A prospective study of resilience and emotions following the terrorist attacks on the United States on September 11th, Who benefits the most from a gratitude intervention in children and adolescents?
Examining positive affect as a moderator. Strengths use, self-concordance and well-being: Multivariate Data Analysis 7. Upper Saddle River, NJ: The application of signature character strengths and positive experiences at work. Lopez New York, NY: Oxford University Press , — A rationale and test for the number of factors in factor analysis. Cutoff criteria for fit indexes in covariance structure analysis: Kausalmodellierung mit Partial Least Squares: Use of partial least squares PLS in strategic management research: The Foundations of Hedonic Psychology.
A self-determination perspective of strengths use at work: Psychological Stress and the Coping Process. Stress, Appraisal and Coping. Realizing Strengths in Yourself and Others. A potential-guided approach to coaching psychology. Using signature strengths in pursuit of goals: Strengths based career counseling: The benefits of frequent positive affect: Does happiness lead to success? Sample size in factor analysis. Hays New York, NY: Oxford University Press , 77— Character strengths and well-being across the life span: Positive psychology and adolescents: Where are we now?
Where to from here? Psychometric Theory , 2nd Edn. Happy people become happier through kindness: On being grateful and kind: