Partial Least Squares Method Research Articles

THE EFFECT OF COMPENSATION AND WORK-LIFE BALANCE ON EMPLOYEE RETENTION THROUGH WORK SATISFACTION (CASE STUDY AT PT SANDANG ASIA MAJU ABADI SEMARANG)

This study aims to determine the effect of compensation and work-life balance on employee retention through job satisfaction at PT Sandang Asia Maju Abadi Semarang. The author distributed questionnaires with a Likert scale model to 100 respondents in data collection techniques and was tested using validity and reliability instrument tests using the PLS (Partial Least Square) method. The data analysis used is the evaluation of the measurement model (outer model) and structural (inner model) using SmartPLS 3.0 software. The results showed that compensation has no direct effect on employee retention, work-life balance has no direct effect on employee retention, job satisfaction has a direct effect on employee retention, compensation has a direct effect on job satisfaction, work-life balance has a direct effect on job satisfaction, compensation has an indirect effect on employee retention through job satisfaction as an intervening variable and work-life balance has an indirect effect on employee retention through job satisfaction as an intervening variable.

Detection of Soluble Solids Content (SSC) in Pears Using Near-Infrared Spectroscopy Combined with LASSO–GWF–PLS Model

The soluble solids content (SSC) of pears is mainly composed of sugars, organic acids, and other soluble substances and is one of the important indices used to measure the sweetness and quality of pear juice. The SSC of pears is mainly composed of sugars, organic acids, amino acids, esters, alcohols, phenols, flavonoids, and other compounds, and different groups within these compounds have different characteristic absorption peaks corresponding to different characteristic wavelengths. Traditional methods such as genetic algorithm (GA) and competitive adaptive reweighted sampling (CARS) models used for screening characteristic wavelengths are mainly based on statistical methods, and characteristic wavelengths are selected by finding the wavelengths related to the changes in the concentration of the target analytes. By ignoring the molecular structure and chemical properties of the target analytes and disregarding the influence of the groups of the compounds in the target analytes on the spectral characteristics, wavelengths that are not related to the target analytes may be selected, thus affecting the accuracy of the analytical results. In this paper, a partial least squares (PLS) model was established based on the characteristic wavelengths of CARS, GA, and LASSO algorithms, and the best least absolute shrinkage and selection operator (LASSO) was selected and compared with the characteristic wavelengths selected by group weighted fusion (GWF). The LASSO regression was validated by 10-fold cross-validation to select the appropriate regularization parameter, and the 33 characteristic wavelengths correlated with the SSC of pears were selected in the full spectral range, and the 9 characteristic wavelengths corresponding to the group response were weighted and fused and input into the PLS regression model. Using an established model, the coefficient of determination (R2) and the root mean square error (RMSE) of the calibration set were 0.992 and 0.177%, respectively, and the R2 and RMSE of the test set were 0.998 and 0.128%, respectively. The R2 of our LASSO–GWF–PLS prediction model was improved from 0.975 to 0.998, indicating that the LASSO–GWF–PLS method has very good prediction ability for detection of SSC in pears.

Electronic Word of Mouth (EWOM) and Impulse Buying Affect Purchasing Decisions on Game Items

This study aims to explain whether there is an influence from Electronic Word of Mouth (EWOM) and impulse buying on purchasing decisions, and knowing whether income will affect impulse buying or not. This study uses an analytical descriptive basic method, in other words, the data collected in accordance with the actual state. This research was conducted through an application called Discord. The sample in this study used probability sampling techniques with sample sampling techniques. The sample that had been obtained in this study was 326 respondents. The data collection technique chosen is through the interview method using a questionnaire. Questionnaire testing through its validity and reliability test. Analysis of the data used in this study is the Structural Equation Model (SEM) method with an alternative method of Partial Least Square (PLS). The results showed that: (1) Ewom has a positive effect on purchasing decisions. (2) Impulse buying has no positive effect on purchasing decisions. (3) Purchasing ability has a positive effect on impulse purchasing decision. (4) purchasing ability has a positive effect on impulse buying. (5) Ewom has a positive effect on impulse buying.

Spectral sensor fusion for prediction of Li and Zr in rocks: Neural network and PLS methods

Integrating information from multiple sensors, known as sensor fusion, is particularly challenging for small datasets where selecting from the plethora of available methods poses a significant challenge in chemometric analysis. This study compares several sensor fusion methods (spanning data-level, feature-level, and decision-level fusion) based on partial least squares (PLS) and convolutional neural network (CNN) models. This study is the first to compare simple sensor fusion methods to the latest multiblock PLS models and parallel-input CNNs, to the best of our knowledge. We demonstrate sensor fusion using a small dataset of 177 rock samples on two prediction tasks: predicting lithium (Li) concentrations and zirconium (Zr) concentrations using three types of spectra, namely X-ray fluorescence (XRF), visible to short-wave infrared (Vis-NIR-SWIR), and laser-induced breakdown spectroscopy (LIBS). The best-performing Li model was PLS using XRF (mean RMSEP: 0.078%) and the best sensor fusion model was ROSA (mean RMSEP: 0.087%). The best Zr model was a high-level fusion of PLS models (mean RMSEP: 0.042%).

On the role of partial least squares in path analysis for the social sciences

We describe the current and potential future roles for partial least squares (PLS) algorithms in path analyses, guided by recent advances in envelope theory. After reviewing the present debate and establishing a context, we conclude that, depending on specific objectives, PLS methods have considerable promise, but that their full potential, while reachable, is not now being realized. The future developments necessary for achieving their full potential in the social sciences are clear and doable, albeit demanding. A critique of covariance-based structural equation modeling (CB-SEM), as it relates to PLS, is given as well. Technical details are available in the appendix.

Green human resource management and job pursuit intention: mediating role of corporate social responsibility and organizational reputation

This study aimed to evaluate whether green human resource management practice encourages prospective candidates to join environmentally responsible enterprises through organizational reputation and corporate social responsibility. The sample was gathered from 402 students from various universities in Bangladesh using a pre-structured questionnaire. The link between the latent components has been explained by analyzing the obtained data using the Partial Least Squares Method (PLSM) of Structural equation Modeling (SEM). The findings indicated that green human resources management has a positive relationship with job pursuit intention and organizational reputation and that corporate social responsibility plays a significant mediating role in this relationship. Students studying in universities in Bangladesh reported having higher job pursuit intentions towards organizations that practice environmental responsibility. The results also show that green human resource management is a source for luring graduated students to pursue careers in green firms and will enable practitioners to combine green human resource management with their conventional human resource practices to employ qualified applicants. The results provide valuable insights into the nature of green human resource management and how the top management is committed to green initiatives in an emerging economy like Bangladesh. Also, the results will help the top management understand how to use green human resource management projects to create corporate social responsibility initiatives and an excellent corporate reputation and attract highly qualified, socially responsible employees to improve environmental performance in organizations. The national policymakers may also get input in designing a green human resource management policy for the economy. Bangladesh is a climate-vulnerable country with a minimal practice of green human resource management in the economy, so the empirical findings of this study will attract researchers, academicians, and practitioners of human resource management to carry out further research in the economy of this area. Finally, this study contributes to the body of knowledge in the still-emerging subject of green human resource management by empirically showing worthy outcomes.

University Teacher Credibility as a Strategy to Motivate Students

University students’ motivation can be affected by several factors, one being their perceptions of teacher behaviour in the classroom. This study aimed to predict university students’ state motivation from their perceptions of teacher credibility. The participants were 344 students from the University of Seville. A structural equation model was used with the partial least squares method (PLS-SEM), a technique based on variance, employed to test, and validate the proposed hypotheses. The results reveal, on the one hand, a positive effect of teacher credibility on state motivation and, on the other hand, that teacher credibility has predictive power and predictive relevance for state motivation. Likewise, there is evidence of predictive validity in that teacher credibility can predict values for new cases of state motivation. Strategies are provided for a university faculty to manage their behaviour in the classroom to increase their students’ state motivation, highlighting the use of PLS-SEM as a data analysis tool suitable for application in higher education.

Factors Affecting Saving Behavior Millennial Generation in Surabaya

The purpose of this study is to shed light on the effects of financial literacy, peer pressure, and personal maturity on the saving habits of the millennial generation in Surabaya. This study employs a quantitative methodology. The population included in this study is the 27–41 year old Surabaya millennial generation. With a total of 110 respondents, purposive sampling was the sample technique utilized in the study. While the PLS (partial least squares) method of data analysis is employed, along with validity, reliability, and hypothesis testing. According to the study's findings, the millennial generation in Surabaya's saving habits have been influenced by factors such as financial knowledge, peer influence, and personal modernism.

Breaking Barriers: The Influence of Teachers’ Attitudes on Inclusive Education for Students with Mild Learning Disabilities (MLDs)

Inclusive educational practices demand social justice where all students with special educational needs have the same right to access education, irrespective of their special needs. Increasingly, across the world, teachers are supporting and defending the inclusion of students with disabilities in mainstream schools and classrooms. This is also the case in Pakistan, the site of this study. However, support and defense are not assurance that such policy is accepted by classroom teachers. Previous research studies have identified some barriers to inclusion and in this regard, the attitudes of teachers have been identified as a barrier worthy of consideration. The present research can contribute to developing insights by suggesting all the factors that can accommodate students with mild learning disabilities (MLDs). The research study aimed to determine the attitudes of teachers towards the education of students with mild learning disabilities, which are due to hereditary and neurobiological reasons that alter the functioning of the brain by affecting one or more intellectual processes associated with learning. The problems of processing can interfere with basic skills of learning, such as reading, writing, and mathematical skills. They can also interfere with higher-order skills, such as organizational skills, abstract reasoning skills, thinking skills, long or short-term memory, and attention span, in an inclusive classroom setting. Using a quantitative research method, data were gathered from N = 230 sample cases of teachers selected through a stratified sampling technique from 10 private primary inclusive schools and 10 private primary non-inclusive schools in Karachi. To identify the attitudes of teachers towards the inclusion of students with MLDs, teachers were asked to respond to validated and reliable tools used in different studies. The research applied variance-based structural equation modeling using the partial least-squares modeling method. Smart PLS 3.0 is software used for variance-based structural equation modeling, and the purpose of using it that the model involves mediation. This tool can show direct and indirect associations among variables simultaneously. The results revealed that there is a weak linear relationship between teachers’ attitudes towards the education of students with a mild learning disability and their practices towards inclusive classroom settings. On the contrary, teachers’ positive attitudes towards mild learning disabilities are predictors of inclusive classroom settings in schools. The weak linear association between teachers’ attitudes and the provision of inclusive classroom settings showed that teachers are not prepared to accommodate mild learning disabilities. However, if teachers’ self-efficacy is increased, then teachers are prepared to accommodate mild learning disabilities. It indicates that teachers with a greater sense of self-efficacy can play a significant role in creating an inclusive environment in schools by employing the provision of relevant resources. The present study recommended certain support mechanisms to school management and provided guiding principles to them on the specific resources required to meet the needs of students with a mild learning disability and to increase the self-efficacy of teachers.

Creating Customer Loyalty Based on Brand Image, Service Quality, Learning Quality, by Mediation of Customer Trust in Purwacaraka Music Studio

The purpose of this research is to understand what contributes to customer loyalty, such as brand image, quality of service, and quality of learning. The analytical method to be used in this study is quantitative analysis using the Smart PLS (Partial Least Squares) method. The Smart PLS method is an appropriate method for examining the relationship between complex construct variables and for understanding the mediating effect between these variables. We will analyze the relationship between brand image, service quality, learning quality, customer trust and customer loyalty at Purwa Caraka Music Studio using data collected through a survey of active customers. The sample used was 200 people. The results showed that customer trust proved to play a mediating role in creating customer loyalty based on brand image, service quality, and learning quality. However, these results have not been explored further in the past, and can be used as a performance evaluation for other Purwa Caraka Music Studio Branches and studies for future marketing strategy programs.

Impact of Brand Trust on Repurchase Intention of Tokopedia

This study aims to examine the effect of brand trust on repurchase intention consumer in Tokopedia. Questionnaires distributed to 390 respondents and the hypothesis is examined using quantitative approach. Data was analyzed using the SEM PLS (Structural Equation Modeling Partial Least Squares) method with SmartPLS. The results are significant and positive effects between brand trust and repurchase intention The implication is that Tokopedia should increase its performance on strengthening brand trust in order to increase repurchase intention

Individual and Family Attributes Related to Succession Planning

Management change is a decisive factor for the longevity of family organizations. In this article, we seek to understand the connections between individual and family attributes and succession planning in family businesses. The literature provided us with support for the elaboration of research hypotheses, which we tested in a sample composed of 264 Brazilian family companies. The analysis of the data, through the Structural Equation Modeling with estimation by the method of Partial Least Squares, allowed us to confirm five of the hypotheses formulated. The results demonstrate that some attributes of the successor and the prior knowledge of the business of the possible successor are positively related to the planning of the succession process. As a contribution, the article provides a better understanding of family organizations, mainly contemplating the relationships between: family and business; and successor and predecessor, variables that impact the succession planning level in different ways.

Determination of Chemical Content of Vanilla Pods (Vanilla planifolia) Non-destructively Using NIR Spectroscopy

This study aims to assess Near infrared (NIR) spectroscopy to predict vanillin and moisture content non-destructively. Twenty-four samples of vanilla pods (@55 grams/sample) from Sumatra and Sulawesi Island were tested. The reflectance spectra of vanilla pod were measured using a NIRFlex N-500 spectrometer at the wavelength of 1000-2500 nm. After that, the vanillin and moisture content were measured using the reference method. The reflectance spectra were transformed to absorbance spectra and several pre-treatment methods of NIR spectra were applied, and the results were calibrated with chemical data using principal component regression (PCR) and partial least square (PLS) methods. The best estimation for vanillin content using absorbance spectra with OSC pre-treatment at 3 PLS factors with the accuracy parameters of r = 0.91, SEC = 1.31%, SEP = 1.34%, CV = 45.60%, RPD = 2.30 with a consistency of 97.96%. The best prediction of vanillin content using PCR method was obtained with SNV pre-treated spectra at 6 PCR factors with the accuracy parameters of r = 0.89, SEC = 1.47%, SEP = 1.49%, CV = 43.21%, RPD = 2.09 and 101.40% consistency. The best estimation for water content using the PLS method is using the SNV pre-treatment spectra with a PLS factor of 5 with the accuracy parameters of r = 0.99, SEC = 2.11%, SEP = 2.13%, CV = 6.12%, RPD = 7.98 with consistency = 98.86%. While using the PCR method, the best estimation for moisture content was also obtained by SNV pre-treated spectra at 4 PCR factors with the accuracy values of r = 0.99, SEC = 2.33%, SEP = 2.25%, CV = 6.25%, RPD = 7.58 and consistency = 103.90%. The PLS calibration provide a better accuracy than the PCR. The NIR spectroscopy associated with the selected pre-treatments and factor numbers of PLS and PCR can be used for determination of chemical content of vanilla pods.

Multi-Model Rice Canopy Chlorophyll Content Inversion Based on UAV Hyperspectral Images

Rice is China’s main crop and its output accounts for 30% of the world’s total annual rice production. Rice growth status is closely related to chlorophyll content (called Soil and Plant Analyzer Development (SPAD) values). The determination of a SPAD value is of great significance to the health status of rice, agricultural irrigation and regulated fertilization. The traditional SPAD value measurement method is not only time-consuming, laborious and expensive but also causes irreparable damage to vegetation. The main aim of the present study is to obtain a SPAD value through the inversion of hyperspectral remote sensing images. In order to achieve this purpose, the hyperspectral image of rice at different growth stages at the canopy scale was first acquired using a hyperspectral imaging instrument equipped with a drone; the spectral characteristics of the rice canopy at different growth stages were analyzed and combined with a ground-level measured SPAD value, the bands with high correlation between the SPAD values and the spectra of the rice canopy at different fertility stages were selected. Subsequently, we combined the spectral characteristics with the continuous projection algorithm to extract the characteristic band and used the PLS method in MATLAB software to analyze and calculate the weight of each type of spectral value and the corresponding canopy SPAD value; we then used the wavelength corresponding to the spectral value with the highest weight as the used band. Secondly, the four methods of univariate regression, partial least squares (PLS) regression, support vector machine (SVM) regression and back propagation (BP) neural network regression are integrated to establish the estimation model of the SPAD value of rice canopy. Finally, the models are used to map the SPAD values of the rice canopy. Research shows that the model with the highest decision coefficient among the four booting stage models is “booting stage-SVR” (R2 = 0.6258), and the model with the highest decision coefficient among the four dairy maturity models is “milk-ripe stage-BP” (R2 = 0.6716), all of which can meet the requirement of accurately retrieving the SPAD value of rice canopy. The above results can provide a technical reference for the accurate, rapid and non-destructive monitoring of chlorophyll content in rice leaves and provide a core band selection basis for large-scale hyperspectral remote sensing monitoring of rice.

Effects of green human resource management on organisational sustainability: the mediating role of corporate social responsibility and organisational citizenship behaviour

PurposeThe paper aims to examine the association of green human resource management (GHRM) with organisational sustainability (OS) through the mediating function of corporate social responsibility (CSR) and organisational citizenship behaviour for the environment (OCBE) in Palestinian health-care organisations.Design/methodology/approachThe paper applied a quantitative method in which data are collected from a survey with 88 human resources and quality managers who work in Palestinian health-care organisations. Furthermore, the structural equation modelling method of partial least squares was used for the data analysis.FindingsThe results demonstrated that GHRM practices positively influenced CSR and OCBE, which eventually influenced OS. Notably, this study offers empirical insights into how to improve OS through CSR, OCBE and GHRM approaches.Originality/valueThis paper presents the implications for achieving the health care sector’s long-term sustainability by linking strategic environmental goals of human resources management practices with practices of social responsibility and organisational citizenship behaviour, which ensures employee participation in formulating policies and strategies for the organization.

New approach for rapid estimation of leaf nitrogen, phosphorus, and potassium contents in apple-trees using Vis/NIR spectroscopy based on wavelength selection coupled with machine learning

Timely and rapid monitoring of apple trees nutrition status is vital for accurate management of nutrient fertilizers in order to improve the yield and quality, as well as to reduce the risk of environmental degradation. As a most frequently used method, tissue analysis used to assess the nutritional status of apple tree leaves is laborious, costly, time-consuming, environmentally unfriendly, and destructive. Ground-based sensors are able to efficiently provide information on nutritional status using leaf spectra reflectance. This research aims to establish a novel cost-effective and non-destructive approach for rapidly estimating the status of nitrogen (N), phosphorus (P), and potassium (K) in apple tree leaves based on Visible/Near-infrared (Vis/NIR) spectroscopy (500–1000 nm) coupled with machine learning. The Vis/NIR spectra of apple trees’ leaves were acquired. Then, leaf chemical contents of NPK elements were considered as reference points. Different pre-processing techniques were used to pre-treat the spectra. Four different chemometrics analysis consist of support vector machine (SVM), Artificial neural network (ANN), Random Forest (RF) and partial least square (PLS) were applied to predict NPK contents in comparison to actual values. In order to simplify the models, the sensitive wavelengths were extracted using three wavelength selection approaches, variable importance in projection (VIP), partial least squares (PLS), and random frog (Rfrog). The extracted feature wavelengths from PLS, VIP, and Rfrog methods were widely distributed in the visible and near-infrared regions of the spectrum. The performance of the developed models was tested using Residual Prediction Deviation (RPD) and the Ratio of Performance to Interquartile Distance (RPIQ). The results demonstrated that among all models, the non-linear modeling methods were superior to the linear model. The best results for estimation of Nitrogen (N), Phosphorus (P), and Potassium (K) elements were achieved by the models of MSC + D2-Rfrog-RF (rp = 0.985, RMSEP = 0.029%, RPD = 8.77, RPIQ = 7.72), SNV + D2-Rfrog-RF (rp = 0.977, RMSEP = 0.0053%, RPD = 6.42, RPIQ = 5.09) and SNV + D2-Rfrog-RF (rp = 0.978, RMSEP = 0.018%, RPD = 8.16, RPIQ = 7.01), respectively. The findings of the current approach may provide an efficient approach to predict in-situ NPK contents of apple trees based on leaf spectral reflectance.

Rapid quantitative analysis of calcium in infant formula powder assisted by long short-term memory with variable importance using laser-induced breakdown spectroscopy.

Calcium is the main mineral responsible for healthy bone growth in infants. Laser-induced breakdown spectroscopy (LIBS) was combined with a variable importance-based long short-term memory (VI-LSTM) for the quantitative analysis of calcium in infant formula powder. First, the full spectra were used to establish PLS (partial least squares) and LSTM models. The R2 and root-mean-square error (RMSE) of the test set (R P2 and R M S E P) were 0.1460 and 0.0093 in the PLS method, respectively, and 0.1454 and 0.0091 in the LSTM model, respectively. To improve the quantitative performance, variable selection based on variable importance was introduced to evaluate the contribution of input variables. The variable importance-based PLS (VI-PLS) model had R P2 and R M S E P of 0.1454 and 0.0091, respectively, whereas the VI-LSTM model had R P2 and R M S E P of 0.9845 and 0.0037, respectively. Compared with the LSTM model, the number of input variables in the VI-LSTM model was reduced to 276, R P2 was improved by 114.63%, and R M S E P was reduced by 46.38%. The mean relative error of the VI-LSTM model was 3.33%. We confirm the predictive ability of the VI-LSTM model for the calcium element in infant formula powder. Thus, combining VI-LSTM modeling and LIBS has great potential for the quantitative elemental analysis of dairy products.

Rapid Quantitative Simultaneous Determination of Vitamin A and Vitamin D in a Skin Ointment Pharmaceutical Formulation Based on Net Analyte Signal and Partial Least Squares using the UV Spectrophotometric Method

Objective: The aim of this study was to propose an easy, fast, and precise method, which can be satisfactorily applied to the simultaneous determination of the drugs in pharmaceutical dosage forms instead of the high-performance liquid chromatography (HPLC) method. Methods: Complicated and costly instruments, as well as spending a lot of time for analysis are the limitations of the chromatography method. Hence, UV spectrophotometric technique based on net analyte signal (NAS) and partial least squares (PLS) was proposed and validated for the simultaneous determination of vitamin A and vitamin D in binary mixtures and commercial skin ointment. The chromatographic technique was implemented to compare with the proposed approach for the simultaneous analysis of drugs. Results: The NAS method was able to determine vitamin A and vitamin D in the concentration range of 1–7 and 2-13 μg mL-1 at λmax=323 and λmax=261, respectively. The limit of detection (LOD) and limit of quantification (LOQ) related to the NAS method were found to be 0.092, 0.053 μg/mL and 0.214, 0.170 μg/mL for vitamin A and vitamin D, respectively. The root mean square error (RMSE) of the test series was 0.0607 and 0.0547 for vitamins A and D, respectively. Recoveries by the PLS method were found in the range of 99.98%-100.49% and 99.57%-100.88% for vitamins A and D, respectively. The results obtained from the NAS and PLS procedures were statistically compared with the reference method (HPLC) via analysis of variance (ANOVA) test, where no significant difference was observed. Conclusion: The spectrophotometry along with NAS and PLS methods are proper, rapid, and economic for the determination of pharmaceutical components in quality control laboratories.

Multi-parameter optimization for Vis–NIR spectroscopic analysis of multiple indicators of soil heavy metal in the tideland reclamation area of the Pearl River Delta

ABSTRACT Using visible and near-infrared (Vis–NIR) spectroscopy combined with novel chemometric methods, the rapid reagent-free simultaneous analysis model for Cu, Zn, Ni, and Cr contents in tideland reclamation soil in the Pearl River Delta was established. Based on Savitzky–Golay (SG) smoothing and partial least squares (PLS) regression, a multi-parameter optimization platform (SG-PLS) covering 264 modes was constructed to select appropriate spectral preprocessing mode for each indicator. The equidistant combination PLS (EC-PLS) method with three cycle parameters was adopted for the first large-scale screening of wavelength models. In addition, wavelength phase-out PLS (WSP-PLS) and repetition rate priority combination methods were used as the secondary optimization method. The well-executed competitive adaptive reweighted sampling PLS was also used for comparison. The validation samples that were not involved in modeling were used to validate the selected three group models. For the four indicators, the method of EC-PLS combined with WSP-PLS achieved the best validation effect. In validation, the root mean square error (SEPV), relative root mean square error (RSEPV), and correlation coefficients (RP,V) of prediction were 2.46 mg kg−1, 4.5%, and 0.959 for Cu; 51.37 mg kg−1, 24.6%, and 0.900 for Zn; 3.59 mg kg−1, 9.1%, and 0.739 for Ni; and 9.15 mg kg−1, 8.4%, and 0.843 for Cr, respectively. Results indicated that low relative error and high prediction correlation confirmed the feasibility of using Vis–NIR spectroscopy to analyze soil heavy metal contents. The proposed multi-parameter, multi-stage integrated optimization algorithm could be applied to a wider field of spectral analysis.

THE PORTUGUESE ACCOUNTANTS: A IMPORTANT ROLE ON MANAGEMENT ACCOUNTING USE? AND IN FIRM PERFORMANCE?

Currently the role of accountants in firms is changing. Increasingly, accountants should have adequate skills not only in terms of financial accounting, but also skills in terms of management accounting that help in the decision-making process and in the performance of firms.The use of management accounting practices, and the respective instruments, has been gaining more and more importance in the business sector, presenting very satisfactory results, contributing greatly to the achievement of a companys competitive advantage over others operating in the same field of activity.There are some studies that suggest a weak influence on the role of management accounting in the decision-making process and in firm or organizational performance, however, they are already somewhat old, namely from the end of the 20th century. However, more recent studies show that many small and medium-sized firms cannot survive in the first years of activity, possibly due to a lack of specialists in decision-making support and they didnt implement Management Accounting (MA).The research objective of this article isto analyse the role of the Portuguese accountant, his skills and how the use of MA can contribute to the firm performance.To meet the research objective, it was decided to apply a quantitative analysis. It was developed a reflective structural equation model (SEM), using a Partial Least Squares method (PLS-SEM), which reveals the influence and impact of the Portuguese accountant and the MA on the firm performance.The results obtained allowed us to conclude that accountants in Portugal recognize the relevance of financial accounting and management accounting for the firm performance, however, they do not give relevance to the complementarity, use and applicability of these two accountings. For accountants in Portugal, management accounting is a complement to the firm performance, not being relevant to financial performance and is less important than financial accounting.