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Journal articles on the topic 'PLS-SEM'

Author: Grafiati
Published: 4 June 2021
Last updated: 8 February 2024

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1

Zlatković, Matea. "Intellectual capital and organizational effectiveness: PLS-SEM approach." Industrija 46, no. 4 (2018): 145–69. http://dx.doi.org/10.5937/industrija46-19478.

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2

ZUHDI, Z., B. SUHARJO, and H. SUMARNO. "PERBANDINGAN PENDUGAAN PARAMETER KOEFISIEN STRUKTURAL MODEL MELALUI SEM DAN PLS-SEM." Journal of Mathematics and Its Applications 15, no. 2 (December 1, 2016): 11. http://dx.doi.org/10.29244/jmap.15.2.11-22.

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Dalam permodelan struktural, terdapat beberapa teknik pendugaan yang dapat digunakan diantaranya SEM dan PLS-SEM. sifat dan tujuan pendugaan kedua metode tersebut perlu diperhatikan. Dalam berbagai kasus para praktisi sering menganggap bahwa ketika ukuran sampel kecil mereka menggunakan PLS-SEM, namun ketika ukuran sampel cukup besar maka menggunakan pendugaan SEM secara tak langsung. Hal ini mengartikan bahwa pendugaan SEM dan PLS-SEM dianggap sama. Selanjutnya, ketika data dan karakteristik model struktural yang digunakan sama pada pendugaan SEM dan PLS-SEM akan menghasilkan hasil parameter penduga yang berbeda. Penelitian ini membandingkan dan mengindentifikasi pendugaan parameter model SEM & PLS-SEM berdasarkan jumlah ukuran sampel yang sama. Data yang digunakan dalam penelitian ini ialah data hipotetik yang dibangkitkan melalui simulasi komputer. Pendugaan parameter model menggunakan LISREL 9.20 & SmartPLS. Hasil menunjukkan bahwa pendugaan SEM optimum untuk akurasi koefisien dan PLS-SEM optimum untuk akurasi prediksi. Nilai rata-rata koefisien dugaan MAPE kedua metode sangat akurat dalam menduga parameter model (<10%). SEM berbasis koefisien digunakan untuk menguji atau mengkonfirmasi teori, sedangkan PLS-SEM berbasis prediksi digunakan untuk membangun teori. Dengan demikian, kedua metode ini tidak dapat dibandingkan karena memiliki sifat pendugaan yang berbeda walaupun menggunakan model dan karekterisitik data simulasi yang sama.
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YILDIZ, Oğuz, and Alpaslan KELLECİ. "Mobil Alışveriş Düzleminde Consistent PLS-SEM ve CB-SEM Yöntemlerinin Karşılaştırılması." İstanbul Gelişim Üniversitesi Sosyal Bilimler Dergisi 10, no. 2 (October 25, 2023): 649–67. http://dx.doi.org/10.17336/igusbd.1014138.

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This paper seeks to examine and compare the regular and consistent PLS-SEM and CB-SEM by employing the augmented TAM, which stands as a proportionately complicated model. The present paper presents the pros and cons of each method and guides researchers and academics concerning which particular method is the most appropriate to employ in their studies. The findings of this paper are twofold: (1) performing CB-SEM and consistent PLS-SEM for reflectively structured models would have more robust outputs and would be more appropriate and beneficial in lieu of executing regular PLS-SEM; (2) consistent PLS-SEM has softer provisions since it does not necessitate a two-step analysis, high sampling sizes and normal distribution requirements, thus performing consistent PLS-SEM appears more viable and practical for researchers.
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Memon, Mumtaz Ali, Ramayah T., Jun-Hwa Cheah, Hiram Ting, Francis Chuah, and Tat Huei Cham. "PLS-SEM STATISTICAL PROGRAMS: A REVIEW." Journal of Applied Structural Equation Modeling 5, no. 1 (March 10, 2021): i—xiv. http://dx.doi.org/10.47263/jasem.5(1)06.

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Partial least squares structural equation modeling (PLS-SEM) is one of the most widely used methods of multivariate data analysis. Although previous research has discussed different aspects of PLS-SEM, little has been done to explain the attributes of the various PLS-SEM statistical applications. The objective of this editorial is to discuss the multiple PLS-SEM applications, including SmartPLS, WarpPLS, and ADANCO. It is written based on information received from the developers via emails as well as our ongoing understanding and experience of using these applications. We hope this editorial will serve as a manual for users to understand the unique characteristics of each PLS-SEM application and make informed decisions on the most appropriate application for their research.
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5

Borkowski, Mateusz. "Social Capital and Economic Development: PLS-SEM Model." Gospodarka Narodowa 314, no. 2 (June 30, 2023): 11–27. http://dx.doi.org/10.33119/gn/163005.

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Celem artykułu jest pomiar kapitału społecznego oraz określenie jego oddziaływania na rozwój gospodarczy wybranych gospodarek europejskich w latach 2017–2020. W pracy wykorzystano metodę modelowania równań strukturalnych z wykorzystaniem cząstkowych najmniejszych kwadratów (PLS-SEM), która umożliwia pomiar oraz identyfikację związków pomiędzy zmiennymi ukrytymi (bezpośrednio nieobserwowalnymi). Ustalono, że zaufanie społeczne jest bardziej determinowane przez zaufanie interpersonalne niż przez zaufanie względem organizacji. Ponadto stwierdzono, że zaufanie społeczne jest najważniejszym elementem ogólnej miary kapitału społecznego. Mniej ważnym w formowaniu tej zmiennej okazała się zmienna społecznych interakcji i postaw. Ustalono także, że pomiędzy zmiennymi kapitału społecznego i rozwoju gospodarczego występuje silny dodatni i istotny statystycznie związek.
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6

Russo, Daniel, and Klaas-Jan Stol. "PLS-SEM for Software Engineering Research." ACM Computing Surveys 54, no. 4 (May 2021): 1–38. http://dx.doi.org/10.1145/3447580.

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Software Engineering (SE) researchers are increasingly paying attention to organizational and human factors. Rather than focusing only on variables that can be directly measured, such as lines of code, SE research studies now also consider unobservable variables, such as organizational culture and trust. To measure such latent variables, SE scholars have adopted Partial Least Squares Structural Equation Modeling (PLS-SEM), which is one member of the larger SEM family of statistical analysis techniques. As the SE field is facing the introduction of new methods such as PLS-SEM, a key issue is that not much is known about how to evaluate such studies. To help SE researchers learn about PLS-SEM, we draw on the latest methodological literature on PLS-SEM to synthesize an introduction. Further, we conducted a survey of PLS-SEM studies in the SE literature and evaluated those based on recommended guidelines.
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7

Kock, Ned. "Common Method Bias in PLS-SEM." International Journal of e-Collaboration 11, no. 4 (October 2015): 1–10. http://dx.doi.org/10.4018/ijec.2015100101.

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The author discusses common method bias in the context of structural equation modeling employing the partial least squares method (PLS-SEM). Two datasets were created through a Monte Carlo simulation to illustrate the discussion: one contaminated by common method bias, and the other not contaminated. A practical approach is presented for the identification of common method bias based on variance inflation factors generated via a full collinearity test. The author's discussion builds on an illustrative model in the field of e-collaboration, with outputs generated by the software WarpPLS. They demonstrate that the full collinearity test is successful in the identification of common method bias with a model that nevertheless passes standard convergent and discriminant validity assessment criteria based on a confirmation factor analysis.
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8

Aghili, Nasim, and Mehdi Amirkhani. "SEM-PLS Approach to Green Building." Encyclopedia 1, no. 2 (June 18, 2021): 472–81. http://dx.doi.org/10.3390/encyclopedia1020039.

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Green buildings refer to buildings that decrease adverse environmental effects and maintain natural resources. They can diminish energy consumption, greenhouse gas emissions, the usage of non-renewable materials, water consumption, and waste generation while improving occupants’ health and well-being. As such, several rating tools and benchmarks have been developed worldwide to assess green building performance (GBP), including the Building Research Establishment Environmental Assessment Method (BREEAM) in the United Kingdom, German Sustainable Building Council (DGNB), Leadership in Energy and Environmental Design (LEED) in the United States and Canada, Comprehensive Assessment System for Built Environment Efficiency (CASBEE) in Japan, Green Star in Australia, Green Mark in Singapore, and Green Building Index in Malaysia. Energy management (EM) during building operation could also improve GBP. One of the best approaches to evaluating the impact of EM on GBP is by using structural equation modelling (SEM). SEM is a commanding statistical method to model testing. One of the most used SEM variance-based approaches is partial least squares (PLS), which can be implemented in the SmartPLS application. PLS-SEM uses path coefficients to determine the strength and significance of the hypothesised relationships between the latent constructs.
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9

Kumar, Deepak S., and Keyoor Purani. "Model specification issues in PLS-SEM." Journal of Hospitality and Tourism Technology 9, no. 3 (October 1, 2018): 338–53. http://dx.doi.org/10.1108/jhtt-09-2017-0105.

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Purpose Prior research in tourism and hospitality domain calls for closer attention to model specification when using partial least square-structural equation modeling (PLS-SEM), including the choice of software and algorithm for PLS model estimation. This paper aims to illustrate the significance of choosing appropriate algorithms for testing the nature of relationships by comparing findings using two different PLS-SEM software packages. Design/methodology/approach Using a field experiment, relationships between visual servicescape aesthetics and affective responses are conceptualized based on literature in environmental psychology and marketing domains. With photographic surrogates as stimuli in two different hospitality service contexts – spa and upscale restaurant – data are collected from 350 respondents. Findings By comparing results of SmartPLS 3.2 and WarpPLS 5.0 software and theoretical understanding from environmental psychology literature, it is illustrated that the results and their interpretations may not be in line with theory if model specifications are not correctly implemented and are not addressed through usage of software with a relevant algorithm to test them. Originality/value The study highlights the implications for model specification issues such as type of variables and nature of relationships that tourism and hospitality researchers often face and also how use of appropriate algorithms can overcome limitations of model testing for complex models and provide empirical rigor to support theory.
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10

Hair, Joe F., Christian M. Ringle, and Marko Sarstedt. "PLS-SEM: Indeed a Silver Bullet." Journal of Marketing Theory and Practice 19, no. 2 (April 2011): 139–52. http://dx.doi.org/10.2753/mtp1069-6679190202.

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11

Al Issa, Hussein-Elhakim, and Mohammed Khalifa Abdelsalam. "Antecedents to Leadership: A CB-SEM and PLS-SEM Validation." International Journal of Sustainable Development and Planning 16, no. 8 (December 30, 2021): 1403–14. http://dx.doi.org/10.18280/ijsdp.160801.

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12

Vasilica-Maria, Margalina, Álvaro Jiménez Sánchez, and Janna Susanne Ehrlich. "A Review of the Use of PLS-SEM in Neuromarketing Research." INDEX COMUNICACIÓN 13, no. 2 (July 15, 2023): 119–46. http://dx.doi.org/10.33732/ixc/13/02arevie.

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Una parte importante en las investigaciones en neuromarketing es la metodología utilizada para el análisis estadístico con el fin de comprender, explicar y predecir el comportamiento de los consumidores. Esta investigación analiza el uso del método PLS-SEM en este ámbito. Un total de 20 artículos, que emplearon al menos una técnica de neuromarketing y realizaron análisis PLS-SEM, se encontraron en las principales bases de datos (i.e., WOS, Scopus y otros). Se observa que a menudo no se utiliza enfoque adecuado para el muestreo y el tratamiento de muestras pequeñas. También se encuentran problemas con la aplicación apropiada de los procedimientos comunes de análisis PLS-SEM para la evaluación de los modelos externo e interno, así como con la aplicación de métodos avanzados. Los futuros estudios deberían evaluar la idoneidad de utilizar un enfoque PLS-SEM, según el objetivo de investigación que apoye dicho método, las condiciones que apoyen su uso y sus limitaciones. Se proporcionan directrices a los investigadores sobre cuándo el PLS-SEM es una herramienta de investigación apropiada en neuromarketing, qué herramientas analíticas deben utilizar y cómo validar y comunicar los resultados.
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13

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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14

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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Sarstedt, Marko, Christian M. Ringle, and Joseph F. Hair. "PLS-SEM: Looking Back and Moving Forward." Long Range Planning 47, no. 3 (June 2014): 132–37. http://dx.doi.org/10.1016/j.lrp.2014.02.008.

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Kaur, Kamaljeet, and Satinder Kumar. "S-commerce: perception analysis using PLS-SEM." International Journal of Business and Globalisation 26, no. 4 (2020): 345. http://dx.doi.org/10.1504/ijbg.2020.10033678.

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Kumar, Satinder, and Kamaljeet Kaur. "S-commerce: perception analysis using PLS-SEM." International Journal of Business and Globalisation 26, no. 4 (2020): 345. http://dx.doi.org/10.1504/ijbg.2020.111646.

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18

Schubring, Sandra, Iris Lorscheid, Matthias Meyer, and Christian M. Ringle. "The PLS agent: Predictive modeling with PLS-SEM and agent-based simulation." Journal of Business Research 69, no. 10 (October 2016): 4604–12. http://dx.doi.org/10.1016/j.jbusres.2016.03.052.

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Becker, Jan-Michael, Christian M. Ringle, and Marko Sarstedt. "ESTIMATING MODERATING EFFECTS IN PLS-SEM AND PLSc-SEM: INTERACTION TERM GENERATION*DATA TREATMENT." Journal of Applied Structural Equation Modeling 2, no. 2 (June 1, 2018): 1–21. http://dx.doi.org/10.47263/jasem.2(2)01.

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When estimating moderating effects in partial least squares structural equation modeling (PLSSEM), researchers can choose from a variety of approaches to model the influence of a moderator on a relationship between two constructs by generating different interaction terms. While prior research has evaluated the efficacy of these approaches in the context of PLS-SEM, the impact of different data treatment options on their performance in the context of standard PLS-SEM and consistent PLS-SEM (PLSc-SEM) is as yet unexplored. Our simulation study addresses these limitations and explores if the choice of approach and data treatment option has a pronounced impact on the methods’ parameter recovery. An empirical application substantiates these findings. Based on our results, we offer recommendations for researchers wishing to estimate moderating effects by means of PLS-SEM and PLSc-SEM.
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Zeng, Ningshuang, Yan Liu, Pan Gong, Marcel Hertogh, and Markus König. "Do right PLS and do PLS right: A critical review of the application of PLS-SEM in construction management research." Frontiers of Engineering Management 8, no. 3 (March 27, 2021): 356–69. http://dx.doi.org/10.1007/s42524-021-0153-5.

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AbstractPartial least squares structural equation modeling (PLS-SEM) is a modern multivariate analysis technique with a demonstrated ability to estimate theoretically established cause-effect relationship models. This technique has been increasingly adopted in construction management research over the last two decades. Accordingly, a critical review of studies adopting PLS-SEM appears to be a timely and valuable endeavor. This paper offers a critical review of 139 articles that applied PLS-SEM from 2002 to 2019. Results show that the misuse of PLS-SEM can be avoided. Critical issues related to the application of PLS-SEM, research design, model development, and model evaluation are discussed in detail. This paper is the first to highlight the use and misuse of PLS-SEM in the construction management area and provides recommendations to facilitate the future application of PLS-SEM in this field.
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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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Nazaina, Nazaina, Hendra Raza, and Nur Faliza Nur Faliza. "Pelatihan Pengolahan Data menggunakan SEM PLS bagi Mahasiswa S1 Akuntansi FEB Universitas Malikussaleh." Jurnal Malikussaleh Mengabdi 2, no. 1 (April 7, 2023): 161. http://dx.doi.org/10.29103/jmm.v2i1.9430.

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Banyak mahasiswa yang belum sepenuhnya paham tentang pengolahan data hasil penelitian yang dilakukan sehingga pada saat sidang skripsi mahasiswa tidak dapat memberikan argumen tentang alat analisis yang digunakan dan cara menginterprestasikan hasil dari alat analisis tersebut.Tujuan dari pelatihan ini adalah meningkatkan pemahaman penerapan metode SEM-PLS dalam mengolah dan menganalisis serta menginterpretasikan out put hasil pengolahan data.dan pendampingan bagi mahasiswa yang menolah data menggunakan software SEM PLS. Penyelesaian permasalahan dilakukan dengan dua cara yaitu pelatihan tentang dasar-dasar penenggunaan SEM PLS, tahapan-tahapan dalam SEM PLS serta interpretasi dari output SEM PLS dan melakukan pendampingan terhadap mitra.Target luaran dari pengabdian ini adalah dari aspek pelatihan adalah adanya peningkatan pemahaman mitra tentang pengolahan data dengan menggunakan SEM PLS. Dari aspek pendampingan membantu mahasiswa yang mengolah data penelitian dengan menggunakan SEM PLS.
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Jr., Joe F. Hair, Lucy M. Matthews, Ryan L. Matthews, and Marko Sarstedt. "PLS-SEM or CB-SEM: updated guidelines on which method to use." International Journal of Multivariate Data Analysis 1, no. 2 (2017): 107. http://dx.doi.org/10.1504/ijmda.2017.087624.

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Hair Jr., Joe F., Lucy M. Matthews, Ryan L. Matthews, and Marko Sarstedt. "PLS-SEM or CB-SEM: updated guidelines on which method to use." International Journal of Multivariate Data Analysis 1, no. 2 (2017): 107. http://dx.doi.org/10.1504/ijmda.2017.10008574.

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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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Ringle, Christian M., and Marko Sarstedt. "Gain more insight from your PLS-SEM results." Industrial Management & Data Systems 116, no. 9 (October 17, 2016): 1865–86. http://dx.doi.org/10.1108/imds-10-2015-0449.

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Purpose The purpose of this paper is to introduce the importance-performance map analysis (IPMA) and explain how to use it in the context of partial least squares structural equation modeling (PLS-SEM). A case study, drawing on the IPMA module implemented in the SmartPLS 3 software, illustrates the results generation and interpretation. Design/methodology/approach The explications first address the principles of the IPMA and introduce a systematic procedure for its use, followed by a detailed discussion of each step. Finally, a case study on the use of technology shows how to apply the IPMA in empirical PLS-SEM studies. Findings The IPMA gives researchers the opportunity to enrich their PLS-SEM analysis and, thereby, gain additional results and findings. More specifically, instead of only analyzing the path coefficients (i.e. the importance dimension), the IPMA also considers the average value of the latent variables and their indicators (i.e. performance dimension). Research limitations/implications An IPMA is tied to certain requirements, which relate to the measurement scales, variable coding, and indicator weights estimates. Moreover, the IPMA presumes linear relationships. This research does not address the computation and interpretation of non-linear dependencies. Practical implications The IPMA is particularly useful for generating additional findings and conclusions by combining the analysis of the importance and performance dimensions in practical PLS-SEM applications. Thereby, the IPMA allows for prioritizing constructs to improve a certain target construct. Expanding the analysis to the indicator level facilitates identifying the most important areas of specific actions. These results are, for example, particularly important in practical studies identifying the differing impacts that certain construct dimensions have on phenomena such as technology acceptance, corporate reputation, or customer satisfaction. Originality/value This paper is the first to offer researchers a tutorial and annotated example of an IPMA. Based on a state-of-the-art review of the technique and a detailed explanation of the method, this paper introduces a systematic procedure for running an IPMA. A case study illustrates the analysis, using the SmartPLS 3 software.
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Suhayat, J., S. Suwatno, and A. D. Buchdadi. "PLS-SEM Model: Explore Factors Affecting Teacher Performance." International Journal of Instruction 16, no. 1 (January 1, 2023): 21–42. http://dx.doi.org/10.29333/iji.2023.1612a.

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Guenther, Peter, Miriam Guenther, Christian M. Ringle, Ghasem Zaefarian, and Severina Cartwright. "Improving PLS-SEM use for business marketing research." Industrial Marketing Management 111 (May 2023): 127–42. http://dx.doi.org/10.1016/j.indmarman.2023.03.010.

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Cheah, Jun-Hwa, Christian Nitzl, José L. Roldán, Gabriel Cepeda-Carrion, and Siegfried P. Gudergan. "A Primer on the Conditional Mediation Analysis in PLS-SEM." ACM SIGMIS Database: the DATABASE for Advances in Information Systems 52, SI (December 9, 2021): 43–100. http://dx.doi.org/10.1145/3505639.3505645.

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Conditional mediation (CoMe) analysis integrates mediation and moderation analyses to examine and test hypotheses about how mediated relationships vary as a function of context, boundaries, or individual differences. Although CoMe analysis can be a crucial element of empirical studies that seek to advance theory in information systems, applications of such analysis are scarce, in general, and in partial least squares structural equation modeling (PLS-SEM), in particular. This paper clarifies conceptual fundamentals of and develops guidelines for CoMe analysis within the PLS-SEM context. Furthermore, the paper outlines the illustrative use of CoMe analysis in PLS-SEM and presents detailed step-by-step procedures to do so in the PLS-SEM setting. Overall, this paper provides researchers and practitioners with the required knowledge to properly carry out, report, and interpret CoMe analysis in PLS-SEM.
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Hair, Joe, Carole L. Hollingsworth, Adriane B. Randolph, and Alain Yee Loong Chong. "An updated and expanded assessment of PLS-SEM in information systems research." Industrial Management & Data Systems 117, no. 3 (April 10, 2017): 442–58. http://dx.doi.org/10.1108/imds-04-2016-0130.

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Purpose Following the call for awareness of accepted reporting practices by Ringle, Sarstedt, and Straub in 2012, the purpose of this paper is to review and analyze the use of partial least squares structural equation modeling (PLS-SEM) in Industrial Management & Data Systems (IMDS) and extend MIS Quarterly (MISQ) applications to include the period 2012-2014. Design/methodology/approach Review of PLS-SEM applications in information systems (IS) studies published in IMDS and MISQ for the period 2010-2014 identifying a total of 57 articles reporting the use of or commenting on PLS-SEM. Findings The results indicate an increased maturity of the IS field in using PLS-SEM for model complexity and formative measures and not just small sample sizes and non-normal data. Research limitations/implications Findings demonstrate the continued use and acceptance of PLS-SEM as an accepted research method within IS. PLS-SEM is discussed as the preferred SEM method when the research objective is prediction. Practical implications This update on PLS-SEM use and recent developments will help authors to better understand and apply the method. Researchers are encouraged to engage in complete reporting procedures. Originality/value Applications of PLS-SEM for exploratory research and theory development are increasing. IS scholars should continue to exercise sound practice by reporting reasons for using PLS-SEM and recognizing its wider applicability for research. Recommended reporting guidelines following Ringle et al. (2012) and Gefen et al. (2011) are included. Several important methodological updates are included as well.
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Shmueli, Galit, Marko Sarstedt, Joseph F. Hair, Jun-Hwa Cheah, Hiram Ting, Santha Vaithilingam, and Christian M. Ringle. "Predictive model assessment in PLS-SEM: guidelines for using PLSpredict." European Journal of Marketing 53, no. 11 (November 11, 2019): 2322–47. http://dx.doi.org/10.1108/ejm-02-2019-0189.

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Purpose Partial least squares (PLS) has been introduced as a “causal-predictive” approach to structural equation modeling (SEM), designed to overcome the apparent dichotomy between explanation and prediction. However, while researchers using PLS-SEM routinely stress the predictive nature of their analyses, model evaluation assessment relies exclusively on metrics designed to assess the path model’s explanatory power. Recent research has proposed PLSpredict, a holdout sample-based procedure that generates case-level predictions on an item or a construct level. This paper offers guidelines for applying PLSpredict and explains the key choices researchers need to make using the procedure. Design/methodology/approach The authors discuss the need for prediction-oriented model evaluations in PLS-SEM and conceptually explain and further advance the PLSpredict method. In addition, they illustrate the PLSpredict procedure’s use with a tourism marketing model and provide recommendations on how the results should be interpreted. While the focus of the paper is on the PLSpredict procedure, the overarching aim is to encourage the routine prediction-oriented assessment in PLS-SEM analyses. Findings The paper advances PLSpredict and offers guidance on how to use this prediction-oriented model evaluation approach. Researchers should routinely consider the assessment of the predictive power of their PLS path models. PLSpredict is a useful and straightforward approach to evaluate the out-of-sample predictive capabilities of PLS path models that researchers can apply in their studies. Research limitations/implications Future research should seek to extend PLSpredict’s capabilities, for example, by developing more benchmarks for comparing PLS-SEM results and empirically contrasting the earliest antecedent and the direct antecedent approaches to predictive power assessment. Practical implications This paper offers clear guidelines for using PLSpredict, which researchers and practitioners should routinely apply as part of their PLS-SEM analyses. Originality/value This research substantiates the use of PLSpredict. It provides marketing researchers and practitioners with the knowledge they need to properly assess, report and interpret PLS-SEM results. Thereby, this research contributes to safeguarding the rigor of marketing studies using PLS-SEM.
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Hair, Joseph F., Jeffrey J. Risher, Marko Sarstedt, and Christian M. Ringle. "When to use and how to report the results of PLS-SEM." European Business Review 31, no. 1 (January 14, 2019): 2–24. http://dx.doi.org/10.1108/ebr-11-2018-0203.

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Purpose The purpose of this paper is to provide a comprehensive, yet concise, overview of the considerations and metrics required for partial least squares structural equation modeling (PLS-SEM) analysis and result reporting. Preliminary considerations are summarized first, including reasons for choosing PLS-SEM, recommended sample size in selected contexts, distributional assumptions, use of secondary data, statistical power and the need for goodness-of-fit testing. Next, the metrics as well as the rules of thumb that should be applied to assess the PLS-SEM results are covered. Besides presenting established PLS-SEM evaluation criteria, the overview includes the following new guidelines: PLSpredict (i.e., a novel approach for assessing a model’s out-of-sample prediction), metrics for model comparisons, and several complementary methods for checking the results’ robustness. Design/methodology/approach This paper provides an overview of previously and recently proposed metrics as well as rules of thumb for evaluating the research results based on the application of PLS-SEM. Findings Most of the previously applied metrics for evaluating PLS-SEM results are still relevant. Nevertheless, scholars need to be knowledgeable about recently proposed metrics (e.g. model comparison criteria) and methods (e.g. endogeneity assessment, latent class analysis and PLSpredict), and when and how to apply them to extend their analyses. Research limitations/implications Methodological developments associated with PLS-SEM are rapidly emerging. The metrics reported in this paper are useful for current applications, but must always be up to date with the latest developments in the PLS-SEM method. Originality/value In light of more recent research and methodological developments in the PLS-SEM domain, guidelines for the method’s use need to be continuously extended and updated. This paper is the most current and comprehensive summary of the PLS-SEM method and the metrics applied to assess its solutions.
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Petter, Stacie, and Yasamin Hadavi. "With Great Power Comes Great Responsibility." ACM SIGMIS Database: the DATABASE for Advances in Information Systems 52, SI (December 9, 2021): 10–23. http://dx.doi.org/10.1145/3505639.3505643.

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Partial least squares (PLS) offers multiple advantages as a composite-based structural equation modeling (SEM) technique. PLS enables scholars to examine the measurement model and structural model simultaneously and often requires fewer assumptions than factor-based SEM techniques. For these reasons and more, PLS offers great power for researchers who wish to use a SEM-based approach to evaluate a research model. However, with the great power of PLS also comes great responsibility. Scholars should determine if PLS is appropriate to use within their context, and scholars should explain their rationale for employing PLS for data analysis. Recognizing the power and responsibility associated with PLS is important since many scholars have called for an abandonment of PLS within the information systems discipline and beyond. We reviewed articles from four premier journals within the information systems field from 2017-2020 that use PLS as an analysis technique. Based on this review, we identify recommendations for scholars seeking to embrace the power and responsibility of using composite-based SEM to analyze research models.
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Aghnia Ilmani, Erdanisa, and Marizsa Herlina. "Multigroup SEM-PLS untuk Pemodelan Kemampuan Literasi Digital Masyarakat Desa." Bandung Conference Series: Statistics 2, no. 2 (July 28, 2022): 96–102. http://dx.doi.org/10.29313/bcss.v2i2.3279.

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Abstract. SEM-PLS (Partial Least Square) aims to test the predictive relationship between constructs by seeing whether there is a relationship or influence between constructs. Based on real data in the field, it often shows that the data pattern is not normally distributed, so a free and flexible distribution method is needed, namely SEM-PLS. One of the SEM-PLS approach methods is MGA-PLS (Multigroup-PLS) to compare data analysis based on sample data because it has different characteristics with 2 or more characters. Due to the development gap between cities and villages, the government has created a digital village program. The smooth running of the digital village program is also supported by the digital literacy capabilities of the community in the village. Then in this period there is still a digital divide between men and women, meaning that the digital use of men and women is still not balanced. The purpose of this study is to identify and model the factors that influence digital literacy skills, and to see whether or not there are differences in characteristics based on gender using the Multigroup Analysis - Partial Least Square (MGA-PLS) method. The exogenous variables are family environment, intensity of online media use, socioeconomic conditions. The endogenous variable used is digital literacy ability. Meanwhile, the moderating variable is gender. The results of the study using bootstrap estimation show that the three variables of the intensity of online media use, socio-economic conditions, and family environment affect digital literacy skills with an R-Square value of 66%. Then the results of research from Multigroup Analysis - Partial Least Square (MGA-PLS) showed that between male and female groups there was no difference in digital literacy ability. Abstrak. SEM-PLS (Partial Least Square) bertujuan untuk menguji hubungan prediktif antar konstruk dengan melihat apakah ada hubungan atau pengaruh antar konstruk. Berdasarkan data real di lapangan seringkali menunjukkan pola data yang tersebar tidak normal, sehingga diperlukan suatu metode yang bebas distribusi (free distribution) dan fleksibel yaitu SEM-PLS. Salah satu metode pendekatan SEM-PLS adalah MGA-PLS (Multigroup-PLS) untuk membandingkan analisis data berdasarkan data sampel karena memiliki karakteristik yang berbeda dengan 2 atau lebih karakter. Akibat adanya kesenjangan pembangunan antara kota dan desa, maka pemerintah membuat program desa digital. Kelancaran jalannya program desa digital didukung juga oleh kemampuan literasi digital masyarakat di desa tersebut. Kemudian dalam masa ini masih terjadi kesenjangan digital antara pria dan wanita, artinya penggunaan digital pria dan wanita masih tidak seimbang. Tujuan dari penelitian ini adalah mengetahui dan memodelkan faktor- faktor yang berpengaruh terhadap kemampuan literasi digitial, serta melihat ada atau tidak perbedaan karakteristik berdasarkan jenis kelamin menggunakan metode Multigroup Analysis - Partial Least Square (MGA-PLS). Variabel eksogen nya adalah lingkungan keluarga, intensitas penggunaan media online, kondisi sosial ekonomi. Variabel endogen yang digunakan adalah kemampuan literasi digital. Sedangkan untuk variabel moderasinya adalah jenis kelamin. Hasil penelitian menggunakan estimasi bootstrap menunjukkan bahwa ketiga variabel intensitas penggunaan media online, kondisi sosial ekonomi, lingkungan keluarga berpengaruh terhadap kemampuan literasi digital dengan nilai R-Square sebesar 66%. Kemudian hasil penelitan dari Multigroup Analysis - Partial Least Square (MGA-PLS) menunjukan bahwa antara kelompok pria dan wanita tidak memiliki perbedaan terhadap kemampuan literasi digital.
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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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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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Amusa, Lateef Babatunde, and Twinomurinzi Hossana. "An empirical comparison of some missing data treatments in PLS-SEM." PLOS ONE 19, no. 1 (January 19, 2024): e0297037. http://dx.doi.org/10.1371/journal.pone.0297037.

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PLS-SEM is frequently used in applied studies as an excellent tool for examining causal-predictive associations of models for theory development and testing. Missing data are a common problem in empirical analysis, and PLS-SEM is no exception. A comprehensive review of the PLS-SEM literature reveals a high preference for the listwise deletion and mean imputation methods in dealing with missing values. PLS-SEM researchers often disregard strategies for addressing missing data, such as regression imputation and imputation based on the Expectation Maximization (EM) algorithm. In this study, we investigate the utility of these underutilized techniques for dealing with missing values in PLS-SEM and compare them with mean imputation and listwise deletion. Monte Carlo simulations were conducted based on two prominent social science models: the European Customer Satisfaction Index (ECSI) and the Unified Theory of Acceptance and Use of Technology (UTAUT). Our simulation experiments reveal the outperformance of the regression imputation against the other alternatives in the recovery of model parameters and precision of parameter estimates. Hence, regression imputation merit more widespread adoption for treating missing values when analyzing PLS-SEM studies.
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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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POLAT, Murat. "EĞİTİM BİLİMLERİNDE PLS-SEM YAKLAŞIMININ KULLANILABİLİRLİĞİ VE BİR UYGULAMA." Social Sciences Studies Journal 4, no. 25 (January 1, 2018): 5325–37. http://dx.doi.org/10.26449/sssj.994.

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Ummu Sakinah S, Ridzwan CR, Ramlee M, and Zaliza H. "CAREER CHALLENGES MODEL AMONG FEMALE ENGINEERS: PLS-SEM ANALYSIS." Malaysian Journal of Public Health Medicine 20, Special1 (August 1, 2020): 243–50. http://dx.doi.org/10.37268/mjphm/vol.20/no.special1/art.710.

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Women are likely to leave the job sector as a result of the crisis between their commitments to the career with the household interest. In response to this issue, this study aims to build a career challenge model that caters to the demand among women in this century. Hence, this study has identified the key factors to the challenges faced by female engineers in pursuing their career as an engineer through the Delphi Modified Technique. The result shows this study looking into the relationship between four independent constructs namely, life balance, childcare, leaves and gender discrimination. Meanwhile, the dependent construct of this study is career challenges faced by women. The scope of the study comprises female engineers with families and 211 respondents were selected to answer the questionnaire. The data obtained were analysed using the PLS-SEM 2.0 software via the algorithm, bootstrapping and blindfolding method. The construction process of this model involves two tests including the construction of the measurement model and the structure model. Testing the measurement model involves internal consistency namely (a) convergent validity and (b) discriminant validity in which these two validities have six analyses; (i) external loading, (ii) composite reliability, (iii) average variance extracted (AVE), (iv) Fornell-Larcker, (v) cross loading, and (vi) Heterotrait-Monotrait Ratio (HTMT). Meanwhile, the structural model testing involves the analysis of (i) Multicollinearity (Inner VIF), (ii) Path Coefficient, (iii) R square (R2), (iv) size effect (f2), and (v) Predictive Relevance (Q2). The findings indicate that gender discrimination and life balance have significant relationships in influencing career challenges. Hence, this model is expected to contribute to the literature of Human Resource Management.
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Yeom, Joon Ho, Jea Hang Lee, and Dae Shik Park. "Medium-sized Cities in Korea : A PLS-SEM Analysis." Korean Journal of Local Government Studies 20, no. 1 (May 31, 2016): 477–502. http://dx.doi.org/10.20484/klog.20.1.20.

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Pramesti, Nectaria Putri, Henricus Priyosulistyo, Akhmad Aminullah, and Ambrosius Koesmargono. "The Bridge Maintenance Factors Model: A PLS-SEM Approach." Civil Engineering and Architecture 9, no. 6 (October 2021): 2027–38. http://dx.doi.org/10.13189/cea.2021.090631.

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Farooq, Muhammad Shoaib, and Maimoona Salam. "Nexus between CSR and DSIW: A PLS-SEM Approach." International Journal of Hospitality Management 86 (April 2020): 102437. http://dx.doi.org/10.1016/j.ijhm.2019.102437.

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DURDYEV, Serdar, Ali IHTIYAR, Audrius BANAITIS, and Derek THURNELL. "The construction client satisfaction model: a PLS-SEM approach." Journal of Civil Engineering and Management 24, no. 1 (March 1, 2018): 31–42. http://dx.doi.org/10.3846/jcem.2018.297.

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The satisfaction of the client is of utmost importance in procuring future projects; however, anecdotally client satisfaction (CS) is not adequately considered in many developing countries. Factors associated with the influence of service quality (SQ) on construction client satisfaction and how it affects their behavioural intentions in Cambodia are investigated. A theoretical structural model was developed to investigate the influence of five SQ factors on CS and their links to the future behavioural intentions of the client. A partial least square approach of the structural equation modelling technique (PLS-SEM) was employed to analyse data collected from a questionnaire survey of 227 participants comprising the clients of small and medium size projects. The structural model adapts 22 attributes across five SQ factors, namely: reliability; responsiveness; assurance; empathy, and tangible factors. PLS-SEM outcomes confirmed that the future behavioural intentions of the client are influenced by their satisfaction over the service excellence provided by construction companies. Moreover, treating every client individually and demonstrating an approachable attitude towards their needs will increase their satisfaction with the experience they had with a contractor. Findings and recommendations of the study may be useful to construction companies who are seeking ways to improve CS in developing countries.
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Ljiljana Šobić, Nikola Bošković, and Danijela Pantović. "ENTREPRENEURIAL ELEMENTS IN RURAL TOURISM - FINDINGS FROM PLS-SEM." Ekonomika poljoprivrede 70, no. 2 (June 19, 2023): 521–35. http://dx.doi.org/10.59267/ekopolj2302521s.

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The aim of this paper is to determine the interrelationship between innovation, proactivity and risk-taking, as a dimension of entrepreneurial orientation and their connection with relational capital, in order to indicate the entrepreneurial behavior that gives the best results for the development of sustainable rural tourism. The research was conducted on the basis of questionnaires collected on the territory of the Republic of Serbia during the year 2022. The data were analyzed using the PLS-SEM method. The results show a positive relationship between proactivity and innovation, between relational capital and innovation, and proactivity and relational capital, while a positive relationship between risktaking and innovation and risk-taking and relational capital was not confirmed. Results provide important elements for making decisions about innovation and competitiveness of rural tourism destinations.
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Kock, Ned. "PLS-based SEM Algorithms: The Good Neighbor Assumption, Collinearity, and Nonlinearity." Information Management and Business Review 7, no. 2 (April 30, 2015): 113–30. http://dx.doi.org/10.22610/imbr.v7i2.1146.

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The partial least squares (PLS) method has been extensively used in information systems research, particularly in the context of PLS-based structural equation modeling (SEM). Nevertheless, our understanding of PLS algorithms and their properties is still progressing. With the goal of improving that understanding, we provide a discussion on the treatment of reflective and formative latent variables in the context of three main algorithms used in PLS-based SEM analyses –PLS regression, PLS Mode A, and PLS Mode B. Two illustrative examples based on actual data are presented. It is shown that the “good neighbor” assumption underlying modes A and B has several consequences, including the following: the inner model influences the outer model in a way that increases inner model coefficients of association and collinearity levels in tandem, and makes measurement model analysis tests dependent on structural model links; instances of Simpson’s paradox tend to occur with Mode B at the latent variable level; and nonlinearity is improperly captured. In spite of these mostly detrimental outcomes, it is argued that modes A and B may have important and yet unexplored roles to play in PLS-based structural equation modeling analyses.
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Šiška, Ladislav. "COMPARING CB-SEM AND PLS-SEM: A CASE SHOWING MANAGEMENT ACCOUNTING IMPACT ON PERFORMANCE." Polish Journal of Management Studies 15, no. 2 (June 2017): 240–49. http://dx.doi.org/10.17512/pjms.2017.15.2.22.

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Dash, Ganesh, and Justin Paul. "CB-SEM vs PLS-SEM methods for research in social sciences and technology forecasting." Technological Forecasting and Social Change 173 (December 2021): 121092. http://dx.doi.org/10.1016/j.techfore.2021.121092.

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Rigdon, Edward E., Marko Sarstedt, and Christian M. Ringle. "On Comparing Results from CB-SEM and PLS-SEM: Five Perspectives and Five Recommendations." Marketing ZFP 39, no. 3 (2017): 4–16. http://dx.doi.org/10.15358/0344-1369-2017-3-4.

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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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