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If you would like to write for this, or any other Emerald publication, then please use our Emerald for Authors service information about how to choose which publication to write for and submission guidelines are available for all. Please visit www.emeraldinsight.com/authors for more information. About Emerald www.emeraldinsight.comEmerald is a global publisher linking research and practice to the benefit of society. The company manages a portfolio of more than 290 journals and over 2,350 books and book series volumes, as well as providing an extensive range of online products and additional customer resources and services.Emerald is both COUNTER 4 and TRANSFER compliant. The organization is a partner of the Committee on Publication Ethics (COPE) and also works with Portico and the LOCKSS initiative for digital archive preservation. Christopher SchlägelFaculty of Economics and Management, Otto von Guericke University Magdeburg, Magdeburg, Gemany Abstract Purpose -Structural equation modeling (SEM) has been widely used to examine complex research models in international business and marketing research. While the covariance-based SEM (CB-SEM) approach is dominant, the authors argue that the field's dynamic nature and the sometimes early stage of theory development more often require a partial least squares SEM (PLS-SEM) approach. The purpose of this paper is to critically review the application of SEM techniques in the field. Design/methodology/approach -The authors searched six journals with an international business (and marketing) focus (Management International Review, Journal of International Business Studies, Journal of International Management, International Marketing Review, Journal of World Business, International Business Review) from 1990 to 2013. The authors reviewed all articles that apply SEM, analyzed their research objectives and methodology choices, and assessed whether the PLS-SEM papers followed the best practices outlined in the past. Findings -Of the articles, 379 utilized CB-SEM and 45 PLS-SEM. The reasons for using PLS-SEM referred largely to sampling and data measurement issues and did not sufficiently build on the procedure's benefits that stem from its design for predictive and exploratory purposes. Thus, the procedure's key benefits, which might be fruitful for the theorizing process, are not being fully exploited. Furthermore, authors need to better follow best practices to truly advance theory building. Research limitations/implications -The authors examined a subset of journals in the field and did not include general management journals that publish international business and The current issue and full text archive of this journal is available on Emerald Insight at: www.emeraldinsight.com/0265-1335.htm marketing-related studies. Fur-thermore, the authors found only limited use of PLS-SEM in the journals the authors considered relevant to the study. Originality/value -The study contributes to the literature by providing researchers seeking to adopt SEM as an analytical method with pra...

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PurposeThis research introduces the combined use of partial least squares–structural equation modeling (PLS-SEM) and necessary condition analysis (NCA) that enables researchers to explore and validate hypotheses following a sufficiency logic, as well as hypotheses drawing on a necessity logic. The authors’ objective is to encourage the practice of combining PLS-SEM and NCA as complementary views of causality and data analysis.Design/methodology/approachThe authors present guidelines describing how to combine PLS-SEM and NCA. These relate to the specification of the research objective and the theoretical background, the preparation and evaluation of the data set, running the analyses, the evaluation of measurements, the evaluation of the (structural) model and relationships and the interpretation of findings. In addition, the authors present an empirical illustration in the field of technology acceptance.FindingsThe use of PLS-SEM and NCA enables researchers to identify the must-have factors required for an outcome in accordance with the necessity logic. At the same time, this approach shows the should-have factors following the additive sufficiency logic. The combination of both logics enables researchers to support their theoretical considerations and offers new avenues to test theoretical alternatives for established models.Originality/valueThe authors provide insights into the logic, assessment, challenges and benefits of NCA for researchers familiar with PLS-SEM. This novel approach enables researchers to substantiate and improve their theories and helps practitioners disclose the must-have and should-have factors relevant to their decision-making.

A. Wold (2006), the originator of the method, characterizes PLS-SEM as an "epoch-making 1960s innovation" that combines econometric prediction with the psychometric modeling of latent variables (also referred to as constructs), which multiple indicators (also referred to as manifest variables) determine. To provide a better understanding of the approach, Figure 1 shows a simple PLS path model with four latent variables, Y 1 to Y 4 (represented by circles), determined as the weighted sum of their assigned indicators x (represented by the rectangles). In other words, in the measurement model (also called the outer model), a block of directly observable indicators represents each latent variable that is not directly observable. In the structural model (also called the inner model), the latent variables have pre-defined and theoretically/conceptually established relationships. The goal of the PLS-SEM approach is to generate latent variable scores that jointly minimize the residuals of the ordinary least squares (OLS) regressions in the model (i.e., maximize the explanation). The resulting latent variable scores are unique and determine the case values of each observation. They also make it possible to predict the indicators (x 7 -x 12 ) of the endogenous or dependent latent variables in the structural model (Y 3 and Y 4 ).