Journal articles: 'Partial Least Squares Structural Equation Modeling (PLS SEM)'

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F. Hair Jr, Joe, Marko Sarstedt, Lucas Hopkins, and Volker G. Kuppelwieser. "Partial least squares structural equation modeling (PLS-SEM)." European Business Review 26, no. 2 (March 4, 2014): 106–21. http://dx.doi.org/10.1108/ebr-10-2013-0128.

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Purpose – The authors aim to present partial least squares (PLS) as an evolving approach to structural equation modeling (SEM), highlight its advantages and limitations and provide an overview of recent research on the method across various fields. Design/methodology/approach – In this review article, the authors merge literatures from the marketing, management, and management information systems fields to present the state-of-the art of PLS-SEM research. Furthermore, the authors meta-analyze recent review studies to shed light on popular reasons for PLS-SEM usage. Findings – PLS-SEM has experienced increasing dissemination in a variety of fields in recent years with nonnormal data, small sample sizes and the use of formative indicators being the most prominent reasons for its application. Recent methodological research has extended PLS-SEM's methodological toolbox to accommodate more complex model structures or handle data inadequacies such as heterogeneity. Research limitations/implications – While research on the PLS-SEM method has gained momentum during the last decade, there are ample research opportunities on subjects such as mediation or multigroup analysis, which warrant further attention. Originality/value – This article provides an introduction to PLS-SEM for researchers that have not yet been exposed to the method. The article is the first to meta-analyze reasons for PLS-SEM usage across the marketing, management, and management information systems fields. The cross-disciplinary review of recent research on the PLS-SEM method also makes this article useful for researchers interested in advanced concepts.

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Zulkifli, Raudhah, Nazim Aimran, Sayang Mohd Deni, and Fatin Najihah Badarisam. "A comparative study on the performance of maximum likelihood, generalized least square, scale-free least square, partial least square and consistent partial least square estimators in structural equation modeling." International Journal of Data and Network Science 6, no. 2 (2022): 391–400. http://dx.doi.org/10.5267/j.ijdns.2021.12.015.

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Structural equation modeling offers various estimation methods for estimating parameters. The most used method in covariance-based structural equation modeling (CB-SEM) is the maximum likelihood (ML) estimator. The ML estimator is typically used when fitting models with normally distributed data. The growth of partial least squares path modeling (PLS-PM), including consistent partial least squares (PLSc), has also been noticed by researchers in the SEM fields. The PLSc has elevated interest in the scholastic setting in measuring the performance of various estimation methods in structural equation modeling. The choice of estimation methods has substantial impact in yielding parameter estimates. There could be a trade-off among the estimation methods’ ability to deal with different types of data based on the model tested. Accordingly, this study aims to compare the performance of ML, generalized least squares (GLS), and scale-free least squares (SFLS) for CB-SEM as well as partial least squares (PLS) and consistent partial least squares (PLSc). Multivariate normal data were generated using Monte Carlo simulation with pre-determined population parameters and sample sizes using R Programming packages. To produce the estimated values, data analysis was performed using AMOS and SmartPLS for CB-SEM and PLS-SEM, respectively. The findings illustrate notable similarities between CB-SEM (ML) and PLS-SEM results when the true indicator loading is certainly high.

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Usakli, Ahmet, and Kemal Gurkan Kucukergin. "Using partial least squares structural equation modeling in hospitality and tourism." International Journal of Contemporary Hospitality Management 30, no. 11 (November 12, 2018): 3462–512. http://dx.doi.org/10.1108/ijchm-11-2017-0753.

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PurposeThe purpose of this study is to review the use of partial least squares-structural equation modeling (PLS-SEM) in the field of hospitality and tourism and thereby to assess whether the PLS-SEM-based papers followed the recommended application guidelines and to investigate whether a comparison of journal types (hospitality vs tourism) and journal qualities (top-tier vs other leading) reveal significant differences in PLS-SEM use.Design/methodology/approachA total of 206 PLS-SEM based papers published between 2000 and April 2017 in the 19 SSCI-indexed hospitality and tourism journals were critically analyzed using a wide range of guidelines for the following aspects of PLS-SEM: the rationale of using the method, the data characteristics, the model characteristics, the model assessment and reporting the technical issues.FindingsThe results reveal that some aspects of PLS-SEM are correctly applied by researchers, but there are still some misapplications, especially regarding data characteristics, formative measurement model evaluation and structural model assessment. Furthermore, few significant differences were found on the use of PLS-SEM between the two fields (hospitality and tourism) and between the journal tiers (top-tier and other leading).Practical implicationsTo enhance the quality of research in hospitality and tourism, the present study provides recommendations for improving the future use of PLS-SEM.Originality/valueThe present study fills a sizeable gap in hospitality and tourism literature and extends the previous assessments on the use of PLS-SEM by providing a wider perspective on the issue (i.e. includes both hospitality and tourism journals rather than the previous reviews that focus on either tourism or hospitality), using a larger sample size of 206 empirical studies, investigating the issue over a longer time period (from 2000 to April, 2017, including the in-press articles), extending the scope of criteria (guidelines) used in the review and comparing the PLS-SEM use between the two allied fields (hospitality and tourism) and between the journal tiers (top-tier and other leading).

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Khan, Gohar F., Marko Sarstedt, Wen-Lung Shiau, Joseph F. Hair, Christian M. Ringle, and Martin P. Fritze. "Methodological research on partial least squares structural equation modeling (PLS-SEM)." Internet Research 29, no. 3 (June 3, 2019): 407–29. http://dx.doi.org/10.1108/intr-12-2017-0509.

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Purpose The purpose of this paper is to explore the knowledge infrastructure of methodological research on partial least squares structural equation modeling (PLS-SEM) from a network point of view. The analysis involves the structures of authors, institutions, countries and co-citation networks, and discloses trending developments in the field. Design/methodology/approach Based on bibliometric data downloaded from the Web of Science, the authors apply various social network analysis (SNA) and visualization tools to examine the structure of knowledge networks of the PLS-SEM domain. Specifically, the authors investigate the PLS-SEM knowledge network by analyzing 84 methodological studies published in 39 journals by 145 authors from 106 institutions. Findings The analysis reveals that specific authors dominate the network, whereas most authors work in isolated groups, loosely connected to the network’s focal authors. Besides presenting the results of a country level analysis, the research also identifies journals that play a key role in disseminating knowledge in the network. Finally, a burst detection analysis indicates that method comparisons and extensions, for example, to estimate common factor model data or to leverage PLS-SEM’s predictive capabilities, feature prominently in recent research. Originality/value Addressing the limitations of prior systematic literature reviews on the PLS-SEM method, this is the first study to apply SNA to reveal the interrelated structures and properties of PLS-SEM’s research domain.

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Hult, G. Tomas M., Joseph F. Hair, Dorian Proksch, Marko Sarstedt, Andreas Pinkwart, and Christian M. Ringle. "Addressing Endogeneity in International Marketing Applications of Partial Least Squares Structural Equation Modeling." Journal of International Marketing 26, no. 3 (September 2018): 1–21. http://dx.doi.org/10.1509/jim.17.0151.

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Partial least squares structural equation modeling (PLS-SEM) has become a key method in international marketing research. Users of PLS-SEM have, however, largely overlooked the issue of endogeneity, which has become an integral component of regression analysis applications. This lack of attention is surprising because the PLS-SEM method is grounded in regression analysis, for which numerous approaches for handling endogeneity have been proposed. To identify and treat endogeneity, and create awareness of how to deal with this issue, this study introduces a systematic procedure that translates control variables, instrumental variables, and Gaussian copulas into a PLS-SEM framework. We illustrate the procedure's efficacy by means of empirical data and offer recommendations to guide international marketing researchers on how to effectively address endogeneity concerns in their PLS-SEM analyses.

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Shiau, Wen-Lung, Marko Sarstedt, and Joseph F. Hair. "Internet research using partial least squares structural equation modeling (PLS-SEM)." Internet Research 29, no. 3 (June 3, 2019): 398–406. http://dx.doi.org/10.1108/intr-10-2018-0447.

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Leguina, Adrian. "A primer on partial least squares structural equation modeling (PLS-SEM)." International Journal of Research & Method in Education 38, no. 2 (January 21, 2015): 220–21. http://dx.doi.org/10.1080/1743727x.2015.1005806.

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Nitzl, Christian, Jose L. Roldan, and Gabriel Cepeda. "Mediation analysis in partial least squares path modeling." Industrial Management & Data Systems 116, no. 9 (October 17, 2016): 1849–64. http://dx.doi.org/10.1108/imds-07-2015-0302.

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Purpose Indirect or mediated effects constitute a type of relationship between constructs that often occurs in partial least squares (PLS) path modeling. Over the past few years, the methods for testing mediation have become more sophisticated. However, many researchers continue to use outdated methods to test mediating effects in PLS, which can lead to erroneous results. One reason for the use of outdated methods or even the lack of their use altogether is that no systematic tutorials on PLS exist that draw on the newest statistical findings. The paper aims to discuss these issues. Design/methodology/approach This study illustrates the state-of-the-art use of mediation analysis in the context of PLS-structural equation modeling (SEM). Findings This study facilitates the adoption of modern procedures in PLS-SEM by challenging the conventional approach to mediation analysis and providing more accurate alternatives. In addition, the authors propose a decision tree and classification of mediation effects. Originality/value The recommended approach offers a wide range of testing options (e.g. multiple mediators) that go beyond simple mediation analysis alternatives, helping researchers discuss their studies in a more accurate way.

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Adi, Arini Annisa, Mohammad Masjkur, and Erfiani Erfiani. "Penerapan Structural Equation Modelling-Partial Least Squares pada Faktor Kemiskinan di Jawa Tengah." Xplore: Journal of Statistics 11, no. 2 (June 26, 2022): 84–95. http://dx.doi.org/10.29244/xplore.v11i2.875.

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Jumlah penduduk miskin di Jawa Tengah pada Maret 2020 sebesar 3,98 juta orang (11,41%), peringkat kedua terbesar di Pulau Jawa. Angka jumlah penduduk miskin yang relatif tinggi menjadi prioritas bagi pemerintah untuk menanggulangi kemiskinan. Salah satu cara penanggulangan kemiskinan adalah dengan mengetahui faktor kemiskinan. Penelitian ini bertujuan untuk mengidentifikasi faktor kemiskinan di Jawa Tengah menggunakan metode Structural Equation Modelling-Partial Least Squares (SEM-PLS). Data yang digunakan dalam penelitian ini adalah data kabupaten/kota di Jawa Tengah tahun 2020. Pada kasus ini, terdapat satu peubah laten eksogen kesehatan dan tiga peubah laten endogen kemiskinan, ekonomi, dan sumberdaya manusia. Permasalahan yang ditemui adalah data amatan relatif kecil yaitu 35 amatan serta sebaran data tidak memenuhi asumsi kenormalan, sehingga analisis yang tepat digunakan dalam penelitian ini adalah Structural Equation Modelling-Partial Least Squares (SEM-PLS). Hasil penelitian menunjukkan bahwa peubah laten ekonomi dan Sumber Daya Manusia memiliki pengaruh positif tetapi tidak signifikan. Peubah laten kesehatan memiliki pengaruh negatif dan signifikan terhadap peubah laten kemiskinan. Nilai Q2 untuk peubah laten kemiskinan adalah 0,333, hal ini menunjukkan bahwa sebesar 33,3% keragaman peubah laten kemiskinan dapat dijelaskan oleh peubah laten ekonomi, kesehatan, dan sumber daya manusia. Kata Kunci: Kemiskinan, Structural Equation Modeling (SEM), Partial Least Square (PLS)

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Miftahuddin, Miftahuddin, Retno Wahyuni Putri, Ichsan Setiawan, and Rina Suryani Oktari. "MODELING OF SEA SURFACE TEMPERATURE BASED ON PARTIAL LEAST SQUARE - STRUCTURAL EQUATION." MEDIA STATISTIKA 14, no. 2 (December 28, 2021): 170–82. http://dx.doi.org/10.14710/medstat.14.2.170-182.

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Variability of Sea Surface Temperature (SST) is one of the climatic features that influence global and regional climate dynamics. Missing data (gaps) in the SST dataset are worth investigating since they may statistically alter the value of the SST change. The partial least square-structural equation modeling (PLS-SEM) approach is used in this work to estimate the causality relationships between exogenous and endogenous latent variables. The findings of this study, which are significant indicators that have a loading factor value > 0.7 are as follows: i) sea surface temperature (oC) as a measure of the latent variable changes in SST, ii) wind speed (m/s) and relative humidity (%) as a measure of the latent variable of weather, and iii) air temperature (oC), long-wave solar radiation (w/m2) as a measure of climate latent variables. The size of the Rsquare value is influenced by the number of gaps. The results of the boostrapping show that the latent variables of weather and climate have a significant effect on changes in SST which are indicated by the value of tstatistics > ttabel. The structural model obtained Changes in SST (η) = -0.330 weather + 0.793 climate + ζ. The model shows that the weather has a negative coefficient, which means that the better the weather conditions, the lower the SST changes. Climate has a positive coefficient, which means that the better the climate, the SST changes will also increase. Rising sea surface temperatures caused by an increase in climate can lead to global warming, impacting El-Nino and La-Nina events.

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Richter, Nicole F., Gabriel Cepeda, José L. Roldán, and Christian M. Ringle. "European management research using Partial Least Squares Structural Equation Modeling (PLS-SEM)." European Management Journal 33, no. 1 (February 2015): 1–3. http://dx.doi.org/10.1016/j.emj.2014.12.001.

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Richter, Nicole Franziska, Gabriel Cepeda, José Luis Roldán, and Christian M. Ringle. "European management research using partial least squares structural equation modeling (PLS-SEM)." European Management Journal 34, no. 6 (December 2016): 589–97. http://dx.doi.org/10.1016/j.emj.2016.08.001.

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Afthanorhan, Asyraf, Zainudin Awang, and Nazim Aimran. "Five Common Mistakes for Using Partial Least Squares Path Modeling (PLS-PM) in Management Research." Contemporary Management Research 16, no. 4 (December 2020): 255–78. http://dx.doi.org/10.7903/cmr.20247.

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The value of Partial Least Squares Path Modeling (PLS-PM) in management research has now been acknowledged, although the PLS-PM was developed for a reason. First, the PLS-PM was developed as an alternative to Covariance based Structural Equation Modeling (CBSEM) when exploratory research is conducted. As far as this method concerned, many researchers are misused or overuse the application of PLS-PM without understanding the basic knowledge in structural equation modeling. Thus, the purpose of this paper is to discuss the five common mistakes (data distributions, sample size limitations, unsatisfactory fitness index, misunderstanding between confirmatory and exploratory research, and poor factor loadings) for using PLS-PM over CB-SEM in management research. We concluded that the researchers should respect these methods and justify their use when conducting the research projects because some of the projects might be better for CB-SEM or PLS-PM.

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Kock, Ned. "Structural Equation Modeling with Factors and Composites." International Journal of e-Collaboration 13, no. 1 (January 2017): 1–9. http://dx.doi.org/10.4018/ijec.2017010101.

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Recent methodological developments building on partial least squares (PLS) techniques and related ideas have significantly contributed to bridging the gap between factor-based and composite-based structural equation modeling (SEM) methods. PLS-SEM is extensively used in the field of e-collaboration, as well as in many other fields where multivariate statistical analyses are employed. The author compares results obtained with four methods: covariance-based SEM with full information maximum likelihood (FIML), factor-based SEM with common factor model assumptions (FSEM1), factor-based SEM building on the PLS Regression algorithm (FSEM2), and PLS-SEM employing the Mode A algorithm (PLSA). The comparison suggests that FSEM1 yields path coefficients and loadings that are very similar to FIML's; and that FSEM2 yields path coefficients that are very similar to FIML's and loadings that are very similar to PLSA's.

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Tirta, I. Made, Nawal Ika Susanti, and Yuliani Setia Dewi. "Structural Equation Modeling of the Factors Affecting the Nutritional Status of Children Under Five in Banyuwangi Region using Recursive (one-way) GSCA." Jurnal ILMU DASAR 16, no. 1 (August 7, 2015): 1. http://dx.doi.org/10.19184/jid.v16i1.534.

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Structural Equation Modeling is one among popular multivariate analysis, especially applied in pschology and marketing. There are two main types of Structural Equation Modeling namely covariance-based or CB-SEM and variance-based or Partial Least Square (PLS)- SEM. Both types have advantages and disadvantage. To overcome its limitation, Generalized Structured Component Analysis (GSCA) was then proposed as an extension of PLS-SEM. In estimating the parameters, GSCA uses Alternating Least Squares (ALS) and in estimating the standard error of the parameter estimates it uses the bootstrap method. In this paper, GSCA is applied to study the causality model of Infant nutritional status, in relation with socio-economic status and infantcare status in Banyuwangi Region. The results show that both socio-economic and infantcare status have significant positive influence on infant nutritional status.Keywords: Alternating least square, generalized structural component analysis, nutritional status of infants, structural equation modelling

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Chin, Wynne, Jun-Hwa Cheah, Yide Liu, Hiram Ting, Xin-Jean Lim, and Tat Huei Cham. "Demystifying the role of causal-predictive modeling using partial least squares structural equation modeling in information systems research." Industrial Management & Data Systems 120, no. 12 (August 4, 2020): 2161–209. http://dx.doi.org/10.1108/imds-10-2019-0529.

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PurposePartial least squares structural equation modeling (PLS-SEM) has become popular in the information systems (IS) field for modeling structural relationships between latent variables as measured by manifest variables. However, while researchers using PLS-SEM routinely stress the causal-predictive nature of their analyses, the model evaluation assessment relies exclusively on criteria designed to assess the path model's explanatory power. To take full advantage of the purpose of causal prediction in PLS-SEM, it is imperative for researchers to comprehend the efficacy of various quality criteria, such as traditional PLS-SEM criteria, model fit, PLSpredict, cross-validated predictive ability test (CVPAT) and model selection criteria.Design/methodology/approachA systematic review was conducted to understand empirical studies employing the use of the causal prediction criteria available for PLS-SEM in the database of Industrial Management and Data Systems (IMDS) and Management Information Systems Quarterly (MISQ). Furthermore, this study discusses the details of each of the procedures for the causal prediction criteria available for PLS-SEM, as well as how these criteria should be interpreted. While the focus of the paper is on demystifying the role of causal prediction modeling in PLS-SEM, the overarching aim is to compare the performance of different quality criteria and to select the appropriate causal-predictive model from a cohort of competing models in the IS field.FindingsThe study found that the traditional PLS-SEM criteria (goodness of fit (GoF) by Tenenhaus, R2 and Q2) and model fit have difficulty determining the appropriate causal-predictive model. In contrast, PLSpredict, CVPAT and model selection criteria (i.e. Bayesian information criterion (BIC), BIC weight, Geweke–Meese criterion (GM), GM weight, HQ and HQC) were found to outperform the traditional criteria in determining the appropriate causal-predictive model, because these criteria provided both in-sample and out-of-sample predictions in PLS-SEM.Originality/valueThis research substantiates the use of the PLSpredict, CVPAT and the model selection criteria (i.e. BIC, BIC weight, GM, GM weight, HQ and HQC). It provides IS researchers and practitioners with the knowledge they need to properly assess, report on and interpret PLS-SEM results when the goal is only causal prediction, thereby contributing to safeguarding the goal of using PLS-SEM in IS studies.

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Cepeda-Carrion, Gabriel, Juan-Gabriel Cegarra-Navarro, and Valentina Cillo. "Tips to use partial least squares structural equation modelling (PLS-SEM) in knowledge management." Journal of Knowledge Management 23, no. 1 (January 14, 2019): 67–89. http://dx.doi.org/10.1108/jkm-05-2018-0322.

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PurposeStructural equation modelling (SEM) has been defined as the combination of latent variables and structural relationships. The partial least squares SEM (PLS-SEM) is used to estimate complex cause-effect relationship models with latent variables as the most salient research methods across a variety of disciplines, including knowledge management (KM). Following the path initiated by different domains in business research, this paper aims to examine how PLS-SEM has been applied in KM research, also providing some new guidelines how to improve PLS-SEM report analysis.Design/methodology/approachTo ensure an objective way to analyse relevant works in the field of KM, this study conducted a systematic literature review of 63 publications in three SSCI-indexed and specific KM journals between 2015 and 2017.FindingsOur results show that over the past three years, a significant amount of KM works has empirically used PLS-SEM. The findings also suggest that in light of recent developments of PLS-SEM reporting, some common misconceptions among KM researchers occurred mainly related to the reasons for using PLS-SEM, the purposes of PLS-SEM analysis, data characteristics, model characteristics and the evaluation of the structural models.Originality/valueThis study contributes to that vast KM literature by documenting the PLS-SEM-related problems and misconceptions. Therefore, it will shed light for better reports in PLS-SEM studies in the KM field.

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Marliana, Reny Rian. "PARTIAL LEAST SQUARE-STRUCTURAL EQUATION MODELING PADA HUBUNGAN ANTARA TINGKAT KEPUASAN MAHASISWA DAN KUALITAS GOOGLE CLASSROOM BERDASARKAN METODE WEBQUAL 4.0." Jurnal Matematika, Statistika dan Komputasi 16, no. 2 (December 19, 2019): 174. http://dx.doi.org/10.20956/jmsk.v16i2.7851.

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AbstractThis paper studied a relationship between the quality of google classroom and student satisfaction. The quality of google classroom measured based on the Webqual 4.0 which is consists of four variables i.e. information quality, service interaction quality, user interface quality and usability. The relationship modeling between these latent variables and the student satisfaction was done by using the Partial Least Square-Structural Equation Modeling (PLS-SEM). Estimation parameters of the model used PLS-SEM algorithm and Ordinary Least Square (OLS) method. Data was collected using a questionnaire to 89 students of google classroom’s Probability and Statistics Course, Odd Semester 2017-2018 at STMIK Sumedang. The result showed the information quality, the service interaction quality and the user interface quality does not have significantly influence of the student satisfaction. Each of the total effects are 0.149; 0.011 and -0.155. While the usability has a significant effect to the student satisfaction positively with total effect 0.707. Keywords : partial least squares, pls-sem, webqual 4.0 AbstrakHubungan antara kualitas google classroom dan tingkat kepuasan mahasiswa dipelajari pada paper ini. Kualitas google classroom diukur berdasarkan metode Webqual 4.0 yang terdiri atas empat variabel laten yaitu kualitas informasi, kualitas interaksi layanan, kualitas antar muka pengguna dan kegunaa. Pemodelan hubungan antara keempat laten variabel tersebut dengan tingkat kepuasan mahasiswa dilakukan dengan menggunakan Partial Least Squares- Structural Equation Modeling (PLS-SEM). Estimasi parameter model dilakukan dengan menggunakan algoritma PLS-SEM yang didasarkan pada metode Ordinary Least Square (OLS). Data penelitian diperoleh melalui penyebaran 89 kuesioner terhadap mahasiswa yang terdaftar pada google classroom mata kuliah Probabilitas dan Statistika pada Semester Gasal 2017-2018 di STMIK Sumedang. Hasil analisis menunjukkan bahwa kualitas informasi, kualitas antar muka penggunan dan kualitas interaksi layanan tidak berpengaruh secara signifikan terhadap tingkat kepuasan mahasiswa dengan total pengaruh berturut-turut 0,149; 0,011 dan -0,155. Sementara variabel kegunaan berpengaruh secara signifikan terhadap tingkat kepuasan mahasiswa dengan total pengaruh sebesar 0,707. Keywords : partial least squares, pls-sem, webqual 4.0

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Thien, Lei Mee, and Meow Yem Tan. "KEPIMPINAN DISTRIBUTIF, KEADAAN DALAM SEKOLAH, DAN KOMITMEN GURU UNTUK BERUBAH: SATU ANALISA PARTIAL LEAST SQUARES[DISTRIBUTIVE LEADERSHIP, IN-SCHOOL CONDITION, AND TEACHERS’ COMMITMENT TO CHANGE: A PARTIAL LEAST SQUARES ANALYSIS]." Journal of Nusantara Studies (JONUS) 4, no. 1 (June 29, 2019): 159. http://dx.doi.org/10.24200/jonus.vol4iss1pp159-185.

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Kajian ini bertujuan untuk mengkaji hubungan antara kepimpinan distributif terhadap komitmen guru untuk berubah dengan keadaan dalam sekolah sebagai mediator. Kajian ini menggunakan reka bentuk kajian kuantitatif dengan kaedah tinjauan. Sampel kajian terdiri daripada 357 orang guru-guru sekolah menengah yang dipilih secara rawak daripada 20 buah sekolah di Daerah Seberang Perai Utara dan Seberang Perai Tengah di Pulau Pinang, Malaysia. Pendekatan Partial Least Squares Structural Equation Modeling (PLS-SEM) dengan perisian SmartPLS 3.2.7 digunakan untuk penganalisaan data. Dapatan kajian ini menunjukkan bahawa kepimpinan distributif mempunyai hubungan positif yang signifikan dengan komitmen guru untuk berubah secara langsung (β=0.472, p<.05). Keadaan dalam sekolah mempunyai kesan pengantaraan yang signifikan dalam hubungan antara kepimpinan distributif dan komitmen guru untuk berubah (β=0.228, p<.05). Dapatan kajian memberi implikasi terhadap pihak pembuat polisi dan pentadbir sekolah bahawa kepimpinan distributif dan keadaan dalam sekolah merupakan elemen yang penting untuk meningkatkan komitmen guru untuk berubah dalam pengajaran abad ke-21.Kata kunci: Keadaan dalam sekolah, kepimpinan distributif, komitmen guru untuk berubah, Partial Least Squares Structural Equation Modeling (PLS-SEM), pembuat polisi.ABSTRACTThis study aims to investigate the relationships between distributed leadership and teachers’ commitment to change with in-school condition as the mediator. This study employed quantitative survey research design. Data consisted of 357 secondary teachers who were selected randomly from 20 schools in northern and central districts in Seberang Prai in Penang, Malaysia. Partial Least Squares Structural Equation Modeling (PLS-SEM) approach with SmartPLS 3.2.7 software was used for data analysis. Findings revealed significant and positive direct relationship between distributed leadership and teachers’ commitment to change (β=0.472, p<.05). In-school condition has significant mediating effects between distributed leadership and teachers’ commitment to change (β=0.228, p<.05). Findings informed implications for policymakers that distributed leadership and in-school condition are two ultimate elements to enhance teachers’ commitment to change in the teaching of 21st century.Keywords: Distributed leadership, in-school condition, Partial Least Squares Structural Equation Modeling (PLS-SEM), policymakers, teachers’ commitment to changeCite as: Thien, L. M. & Tan, M. Y. (2019). Kepimpinan distributif, keadaan dalam sekolah, dan komitmen guru untuk berubah: Satu analisa partial least squares. Journal of Nusantara Studies, 4(1), 159-185. http://dx.doi.org/10.24200/jonus.vol4iss1pp159-185

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Hair, Joseph F., Marko Sarstedt, and Christian M. Ringle. "Rethinking some of the rethinking of partial least squares." European Journal of Marketing 53, no. 4 (April 8, 2019): 566–84. http://dx.doi.org/10.1108/ejm-10-2018-0665.

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PurposePartial least squares structural equation modeling (PLS-SEM) is an important statistical technique in the toolbox of methods that researchers in marketing and other social sciences disciplines frequently use in their empirical analyses. The purpose of this paper is to shed light on several misconceptions that have emerged as a result of the proposed “new guidelines” for PLS-SEM. The authors discuss various aspects related to current debates on when or when not to use PLS-SEM, and which model evaluation metrics to apply. In addition, this paper summarizes several important methodological extensions of PLS-SEM researchers can use to improve the quality of their analyses, results and findings.Design/methodology/approachThe paper merges literature from various disciplines, including marketing, strategic management, information systems, accounting and statistics, to present a state-of-the-art review of PLS-SEM. Based on these findings, the paper offers a point of orientation on how to consider and apply these latest developments when executing or assessing PLS-SEM-based research.FindingsThis paper offers guidance regarding situations that favor the use of PLS-SEM and discusses the need to consider certain model evaluation metrics. It also summarizes how to deal with endogeneity in PLS-SEM, and critically comments on the recent proposal to adjust PLS-SEM estimates to mimic common factor models that are the foundation of covariance-based SEM. Finally, this paper opposes characterizing common concepts and practices of PLS-SEM as “out-of-date” without providing well-substantiated alternatives and solutions.Research limitations/implicationsThe paper paves the way for future discussions and suggests a way forward to reach consensus regarding situations that favor PLS-SEM use and its application.Practical implicationsThis paper offers guidance on how to consider the latest methodological developments when executing or assessing PLS-SEM-based research.Originality/valueThis paper complements recently proposed “new guidelines” with the aim of offering a counter perspective on some strong claims made in the latest literature on PLS-SEM. It also clarifies some misconceptions regarding the application of PLS-SEM.

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Ross, James, Leslie Nuñez, and Chinh Chu Lai. "Partial least squares structural equation modeling of chemistry attitude in introductory college chemistry." Chemistry Education Research and Practice 19, no. 4 (2018): 1270–86. http://dx.doi.org/10.1039/c7rp00238f.

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Students’ decisions to enter or persist in STEM courses is linked with their affective domain. The influence of factors impacting students’ affective domain in introductory college chemistry classes, such as attitude, is often overlooked by instructors, who instead focus on students’ mathematical abilities as sole predictors of academic achievement. The current academic barrier to enrollment in introductory college chemistry classes is typically a passing grade in a mathematics prerequisite class. However, mathematical ability is only a piece of the puzzle in predicting preparedness for college chemistry. Herein, students’ attitude toward the subject of chemistry was measured using the original Attitudes toward the Subject of Chemistry Inventory (ASCI). Partial least squares structural equation modeling (PLS-SEM) was used to chart and monitor the development of students’ attitude toward the subject of chemistry during an introductory college chemistry course. Results from PLS-SEM support a 3-factor (intellectual accessibility,emotional satisfaction, andinterestandutility) structure, which could signal the distinct cognitive, affective, and behavioral components of attitude, according to its theoretical tripartite framework. Evidence of a low-involvement hierarchy of attitude effect is also presented herein. This study provides a pathway for instructors to identify at-risk students, exhibiting low affective characteristics, early in a course so that academic interventions are feasible. The results presented here have implications for the design and implementation of teaching strategies geared toward optimizing student achievement in introductory college chemistry.

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Cepeda-Carrión, Gabriel, Joseph F. Hair, Christian M. Ringle, José Luis Roldán, and Jerónimo García-Fernández. "Guest editorial: Sports management research using partial least squares structural equation modeling (PLS-SEM)." International Journal of Sports Marketing and Sponsorship 23, no. 2 (April 5, 2022): 229–40. http://dx.doi.org/10.1108/ijsms-05-2022-242.

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Gye-Soo, Kim. "Partial Least Squares Structural Equation Modeling(PLS-SEM): An application in Customer Satisfaction Research." International Journal of u- and e- Service, Science and Technology 9, no. 4 (April 30, 2016): 61–68. http://dx.doi.org/10.14257/ijunesst.2016.9.4.07.

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Amriza, Rona Nisa Sofia, and Khairun Nisa Meiah Ngafidin. "ANALISIS PENGARUH PLATFORM SOSIAL MEDIA TERHADAP PENYEBARAN INFORMASI BENCANA." JSiI (Jurnal Sistem Informasi) 8, no. 2 (September 11, 2021): 82–87. http://dx.doi.org/10.30656/jsii.v8i2.3639.

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Abstrak- Sosial media menjadi platform yang sangat berperan dalam menyebarkan informasi, berita, dan memberikan informasi bencana secara cepat dan tepat. Banyak informasi berharga yang dapat diperoleh dalam platform ini. Penelitian ini menganalisis pengaruh platform sosial media terhadap intensi masyarakat untuk menyebarkan informasi bencana yang dipengaruhi oleh mediator personal yaitu altruisme dan efikasi diri. Penelitian ini mengobservasi penyebab seseorang memiliki intensi untuk menyebarkan informasi bencana. Structural Equation Modeling Partial Least Squares (SEM-PLS) digunakan untuk melakukan uji hipotesis. Dari penelitian ini kami menemukan bahwa mediator altruisme dan efikasi diri dalam platform sosial media berpengaruh secara signifikan terhadap intensi seseorang untuk menyebarkan informasi bencana. Kata Kunci: Sosial Media, Penyebaran Informasi, Penyebaran Informasi Bencana, Structural Equation Modeling, Partial Least Squares, SEM-PLS

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Serrato González, Hugo, Ignacio Méndez Ramírez, and Odette Lobato Calleros. "The sampling design effect on partial least squares algorithm." RECI Revista Iberoamericana de las Ciencias Computacionales e Informática 5, no. 9 (June 21, 2016): 84. http://dx.doi.org/10.23913/reci.v5i9.44.

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The objective of this article is to analyze the effect of the probability sampling’s selection on the estimated results in Structural Equation Modeling (SEM) using the Partial Least Squares (PLS) algorithm.The idea leading this work is to estimate the satisfaction level of government service users in a large and dispersed population, for which a sample design with an equal selection probability is not a feasible option. This study is based on the analysis of the sampling distributions of estimators under different sampling designs.It is shown that the probability of selection of the units behind the sampling design affect the results of the PLS algorithm, both the scores of latent variables and the impacts between them.To the author's knowledge, this issue has not been addressed before in the literature.

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ÇAKIR, Süleyman. "MODELING NATIONAL INNOVATION SYSTEMS OF EU COUNTRIES USING PARTIAL LEAST SQUARES STRUCTURAL EQUATION MODELING (PLS-SEM)." Hacettepe Üniversitesi İktisadi ve İdari Bilimler Fakültesi Dergisi 35, no. 3 (September 29, 2017): 19–41. http://dx.doi.org/10.17065/huniibf.340694.

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Kusuma, Wirajaya, Rifani Nur Sindy Setiawan, Kirti Verma, and Carina Firstca Utomo. "Structural Equation Modeling-Partial Least Square for Poverty Modeling in Papua Province." Jurnal Varian 4, no. 2 (April 30, 2021): 79–90. http://dx.doi.org/10.30812/varian.v4i2.852.

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Poverty in Papua Province in 2018 has increased from the previous year. The poverty rate in Papua Province in March 2018 reached 27,74%. This study aims to analyze the factors that influence it so that it can be handled properly. The research method used in this research is Structural Equation Modeling (SEM) with the Partial Least Squares (PLS) approach. The research variables used consisted of 4 latent variables (Poverty, Economy, Human Resources (HR), and Health) with 16 indicators (manifest variables). Based on the analysis that has been done, it is found that economic and health variables have a negative and significant effect on poverty with path coefficients of -0,421 and -0,270, respectively. The health variable has a positive and significant effect on HR with a path coefficient of 0,496. Meanwhile, the HR variable has a positive and significant effect on the economy with a path coefficient of 0,801. It can be concluded that there are two variables that have a significant effect on poverty in Papua Province, including the economy and health.

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Henseler, Jörg, Christian M. Ringle, and Marko Sarstedt. "Testing measurement invariance of composites using partial least squares." International Marketing Review 33, no. 3 (May 9, 2016): 405–31. http://dx.doi.org/10.1108/imr-09-2014-0304.

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Purpose – Research on international marketing usually involves comparing different groups of respondents. When using structural equation modeling (SEM), group comparisons can be misleading unless researchers establish the invariance of their measures. While methods have been proposed to analyze measurement invariance in common factor models, research lacks an approach in respect of composite models. The purpose of this paper is to present a novel three-step procedure to analyze the measurement invariance of composite models (MICOM) when using variance-based SEM, such as partial least squares (PLS) path modeling. Design/methodology/approach – A simulation study allows us to assess the suitability of the MICOM procedure to analyze the measurement invariance in PLS applications. Findings – The MICOM procedure appropriately identifies no, partial, and full measurement invariance. Research limitations/implications – The statistical power of the proposed tests requires further research, and researchers using the MICOM procedure should take potential type-II errors into account. Originality/value – The research presents a novel procedure to assess the measurement invariance in the context of composite models. Researchers in international marketing and other disciplines need to conduct this kind of assessment before undertaking multigroup analyses. They can use MICOM procedure as a standard means to assess the measurement invariance.

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Lenggogeni, Sari. "Why Is The Partial Least Square Important For Tourism Studies." International Journal of Tourism, Heritage and Recreation Sport 1, no. 2 (December 30, 2019): 7–15. http://dx.doi.org/10.24036/ijthrs.v1i2.27.

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Although the multiple regression method has been applied to exploratory research on most tourism studies, there is lack of understanding on studies that present a well-justified rationale in choosing a robust statistical tool for data analysis. This research note aims to review why tourism researchers are encouraged to use the Partial Least Squares Structural Equation Modelling (PLS-SEM) method to address this research problem. This article provides rationale, comparisons among techniques for multiple regression-based papers and suggestions for tourism researchers to justify why PLS-SEM is important for exploratory studies.

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Mukid, Moch Abdul, Bambang Widjanarko Otok, and Agus Suharsono. "Segmentation in Structural Equation Modeling Using a Combination of Partial Least Squares and Modified Fuzzy Clustering." Symmetry 14, no. 11 (November 16, 2022): 2431. http://dx.doi.org/10.3390/sym14112431.

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The application of a structural equation modeling (SEM) assumes that all data follow only one model. This assumption may be inaccurate in certain cases because individuals tend to differ in their responses, and failure to consider heterogeneity may threaten the validity of the SEM results. This study focuses on unobservable heterogeneity, where the difference between two or more data sets does not depend on observable characteristics. In this study, we propose a new method for estimating SEM parameters containing unobserved heterogeneity within the data and assume that the heterogeneity arises from the outer model and inner model. The method combines partial least squares (PLS) and modified fuzzy clustering. Initially, each observation was randomly assigned weights in each selected segment. These weights continued to be iteratively updated using a specific objective function. The sum of the weighted residual squares resulting from the outer and inner models of PLS-SEM is an objective function that must be minimized. We then conducted a simulation study to evaluate the performance of the method by considering various factors, including the number of segments, model specifications, residual variance of endogenous latent variables, residual variance of indicators, population size, and distribution of latent variables. From the simulation study and its application to the actual data, we conclude that the proposed method can classify observations into correct segments and precisely predict SEM parameters in each segment.

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Danks, Nicholas P., Pratyush N. Sharma, and Marko Sarstedt. "Model selection uncertainty and multimodel inference in partial least squares structural equation modeling (PLS-SEM)." Journal of Business Research 113 (May 2020): 13–24. http://dx.doi.org/10.1016/j.jbusres.2020.03.019.

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Sarstedt, Marko, Christian M. Ringle, Donna Smith, Russell Reams, and Joseph F. Hair. "Partial least squares structural equation modeling (PLS-SEM): A useful tool for family business researchers." Journal of Family Business Strategy 5, no. 1 (March 2014): 105–15. http://dx.doi.org/10.1016/j.jfbs.2014.01.002.

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Perry, Carlos Monge, Jesús Cruz Álvarez, and Jesús Fabián López. "MANUFACTURING AND CONTINUOUS IMPROVEMENT AREAS USING PARTIAL LEAST SQUARE PATH MODELING WITH MULTIPLE REGRESSION COMPARISON." CBU International Conference Proceedings 2 (July 1, 2014): 15–26. http://dx.doi.org/10.12955/cbup.v2.442.

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Structural equation modeling (SEM) has traditionally been deployed in areas of marketing, consumer satisfaction and preferences, human behavior, and recently in strategic planning. These areas are considered their niches; however, there is a remarkable tendency in empirical research studies that indicate a more diversified use of the technique. This paper shows the application of structural equation modeling using partial least square (PLS-SEM), in areas of manufacturing, quality, continuous improvement, operational efficiency, and environmental responsibility in Mexico’s medium and large manufacturing plants, while using a small sample (n = 40). The results obtained from the PLS-SEM model application mentioned, are highly positive, relevant, and statistically significant. Also shown in this paper, for purposes of validity, reliability, and statistical power confirmation of PLS-SEM, is a comparative analysis against multiple regression showing very similar results to those obtained by PLS-SEM. This fact validates the use of PLS-SEM in areas of untraditional scientific research, and suggests and invites the use of the technique in diversified fields of the scientific research

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Soelaiman, Nur Fauzi, Sharifah Sakinah Syed Ahmad, Othman Mohd, Rosyid Ridlo Al Hakim, and Hexa Apriliana Hidayah. "Modeling the civil servant discipline in Indonesia: partial least square-structural equation modeling approach." Asean International Journal of Business 1, no. 1 (January 26, 2022): 43–58. http://dx.doi.org/10.54099/aijb.v1i1.72.

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Purpose – This paper seeks to discover the factors that influence the supervisor to give the punishment level to civil servant staff—the data being used is a questionnaire to several civil servants in public academic institutions. Methodology/approach – This research used computational tools to classify transgressions into punishment categories (light, medium, or severe) with the model using the data science technique based on the partial least square-structural equation modeling (PLS-SEM) approach. Findings – It was found that the model of civil servant discipline in Indonesia is based on 14 hypotheses from bootstrapping technique and by using data science technique to support the result analysis of PLS-SEM. Novelty/value – This research contributed to providing civil servant supervisors to understand factors that influence the discipline of their staff, so it can be used to determine the punishment categorization.

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Leclercq-Machado, Luigi, Aldo Alvarez-Risco, Verónica García-Ibarra, Sharon Esquerre-Botton, Flavio Morales-Ríos, Maria de las Mercedes Anderson-Seminario, Shyla Del-Aguila-Arcentales, Neal M. Davies, and Jaime A. Yáñez. "Consumer Patterns of Sustainable Clothing Based on Theory of Reasoned Action: Evidence from Ecuador." Sustainability 14, no. 22 (November 9, 2022): 14737. http://dx.doi.org/10.3390/su142214737.

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Corporations need to understand the factors that influence purchase intention. The current study aimed to understand sustainable clothing patterns in Ecuador. A total of 343 Ecuadorian consumers completed an online survey; the results were analyzed with partial least squares structural equation modeling (PLS-SEM). As the outcome, attitude was predicted by perceived environmental knowledge (PEK) and environmental concern (EC). PEK and EC are positively correlated to attitudes towards purchasing sustainable clothing. Additionally, attitude mediated the relationship between these two variables and purchase intention. As measured by PEK, attitude is the most critical factor in determining purchase intention, based on importance performance map analysis (IPMA). The research findings may support firms’ marketing and selling strategies to demonstrate that their brands are environmentally green and generate greater consumer interest in current and future customers. The novelty of these findings is supported by the partial least squares structural equation modeling (PLS-SEM) technique results.

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Sarstedt, Marko, Christian M. Ringle, Jun-Hwa Cheah, Hiram Ting, Ovidiu I. Moisescu, and Lacramioara Radomir. "Structural model robustness checks in PLS-SEM." Tourism Economics 26, no. 4 (January 23, 2019): 531–54. http://dx.doi.org/10.1177/1354816618823921.

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Partial least squares structural equation modeling (PLS-SEM) has become a standard tool for analyzing complex inter-relationships between observed and latent variables in tourism and numerous other fields of scientific inquiry. Along with the recent surge in the method’s use, research has contributed several complementary methods for assessing the robustness of PLS-SEM results. Although these improvements are documented in extant literature, research on tourism has been slow to adopt the relevant complementary methods. This article illustrates the use of recent advances in PLS-SEM, designed to ensure structural model results’ robustness in terms of nonlinear effects, endogeneity, and unobserved heterogeneity in a PLS-SEM framework. Our overarching aim is to encourage the routine use of these complementary methods to increase methodological rigor in the field.

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Padlee, Siti Falindah, Siti Zulaieqa Mohd Hat, Hayatul Safrah Salleh, and Siti Nur. "Modeling Service Quality and Satisfaction of Evacuation Center Using Partial Least Squares Structural Equation Modeling (PLS-SEM)." International Journal of Service Science, Management, Engineering, and Technology 12, no. 3 (May 2021): 20–33. http://dx.doi.org/10.4018/ijssmet.2021050102.

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The objective of the study was to examine the relationship between evacuees' perception of service quality and their level of satisfaction on the services provided at the evacuation center. The measurement of the relationship between the constructs was analyzed by using the PLS-SEM. About 600 questionnaires were administered in three locations—Kemaman (Terengganu), Kuala Krai (Kelantan), and Temerloh (Pahang)—that are severely affected by floods during the monsoon season from November to March. The results revealed that evacuees' perceived service quality on the seven dimensions of services such as food catering, health and safety, transportation, volunteers, site services, telecommunication, and special facilities for special needs significantly influence their overall satisfaction. The outcome was the identification of seven key service variables of an evacuation center that have impacted to evacuees' overall satisfaction. This study hopes to assist the government in providing continuous quality of services to evacuees in the evacuation center in Malaysia.

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Sukimin, Sukimin, Eva Fidriani, Miswaty Miswaty, and Juwari Juwari. "ANALISIS PENGARUH FAKTOR KEPUTUSAN PEMBELIAN DENGAN STRUCTURAL EQUATION MODELING PARTIAL LEAST SQUARE." MEDIA RISET EKONOMI [MR.EKO] 1, no. 2 (October 26, 2022): 19–27. http://dx.doi.org/10.36277/mreko.v1i2.227.

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The purpose of this paper is to idendify the factors affecting consumers’ purchase decision making at Angkringan in Balikpapan. Data were collected by distributing questionnaires to 177Balikpapan consumes who were actively buying food of angkringan. The data were analyzed using the partial least squares structural equation model (PLS-SEM) and multi group analysis (MGA) in SMARTPLS software.. The results show that the product and location have an impact on purchasing decisions. This study illustrates that the determination of product and location are important factors that need to be considered to increase sales due to increased purchasing decisions by consumers. The purchase decision considers the convenience and location that is suitable for buyers to come to the place of business, ease of access and becomes a determinant for buyers and influences buyers' decisions. Suggestions from this study that paying attention to location and product quality will increase purchases.

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Chan, Sane Hwui, and Yoon Fah Lay. "EXAMINING THE RELIABILITY AND VALIDITY OF RESEARCH INSTRUMENTS USING PARTIAL LEAST SQUARES STRUCTURAL EQUATION MODELING (PLS-SEM)." Journal of Baltic Science Education 17, no. 2 (April 25, 2018): 239–51. http://dx.doi.org/10.33225/jbse/18.17.239.

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Understanding teacher’s behavioural intention in teaching science becomes a foundation to improve teacher education programme. Prior to the evaluation of the interrelation between teacher self-efficacy beliefs, teaching motivation, attitudes towards teaching science, and behavioural intention in teaching science, this research examines the reliability and validity of instruments used to measure the constructs. ‘Science Teaching Efficacy Belief Instrument-Form B’ (STEBI-B) was used to measure pre-service science teachers’ self-efficacy beliefs. ‘Work Tasks Motivation Scale for Teachers’ (WTMST) was used to measure teaching motivation. ‘Dimensions of Attitude towards Science’ (DAS) was used to measure attitudes towards teaching science whereas the ‘Behavioural Intention Scale’ was used to measure behavioural intention in teaching science among pre-service science teachers. Partial Least Square-Structural Equation Modelling approach was used to evaluate the reliability and validity of the instruments. Research findings concluded all instruments are valid and reliable to be used in future research. Key words: self-efficacy beliefs, teaching motivation, attitudes towards teaching science, behavioural intention, partial least square-structural equation modeling.

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Paris, Muhammad Amin. "PERBANDINGAN ANTARA UNWEIGHTED LEAST SQUARES (ULS) DAN PARTIAL LEAST SQUARES (PLS) DALAM PEMODELAN PERSAMAAN STRUKTURAL (STUDI KASUS MODEL ANALISIS PRESTASI BELAJAR MAHASISWA TAHUN PERTAMA PROGRAM STUDI S1 MATEMATIKA FMIPA-INSTUTUT PERTANIAN BOGOR)." Jurnal Pendidikan Matematika 2, no. 1 (April 19, 2017): 21. http://dx.doi.org/10.18592/jpm.v2i1.1165.

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Structural Equation Modeling (SEM) is one of multivariate techniques that can estimates a series of interrelated dependence relationships from a number of endogenous and exogenous variables, as well as latent (unobserved) variables simultaneously. Estimation of Parameter methods that is often applied in SEM are Maximum Likelihood (ML), Weighted Least Squares (WLS), Unweighted Least Squares (ULS), Generalized Least Squares (GLS) and Partial Least Squares (PLS). This research aims to compare ULS method and PLS method in estimating parameter model of achievement of student learning in first year undergraduate Mathematics students, FMIPA, Bogor Agricultural University ( IPB). This research use secondary and primary data which amounts to 112. The result of this research indicates that ULS method is more accurate than PLS methods. The analysis done with ULS method shows that motivation, capability and environmental had an effect to achievement of student learning.

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Ciavolino, Enrico, Lucrezia Ferrante, Giovanna Alessia Sternativo, Jun-Hwa Cheah, Simone Rollo, Tiziana Marinaci, and Claudia Venuleo. "A confirmatory composite analysis for the Italian validation of the interactions anxiousness scale: a higher-order version." Behaviormetrika 49, no. 1 (October 2, 2021): 23–46. http://dx.doi.org/10.1007/s41237-021-00151-x.

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AbstractThis study examined the factor structure and model specifications of the Interaction Anxiousness Scale (IAS) with confirmatory composite analysis (CCA) using partial least squares-structural equation modeling (PLS-SEM) with a sample of Italian adolescents ($$n = 764$$ n = 764 ). The CCA and PLS-SEM results identified the reflective nature of the IAS sub-scale scores, supporting an alternative measurement model of the IAS scores as a second-order reflective–reflective model.

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Tabet, Saundra M., Glenn W. Lambie, Shiva Jahani, and S. Mostafa Rasoolimanesh. "An Analysis of the World Health Organization Disability Assessment Schedule 2.0 Measurement Model Using Partial Least Squares–Structural Equation Modeling." Assessment 27, no. 8 (March 15, 2019): 1731–47. http://dx.doi.org/10.1177/1073191119834653.

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The researchers examined the factor structure and model specifications of the World Health Organization Disability Assessment Schedule 2.0 (WHODAS 2.0) with confirmatory tetrad analysis (CTA) using partial least squares–structural equation modeling (PLS-SEM) with a sample of adult clients ( N = 298) receiving individual therapy at a university-based counseling research center. The CTA and PLS-SEM results identified the formative nature of the WHODAS 2.0 subscale scores, supporting an alternative measurement model of the WHODAS 2.0 scores as a second-order formative–formative model.

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Zapata, Soledad, and Laura Martínez. "The Level of Involvement with the Olimpic Games and its Influence in Sport Sponsorship." Review of European Studies 10, no. 3 (July 26, 2018): 94. http://dx.doi.org/10.5539/res.v10n3p94.

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In this research, intend to demonstrate the influence of the consumer involvement in a finer brand purchase intent of the sponsor, and generate an effect on the consumers "goodwill" towards the sponsor and also to perceive a greater fit between sponsor and event, and finally cause the consumer better exposure in the event. This was chosen the Rio 2016 Olympic Games. In this study, different analyses have been conducted to verify reliability and factorial scales of charges. As well as analyses to contrast the hypotheses: ANOVAS and structural equations using the SmartPLS program (is a software with graphical user interface for variance-based structural equation modeling (SEM) using the partial least squares (PLS) path modeling method[1]), to check the fit for the model. It is interesting to highlight the contribution to this research, because if organizations look for sporting events with a public involved with them. Consequently will get a bigger intention to purchase the sponsor's brand, a finer perception of both the goodwill and the fit between the event and the sponsor, and finally a larger exposure in the event and accordingly, to the promotions made by sponsor brands. [1] Wong, K. K. K. (2013). Partial least squares structural equation modeling (PLS-SEM) techniques using SmartPLS. Marketing Bulletin, 24(1), 1-32.

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Mooi, Zhi Yin. "Mobile-social media shopping: a partial least squares-structural equation modelling (PLS-SEM) approach." International Journal of Modelling in Operations Management 7, no. 1 (2018): 1. http://dx.doi.org/10.1504/ijmom.2018.095659.

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Mooi, Zhi Yin. "Mobile-social media shopping: a partial least squares-structural equation modelling (PLS-SEM) approach." International Journal of Modelling in Operations Management 7, no. 1 (2018): 1. http://dx.doi.org/10.1504/ijmom.2018.10016850.

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Durdyev, Serdar, Syuhaida Ismail, Ali Ihtiyar, Nur Fatin Syazwani Abu Bakar, and Amos Darko. "A partial least squares structural equation modeling (PLS-SEM) of barriers to sustainable construction in Malaysia." Journal of Cleaner Production 204 (December 2018): 564–72. http://dx.doi.org/10.1016/j.jclepro.2018.08.304.

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Tabet, Saundra M., Glenn W. Lambie, Shiva Jahani, and S. Mostafa Rasoolimanesh. "The Factor Structure of Outcome Questionnaire–45.2 Scores Using Confirmatory Tetrad Analysis–Partial Least Squares." Journal of Psychoeducational Assessment 38, no. 3 (April 15, 2019): 350–68. http://dx.doi.org/10.1177/0734282919842035.

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The researchers employed a confirmatory tetrad analysis (CTA) using partial least squares–structural equation modeling (PLS-SEM) with Outcome Questionnaire–45.2 (OQ-45) data, examining the measurement model of the OQ-45 scores with a sample of male adult clients ( N = 1,558) receiving individual therapy at a university-based community counseling and research center (UBCCRC). Using CTA-PLS, this study examined the reflective and formative nature of each of the OQ-45 items and dimensions. These results identified the innovative second-order formative–formative three-factor model as a best alternative measurement model to represent and calculate the scores of OQ-45 scale.

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Barcia, Kleber F., Lizzi Garcia-Castro, and Jorge Abad-Moran. "Lean Six Sigma Impact Analysis on Sustainability Using Partial Least Squares Structural Equation Modeling (PLS-SEM): A Literature Review." Sustainability 14, no. 5 (March 5, 2022): 3051. http://dx.doi.org/10.3390/su14053051.

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The Lean Six Sigma (LSS) philosophy and sustainability have become topics of interest since the 1990s; they have generally been analyzed together since 2012. Numerous professionals, managers, and researchers have sought methodologies by which to assess their impact and know their effectiveness within companies. During the past decade, the application of partial least squares structural equation modeling (PLS-SEM) has been widely accepted in various modeling, prediction, or multivariate analyses as a way to measure the impact of LSS on sustainability. This study conducts a literature review to identify the use of PLS-SEM in measuring the impact of LSS on sustainability. A systematic review methodology has been employed, applying five search criteria to three scientific database platforms. This approach has been helpful to identify PLS-SEM as a valuable methodology for measuring the impact of LSS on sustainability. One of the research findings is that LSS practices positively impact 83% of economic indicators, 78% of environmental indicators, and 70% of social indicators. This article creates a theoretical foundation for future research on these issues, outlining research opportunities to generate future studies. It also allows researchers and managers who are interested in improving sustainability indicators to access valuable knowledge regarding what types of LSS tools could be used.

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Ali, Faizan, Woo Gon Kim, Jun (Justin) Li, and Cihan Cobanoglu. "A comparative study of covariance and partial least squares based structural equation modelling in hospitality and tourism research." International Journal of Contemporary Hospitality Management 30, no. 1 (January 8, 2018): 416–35. http://dx.doi.org/10.1108/ijchm-08-2016-0409.

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Purpose Structural equation modelling (SEM) has increasingly been used by hospitality and tourism researchers to examine complex relationships. This paper aims to highlight the benefits and limitations of SEM for hospitality and tourism research and compare its two main approaches, i.e. covariance-based SEM (CB-SEM) and partial least squares-SEM (PLS-SEM). Design/methodology/approach By using a comparative approach, this study parallels SEM’s two main approaches, i.e. CB-SEM and PLS-SEM, using three different examples from hospitality and tourism industry. Both the approaches are compared side by side in terms of assumptions, validity and reliability of measurement models, item retention and loadings, strength and significance of path relationships and coefficient of determinations. Findings The findings show that even though both methods analyse measurement theory and structural path models, there are relatively higher advantages for hospitality and tourism researchers in applying PLS-SEM. Research limitations/implications Because of the limitations of only using three examples, the results and trends generated in this study may not be generalized to all research in hospitality and tourism discipline. Moreover, the Likert scale has been used to measure the constructs in both the studies, which may have biased the results. Originality value This study is the first to compare the usage of both the SEM approaches in hospitality and tourism research. The findings of this study provide significant implications and directions for hospitality and tourism researchers to apply PLS-SEM in the future.

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Hossain, Afzal, Shahedul Hasan, Sumayya Begum, and Mohammad Amzad Hossain Sarker. "Consumers' Online Buying Behaviour during COVID-19 Pandemic Using Structural Equation Modeling." Transnational Marketing Journal 10, no. 2 (August 31, 2022): 311–34. http://dx.doi.org/10.33182/tmj.v10i2.1709.

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Due to the pandemic, businesses turned to alternatives and took up online marketing. E-marketing is a versatile tool for streamlining business processes, reducing managerial costs, reducing turnaround time, maintaining social distance, staying at home, protecting against viruses, and illuminating relationships with customers and business partners. Therefore, this research examined the factors affecting consumers' online purchase behaviour during the COVID-19 pandemic using partial least square structural equation modeling (PLS-SEM). Both quantitative and descriptive analysis methods were used. A standardized questionnaire was used to collect data from a sample of 200 local consumers in Bangladesh. A partial least square structural equation modeling (PLS-SEM) approach was used to evaluate the data and test the hypotheses. PLS-SEM showed that web design, price, administrative and product had a positive and significant relationship with consumers' online buying behaviour during the pandemic. This research adds theoretical contributions by evaluating the changes of consumers’ online buying behaviour during the COVID-19 pandemic.

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Journal articles on the topic 'Partial Least Squares Structural Equation Modeling (PLS SEM)'

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