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Purpose The purpose of this paper is to present integrated generalized structured component analysis (IGSCA) as a versatile approach for estimating models that contain both components and factors as statistical proxies for the constructs. The paper sets out to discuss the how-tos of using IGSCA by explaining how to specify, estimate, and evaluate different types of models. The paper’s overarching aim is to make business researchers aware of this promising structural equation modeling (SEM) method. Design/methodology/approach By merging works of literature from various fields of science, the paper provides an overview of the steps that are required to run IGSCA. Findings from conceptual, analytical and empirical articles are combined to derive concrete guidelines for IGSCA use. Finally, an empirical case study is used to illustrate the analysis steps with the GSCA Pro software. Findings Many of the principles and metrics known from partial least squares path modeling – the most prominent component-based SEM method – are also relevant in the context of IGSCA. However, there are differences in model specification, estimation and evaluation (e.g. assessment of overall model fit). Research limitations/implications Methodological developments associated with IGSCA are rapidly emerging. The metrics reported in this paper are useful for current applications, but researchers should follow the latest developments in the field. Originality/value To the best of the authors’ knowledge, this is the first paper to offer guidelines for IGSCA use and to illustrate the method's application by means of the GSCA Pro software. The recommendations and illustrations guide researchers who are seeking to conduct IGSCA studies in business research and practice.

Purpose Partial least squares structural equation modeling (PLS-SEM) has attracted much attention from both methodological and applied researchers in various disciplines – also in hospitality management research. As PLS-SEM is relatively new compared to other multivariate analysis techniques, there are still numerous open questions and uncertainties in its application. This study aims to address this important issue by offering guidance regarding its use in contexts with which researchers struggle. Design/methodology/approach The authors examine the most prominent questions and answers posed in a well-known PLS-SEM discussion forum. The authors do so by using a text analysis technique to identify the most salient topics. Findings The data analysis identifies three salient PLS-SEM topics (i.e. bootstrapping and significance testing, higher-order constructs and moderation). Research limitations/implications The results allow us to address the PLS-SEM community’s main methodological issues. The authors discuss each area separately and provide explanations and guidelines. Practical implications The guidelines on the most important PLS-SEM topics provide decision-making and application aids. In this way, the authors make a decisive contribution to clarifying ambiguities when applying the PLS-SEM method in hospitality management research and other disciplines. Originality/value There has as yet been no systematic analysis of this kind in the field of PLS-SEM; the authors, therefore, present the first research results. The findings and recommendations provide guidance for PLS-SEM applications in hospitality research and practice.

Purpose Generalized structured component analysis (GSCA) and partial least squares path modeling (PLSPM) are two key component-based approaches to structural equation modeling that facilitate the analysis of theoretically established models in terms of both explanation and prediction. This study aims to offer a comparative evaluation of GSCA and PLSPM in a predictive modeling framework. Design/methodology/approach A simulation study compares the predictive performance of GSCA and PLSPM under various simulation conditions and different prediction types of correctly specified and misspecified models. Findings The results suggest that GSCA with reflective composite indicators (GSCAR) is the most versatile approach. For observed prediction, which uses the component scores to generate prediction for the indicators, GSCAR performs slightly better than PLSPM with mode A. For operative prediction, which considers all parameter estimates to generate predictions, both methods perform equally well. GSCA with formative composite indicators and PLSPM with mode B generally lag behind the other methods. Research limitations/implications Future research may further assess the methods’ prediction precision, considering more experimental factors with a wider range of levels, including more extreme ones. Practical implications When prediction is the primary study aim, researchers should generally revert to GSCAR, considering its performance for observed and operative prediction together. Originality/value This research is the first to compare the relative efficacy of GSCA and PLSPM in terms of predictive power.

With the increasing prominence of partial least squares structural equation modeling (PLS-SEM) in business research, the use of latent class analyses for identifying and treating unobserved heterogeneity has also gained momentum. Researchers have introduced various latent class approaches in a PLS-SEM context, of which finite mixture PLS (FIMIX-PLS) plays a central role due to its ability to identify heterogeneity and indicate a suitable number of segments to extract from the data. However, applying FIMIX-PLS requires researchers to make several choices that, if incorrect, could lead to wrong results and false conclusions. Addressing this concern, we present the results of a systematic review of FIMIX-PLS applications published in major business research journals. Our review provides an overview of the interdependencies between researchers’ choices and identifies potential problem areas. Based on our results, we offer concrete guidance on how to prevent common pitfalls when using FIMIX-PLS, and identify future research areas.

Multiple scales measuring a customer's, or consumer's, engagement (CE) with a brand or specific brand elements (e.g., advertising/social media content) have been proposed in the literature, offering researchers different options to gauge CE. However, the myriad proposed operationalizations can yield confusion among scholars regarding how to best capture CE, exposing a growing issue for CE research. Addressing this issue, we take stock of major scales measuring a CE with a brand or specific brand elements. To achieve this objective, we performed a systematic review of major CE scale development articles (2005 to January 2023) using the Preferred Reporting Items for Systematic reviews and Meta-Analyses (PRISMA) approach. We systematically evaluated these scales in terms of their respective CE conceptualization, dimensionality, itemization, and underlying theoretical perspective. We also identify potential scale-related risks, or pitfalls, exposing important insight for CE researchers. Overall, the results suggest the existence of theoretical contamination in specific CE measures (e.g., through the inclusion of related concepts in the proposed CE definition), compromising their theoretical rigor and raising a need for scholars to verify the theoretical underpinnings of their adopted CE scales.

There are broadly two dimensions on which researchers can evaluate their statistical models: explanatory power and predictive power. Using data on job satisfaction in ageing workforces, we empirically highlight the importance of distinguishing between these two dimensions clearly by showing that a model with a certain degree of explanatory power can produce vastly different levels of predictive power and vice versa—in the same and different contexts. In a further step, we review all the papers published in three top‐tier human resource management journals between 2014 and 2018 to show that researchers generally confuse explanation and prediction. Specifically, while almost all authors rely solely on explanatory power assessments (i.e., assessing whether the coefficients are significant and in the hypothesised direction), they also derive practical recommendations, which inherently result from a predictive scenario. Based on our results, we provide HRM researchers recommendations on how to improve the rigour of their explanatory studies.

Evaluations of convergent and discriminant validity are generally conducted by analyzing constructs in isolation or by comparing pairs of latent variables. These approaches ignore the broader nomological network that is intrinsic to a measure’s construct validity, and fail to test the implications of either perfect correlations (convergence) or imperfect correlations (divergence). This paper proposes congruence assessment as a useful approach to simultaneously examining the relationships between multiple latent variables within nomological networks. Two measures are congruent if they have proportionally equal correlations with other constructs. We present measures for quantifying congruence within nomological networks, discuss statistical tests of significance, and demonstrate their performance in simulation studies. We reanalyze three published studies to contrast findings from congruence assessment versus traditional criteria for convergent and discriminant validity. We also discuss methodological and theoretical implications of congruence assessment, and suggest future research directions for both covariance- and composite-based structural equation modeling.

Purpose This study aims to propose guidelines for the joint use of partial least squares structural equation modeling (PLS-SEM) and fuzzy-set qualitative comparative analysis (fsQCA) to combine symmetric and asymmetric perspectives in model evaluation, in the hospitality and tourism field. Design/methodology/approach This study discusses PLS-SEM as a symmetric approach and fsQCA as an asymmetric approach to analyze structural and configurational models. It presents guidelines to conduct an fsQCA based on latent construct scores drawn from PLS-SEM, to assess how configurations of exogenous constructs produce a specific outcome in an endogenous construct. Findings This research highlights the advantages of combining PLS-SEM and fsQCA to analyze the causal effects of antecedents (i.e., exogenous constructs) on outcomes (i.e., endogenous constructs). The construct scores extracted from the PLS-SEM analysis of a nomological network of constructs provide accurate input for performing fsQCA to identify the sufficient configurations required to predict the outcome(s). Complementing the assessment of the model’s explanatory and predictive power, the fsQCA generates more fine-grained insights into variable relationships, thereby offering the means to reach better managerial conclusions. Originality/value The application of PLS-SEM and fsQCA as separate prediction-oriented methods has increased notably in recent years. However, in the absence of clear guidelines, studies applied the methods inconsistently, giving researchers little direction on how to best apply PLS-SEM and fsQCA in tandem. To address this concern, this study provides guidelines for the joint use of PLS-SEM and fsQCA.

Most project management research focuses almost exclusively on explanatory analyses. Evaluation of the explanatory power of statistical models is generally based on F-type statistics and the R 2 metric, followed by an assessment of the model parameters (e.g., beta coefficients) in terms of their significance, size, and direction. However, these measures are not indicative of a model’s predictive power, which is central for deriving managerial recommendations. We recommend that project management researchers routinely use additional metrics, such as the mean absolute error or the root mean square error, to accurately quantify their statistical models’ predictive power.

The emergence of digital data and the methods used to analyze them are revolutionizing marketing research. The vast quantity of data offers marketing researchers countless opportunities to better p...

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.

Examining the efficacy of composite-based structural equation modeling (SEM) features prominently in research. However, studies analyzing the efficacy of corresponding estimators usually rely on factor model data. Thereby, they assess and analyze their performance on erroneous grounds (i.e., factor model data instead of composite model data). A potential reason for this malpractice lies in the lack of available composite model-based data generation procedures for prespecified model parameters in the structural model and the measurements models. Addressing this gap in research, we derive model formulations and present a composite model-based data generation approach. The findings will assist researchers in their composite-based SEM simulation studies.

Mediation and conditional process analyses have become popular approaches for examining the mechanisms by which effects operate and the factors that influence them. To estimate mediation models, researchers often augment their structural equation modeling (SEM) analyses with additional regression analyses using the PROCESS macro. This duality is surprising considering that research has long acknowledged the limitations of regression analyses when estimating models with latent variables. In this article, we argue that much of the confusion regarding SEM’s efficacy for mediation analyses results from a singular focus on factor-based methods, and there is no need for a tandem use of SEM and PROCESS. Specifically, we highlight that composite-based SEM methods overcome the limitations of both regression and factor-based SEM analyses when estimating even highly complex mediation models. We further conclude that composite-based SEM methods such as partial least squares (PLS-SEM) are the preferred and superior approach when estimating mediation and conditional process models, and that the PROCESS approach is not needed when mediation is examined with PLS-SEM.

Marketing has started exploring ambient scents’ diverse effects and the psychological mechanisms through which they affect consumer behaviour. Recent research focuses on ambient scents’ perceived temperature’s impact on consumer behaviour. In this research, we first replicate prior research by showing that consumers exposed to a warm (vs. cool) ambient scent prefer premium over regular brands – also in an FMCG context. Broadening the perspective, we show that ambient scent’s effect can be generalized to general elections. Specifically, we present initial evidence that the diffusion of a warm (vs. cool) ambient scent nudges potential voters to opt for right-wing instead of moderate political parties. We conjecture that the effect of perceived scent temperature on brand preferences and voting behaviour is a symbolic way of compensating for temperature perceptions.

Partial least squares structural equation modeling (PLS-SEM) is an essential element of marketing researchers' methodological toolbox. During the last decade, the PLS-SEM field has undergone massive developments, raising the question of whether the method's users are following the most recent best practice guidelines. Extending prior research in the field, this paper presents the results of a new analysis of PLS-SEM use in marketing research, focusing on articles published between 2011 and 2020 in the top 30 marketing journals. While researchers were more aware of the when's and how's of PLS-SEM use during the period studied, we find that there continues to be some delay in the adoption of model evaluation's best practices. Based on our review results, we provide recommendations for future PLS-SEM use, offer guidelines for the method's application, and identify areas of further research interest.

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