Prediction-oriented modeling in business research by means of PLS path modeling: Introduction to a JBR special section

1 Prediction-oriented modeling in business research by means of PLS path-modeling Gabriel Cepeda Carrión, Universidad de Sevilla Jörg Henseler, University of Twente and Universidade Nova de Lisboa Christian M. Ringle, Hamburg University of Technology (TUHH) and The University of Newcastle José Luis Roldán, Universidad de Sevilla March 2016 Send correspondence to Gabriel Cepeda Carrión, Department of Business Management and Marketing, Universidad de Sevilla, Av. Ramón y Cajal, 1, 41018, Sevilla, Spain (E-mail: ga[email protected]); Jörg Henseler, Department of Design, Faculty of Engineering Technology, University of Twente, 7500 AE Enschede, The Netherlands (E-mail: j.hens[email protected]); Christian M. Ringle, Hamburg University of Technology (TUHH), Am-Schwarzenberg- Campus 4 (D), 21073 Hamburg, Germany (E-mail: [email protected]); José Luis Roldán, Department of Business Management and Marketing, Universidad de Sevilla, Av. Ramón y Cajal, 1, 41018, Sevilla, Spain (E-mail: [email protected]). 2 Prediction-oriented modeling in business research by means of PLS path-modeling Abstract Under the main theme “prediction-oriented modeling in business research by means of partial least squares path modeling” (PLS), the special issue presents 17 papers. Most contributions include content from presentations at the 2nd International Symposium on Partial Least Squares Path Modeling: The Conference for PLS Users, which took place at the Universidad de Sevilla (Spain) from June 16 to 19, 2015. This conference provided PLS users with a platform for the fruitful exchange of ideas on variance-based structural equation modeling. At the same time, the conference addressed the latest methodological advances and their use in research practice. Finally, the conference resumed and enriched the ongoing discussion on the strengths and weaknesses of PLS. Researchers often emphasize that predictive capabilities is a strength of the PLS method. Nevertheless, methodological advances and applications in this direction are rare. The scientific committee therefore selected high-quality papers that mainly advance PLS and prediction. The special issue editors believe this special issues will become the starting point for a more intensive use of predictive modeling in the social sciences discipline and for additional advances that will exploit PLS’ capabilities in this area. Keywords: Partial least squares; prediction-oriented modeling; business research; quantitative methods 3 Prediction-oriented modeling in business research by means of partial least squares path modeling 1. Introduction Partial least squares path modeling (PLS; Chin, 1998; Dijkstra, 2010; Lohmöller, 1989; Wold, 1982) is a variance-based method to estimate composite-based path models (Hair, Hult, Ringle, & Sarstedt, 2017; Henseler, Dijkstra, Sarstedt, Ringle, Diamantopoulos, Straub, Ketchen, Hair, Hult, & Calantone, 2014). As numerous review studies show, PLS enjoys rapidly increasing usage in various business disciplines, such as accounting (Lee, Petter, Fayard, & Robinson, 2011), family business (Sarstedt, Ringle, Smith, Reams, & Hair, 2014b), management information systems (Ringle, Sarstedt, & Straub, 2012), marketing (Hair, Sarstedt, Ringle, & Mena, 2012b), operations management (Peng & Lai, 2012), strategic management (Hair, Sarstedt, Pieper, & Ringle, 2012a), and tourism (do Valle & Assaker, 2015). Even though the popularity of PLS continues to increase, researchers often call for more rigor when applying the method (e.g., Hair, Ringle, & Sarstedt, 2013; Rigdon, Becker, Rai, Ringle, Diamantopoulos, Karahanna, Straub, & Dijkstra, 2014; Roldán & Sánchez- Franco, 2012; Sarstedt, Ringle, Henseler, & Hair, 2014a). At the same time, in the context of PLS, Rigdon (2014, p. 166) notes: “We have seen a long period where our choice of statistical tools has shaped our research goals, exalting parameter estimation and fit assessment and neglecting prediction. In the future, we need to have our choice of goals shaping our tools.” However, new PLS developments primarily address the method’s explanatory and confirmatory capabilities so far. Advances of the PLS include consistent PLS estimates for factor models (Dijkstra & Henseler, 2015b), the confirmatory tetrad analysis for testing the kind of measurement model and construct (Gudergan, Ringle, Wende, & Will, 2008), the heterotrait-monotrait ratio of correlations (HTMT) for assessing discriminant validity (Henseler, Ringle, & Sarstedt, 2015), methods for uncovering unobserved heterogeneity (e.g., 4 Becker, Rai, Ringle, & Völckner, 2013; Ringle, Sarstedt, & Schlittgen, 2014), different multigroup analysis approaches (Chin & Dibbern, 2010; Sarstedt, Henseler, & Ringle, 2011), testing measurement invariance of composites (Henseler, Ringle, & Sarstedt, 2016), as well as the use of PLS for non-recursive models and overall goodness-of-fit measures (Dijkstra & Henseler, 2015a). All these changes culminate in revised guidelines for PLS’ confirmatory research use (Henseler, Hubona, & Ray, 2016), as well as updated primers and new advanced PLS textbooks (Hair et al., 2017; Hair, Sarstedt, Ringle, & Gudergan, 2018). While PLS researchers concentrate their efforts largely on confirmatory research, PLS’ use for predictive research problems is somewhat out of focus. Yet researchers often emphasize its predictive capabilities as a strength of the PLS method (e.g., Hair, Ringle, & Sarstedt, 2011). At the moment, methodological advances and applications in this direction are rare. Against this background, the “2nd International Symposium on Partial Least Squares Path Modeling: The Conference for PLS Users” worked with the Journal of Business Research (JBR) to develop a special issue that includes advances in PLS and prediction, as well as substantial PLS applications in marketing and management research that mainly have an orientation towards prediction. The purpose of the special issue is to establish a starting point for the more intensive use of predictive modeling in the social sciences discipline and to initiate additional advances to exploit PLS’ capabilities in this area. Most papers included in the special issue are extended versions of papers presented at the 2nd International Symposium on Partial Least Squares Path Modeling: The Conference for PLS Users. 2. The 2nd International Symposium on PLS: The Conference for PLS Users From June 16 to 19, 2015, the Universidad de Sevilla, Spain, hosted the conference for PLS users. Gabriel Cepeda Carrión (University of Seville), Jörg Henseler (University of Twente and Universidade Nova de Lisboa), Christian M. Ringle (Hamburg University of 5 Technology, TUHH, and The University of Newcastle), and José Luis Roldán (University of Seville) chaired the conference. The International Symposium on PLS was an ideal place to answer the challenges pertaining to advanced issues in PLS, such as its orientation towards prediction. The conference gathered scholars from more than 30 countries, who presented their contributions and obtained rich feedback from the participants. Given the tradition of the Department of Business Administration and Marketing at the Universidad de Sevilla regarding the application and diffusion of the PLS technique, the University was a perfect venue for this PLS meeting. The scientific program committee handled more than 80 submissions and decided on the final conference program; this committee comprised the following members: Francisco J. Acedo (Universidad de Sevilla), Sönke Albers (Kühne Logistics University), Tomàs Aluja (Universitat Politècnica de Catalunya, Spain), Nicholas J. Ashill (American University of Sharjah), Jan-Michael Becker (University of Cologne), Diogenes Bido (Universidade Presbiteriana Mackenzie), Pedro Simoes Coelho (Universidade Nova de Lisboa), Tim Coltman (University of Wollongong), Timothy Devinney (The Universtiy of Leeds), Theo Dijkstra (University of Groningen), Andreas Eggert (University of Paderborn), Vincenzo Esposito Vinzi (ESSEC), John Ettlie (Rochester Institute of Technology), Siegfried Gudergan (The University of Newcastle), Michael Haenlein (ESCP, France), Joe Hair (Kennesaw State University, U.S.A.), Andrew Hardin (University of Nevada), Geoffrey Hubona (Virginia Commonwealth University), John Hulland (University of Georgia), Tomas Hult (Michigan State University), Izani Ibrahim (University Kebangsaan Malaysia), Surinder Kahai (Binghamton University, State University of New York), Byron Keating (University of Canberra), George Marcoulides (University of California, Santa Barbara), David Midgley (INSEAD), Yuichi Mori (Okayama University), Aron O'Cass (University of Tasmania), Shintaro Okazaki (Universidad Autónoma de Madrid), David Xiaosong Peng (University of 6 Houston), Arun Rai (Georgia State University), Thurasamy Ramayah (University Sains Malaysia), Edward E. Rigdon (Georgia State University), James Robins (Vienna University of Economics and Business), Gastón Sánchez (University of California, Berkeley), Marko Sarstedt (Otto-von-Guericke-University Magdeburg), Holger Schiele (University of Twente), Rainer Schlittgen (University of Hamburg), Judit Simon (Corvinus University of Budapest), Rudolf Sinkovics (Manchester Business School), Detmar Straub (Georgia State University), Dirk Temme (University of Wuppertal), Michel Tenenhaus (HEC), Jason Bennett Thatcher (Clemson University), Ron Thompson (Wake Forest University), Sunil Venaik (The University of Queensland), and Martin Wetzels (Maastricht University). Many of the conference papers and sessions offered important developments in PLS’ path modeling use. The conference primarily increased the applicability of PLS in the management and marketing field. Most of the sessions were truly unmissable. The plenary sessions had noteworthy keynote speakers in the PLS field, such as Edward E. Rigdon (Georgia State University), Theo K. Dijkstra (University of Groningen), Joseph F. Hair (Kennesaw State University), and Wynne W. Chin (University of Houston). The following videos of the conference’s key moments are available:  Conference opening: http://tv.us.es/videoembed/?numberpost=30259  Keynote talk I: Reconciling composite-based and factor-based approaches to structural equation modeling (Edward E. Rigdon): http://tv.us.es/videoembed/?numberpost=30263  Keynote talk II: PLS & CB SEM: A weary and a fresh look at presumed antagonists (Theo K. Dijkstra): http://tv.us.es/videoembed/?numberpost=30269  Keynote talk III: On partial least squares’ variance-based component SEM (VBSEM) versus covariance-based SEM (CBSEM) for confirmatory analysis: It’s all about the 7 components and variance explained (Wynne W. Chin): http://tv.us.es/videoembed/?numberpost=30273  Featured talk: Confirmatory composite analysis (Jörg Henseler): http://tv.us.es/videoembed/?numberpost=30277  Panel session: The future of PLS (Discussants: Edward E. Rigdon, Theo K. Dijkstra, Galit Shmueli. Moderator: Jörg Henseler): http://tv.us.es/videoembed/?numberpost=30281 Several sessions, and specifically the panel session, were devoted to this special issue’s topic: the prediction-oriented nature of PLS. 3. PLS and its prediction-oriented results assessment Researchers and practitioners appreciate PLS’ various advantageous features for practical applications (e.g., Hair et al., 2012b). Orientation towards prediction has been one of PLS’ key building blocks since its creation (Jöreskog & Wold, 1982; Wold, 1985). Recent conceptual (Chin, 2010a; Sarstedt et al., 2014a) and empirical studies (Becker, Rai, & Rigdon, 2013; Evermann & Tate, 2012) substantiate the suitability of PLS path modeling for predictive purposes. However, when researchers come to assessing the predictive capabilities of PLS, the Stone-Geisser Q² criterion (Geisser, 1974; Stone, 1974) and the q² predictive effect size, which the blindfolding routine deliver (e.g., Hair et al., 2017), are the only standard evaluation criteria thus far (Chin, 1998, 2010b). The special issue thus aims at providing methodological advances in PLS prediction and management and marketing applications that address this issue. The JBR editor-in-chief, Arch Woodside, the special issue editors, and the reviewers focus on the use of established predictive validity assessment. In addition, they suggest the use of holdout samples in PLS, as 8 Hair et al. (2012b) call for, and the use of fuzzy-set qualitative comparative analysis (fsQCA) to test for causal asymmetry (Gigerenzer & Brighton, 2009; Woodside, 2013). 3.1. Assessing the Predictive Validity of PLS Path Models Using Holdout Samples There are strong recommendations for using cross-validation by means of holdout samples as a standard results assessment routine in PLS (Hair et al., 2012b). The holdout sample assessment allows for determining how well a predictive model will perform in practice, especially when the analysis follows prediction-oriented goals (Ebbes, Papies, & van Heerde, 2011). The use of holdout samples can encourage analysts to find a balance between model fit and prediction capability (Schorfheide & Wolpin, 2012). However, researchers rarely use holdout samples when evaluating their PLS results. Possible reasons for this may be the scarcity of guidelines on the use of holdout samples in the PLS context resulting in a lack of analysts’ knowledge about conducting holdout sample assessments, or the lack of holdout sample implementation in leading PLS software packages. As a point of departure for the more frequent use of holdout samples, the guest editors present an eight-step PLS procedure (Figure 1 and Figure 2). To begin with (Step 1), randomly divide your sample into a training sample (comprising more than half of the data, e.g. see Steckel & Vanhonacker, 1993) and a holdout sample (the remaining observations). Then create two data sets from your original set of data. Next, in Step 2, estimate the PLS path model parameters, using the training sample, and save the results. In Step 3, standardize the holdout sample data (i.e., subtract the mean of each indicator of the holdout sample and divide each mean by its standard deviation). Alternatively, leading PLS software applications provide a standardized data matrix in their results report. Thereafter, create construct scores for the holdout sample as linear combinations of the respective indicators and the weights 9 obtained from the training sample (Step 4). Next, standardize the construct scores of the holdout sample (mean value subtraction and standard deviation division; Step 5). Figure 1 here. In Step 6, create prediction scores for each endogenous construct as a linear combination of the exogenous constructs (using the scores obtained from Step 4) and the path coefficients from the training data model estimation. Now, determine the R² value (Step 7). Calculate the proportion of explained variance (R²) as the squared correlation of the prediction scores and the construct scores for each endogenous construct of the holdout sample. Finally, in Step 8, report the R² values of the holdout sample and compare them with the R² values obtained in the training sample. Both R² values should be fairly similar. This kind of cross-validation substantiates how the statistical analysis results will generalize to an independent data set, as well as how well a predictive model will perform in practice. Figure 2 here. Researchers can use this step-by-step approach to conduct the cross-validation of their PLS results. They may want to use different statistics, such as the root mean square error (RMSE) or the mean absolute error (MAE, e.g. see Greene, 2011). The guest editors’ expectations are that leading PLS software applications will offer the use of holdout samples to conduct crossvalidation routines. Thereby, this kind of assessment will become a standard routine for result analysis and reporting. 3.2. Testing causal asymmetry Woodside (2013) emphasizes the importance of an overview for exploring actual phenomena and the appropriateness of fuzzy-set qualitative comparative analysis (fsQCA; Ragin, 2008, 2009) for analyzing data focuses on a case-level recipe that explains the influence of all the ingredients, rather than the variables’ unidirectional net effects. FsQCA is therefore a strong tool for testing causal asymmetry (Fiss, 2011; Marx, Rihoux, & Ragin, 16 attitude toward the brand, and brand loyalty for the methodology. In addition, using PLS, they perform a prediction-oriented segmentation (POS) that demonstrates the existence of three different groups of people with different reactions toward counterfeiting: (1) resistant and brand attached, (2) non-resistant, and (3) detached. The stability of this segmentation is adequate, as is the causal asymmetry of data. 4.14. Testing the predictive power of PLS through cross-validation in banking Nuria Reguera-Blanco, Antonio Blanco-Oliver, and David Martin-Ruiz analyze the effects of customers’ level of e-banking usage on the relationship quality (satisfaction, trust, and commitment) at a Spanish savings bank. The methodology uses PLS path modeling on a primary dataset obtained through an online survey of almost a thousand customers. The authors also undertake predictive validation testing of the models, using holdout samples and testing for causal asymmetry. 4.15. Customer equity and predictive CLV in Spanish telecommunication services Jose Ramon Segarra Moliner and Miguel Angel Moliner Tena analyze the multidimensionality of the perceived value, brand, and relationship of productivity models, as well as predicting the economic results. They develop a predictive model by analyzing four competing firms. The study context comprises the customers of Spanish telecommunication firms. 4.16. Understanding Chinese tourists’ food consumption in the United States Kaiyang Wu, Carola Raab, Wen Chang, and Anjala Krishen apply the Theory of Planned Behavior (TPB) and use the PLS-SEM method to evaluate different factors that influence 17 Chinese tourists’ consumption behavior toward local food in the United States. The results of this study show that concerns regarding food safety and table manners could aggravate their attitudes toward consuming unfamiliar local foods. However, communication and the food’s sensory appeal are not significant in predicting their attitudes toward local food. 4.17. Entrepreneurial orientation in the hotel industry: Multi-group analysis of quality certification Felipe Hernandez-Perlines analyzes the impact of entrepreneurial orientation on business performance in the hotel sector. Two research methods: PLS-SEM and fsQCA (25 cases) corroborate the results. This twofold confirmation provides his proposed model with validity and robustness, because fsQCA allows for overcoming the drawbacks of PLS-SEM. Thus, this study makes a twofold contribution: a) An analysis of the impact of entrepreneurial orientation on business performance in the hotel sector and b) evidence that quality certification plays a moderating role in this impact. Concluding Remarks and Acknowledgements Researchers often emphasize predictive capabilities as a strength of the PLS method. However, methodological advances and applications in this direction are rare. Consequently, the scientific committee selected high-quality papers for publication that mainly advance PLS and prediction. The JBR special issue provides a forum for topical issues that demonstrate PLS path modeling’s usefulness in management and business applications. A description of the method, its empirical applications, and potential methodological advancements that increase its usefulness for research and practice are specifically emphasized. As such, the special issue aims at two audiences: academics involved in the fields of marketing and management and practitioners, such as consultants. Accordingly, theoretical, methodological, 18 and empirical manuscripts were considered as long as the topic had strong implications for business research and practice. The special issue editors believe that this special issue will be the starting point for a more intensive use of predictive modeling in the social sciences discipline and for additional advances that will exploit PLS’ capabilities in this area. The guest editors and authors gratefully acknowledge Arch G. Woodside’s valuable comments, encouraging support, and suggestions during the preparation of this special issue. The reviewers also deserve the heartfelt recognition of the special editors for their remarkable contribution to the quality of this special issue on the very specific topic of PLS’ predictionoriented nature. As usual, they were diligent, meticulous, constructive, and extremely competent. The special issues editors specifically express their gratitude to the following reviewers: Vicenzo Esposito Vinzi (ESSEC Business School), Galit Shmueli (National Tsing Hua University), Theo Dijkstra (University of Groningen), Florian Schuberth (Universität Würzburg), Tomás Aluja (Universidad Politecnica de Cataluña), Jan-Michael Becker (University of Cologne), Carsten Gelhard (University of Twente), David Martin (Universidad de Sevilla), Ricarda Bouncken (University of Bayreuth), Marko Sarstedt (Otto von Guericke Universität Magdeburg), Asyraf Afthanorfhan (Universiti Sultan Zainal Abidi), Joseph F. Hair (Kennesaw State University), Alexander Benlian (Technische Universität Darmstadt), Joerg Evermann (Memorial University of Newfoundland), José Ramón Segarra (Universidad Jaume I), Craig Lee (University of South Australia), Jose Benitez (Universidad de Granada), Holger Schiele (University of Twente), Liad Wagman (Illinois Institute of Technology), Christian Nitzl (Universität der Bundeswehr München), Yen Nee Gooh (Universiti Sains Malaysia), Francisco Javier Rondán (Universidad de Sevilla), Pauline Ash Ray (Thomas University), Macario Rodriguez (Junta de Andalucia), Laura Trinchera (Neoma Business School), Sandra Streukens (Universiteit Hasselt), Antonio Blanco (Universidad de Sevilla), Volker Seiler (Paderborn University), Julen Castillo (Universidad del Pais Vasco), Jose Ángel 19 López (Universidad de Extremadura), Pratyush Nidhi Sharma (University of Delaware), Felipe Hernandez-Perlines (Universidad de Castilla La Mancha), Miguel Ángel Moliner (Universidad Jaume I), Antonio L. 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