This paper emphasizes that paying greater attention to how human resource management (HRM) systems are conceptualized in empirical studies could provide more actionable insights and increase the impact of HRM systems research. We advocate the use of formative measurement models, arguing that this approach aligns better with the concept of HRM systems, and allows for a nuanced understanding of how each HRM practice and the system contribute to the outcomes of interest. In the same vein, we advocate the use of hierarchical component models, which allow a multi-level conceptualization representing HRM practices, the HRM system, and their intermediate levels of abstraction (e.g. ability, motivation, and opportunities as subcomponents of high-performance work systems). As a result, HRM systems research can move beyond general assertions and instead offer specific and actionable recommendations. We discuss and illustrate how these conceptual ideas can be implemented in partial least squares-structural equation modeling (PLS-SEM), and enriched by predictive model evaluation following state-of-the-art guidelines.

Structural equation modeling (SEM) using partial least squares (PLS) has received considerable attention in recent years. We address the increasing fragmentation of PLS-SEM-related research across multiple fields of scientific inquiry by presenting a bibliometric analysis’s results of n = 9,150 documents from the Web of Science database. We identify the main themes by using bibliometric content analysis to explore the PLS-SEM knowledge structure’s definition, its main drivers, and the interplay between the methodology and the application themes over time. Furthermore, we document the dynamics of the PLS-SEM knowledge structure over four periods spanning 1995–2022, unveiling a surge in scientific production and connections among thematic areas due to topic evolution and hybridization. Finally, we investigate the driving forces behind these trends and the relationship between methodology and application themes, providing an integrative view and insights into PLS-SEM research across disciplines.

Covariance-based structural equation modeling (CB-SEM) enables researchers to estimate models with hypothesized cause-effect relationships between latent variables (i.e., constructs), each of which is operationalized by several items (i.e., indicators). To conduct CB-SEM analyses, researchers can rely on a range of software applications. However, many of these applications require researchers to engage in sometimes complicated and error-prone programming tasks. While IBM SPSS AMOS provides a graphical user interface (GUI), it does not fully meet the expectations of contemporary software. In order to address these challenges, the statistical SmartPLS 4 software has recently introduced a new CB-SEM module, which improves the user experience through a modern and intuitive graphical interface and comprehensive result reports. This tutorial describes the key CB-SEM analysis steps (i.e., model setup, estimation, and results evaluation) using the SmartPLS software.

PurposeThis paper aims to discuss recent criticism related to partial least squares structural equation modeling (PLS-SEM).Design/methodology/approachUsing a combination of literature reviews, empirical examples, and simulation evidence, this research demonstrates that critical accounts of PLS-SEM paint an overly negative picture of PLS-SEM's capabilities.FindingsCriticisms of PLS-SEM often generalize from boundary conditions with little practical relevance to the method's general performance, and disregard the metrics and analyses (e.g., Type I error assessment) that are important when assessing the method's efficacy.Research limitations/implicationsWe believe the alleged "fallacies" and "untold facts" have already been addressed in prior research and that the discussion should shift toward constructive avenues by exploring future research areas that are relevant to PLS-SEM applications.Practical implicationsAll statistical methods, including PLS-SEM, have strengths and weaknesses. Researchers need to consider established guidelines and recent advancements when using the method, especially given the fast pace of developments in the field.Originality/valueThis research addresses criticisms of PLS-SEM and offers researchers, reviewers, and journal editors a more constructive view of its capabilities.

Die Partial Least Squares Strukturgleichungsmodellierung (PLS-SEM) hat sich in der wirtschafts- und sozialwissenschaftlichen Forschung als geeignetes Verfahren zur Schätzung von Kausalmodellen behauptet. Dank der Anwenderfreundlichkeit des Verfahrens und der vorhandenen Software ist es auch in der Praxis etabliert.

Endogeneity in regression models is a key marketing research concern. The Gaussian copula approach offers an instrumental variable (IV)-free technique to mitigate endogeneity bias in regression models. Previous research revealed substantial finite sample bias when applying this method to regression models with an intercept. This is particularly problematic as models in marketing studies almost always require an intercept. To resolve this limitation, our research determines the bias’s sources, making several methodological advances in the process. First, we show that the cumulative distribution function estimation’s quality strongly affects the Gaussian copula approach’s performance. Second, we use this insight to develop an adjusted estimator that improves the Gaussian copula approach’s finite sample performance in regression models with (and without) an intercept. Third, as a broader contribution, we extend the framework for copula estimation to models with multiple endogenous variables on continuous scales and exogenous variables on discrete and continuous scales, and non-linearities such as interaction terms. Fourth, simulation studies confirm that the new adjusted estimator outperforms the established ones. Further simulations also underscore that our extended framework allows researchers to validly deal with multiple endogenous and exogenous regressors, and the interactions between them. Fifth, we demonstrate the adjusted estimator and the general framework’s systematic application, using an empirical marketing example with real-world data. These contributions enable researchers in marketing and other disciplines to effectively address endogeneity problems in their models by using the improved Gaussian copula approach.

Purpose Partial least squares structural equation modeling (PLS-SEM) has become an established social sciences multivariate analysis technique. Since quality management researchers also increasingly using PLS-SEM, this growing interest calls for guidance. Design/methodology/approachBased on established guidelines for applying PLS-SEM and evaluating the results, this research reviews 107 articles applying the method and published in eight leading quality management journals. FindingsThe use of PLS-SEM in quality management often only draws on limited information and analysis results. The discipline would benefit from the method's more comprehensive use by following established guidelines. Specifically, the use of predictive model assessment and more advanced PLS-SEM analyses harbors the potential to provide more detailed findings and conclusions when applying the method. Research limitations/implicationsThis research provides first insights into PLS-SEM's use in quality management. Future research should identify the key areas and the core quality management models that best support the method's capabilities and researchers' goals. Practical implicationsThe results of this analysis guide researchers who use the PLS-SEM method for their quality management studies. Originality/valueThis is the first article to systematically review the use of PLS-SEM in the quality management discipline.

As companies in the manufacturing and construction industries strive to meet the EU circular economy (CE) targets, they need to develop new capabilities to implement CE activities that can positively influence their product/service innovations. However, companies in both industries, and beyond, still struggle to develop internal capabilities to innovate products and services that would help them in implementing CE principles and move towards the CE. The objective of this research is to analyze the types of innovation capabilities that are needed to enable CE implementation and achieve product/service innovations in two different industrial sectors. Prior research has focused on innovating and implementing circular business models and elaborated less on the innovation capability types. We collected survey data in December 2021-January 2022 that consists of responses from companies operating in Germany (n = 177), including employees in manufacturing (n = 87) and construction companies (n = 90). The results from the partial least squares structural equation modeling (PLS-SEM) based on measurement models from the literature indicate that employees in both sectors overall perceive higher levels of CE implementation capability as important for the company's product/service innovations. Furthermore, the results reveal differences in the way CE innovation capability and IT resource orchestration capability influence CE implementation and product/service innovations in the two sectors. Our study offers theoretical implications on how dynamic capabilities are associated with CE innovations and how they influence companies' product/service innovations based on empirical evidence from two industrial sectors. Those capabilities that are crucial for circular product/service innovations need to be associated with CE implementation capabilities. The results further advise practitioners in the development of CE innovation and CE implementation capabilities and how they are linked to IT resource orchestration capability and provide evidence on their relevance to creating product/service innovations.

Recent research on partial least squares structural equation modeling (PLS–SEM) extended the classic importance–performance map analysis (IPMA) by taking the results of a necessary condition analysis (NCA) into consideration. By also highlighting necessary conditions, the combined importance–performance map analysis (cIPMA) offers a tool that enables better prioritization of management actions to improve a key target construct. In this article, we showcase a cIPMA’s main steps when using the SmartPLS 4 software. Our illustration draws on the technology acceptance model (TAM) used in the cIPMA’s original publication, which features prominently in business research.

Purpose The purpose of this paper is to assess the appropriateness of equal weights estimation (sumscores) and the application of the composite equivalence index (CEI) vis-à-vis differentiated indicator weights produced by partial least squares structural equation modeling (PLS-SEM). Design/methodology/approach The authors rely on prior literature as well as empirical illustrations and a simulation study to assess the efficacy of equal weights estimation and the CEI. Findings The results show that the CEI lacks discriminatory power, and its use can lead to major differences in structural model estimates, conceals measurement model issues and almost always leads to inferior out-of-sample predictive accuracy compared to differentiated weights produced by PLS-SEM. Research limitations/implications In light of its manifold conceptual and empirical limitations, the authors advise against the use of the CEI. Its adoption and the routine use of equal weights estimation could adversely affect the validity of measurement and structural model results and understate structural model predictive accuracy. Although this study shows that the CEI is an unsuitable metric to decide between equal weights and differentiated weights, it does not propose another means for such a comparison. Practical implications The results suggest that researchers and practitioners should prefer differentiated indicator weights such as those produced by PLS-SEM over equal weights. Originality/value To the best of the authors’ knowledge, this study is the first to provide a comprehensive assessment of the CEI’s usefulness. The results provide guidance for researchers considering using equal indicator weights instead of PLS-SEM-based weighted indicators.

The ongoing scientific discourse surrounding the replication crisis in behavioral research, including management information systems (MIS) research, underscores the importance of innovative and rigorous approaches to theory development and validation. This article proposes the EP-mixed framework, which addresses the necessity of an ontological distinction between explanation and prediction in MIS theories, along with the epistemological challenges associated with conflating exploratory and confirmatory research during the design of robust, replicable theories. EP-mixed refers to theories that explain and predict (i.e., EP theories) developed using a mixed mode that combines the strengths of both exploratory and confirmatory research. The EP-mixed framework guides researchers in selecting appropriate analytical approaches based on their research goals and the type of theory being developed. While it can be applied in conjunction with a broad spectrum of statistical methods to enhance the robustness and replicability of MIS theories, we elaborate on the predictive analytic tools available in partial least squares structural equation modeling (PLS-SEM) as an exemplar for operationalizing the framework.

Die Partial Least Squares Strukturgleichungsmodellierung (PLS-SEM) hat sich in der wirtschafts- und sozialwissenschaftlichen Forschung als geeignetes Verfahren zur Schätzung von Kausalmodellen behauptet. Dank der Anwenderfreundlichkeit des Verfahrens und der vorhandenen Software ist es auch in der Praxis etabliert.

Scientific research demands robust findings, yet variability in results persists due to researchers' decisions in data analysis. Despite strict adherence to state-of the-art methodological norms, research results can vary when analyzing the same data. This article aims to explore this variability by examining the impact of researchers' analytical decisions when using different approaches to structural equation modeling (SEM), a widely used method in innovation management to estimate cause-effect relationships between constructs and their indicator variables. For this purpose, we invited SEM experts to estimate a model on absorptive capacity's impact on organizational innovation and performance using different SEM estimators. The results show considerable variability in effect sizes and significance levels, depending on the researchers' analytical choices. Our research underscores the necessity of transparent analytical decisions, urging researchers to acknowledge their results' uncertainty, to implement robustness checks, and to document the results from different analytical workflows. Based on our findings, we provide recommendations and guidelines on how to address results variability. Our findings, conclusions, and recommendations aim to enhance research validity and reproducibility in innovation management, providing actionable and valuable insights for improved future research practices that lead to solid practical recommendations.

This research offers a novel approach that extends the application of importance-performance map analysis (IPMA) in partial least squares structural equation modeling (PLS-SEM) by incorporating findings from a necessary condition analysis (NCA). The IPMA comprises assessing latent variables and their indicators' importance and performance, while an NCA introduces an additional dimension by identifying factors that are crucial for achieving the desired outcomes. An NCA employs necessity logic to identify the must-have factors required for an outcome, while PLS-SEM follows an additive sufficiency logic to identify the should-have factors that contribute to high performance levels. Integrating these two logics into the performance dimension is particularly valuable for prioritizing actions that could improve the target outcomes, such as customer satisfaction and employee commitment. Although the combined use of PLS-SEM and NCA is a recent suggestion, this study is the first to combine them with an IPMA (i.e., in a combined IPMA; cIPMA). A case study illustrates the combined use of PLS-SEM and an NCA to undertake a cIPMA. This innovative approach enhances researchers' and practitioners' decision making, enabling them to prioritize their efforts effectively.

This study aims to contribute to the existing literature on higher education marketing by proposing and empirically testing a theoretical model linking higher education quality, student satisfaction, and subjective well-being. The bottom-up spill over theory, the stimulus-organism-response theory, and the expectancy-disconfirmation theory, inform the development of the theoretical model of the study. A cross-sectional survey design is adopted, and data are collected from a sample of students from Mauritian Universities. The model is estimated and tested using a variance-based and prediction-oriented approach to structural equation modelling, specifically partial least squares structural equation modelling (PLS-SEM). The results demonstrate that approximately one-fifth of university students' subjective well-being is explained by the quality of their student life and their satisfaction with higher education services. Based on these empirical results, we discuss and present key implications for higher education marketing.

Die Partial Least Squares Strukturgleichungsmodellierung (PLS-SEM) hat sich in der wirtschafts- und sozialwissenschaftlichen Forschung als geeignetes Verfahren zur Schätzung von Kausalmodellen behauptet. Dank der Anwenderfreundlichkeit des Verfahrens und der vorhandenen Software ist es auch in der Praxis etabliert.

Die Partial Least Squares Strukturgleichungsmodellierung (PLS-SEM) hat sich in der wirtschafts- und sozialwissenschaftlichen Forschung als geeignetes Verfahren zur Schätzung von Kausalmodellen behauptet. Dank der Anwenderfreundlichkeit des Verfahrens und der vorhandenen Software ist es auch in der Praxis etabliert.

Die Partial Least Squares Strukturgleichungsmodellierung (PLS-SEM) hat sich in der wirtschafts- und sozialwissenschaftlichen Forschung als geeignetes Verfahren zur Schätzung von Kausalmodellen behauptet. Dank der Anwenderfreundlichkeit des Verfahrens und der vorhandenen Software ist es auch in der Praxis etabliert.