Prelims

Applying Partial Least Squares in Tourism and Hospitality Research

ISBN: 978-1-78756-700-9, eISBN: 978-1-78756-699-6

Publication date: 14 December 2018

Citation

(2018), "Prelims", Ali, F., Rasoolimanesh, S.M. and Cobanoglu, C. (Ed.) Applying Partial Least Squares in Tourism and Hospitality Research, Emerald Publishing Limited, Bingley, pp. i-xviii. https://doi.org/10.1108/978-1-78756-699-620181014

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Emerald Publishing Limited

Copyright © 2019 Emerald Publishing Limited


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Applying Partial Least Squares in Tourism and Hospitality Research

Title Page

Applying Partial Least Squares in Tourism and Hospitality Research

Editors:

Faizan Ali

University of South Florida Sarasota-Manatee, USA

S. Mostafa Rasoolimanesh

Taylor’s University, Malaysia

Cihan Cobanoglu

University of South Florida Sarasota-Manatee, USA

United Kingdom – North America – Japan India – Malaysia – China

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First edition 2019

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ISBN: 978-1-78756-700-9 (Print)

ISBN: 978-1-78756-699-6 (Online)

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Contents

Preface vii
Foreword xi
About the Authors xvii
Chapter 1 Minimum Sample Size Estimation in PLS-SEM: An Application in Tourism and Hospitality Research
Ned Kock
1
Chapter 2 New Guidelines for the Use of PLS Path Modeling in Hospitality, Travel, and Tourism Research
Jörg Henseler, Tobias Müller and Florian Schuberth
17
Chapter 3 Predictions from Partial Least Squares Models
Nicholas P. Danks and Soumya Ray
35
Chapter 4 PLS Path Modeling in Hospitality and Tourism Research: The Golden Age and Days of Future Past
Hengky Latan
53
Chapter 5 Hotel Employees’ Use of Smartphones and Performance: Reflective–Formative Estimation Approach
Minwoo Lee, Kawon Kim, Kyung Young Lee and Jung Hwa Hong
85
Chapter 6 Loyalty to Rural Tourism in Brazil: The Moderating Effect of the Purchase Frequency
Mara Mataveli and Alfonso J. Gil
109
Chapter 7 HRM Practices and Employee Retention: The Moderating Effect of Work Environment
Palwasha Bibi, Ashfaq Ahmad and Abdul H. A. Majid
129
Chapter 8 Do Museums’ Websites Boost Visitors’ Intentions? A PLS Multigroup Comparison
Jesús García-Madariaga, Nuria Recuero Virto, María Francisca Blasco López and Joaquin Aldas Manzano
153
Chapter 9 The Effect of High and Low Environmental Consciousness Regarding Brazilian Restaurants: A Multigroup Analysis Using PLS
Carlos Alberto Alves, Claudio José Stefanini and Leonardo Aureliano da Silva
185
Chapter 10 Analyzing the Effects of Online and Offline Communication in the Hotel and Restaurant Industry: The PLS ApproachMaja Šerić and Đurđana Ozretić-Došen 211
About the Editors 233
Index 235

Preface

Faizan Ali (University of South Florida Sarasota Manatee)

S. Mostafa Rasoolimanesh (Taylor’s University)

Cihan Cobanoglu (University of South Florida Sarasota Manatee)

Partial least squares-structural equation modeling (PLS-SEM) is a multivariate statistical technique and its usage in various disciplines is increasing. Considering this increase in the application of PLS-SEM, numerous scholars have reviewed its usage in accounting, business research, strategic management, marketing, management information system, tourism and hospitality research, etc. Review studies on the usage of PLS-SEM in tourism (do Valle & Assaker, 2016) and hospitality research (Ali, Rasoolimanesh, Sarstedt, Ringle, & Ryu, 2018) indicate an increasing dissemination of PLS-SEM in tourism and hospitality research. Researchers in tourism and hospitality seem to be aware of sample size issues in PLS-SEM, which have attracted considerable attention in recent years. In addition, the reporting practices regarding the assessment of reflective measurement models are clearly above standard but still warrant improvement. This is particularly true regarding discriminant validity assessment, which draws on metrics that recent research has debunked as ineffective in a PLS-SEM context. Similarly, the structural model assessment practices compare well with those in other disciplines but should consider more recent metrics that allow for assessing a model’s out-of-sample predictive power. However, other aspects, such as formative measurement model assessment, clearly require improvement. Hospitality researchers disregard fundamental validation steps such as convergent validity and multicollinearity assessment.

While these studies indicate an increase in the application of PLS-SEM in tourism and hospitality research over last few years, it is noteworthy that PLS-SEM is clearly under-utilized as compared to the extensively used covariance-based SEM in these disciplines. Apart from providing insights into reporting practices, these review papers (Ali et al., 2018; do Valle & Assaker, 2016) also indicated that tourism and hospitality researchers seem unaware of recent advances/complimentary analysis techniques in the field. These advances and techniques clearly extend the scope of the analyses and help researchers gain more insights from the model and the data. Extensions include, but are not limited to, the weighted PLS algorithm, consistent PLS, methods for uncovering unobserved heterogeneity and impact-performance map analyses. Hence, we are editing this handbook to provide tourism and hospitality researchers with the foundations when adopting the PLS-SEM method in their research.

This handbook on the “Applying Partial Least Squares in Tourism and Hospitality Research” includes 10 chapters, representing a comprehensive application of the current, original and the most advanced research in the domain of PLS methods with specific reference to their use in tourism and hospitality research. While most of the chapters comprise a thorough discussion of applications to problems from tourism and hospitality research, others focus on some key aspects of PLS analysis with a didactic approach. This handbook serves as both an introduction for those without prior knowledge of PLS and as a comprehensive reference for researchers and practitioners interested in the most recent advances in PLS methodology.

The use of PLS-SEM in tourism and hospitality research is on the rise, a trend that is in line with what has been taking place in many other fields where advanced multivariate statistical methods are employed. One of the most fundamental issues in PLS-SEM is that of minimum sample size estimation, where the “10-times rule” has been a favorite due to its simplicity of application, even though it tends to yield grossly imprecise estimates. In Chapter 1, Ned Kock discuss two related methods, based on mathematical equations, as alternatives for minimum sample size estimation in PLS-SEM: the inverse square root method and the gamma-exponential method. The application of the methods is illustrated based on a model derived from a tourism and hospitality research study. Both the methods are implemented in one of the leading PLS-SEM software tools, WarpPLS, starting in version 6.0.

There are five types of research that can be distinguished in the context of PLS-PM: (1) confirmatory, (2) explanatory, (3) predictive, (4) descriptive, and (5) exploratory. Each research type needs to be considered to select the appropriate assessment criteria. Chapter 2, by Jörg Henseler, Tobias Müller, and Florian Schuberth, sheds some light to these five research types and explains the differences by presenting empirical examples from the literature in hospitality, travel, and tourism (HTT) research. This chapter introduces new guidelines and enhancements for the use of PLS-PM in causal HTT research to assess overall model fit by using consistent PLS (PLSc) in combination with the bootstrap-based test, to measure discriminant validity with the heterotrait-monotrait ratio of correlations and assess the reliability of reflectively measured constructs via Dijkstra and Henseler’s ρ A .

Apart from the theoretic explanations offered by the empirical models in the research papers, practitioners are also interested in the practical implications that they can apply to future cases. Being able to provide predictive diagnoses is an increasingly important issue linking theory and practice, and empirical researchers in tourism and hospitality should heed the call for predictive evaluations of their theoretical models. Fortunately, PLS path models are uniquely suited to predictive analytics. Chapter 3, by Nicholas P. Danks and Soumya Ray, offers a review of the emerging predictive methodology for PLS path models and a practical guide to what researchers can do to diagnose the predictive qualities of their models. These discussions are followed by a demonstration on a well-regarded model and dataset from the tourism literature.

Chapter 4 is contributed by Hengky Latan. It aims to update the field of knowledge regarding recent advances in PLS path modeling. This chapter uses eight assessment criteria that have been adapted in accordance with recent advances in PLS-PM. Specifically, this chapter explores all recent advances in the application of each PLS-PM technique. This chapter highlights serious misconceptions surrounding the use of PLS-PM in many disciplines, including hospitality and tourism research. This chapter also contributes to the improved practices and application of PLS-PM by proposing a new framework for reporting the results of PLS-PM.

Chapter 5 is contributed by Minwoo Lee, Kawon Kim, Kyung Young Lee, and Jung Hwa Hong. It is an application of PLS-SEM to identify smart-computing functions of smartphone’s use at the workplace in the hospitality industry and examine the impact of using smart-computing functions on Mintzberg’s managerial role performance and overall performance improvement. This chapter presents how both reflectively measured constructs and formatively measured constructs can be tested by using PLS-SEM.

Chapter 6, contributed by Mara Mataveli and Alfonso J. Gil, is an application of PLS-SEM to examine the impact of motivations on rural tourism on loyalty. In addition, this chapter also uses and reports moderating as well as mediation analysis.

Chapter 7, contributed by Palwasha Bibi, Ashfaq Ahmad, and Abdul H. A. Majid is also an application of PLS-SEM to measure the relationships between compensation, training and development, performance appraisal and employee retention, and the moderating role of work environment on the relationships between compensation, training and development, performance appraisal, and employee retention.

Chapter 8, contributed by Jesús García-Madariaga, Nuria Recuero Virto, María Francisca Blasco López, and Joaquin Aldas-Manzano, aims to identify how features of museum websites explain visitors’ intentions to visit the museum as well as revisit intentions to the website. This chapter applies multigroup analysis (MGA) to assess visitors’ intentions across the websites of the two most visited museums of Spain: Prado Museum and Reina Sofia Museum.

Chapter 9, contributed by Carlos Alberto Alves, Claudio José Stefanini, and Leonardo Aureliano da Silva, applies PLS-SEM and MGA to investigate if the presence or absence of an environmental conscious can change the relationship between environmental practices, environmental image, and attachment, and their effects on customer loyalty in restaurants based on the theory of reasoned action.

Chapter 10, contributed by Maja Šerić and Đurđana Ozretić-Došen, examines whether consumers’ perceptions of online and offline communication consistency can increase their perceived service quality and brand loyalty in hospitality by applying PLS-SEM and MGA.

Even though the discussion on PLS method is increasing, its application in tourism and hospitality is under-whelming. Consequently, editors for this handbook selected high-quality papers for publication where some of them advance and explain the recent advances of PLS-SEM and others report application of the method. The handbook provides a forum for topical issues that demonstrate PLS path modeming’s usefulness in tourism and hospitality applications. A description of the method, its empirical applications, and potential methodological advancements, which increase its usefulness for research and practice, are specifically emphasized. The editors believe that this handbook will be the starting point for a more intensive use of PLS-SEM in the tourism and hospitality discipline and for additional advances that will exploit PLS’s capabilities in this area. The editors and authors gratefully acknowledge Christian M. Ringle and Marko Sarstedt’s comments, encouraging support, and suggestions during the preparation of this handbook. The reviewers also deserve the heartfelt recognition of the editors for their remarkable contribution to the quality of this handbook. As usual, they were diligent, meticulous, constructive, and extremely competent. The editors specifically express their gratitude to the following reviewers: Babak Taheri (Heriot-Watt University), Chritian M. Ringle (Hamburg University of Technology), Gabriel Cepeda-Carrión (Universidad de Sevilla), Hengky Latan (STIE Bank BPD Jateng), José L. Roldán (Universidad de Sevilla), Jun-Hwa Cheah (Universiti Teknologi Malaysia), Marko Sarstedt (Otto von Guericke Universität Magdeburg), Murad Ali (King Abdulaziz University), and Rob Hallak (University of South Australia).

References Ali, Rasoolimanesh, Sarstedt, Ringle, & Ryu, 2018Ali, F., Rasoolimanesh, S. M., Sarstedt, M., Ringle, C. M. & Ryu, K. (2018). An assessment of the use of partial least squares structural equation modeling (PLS-SEM) in hospitality research. International Journal of Contemporary Hospitality Management, 30(1), 514538. do Valle, & Assaker, 2016do Valle, P. O. & Assaker, G. (2016). Using partial least squares structural equation modeling in tourism research: A review of past research and recommendations for future applications. Journal of Travel Research, 55(6), 695708.

Foreword

Since its introduction by Herman O. A. Wold (1982) and Jan-Bernd Lohmöller (1989), partial least squares structural equation modeling (PLS-SEM) has undergone a broad adoption and numerous advances. The increasing dissemination of PLS-SEM in applied business is rooted in Wynne W. Chin’s (1995, 1998) introductory articles and the availability of several software packages for PLS-SEM such as PLS-Graph, matrixpls, SmartPLS*, and WarpPLS, whereby a recent software review considers SmartPLS (Ringle et al., 2015) being “the most comprehensive” one (Kumar and Purani, 2018). Today, several textbooks (Garson, 2016; Hair, Hult, Ringle, & Sarstedt, 2017; Hair, Sarstedt, Ringle, & Gudergan, 2018; Ramayah, Cheah, Chuah, Ting, & Memon, 2016) and handbook articles (Esposito Vinzi, Chin, Henseler, & Wang, 2010; Henseler, Ringle, & Sarstedt, 2012; Rigdon, 2013; Sarstedt, Ringle, & Hair, 2017) provide researchers with the foundations when adopting the PLS-SEM method in their research. Numerous review studies on the use of PLS-SEM in various business research disciplines such as – accounting (Lee, Petter, Fayard, & Robinson, 2011; Nitzl, 2016), family business (Sarstedt, Ringle, Smith, Reams, & Hair, 2014), group and organization management (Sosik, Kahai, & Piovoso, 2009), hospitality management (Ali, Rasoolimanesh, Sarstedt, Ringle, & Ryu, 2018), human resource management (Ringle, Sarstedt, Mitchell, & Gudergan, 2018), information systems (Hair, Hollingsworth, Randolph, & Chong, 2017; Ringle, Sarstedt, & Straub, 2012), international marketing research (Henseler, Ringle, & Sinkovics, 2009; Richter, Sinkovics, Ringle, & Schlägel, 2016), marketing (Hair, Sarstedt, Ringle, & Mena, 2012), operations management (Peng & Lai, 2012), psychology (Willaby, Costa, Burns, MacCann, & Roberts, 2015), strategic management (Hair, Sarstedt, Pieper, & Ringle, 2012), supply chain management (Kaufmann & Gaeckler, 2015), and tourism (do Valle & Assaker, 2016) – not only substantiate the wide adoption of the method, but also provide an overview how researchers used PLS-SEM in their studies.

Accompanying the rapid pace of development, PLS-SEM has also witnessed controversies, with researchers sometimes even questioning the method’s raison d’être (Rönkkö, Antonakis, McIntosh, & Edwards, 2016; Rönkkö & Evermann, 2013; Rönkkö, McIntosh, & Antonakis, 2015). However, most of the criticism has been refuted as inaccurate (Henseler et al., 2014) or grounded in different measurement philosophies (Rigdon, Sarstedt, & Ringle, 2017; Sarstedt, Hair, Ringle, Thiele, & Gudergan, 2016). These criticisms, however, helped furthering the method’s theory base in terms of measurement and model estimation, triggering a wide range of follow-up research. New developments in PLS-SEM range from new estimators (e.g., Dijkstra & Henseler, 2015; Dolce, Esposito Vinzi, & Lauro, 2018; Schuberth & Cantaluppi, 2017) and model evaluation metrics (e.g., Aguirre-Urreta & Rönkkö, 2018; Franke & Sarstedt, in press; Henseler, Ringle, & Sarstedt, 2015; Sharma, Sarstedt, Shmueli, Thiele, & Kim, 2017; Shmueli, Ray, Velasquez Estrada, & Chatla, 2016) to complementary methods such as methods for uncovering unobserved heterogeneity (e.g., Ringle, Sarstedt, & Schlittgen, 2014; Schlittgen, Ringle, Sarstedt, & Becker, 2016), different multigroup analysis approaches (Matthews, 2018), testing measurement invariance of composites (Henseler, Ringle, & Sarstedt, 2016), and endogeneity assessment (Hult, Hair, Proksch, Sarstedt, Pinkwart, & Ringle, 2018). These advances have greatly extended researchers’ methodological toolbox (Khan et al., 2018) and fueled the adoption of PLS-SEM in the social sciences and other fields.

This handbook by Faizan Ali, S. Mostafa Rasoolimanesh, and Cihan Cobanoglu on PLS-SEM application in tourism and hospitality research represents another important contribution to progress on the method. We would like to thank Faizan Ali, S. Mostafa Rasoolimanesh, and Cihan Cobanoglu for the effort of developing this important handbook. Congratulations to a job done well!

Christian M. Ringle Hamburg University of Technology (TUHH), Hamburg, Germany, and the University of Waikato, Hamilton, New ZealandMarko SarstedtOtto-von-Guericke University Magdeburg, Magdeburg, Germany, and Monash University Malaysia

References Aguirre-Urreta, & Rönkkö, 2018Aguirre-Urreta, M. I. & Rönkkö, M. (2018). Statistical inference with PLSc using bootstrap confidence intervals. MIS Quarterly, forthcoming. Ali, Rasoolimanesh, Sarstedt, Ringle, & Ryu, 2018Ali, F., Rasoolimanesh, S. M., Sarstedt, M., Ringle, C. M. & Ryu, K. (2018). An assessment of the use of partial least squares structural equation modeling (PLS-SEM) in hospitality research. International Journal of Contemporary Hospitality Management, 30(1), 514538. Chin, 1995Chin, W. W. (1995). Partial least squares is to LISREL as principal components analysis is to common factor analysis. Technology Studies, 2(2), 315319. Chin, 1998Chin, W. W. (1998). The partial least squares approach to structural equation modeling. In G. A. Marcoulides (Ed.), Modern methods for business research (pp. 295358). Mahwah, NJ: Erlbaum. Chin, 2003Chin, W. W. (2003). PLS-Graph 3.0. Houston: Soft Modeling. Dijkstra, & Henseler, 2015Dijkstra, T. K. & Henseler, J. (2015). Consistent partial least squares path modeling. MIS Quarterly, 39(2), 297316. do Valle, & Assaker, 2016do Valle, P. O. & Assaker, G. (2016). Using partial least squares structural equation modeling in tourism research: A review of past research and recommendations for future applications. Journal of Travel Research, 55(6), 695708. Dolce, Esposito Vinzi, & Lauro, 2018Dolce, P., Esposito Vinzi, V. & Lauro, N. C. (2018). Non-symmetrical composite-based path modeling. Advances in data analysis and classification, forthcoming. Esposito Vinzi, Chin, Henseler, & Wang, 2010Esposito Vinzi, V., Chin, W. W., Henseler, J. & Wang, H. (Eds.), (2010). Handbook of partial least squares: Concepts, methods and applications (Springer handbooks of computational statistics series, vol. II). New York, NY: Springer. Franke, & SarstedtFranke, G. R. & Sarstedt, M. (in press). Heuristics versus statistics in discriminant validity testing: A comparison of four procedures. Internet Research, forthcoming. Garson, 2016Garson, G. D. (2016). Partial least squares regression and structural equation models. Asheboro, NC: Statistical Associates. Hair, Hollingsworth, Randolph, & Chong, 2017Hair, J. F., Hollingsworth, C. L., Randolph, A. B. & Chong, A. Y. L. (2017). An updated and expanded assessment of PLS-SEM in information systems research. Industrial Management & Data Systems, 117(3), 442458. Hair, Hult, Ringle, & Sarstedt, 2017Hair, J. F., Hult, G. T. M., Ringle, C. M. & Sarstedt, M. (2017). A primer on partial least squares structural equation modeling (PLS-SEM) (2nd ed.). Thousand Oaks, CA: Sage. Hair, Sarstedt, Pieper, & Ringle, 2012Hair, J. F., Sarstedt, M., Pieper, T. M. & Ringle, C. M. (2012). The use of partial least squares structural equation modeling in strategic management research: A review of past practices and recommendations for future applications. Long Range Planning, 45(5–6), 320340. Hair, Sarstedt, Ringle, & Gudergan, 2018Hair, J. F., Sarstedt, M., Ringle, C. M. & Gudergan, S. P. (2018). Advanced issues in partial least squares structural equation modeling (PLS-SEM). Thousand Oaks, CA: Sage. Hair, Sarstedt, Ringle, & Mena, 2012Hair, J. F., Sarstedt, M., Ringle, C. M. & Mena, J. A. (2012). An assessment of the use of partial least squares structural equation modeling in marketing research. Journal of the Academy of Marketing Science, 40(3), 414433. Henseler, Dijkstra, 2015Henseler, J., Dijkstra, T. K. (2015). ADANCO 2.0. Kleve: Composite Modeling, http://www.compositemodeling.com. Henseler, Dijkstra, Sarstedt, Ringle, Diamantopoulos, Straub, Ketchen, Jr., Hair, Hult, Calantone, 2014Henseler, J., Dijkstra, T. K., Sarstedt, M., Ringle, C. M., Diamantopoulos, A., Straub, D. W., Ketchen, D. J., Jr., Hair, J. F., Hult, G. T. M., Calantone, R. J. (2014). Common beliefs and reality about partial least squares: Comments on Rönkkö & Evermann (2013). Organizational Research Methods, 17(2), 182209. Henseler, Ringle, & Sarstedt, 2012Henseler, J., Ringle, C. M. & Sarstedt, M. (2012). Using partial least squares path modeling in international advertising research: Basic concepts and recent issues. In S. Okazaki (Ed.), Handbook of research in international advertising (pp. 252276). Cheltenham: Edward Elgar Publishing. Henseler, Ringle, & Sarstedt, 2015Henseler, J., Ringle, C. M. & Sarstedt, M. (2015). A new criterion for assessing discriminant validity in variance-based structural equation modeling. Journal of the Academy of Marketing Science, 43(1), 115135. Henseler, Ringle, & Sarstedt, 2016Henseler, J., Ringle, C. M. & Sarstedt, M. (2016). Testing measurement invariance of composites using partial least squares. International Marketing Review, 33(3), 405431. Henseler, Ringle, & Sinkovics, 2009Henseler, J., Ringle, C. M. & Sinkovics, R. R. (2009). The use of partial least squares path modeling in international marketing. In R. R. Sinkovics & P. N. Ghauri (Eds.), Advances in international marketing (Vol. 20, pp. 277320). Bingley: Emerald Publishing. Hult, Hair, Proksch, Sarstedt, Pinkwart, & Ringle, 2018Hult, G. T. M., Hair, J. F., Proksch, D., Sarstedt, M., Pinkwart, A. & Ringle, C. M. (2018). Addressing endogeneity in international marketing applications of partial least squares structural equation modeling. Journal of International Marketing, forthcoming. Kaufmann, & Gaeckler, 2015Kaufmann, L. & Gaeckler, J. (2015). A structured review of partial least squares in supply chain management research. Journal of Purchasing and Supply Management, 21(4), 259272. Khan, Sarstedt, Shiau, Hair, Ringle, & Fritze, 2018Khan, G. F., Sarstedt, M., Shiau, W.-L., Hair, J. F., Ringle, C. M. & Fritze, M. (2018). Methodological research on partial least squares structural equation modeling (PLS-SEM): An analysis based on social network approaches. Internet Research, forthcoming. Kumar, & Purani, 2018Kumar, D. S., & Purani, K. (2018). Model specification issues in PLS-SEM: Illustrating linear and non-linear models in hospitality services context. Journal of Hospitality and Tourism Technology. Lee, Petter, Fayard, & Robinson, 2011Lee, L., Petter, S., Fayard, D. & Robinson, S. (2011). On the use of partial least squares path modeling in accounting research. International Journal of Accounting Information Systems, 12(4), 305328. Lohmöller, 1989Lohmöller, J.-B. (1989). Latent variable path modeling with partial least squares. Heidelberg, Germany: Physica. Matthews, 2018Matthews, L. (2018). Applying multi-group analysis in PLS-SEM: A step-by-step process. In H. Latan & R. Noonan (Eds.), Partial least squares structural equation modeling: Basic concepts, methodological issues and applications (pp. 219243). Heidelberg, Germany: Springer. Nitzl, 2016Nitzl, C. (2016). The use of partial least squares structural equation modelling (PLS-SEM) in management accounting research: Directions for future theory development. Journal of Accounting Literature, 37(December), 1935. Peng, & Lai, 2012Peng, D. X. & Lai, F. (2012). Using partial least squares in operations management research: A practical guideline and summary of past research. Journal of Operations Management, 30(6), 467480. Ramayah, Cheah, Chuah, Ting, & Memon, 2016Ramayah, T., Cheah, J.-H., Chuah, F., Ting, H. & Memon, M. A. (2016). Partial least squares structural equation modeling (PLS-SEM) using SmartPLS 3.0: An updated and practical guide to statistical analysis. Singapore: Pearson. Richter, Sinkovics, Ringle, & Schlägel, 2016Richter, N. F., Sinkovics, R. R., Ringle, C. M. & Schlägel, C. (2016). A critical look at the use of SEM in international business research. International Marketing Review, 33(3), 376404. Rigdon, 2013Rigdon, E. E. (2013). Partial least squares path modeling. In G. R. Hancock & R. O. Mueller (Eds.), Structural equation modeling. A second course (2nd ed., pp. 81116). Charlotte, NC: Information Age Publishing. Rigdon, Sarstedt, & Ringle, 2017Rigdon, E. E., Sarstedt, M. & Ringle, C. M. (2017). On comparing results from CB-SEM and PLS-SEM. Five perspectives and five recommendations. Marketing ZFP, 39(3), 416. Ringle, Sarstedt, Mitchell, & Gudergan, 2018Ringle, C. M., Sarstedt, M., Mitchell, R. & Gudergan, S. P. (2018). Partial least squares structural equation modeling in HRM research. The International Journal of Human Resource Management, forthcoming. Ringle, Sarstedt, & Schlittgen, 2014Ringle, C. M., Sarstedt, M. & Schlittgen, R. (2014). Genetic algorithm segmentation in partial least squares structural equation modeling. OR Spectrum, 36(1), 251276. Ringle, Sarstedt, & Straub, 2012Ringle, C. M., Sarstedt, M. & Straub, D. W. (2012). A critical look at the use of PLS-SEM in MIS quarterly. MIS Quarterly, 36(1), iiixiv. Ringle, Wende, & Becker, 2015Ringle, C. M., Wende, S. & Becker, J.-M. (2015). SmartPLS 3. Bönningstedt: SmartPLS. Retrieved from http://www.smartpls.com. Ringle, Wende, & Will, 2005Ringle, C. M., Wende, S. & Will, A. (2005). SmartPLS 2. Hamburg: SmartPLS. Retrieved from www.smartpls.de. Rönkkö, Antonakis, McIntosh, & Edwards, 2016Rönkkö, M., Antonakis, J., McIntosh, C. N. & Edwards, J. R. (2016). Partial least squares path modeling: Time for some serious second thoughts. Journal of Operations Management, 47–48(November), 927. Rönkkö, & Evermann, 2013Rönkkö, M. & Evermann, J. (2013). A critical examination of common beliefs about partial least squares path modeling. Organizational Research Methods, 16(3), 425448. Rönkkö, McIntosh, & Antonakis, 2015Rönkkö, M., McIntosh, C. N. & Antonakis, J. (2015). On the adoption of partial least squares in psychological research: Caveat emptor. Personality and Individual Differences, 87, 7684. Sarstedt, Hair, Ringle, Thiele, & Gudergan, 2016Sarstedt, M., Hair, J. F., Ringle, C. M., Thiele, K. O. & Gudergan, S. P. (2016). Estimation issues with PLS and CBSEM: Where the bias lies! Journal of Business Research, 69(10), 39984010. Sarstedt, Ringle, & Hair, 2017Sarstedt, M., Ringle, C. M. & Hair, J. F. (2017). Partial least squares structural equation modeling. In C. Homburg, M. Klarmann & A. Vomberg (Eds.), Handbook of market research. Heidelberg, Germany: Springer. Sarstedt, Ringle, Smith, Reams, & Hair, 2014Sarstedt, M., Ringle, C. M., Smith, D., Reams, R. & Hair, J. F. (2014). Partial least squares structural equation modeling (PLS-SEM): A useful tool for family business researchers. Journal of Family Business Strategy, 5(1), 105115. Schlittgen, Ringle, Sarstedt, & Becker, 2016Schlittgen, R., Ringle, C. M., Sarstedt, M. & Becker, J.-M. (2016). Segmentation of PLS path models by iterative reweighted regressions. Journal of Business Research, 69(10), 45834592. Schuberth, & Cantaluppi, 2017Schuberth, F. & Cantaluppi, G. (2017). Ordinal consistent partial least squares. In H. Latan & R. Noonan (Eds.), Partial least squares path modeling: Basic concepts, methodological issues and applications (pp. 109150). Cham, Switzerland: Springer International Publishing. Sharma, Sarstedt, Shmueli, Thiele, & Kim, 2017Sharma, P. N., Sarstedt, M., Shmueli, G., Thiele, K. O. & Kim, K. H. (2017). Model selection in MIS research using PLS-SEM. Working Paper. Shmueli, Ray, Velasquez Estrada, & Chatla, 2016Shmueli, G., Ray, S., Velasquez Estrada, J. M. & Chatla, S. B. (2016). The elephant in the room: Evaluating the predictive performance of PLS models. Journal of Business Research, 69(10), 45524564. Sosik, Kahai, & Piovoso, 2009Sosik, J. J., Kahai, S. S. & Piovoso, M. J. (2009). Silver bullet or voodoo statistics? A primer for using the partial least squares data analytic technique in group and organization research. Group Organization Management, 34(1), 536. Willaby, Costa, Burns, MacCann, & Roberts, 2015Willaby, H. W., Costa, D. S. J., Burns, B. D., MacCann, C. & Roberts, R. D. (2015). Testing complex models with small sample sizes: A historical overview and empirical demonstration of what partial least squares (PLS) can offer differential psychology. Personality and Individual Differences, 84, 7378. Wold, 1982Wold, H. O. A. (1982). Soft modeling: The basic design and some extensions. In K. G. Jöreskog & H. O. A. Wold (Eds.), Systems under indirect observations: Part II (pp. 154). Amsterdam, The Netherlands: North-Holland.

About the Authors

Ashfaq Ahmad is an Assistant Professor at College of Business, Universiti Utara Malaysia, Kedah, Malaysia.

Carlos Alberto Alves is a Professor of the Graduate Program in Hospitality at Universidade Anhembi Morumbi, São Paulo, Brazil.

Joaquin Aldas-Manzano is a Professor of Marketing in the Department of Marketing, Faculty of Economics, Universitat de València, Researcher of Valencia Economics Research Institute (Ivie), and Visiting Research Fellow of the University of Glasgow, Scotland, UK.

Leonardo Aureliano da Silva is a Professor of the Graduate Program in Food and Beverage Management at Universidade Anhembi Morumbi and a Professor at Escola Superior de Propaganda e Marketing – ESPM.

Palwasha Bibi is an Assistant Professor at College of Business, Universiti Utara Malaysia, Kedah, Malaysia.

Nicholas P. Danks is a PhD Candidate at the Institute of Service Science in National Tsing Hua University, Taiwan.

Jesús García-Madariaga is a Coordinator of Marketing Discipline at Complutense University. He is currently an Associate Professor in Economics and Business Administration, Director of the University Research team “Markco2,” and Visiting Research Fellow of the California Polytechnic University, San Luis Obispo, CA, USA.

Alfonso J. Gil is an Assistant Professor in Business Administration at the University of La Rioja, La Rioja. He is a Tutor Professor at the National Distance Education University UNED, Madrid, Spain, and a Visiting Professor at the State University of the West of Paraná, Brazil.

Jörg Henseler is the Chair of Product-Market Relations and heads the Department of Design, Production & Management at the Faculty of Engineering Technology of the University of Twente, Enschede, The Netherlands. Moreover, he is visiting professor at Nova Information Management School, Universidade Nova de Lisboa, Portugal.

Jung Hwa Hong is an Associate Professor in Hotel Management and Dean of Division of Tourism in the University of Dongseo in South Korea.

Claudio José Stefanini is a Professor of the Graduate Program in Hospitality at Universidade Anhembi Morumbi, São Paulo, Brazil.

Kawon Kim is an Assistant Professor at the School of Hotel, Restaurant, and Tourism Management at the University of South Carolina.

Ned Kock is a Killam Distinguished Professor and Chair of the Division of International Business and Technology Studies, A.R. Sanchez, Jr. School of Business, at Texas A&M International University.

Hengky Latan is a Senior Researcher in the area of accounting. He has worked at the STIE Bank BPD Jateng in Department of Accounting, and at the HLC Consulting, Indonesia.

Kyung Young Lee is an Associate Professor in the Rowe School of Business at Dalhousie University, Canada.

Minwoo Lee is an Assistant Professor at Conrad N. Hilton College of Hotel and Restaurant Management, University of Houston, USA.

Maria Francisca Blasco López is a Senior Lecturer at the Universidad Complutense de Madrid. She is currently the Vice Dean and Director of the MBA in Marketing Management and the master’s degree program in Neuromarketing and Consumer Behaviour at the Universidad Complutense de Madrid.

Abdul H. A. Majid is an Associate Professor at College of Business, Universiti Utara Malaysia, Kedah, Malaysia.

Mara Mataveli is a Researcher in Economics and Business Administration at the University of La Rioja, Spain.

Tobias Müller is an external PhD candidate at the Chair of Product-Market Relations of the University of Twente, Enschede, The Netherlands.

Đurđana Ozretić-Došen is a Professor at the Marketing Department of the Faculty of Economics & Business, University of Zagreb, Croatia.

Soumya Ray is an Associate Professor at the Institute of Service Science in National Tsing Hua University, Taiwan.

Florian Schuberth is an Assistant Professor at the Chair of Product-Market Relations of the University of Twente, Enschede, The Netherlands.

Maja Šerić is an Assistant Professor at the Department of Marketing of the Faculty of Economics, University of Valencia, Spain.

Nuria Recuero Virto is a Post-doctoral Researcher at Universidad Complutense de Madrid, with a special grant of Santander Bank.