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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Wagner’s Law, Keynesian view, Public expenditures, ARDL and ‘Bounds test’, H50, C22, O23, E62,

Purpose -The primary objective of this paper is to examine the factors that underpin the pervasiveness of money laundering. An empirical method was used to study the relationship between technology (information and communication technology infrastructure), quality of human capital, efficiency of the legal framework, ethical behavior of firms (corporate governance) and capacity for innovation on the pervasiveness of money laundering in developed and developing countries. Based on the empirical findings, key strategies and policies to reduce the pervasiveness of money laundering were examined in this paper. Design/methodology/approach -The sample period for this study was 2004-2005 entailing 88 developed and developing countries. The ordinary least square method was used in this paper to examine the impact of the above-mentioned factors on the pervasiveness of money laundering. Findings -The empirical analysis showed that efficient legal framework with good corporate governance lower the pervasiveness of money laundering activities. The empirical analysis also showed that a high-innovative capacity contribute negatively to the pervasiveness of money laundering activities.Research limitations/implications -One of the limitations of this study is the lack of quality data measuring pervasiveness of money laundering patterns over a longer period of time. Over the next two years, as more data becomes available, a more robust econometric modeling framework called the dynamic panel data method can be used to assess the impact of the above-mentioned factors on the pervasiveness of money laundering. This new method will not only capture the factors contributing to variations of pervasiveness of money laundering across the different countries but also across the time period. Practical implications -Strategies to reduce the pervasiveness of money laundering in developing countries are discussed. Originality/value -While there are numerous studies in the literature that critically examine factors that contribute to money laundering, the number of empirical studies that examined the factors that contribute to money laundering are rather scarce. This study hopes to fill this gap in the literature.

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