Partial Least Squares Structural Equation Modeling: Recent Advances in Banking and Finance PDF
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International Series in Operations Research & Management Science Necmi K. Avkiran Christian M. Ringle Editors Partial Least Squares Structural Equation Modeling Recent Advances in Banking and Finance International Series in Operations Research & Management Science Volume 267 Series Editor Camille C. Price Department of Computer Science, Stephen F. Austin State University, TX, USA Associate Series Editor Joe Zhu School of Business, Worcester Polytechnic Institute, MA, USA Founding Series Editor Frederick S. Hillier Stanford University, CA, USA More information about this series at http://www.springernature.com/series/6161 Necmi K. Avkiran • Christian M. Ringle Editors Partial Least Squares Structural Equation Modeling Recent Advances in Banking and Finance Editors Necmi K. Avkiran Christian M. Ringle School of Business Institute of HRM University of Queensland Hamburg University of Technology St Lucia, QLD, Australia Hamburg, Germany The University of Newcastle Faculty of Business and Law Callaghan Callaghan, NSW, Australia ISSN 0884-8289 ISSN 2214-7934 (electronic) International Series in Operations Research & Management Science ISBN 978-3-319-71690-9 ISBN 978-3-319-71691-6 (eBook) https://doi.org/10.1007/978-3-319-71691-6 Library of Congress Control Number: 2018931193 © Springer International Publishing AG 2018 This work is subject to copyright. All rights are reserved by the Publisher, whether the whole or part of the material is concerned, specifically the rights of translation, reprinting, reuse of illustrations, recitation, broadcasting, reproduction on microfilms or in any other physical way, and transmission or information storage and retrieval, electronic adaptation, computer software, or by similar or dissimilar methodology now known or hereafter developed. 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Printed on acid-free paper This Springer imprint is published by Springer Nature The registered company is Springer International Publishing AG The registered company address is: Gewerbestrasse 11, 6330 Cham, Switzerland Preface We conceived this book because of the absence of partial least squares structural equation modelling (PLS-SEM) in banking and finance disciplines. Yet, the PLS- SEM method has been broadly accepted and used in disciplines such as accounting, health care, hospitality management, management information systems, marketing, operations management, strategic management, supply chain management, and tourism. Besides, the method enjoys an increasing application in a wide range of additional disciplines such as economics, engineering, environmental sciences, medicine, political sciences, and psychology (Richter et al. 2016). Against this background, we also expect an adoption of PLS-SEM in the banking and finance disciplines. As a causal-predictive method, PLS-SEM has a wide spectrum of practical appli- cations to managerial challenges. Unfortunately, secondary data frequently found in business databases are unlikely to satisfy such constraints as homogeneity in the population, and measurement errors being uncorrelated. With the ever-increasing availability of secondary data, PLS-SEM’s soft modelling approach fits exploratory research, where theory has not been fully developed. Using the PLS-SEM approach is recommended when (a) the objective is explaining and predicting target con- structs and/or detecting important driver constructs, (b) the structural model has formatively measured constructs, (c) the model is complex (with many constructs and indicators), (d) the researcher is working with a small sample size (due to a small population size), and (e) the researcher intends to use latent variable scores in follow-up studies (Sarstedt et al. 2017). PLS-SEM is relatively robust with non- normal data. However, researchers should not use the latter characteristic and/or small sample sizes as the sole argument for selecting PLS-SEM but focus on the goal of their empirical analysis (Rigdon 2016). This is important to proactively attend to potential criticism that has been put forward with regard to PLS-SEM (for further details on this debate, see Sarstedt et al. 2016) The applications in this handbook further pioneer PLS-SEM adoptions in the banking and finance disciplines. New PLS-SEM developments will further expand the method’s usefulness to banking and finance studies. These advances primarily address both the method’s explanatory and predictive capabilities. Examples of v vi Preface recent enhancements include methods for uncovering unobserved heterogeneity, different multi-group analysis approaches, testing measurement invariance of com- posites, overall goodness-of-fit measures, and novel approaches of prediction- oriented results evaluations (Richter et al. 2016; Sarstedt et al. 2017). We also expect that the PLS-SEM method will experience extensions in the direction of longitudi- nal data analysis and multilevel modelling, which will become particularly benefi- cial for the banking and finance disciplines given the characteristics of data usually used in such studies. As a final comment, we would like to gratefully acknowledge the reviewers who contributed to this book: Jac Birt, Allan Hodgson, Rand Low, Lin Mi, and David Smith. St Lucia, QLD, Australia Necmi K. Avkiran Hamburg, Germany Christian M. Ringle Callaghan, NSW, Australia References Richter, N. F., Carrión, G. C., Roldán, J. L., & Ringle, C. M. (2016). European management research using partial least squares structural equation modeling (PLS- SEM): Editorial. European Management Journal, 34(6), 589–597. Rigdon, E. E. (2016). Choosing PLS path modeling as analytical method in European management research: A realist perspective. European Management Journal, 34(6), 598–605. Sarstedt, 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), 3998–4010. Sarstedt, M., Ringle C. M., & Hair, J. F. (2017). Partial least squares structural equation mod- eling. In C. Homburg, M. Klarmann, & A. Vomberg (Eds.), Handbook of market research. Heidelberg: Springer. Contents 1 R ise of the Partial Least Squares Structural Equation Modeling: An Application in Banking . . . . . . . . . . . . . . . . . . . . . . . . . 1 Necmi K. Avkiran 2 B ank Soundness: A PLS-SEM Approach . . . . . . . . . . . . . . . . . . . . . . . 31 Charmele Ayadurai and Rasol Eskandari 3 T he Customer Loyalty Cascade and Its Impact on Profitability in Financial Services . . . . . . . . . . . . . . . . . . . . . . . . . . 53 Anne-Kathrin Hegner-Kakar, Nicole F. Richter, and Christian M. Ringle 4 C orporate Reputation: The Importance of Service Quality and Relationship Investment . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 77 Lăcrămioara Radomir and Alan Wilson 5 T he Compliance Index Model: Mitigating Compliance Risks by Applying PLS-SEM to Measure the Perceived Effectiveness of Compliance Programs . . . . . . . . . . . . . . . . . . . . . . . . . 125 Sebastian Rick and Ralf Jasny 6 W hy Should PLS-SEM Be Used Rather Than Regression? Evidence from the Capital Structure Perspective . . . . . . . . . . . . . . . . 171 Nur Ainna Ramli, Hengky Latan, and Gilbert V. Nartea 7 M anagement Accounting and Partial Least Squares-Structural Equation Modelling (PLS- SEM): Some Illustrative Examples . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 211 Christian Nitzl Index . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 231 vii Contributors Necmi K. Avkiran School of Business, University of Queensland, St Lucia, QLD, Australia Charmele Ayadurai School of Business and Law, University of Salford, Manchester, UK Rasol Eskandari School of Business and Law, University of Salford, Manchester, UK Anne-Kathrin Hegner-Kakar Hamburg University of Technology (TUHH), Hamburg, Germany Fashion and Lifestyle, GfK SE, Nürnberg, Germany Ralf Jasny Faculty 3: Business and Law, Frankfurt University of Applied Sciences, Frankfurt, Germany Hengky Latan Department of Accounting, STIE Bank BPD Jateng, Semarang, Indonesia Department of Accounting, Petra Christian University, Surabaya, Indonesia Gilbert V. Nartea Department of Economics and Finance College of Business and Law, University of Canterbury, Christchurch, New Zealand Christian Nitzl University of the German Armed Forces Munich, Neubiberg, Germany Lăcrămioara Radomir Department of Marketing, Faculty of Economics and Business Administration, Babes¸-Bolyai University, Cluj-Napoca, Romania Nur Ainna Ramli Faculty of Economics and Muamalat, University Sains Islam Malaysia, Bandar Baru Nilai, Negeri Sembilan, Malaysia Nicole F. Richter University of Southern Denmark, Sønderborg, Denmark ix x Contributors Sebastian Rick Governance & Assurance Services, KPMG AG Wirtschaftsprü- fungsgesellschaft, The SQUAIRE, Frankfurt, Germany Christian M. Ringle Institute of HRM, Hamburg University of Technology, Hamburg, Germany The University of Newcastle, Faculty of Business and Law Callaghan, Callaghan, NSW, Australia Alan Wilson Department of Marketing, Strathclyde Business School, University of Strathclyde, Glasgow, UK Chapter 1 Rise of the Partial Least Squares Structural Equation Modeling: An Application in Banking Necmi K. Avkiran Abstract Researchers across a wide range of disciplines exploited the capabilities of partial least squares structural equation modeling (PLS-SEM). The rise in popu- larity of PLS-SEM is particularly noticeable 2013 onwards. The banking and finance discipline, however, hardly exploits the advantages of the PLS-SEM approach. PLS-SEM can be used for prediction and exploration in complex models with relaxed expectations on data. PLS-SEM is useful in identifying relationships between constructs. If the primary objective is theory development, PLS-SEM is appropriate. Keywords Banking ∙ Managerial competency ∙ PLS-SEM ∙ CB-SEM ∙ IPMA ∙ GSCA 1.1 Introduction Researchers across a wide range of disciplines exploited the capabilities of partial least squares structural equation modeling (PLS-SEM) in their studies. These dis- ciplines include, for example, accounting (Lee et al. 2011; Nitzl 2016), family business (Sarstedt et al. 2014), health care (Avkiran 2017), international business (Richter et al. 2016b), management information systems (Hair et al. 2017a; Ringle et al. 2012), marketing (Hair et al. 2012c), operations management (Peng and Lai 2012), psychology (Willaby et al. 2015), strategic management (Hair et al. 2012b), supply chain management (Kaufmann and Gaeckler 2015), and tourism (do Valle and Assaker 2016). Figure 1.1 shows the rise of PLS-SEM (e.g., Wold 1982; Lohmöller 1989), as a variance-based structural equation modeling method (SEM), in literature alongside the traditionally methodological sibling, the N.K. Avkiran (*) School of Business, University of Queensland, St Lucia, QLD, Australia e-mail: [email protected] © Springer International Publishing AG 2018 1 N.K. Avkiran, C.M. Ringle (eds.), Partial Least Squares Structural Equation Modeling, International Series in Operations Research & Management Science 267, https://doi.org/10.1007/978-3-319-71691-6_1
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