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This study aims to examine the role of an overall model fit assessment in the context of partial least squares path modeling (PLS-PM). In doing so, it will explain when it is important to assess the overall model fit and provides ways of assessing the fit of composite models. Moreover, it will resolve major concerns about model fit assessment that have been raised in the literature on PLS-PM.

This paper explains when and how to assess the fit of PLS path models. Furthermore, it discusses the concerns raised in the PLS-PM literature about the overall model fit assessment and provides concise guidelines on assessing the overall fit of composite models.

This study explains that the model fit assessment is as important for composite models as it is for common factor models. To assess the overall fit of composite models, researchers can use a statistical test and several fit indices known through structural equation modeling (SEM) with latent variables.

Researchers who use PLS-PM to assess composite models that aim to understand the mechanism of an underlying population and draw statistical inferences should take the concept of the overall model fit seriously.

To facilitate the overall fit assessment of composite models, this study presents a two-step procedure adopted from the literature on SEM with latent variables.

This paper clarifies that the necessity to assess model fit is not a question of which estimator will be used (PLS-PM, maximum likelihood, etc). but of the purpose of statistical modeling. Whereas, the model fit assessment is paramount in explanatory modeling, it is not imperative in predictive modeling.

This study aims to evaluate the usage of partial least squares structural equation modeling (PLS-SEM) in journals related to logistics and supply chain management (LSCM).

Based on a structured literature review approach, the authors reviewed 401 articles in the field of LSCM applying PLS-SEM published in 15 major journals between 2014 and 2022. The analysis focused on reasons for using PLS-SEM, measurement model and structural model evaluation criteria, advanced analysis techniques and reporting practices.

LSCM researchers sometimes did not clarify the reasons for using PLS-SEM, such as sample size, complex models and non-normal distributions. Additionally, most articles exhibit limited use of measurement models and structural model evaluation techniques, leading to inappropriate use of assessment criteria. Furthermore, progress in the practical implementation of advanced analysis techniques is slow, and there is a need for improved transparency in reporting analysis algorithms.

This study contributes to the field of LSCM by providing clear criteria and steps for using PLS-SEM, enriching the understanding and advancement of research methodologies in this field.

Expanded awareness and application of recent PLS-SEM reporting practices were again called for by Hair (2022) in his PLS 2022 Keynote Address. This paper aims to analyze and extend the application of PLS-SEM in Industrial Management and Data Systems (IMDS) to focus on trends emerging in the more recent 2016–2022 period.

A review of PLS-SEM applications in information systems studies published in IMDS and MISQ for the period 2012–2022 identifies and comments on a total of 135 articles. Selected emerging advanced analytical PLS-SEM applications are also highlighted to expand awareness of their value in more rigorously evaluating model results.

There is a continually increasing maturity of the information systems field in applying PLS-SEM, particularly for IMDS authors. Model complexity and improved prediction assessment as well as other advanced analytical options are increasingly identified as reasons for applying PLS-SEM.

Findings demonstrate the continued use and acceptance of PLS-SEM as a useful alternative research methodology within IS. PLS-SEM is the preferred SEM method in many research settings, but particularly when the research objective is prediction to the population, mediation and mediated moderation, formative constructs are specified, constructs must be modeled as higher-order and for competing model comparisons.

This update on PLS-SEM applications and recent methodological developments will help authors to better understand and apply the method, as well as publish their work. Researchers are encouraged to engage in more complete analyses and include enhanced reporting procedures.

Applications of PLS-SEM for prediction, theory testing and confirmation are increasing. Information systems scholars should continue to exercise sound practice by reporting reasons for using PLS-SEM and recognizing its wider applicability for both exploratory and confirmatory research.

Structural equation modeling (SEM) is a well-established and frequently applied method in various disciplines. New methods in the context of SEM are being introduced in an ongoing manner. Since formal proof of statistical properties is difficult or impossible, new methods are frequently justified using Monte Carlo simulations. For SEM with covariance-based estimators, several tools are available to perform Monte Carlo simulations. Moreover, several guidelines on how to conduct a Monte Carlo simulation for SEM with these tools have been introduced. In contrast, software to estimate structural equation models with variance-based estimators such as partial least squares path modeling (PLS-PM) is limited.

As a remedy, the R package cSEM which allows researchers to estimate structural equation models and to perform Monte Carlo simulations for SEM with variance-based estimators has been introduced. This manuscript provides guidelines on how to conduct a Monte Carlo simulation for SEM with variance-based estimators using the R packages cSEM and cSEM.DGP.

The author introduces and recommends a six-step procedure to be followed in conducting each Monte Carlo simulation.

For each of the steps, common design patterns are given. Moreover, these guidelines are illustrated by an example Monte Carlo simulation with ready-to-use R code showing that PLS-PM needs the constructs to be embedded in a nomological net to yield valuable results.

With new hybrid working models in place post COVID-19, it is requisite that knowledge workers (KWs) stay agile. Knowledge-oriented leadership (KOL) can help employees with essential knowledge acquisition (KA) facilitating the journey toward hybrid work agility (HWA). This study, thus, aims to explore the impact of KOL and KA on HWA and reveal whether this effect stems uniformly from a single homogenous population or if there is unobserved heterogeneity leading to identifiable segments of agile KWs.

Data was collected through stratified sampling from 416 employees from 20 information technology enabled services companies involved in knowledge-intensive tasks. Partial least squares (PLS) structural equation modeling approach, using SMART PLS 4.0, has been applied to examine the effect of KOL and KA on HWA. Finite mixture PLS, PLS prediction-oriented segmentation and multigroup analysis have been used to identify segments, test segment-specific path models and analyze the significance of the differences in the path coefficients for unobserved heterogeneity. Predictive relevance of the model has been determined using PLS Predict.

Results indicate that KOL contributes to employees’ KA and HWA. A significant positive relationship is also reported between KA and HWA. The model has medium predictive relevance. A two-segment solution has been delineated, wherein independent agile KWs (who value autonomy and personal agency over leadership for KA) and dependent agile KWs (who depend on leaders for relational and structural support for KA) have been identified. Thus, KOL and KA play a differential role in determining HWA.

The authors’ major contribution to the knowledge body constitutes the determination of antecedents of HWA and a typology of agile KWs. Future researchers may conduct segment-wise qualitative analysis to delineate other variables that contribute to HWA.

Technological advances necessitate that knowledge-intensive industries foster agility in employees for strategic agility of the organization. For effecting agile adaption of an organization to the knowledge economy conditions, it is pertinent that the full potential of this human resource be used. By profiling HWA of KWs on the basis of dimensions of KOL and the level of their KA, organizations will be able to help employees adapt better to rapidly changing work conditions.

HWA is a novel concept and very germane in a hybrid working environment. To the best of the authors’ knowledge, this is the first study to examine the effects of the dimensions of KOL and KA in relation to HWA, along with an empirical examination of unobserved heterogeneity in the aforementioned relationship.

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).

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.

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.

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.

The results suggest that researchers and practitioners should prefer differentiated indicator weights such as those produced by PLS-SEM over equal weights.

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.

This study aims to determine whether partial least squares path modeling (PLS) is fit for purpose for scholars holding scientific realist views.

The authors present the philosophical foundations of scientific realism and constructivism and examine the extent to which PLS aligns with them.

PLS does not align with scientific realism but aligns well with constructivism.

Research is needed to assess PLS’s fit with instrumentalism and pragmatism.

PLS has no utility as a realist scientific tool but may be of interest to constructivists.

To the best of the authors’ knowledge, this study is the first to assess PLS’s alignments and mismatches with constructivist and scientific realist perspectives.

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.

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.

The results suggest that GSCA with reflective composite indicators (GSCA_R) is the most versatile approach. For observed prediction, which uses the component scores to generate prediction for the indicators, GSCA_R 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.

Future research may further assess the methods’ prediction precision, considering more experimental factors with a wider range of levels, including more extreme ones.

When prediction is the primary study aim, researchers should generally revert to GSCA_R, considering its performance for observed and operative prediction together.

This research is the first to compare the relative efficacy of GSCA and PLSPM in terms of predictive power.

Researchers often stress the predictive goals of their partial least squares structural equation modeling (PLS-SEM) analyses. However, the method has long lacked a statistical test to compare different models in terms of their predictive accuracy and to establish whether a proposed model offers a significantly better out-of-sample predictive accuracy than a naïve benchmark. This paper aims to address this methodological research gap in predictive model assessment and selection in composite-based modeling.

Recent research has proposed the cross-validated predictive ability test (CVPAT) to compare theoretically established models. This paper proposes several extensions that broaden the scope of CVPAT and explains the key choices researchers must make when using them. A popular marketing model is used to illustrate the CVPAT extensions’ use and to make recommendations for the interpretation and benchmarking of the results.

This research asserts that prediction-oriented model assessments and comparisons are essential for theory development and validation. It recommends that researchers routinely consider the application of CVPAT and its extensions when analyzing their theoretical models.

The findings offer several avenues for future research to extend and strengthen prediction-oriented model assessment and comparison in PLS-SEM.

Guidelines are provided for applying CVPAT extensions and reporting the results to help researchers substantiate their models’ predictive capabilities.

This research contributes to strengthening the predictive model validation practice in PLS-SEM, which is essential to derive managerial implications that are typically predictive in nature.

This study aims to identify the effects of innovation types on the service firm’s financial and nonfinancial performance as well as mediation and moderation effects of innovation and the firms’ performance linkages in the Indian service sector.

This study uses combined data from the World Bank innovation survey 2014 and World Bank enterprise survey (WBES) 2014 for India. It classified innovations into technological innovation (service and process) and nontechnological innovation (organizational and marketing) and used financial and nonfinancial performance measures. This study applies variance-based partial least square structural equation modeling (PLS-SEM) using Smart PLS 3 software.

The study results suggest that service innovation has the highest significant effect on a firm’s financial and nonfinancial performance, followed by process innovation. Marketing and organizational innovation have a long route to contribute to a firm’s financial performance via innovative and nonfinancial performance. The study results do not find any synergy effects of innovation types. Multi-group analysis (MGA) results suggest several significant distinctions in the path relationships between small and medium-sizes and large firms.

This study provides several crucial policy suggestions for the managers and policymakers concerning the effects of service and process innovation on service firms’ performance in India and the mediating factors of these relationships. The study suggests that managers should pay the highest importance to service innovation to swiftly and markedly surge service firms’ financial and nonfinancial performances. In contrast, a service firm’s innovative performance mainly results from its organizational and marketing innovations.