Partial least squares structural equation modeling (PLS-SEM) has become a popular method for estimating path models with latent variables and their relationships. A common goal of PLS-SEM analyses is to identify key success factors and sources of competitive advantage for important target constructs such as customer satisfaction, customer loyalty, behavioral intentions, and user behavior. Building on an introduction of the fundamentals of measurement and structural theory, this chapter explains how to specify and estimate path models using PLS-SEM. Complementing the introduction of the PLS-SEM method and the description of how to evaluate analysis results, the chapter also offers an overview of complementary analytical techniques. A PLS-SEM application of the widely recognized corporate reputation model illustrates the method.

Partial Least Squares Structural Equation Modeling

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.,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.,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.,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.,This paper offers clear guidelines for using PLSpredict, which researchers and practitioners should routinely apply as part of their PLS-SEM analyses.,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.

Predictive model assessment in PLS-SEM: guidelines for using PLSpredict

Purpose Structural equation modeling (SEM) depicts one of the most salient research methods across a variety of disciplines, including hospitality management. While for many researchers, SEM is equivalent to carrying out covariance-based SEM, recent research advocates the use of partial least squares structural equation modeling (PLS-SEM) as an attractive alternative. We systematically examine how PLS-SEM has been applied in major hospitality research journals with the aim of providing important guidance and, if necessary, opportunities for realignment in future applications. As PLS-SEM in hospitality research is still in an early stage of development, critically examining its use holds considerable promise in order to counteract misapplications which otherwise might reinforce over time. Design/methodology/approach We reviewed all PLS-SEM studies published in six SSCI-indexed hospitality management journals between 2001 and 2015. Tying in with prior studies in the field, our review covers reasons for usin...

An assessment of the use of partial least squares structural equation modeling (PLS-SEM) in hospitality research

Partial least squares structural equation modeling (PLS-SEM) has become a key multivariate analysis technique that human resource management (HRM) researchers frequently use. While most disciplines...

Partial least squares structural equation modeling in HRM research

How to perform and report an impactful analysis using partial least squares: Guidelines for confirmatory and explanatory IS research

Following the call for awareness of accepted reporting practices by Ringle, Sarstedt, and Straub in 2012, the purpose of this paper is to review and analyze the use of partial least squares structural equation modeling (PLS-SEM) in Industrial Management & Data Systems (IMDS) and extend MIS Quarterly (MISQ) applications to include the period 2012-2014.,Review of PLS-SEM applications in information systems (IS) studies published in IMDS and MISQ for the period 2010-2014 identifying a total of 57 articles reporting the use of or commenting on PLS-SEM.,The results indicate an increased maturity of the IS field in using PLS-SEM for model complexity and formative measures and not just small sample sizes and non-normal data.,Findings demonstrate the continued use and acceptance of PLS-SEM as an accepted research method within IS. PLS-SEM is discussed as the preferred SEM method when the research objective is prediction.,This update on PLS-SEM use and recent developments will help authors to better understand and apply the method. Researchers are encouraged to engage in complete reporting procedures.,Applications of PLS-SEM for exploratory research and theory development are increasing. IS scholars should continue to exercise sound practice by reporting reasons for using PLS-SEM and recognizing its wider applicability for research. Recommended reporting guidelines following Ringle et al. (2012) and Gefen et al. (2011) are included. Several important methodological updates are included as well.

An updated and expanded assessment of PLS-SEM in information systems research

We investigated the role automated behavior plays in contributing to security breaches. Using different forms of phishing, combined with multiple neurophysiological tools, we were able to more fully understand the approaches participants took when they engaged with a phishing campaign. The four participants of this pilot study ranged in their individual characteristics of gender and IT experience while controlling for age. It seems the biggest factor for awareness and successfully resisting a phishing campaign may be proximity of security training to engagement with that campaign. Neurophysiological tools helped illustrate the thought processes behind participants’ statements and actions; combined with consideration of individual characteristics, these tools help shed more light on human behavior. In the future, we plan to further enhance our testing environment by incorporating an emergent model that considers work task complexity and incorporate more industry participants with a range of IT experience.

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Traditional SETA No More: Investigating the Intersection Between Cybersecurity and Cognitive Neuroscience

With the proliferation of mobile device types and variety of tasks being performed on those devices, it is necessary to examine how this pairing changes with individuals. NeuroIS offers complementary tools to traditional survey tools helping researchers delve into users’ perceptions while they are engaged in different tasks. Through analysis of neurophysiological data we may better understand activities performed on mobile devices and help provide more customized user experiences. A two-part preliminary study is described as a pre-cursor to a larger, focused experiment utilizing EEG and eye-tracking on mobile device usage.

Carole L. Hollingsworth

Papers

An updated and expanded assessment of PLS-SEM in information systems research

Traditional SETA No More: Investigating the Intersection Between Cybersecurity and Cognitive Neuroscience

Using NeuroIS to Better Understand Activities Performed on Mobile Devices