# Partial Least Squares Structural Equation Modeling (PLS-SEM) in business research: A simple guide for novice researchers

Ismail Abdi Changalima, Michael Patrick Chuwa

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Ismail Abdi Changalima, Michael Patrick Chuwa. Partial Least Squares Structural Equation Modeling (PLS-SEM) in business research: A simple guide for novice researchers. International Journal of Research in Business and Social Science (2147-4478), 2025, 14 (9), pp.497-506. 10.20525/ijrbs.v14i9.4601 . hal-05475743

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Business & Social Science
IJRBS

Research in Business & Social Science

IJRBS VOL 14 NO 9 (2025) ISSN: 2147-4478

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# Partial least squares structural equation modeling (PLS-SEM) in business research: A simple guide for novice researchers

Ismail Abdi Changalima (a)* Michael Patrick Chuwa (b)

<figure><img src="image_3.png" /></figure>

Crossref

## ARTICLE INFO

Article history:

Received 25 September 2025

Received in rev. form 27 Oct 2025

Accepted 21 November 2025

Keywords:

PLS-SEM; Structural Equation Modeling; Pls Path Modeling; Variance-Based SEM; Composite-Based Path Models; SmartPLS

JEL Classification: B23, C50, C55

## ABSTRACT

This review provides a comprehensive, step-by-step guide to the application of partial least squares structural equation modeling (PLS-SEM) for novice researchers. This is a conceptual and literature-based review that focuses on best practices and PLS-SEM literature. It highlights the rationale for using PLS-SEM, sample size, software tools, and essential metrics in PLS-SEM analysis. Drawing on best practices and recent literature, the review offers a framework for conducting and reporting PLS-SEM analysis. The review presents essential metrics such as outer loadings, Cronbach's alpha coefficients, average variance extracted (AVE), composite reliability, cross-loadings, Heterotrait-Monotrait ratio of correlations (HTMT), the Fornell-Larcker criterion, variance inflation factor (VIF), and redundancy analysis. Moreover, for more consistent results, the paper recommends researchers to employ 10,000 bootstrap subsamples and Bias-corrected and accelerated (BCa) bootstrap in assessing the structural model. Insights regarding path coefficients, p-values, R-square (R²), f-square (f²), and Q-square (Q²), are also presented. Furthermore, the review underscores the trade-off between predictive power and model fit when applying PLS-SEM. The presented practical insights alert novice researchers in avoiding common pitfalls and enhance the methodological rigor of empirical research that utilizes PLS-SEM. This step-by-step guide supports early-career researchers and contributes to the ongoing debates on improving methodological clarity and transparency.

© 2025 by the authors. Licensee SSBFNET, Istanbul, Turkey. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).

## Introduction

Structural equation modeling is a second-generation multivariate data analysis technique that enables the examination of complex relationships among observed and latent variables (Hair et al., 2010, 2025; Magno et al., 2024; Ringle et al., 2023). There are two basic approaches to structural equation modeling which include covariance-based structural equation modeling (CB-SEM) and partial least squares structural equation modeling (PLS-SEM) (Hair et al., 2017, 2024; Hair & Alamer, 2022; Hair & Sabol, 2025). The application of both approaches has revolutionized analytical procedures in most of quantitative research domains, including business research. Nevertheless, over the recent years, PLS-SEM has emerged as one of the most robust and relevant analytical approach, particularly in the context of quantitative research across various domains such as social sciences, business, and management research (Amani, 2025; Changalima, 2024a; de Andrés-Sánchez et al., 2025; Erna & Mutaqin, 2023; Harun et al., 2025; Ismail et al., 2025; Karikari et al., 2022; Ndiango et al., 2024; Nwosu et al., 2025; Osei-Appiah et al., 2024). This approach estimates partial model structures by integrating principal component analysis with ordinary least squares regression techniques (Hair et al., 2019; Mateos-Aparicio, 2011). It is a composite-based approach to structural equation modeling as it considers latent variables (constructs) as composites of their respective observed variables (items) (Hair et al., 2019). The increasing use of PLS-SEM in business research reflects its suitability for addressing complex theoretical models that involve multiple constructs, variables and interrelationships (Guenther et al., 2023; Hair et al., 2019, 2021a; Memon et al., 2021).

<footnote>(a) Department of Business Administration and Management, The University of Dodoma, Dodoma, Tanzania.</footnote>

<footnote>(b) Department of Maritime Transport Management, National Institute of Transport, Dar es Salaam, Tanzania.</footnote>

<footnote>* Corresponding author. ORCID ID: 0000-0002-6216-0558</footnote>
Moreover, unlike CB-SEM, the growing preference for PLS-SEM is attributed by its ability to effectively handle complex models that include both reflective and formative constructs, even in situations where sample sizes are relatively small or data distributions deviate from normality (Akter et al., 2017; Becker et al., 2023; Guenther et al., 2023; Hair et al., 2019, 2024). These features have made PLS-SEM a valuable analytical technique in modern business research where complexity and data limitations are common. Despite the growing popularity of PLS-SEM, some of novice researchers continue to face challenges in applying quantitative techniques, including PLS-SEM, due to methodological constraints. Addressing this matter is crucial, particularly given that researchers and academics increasingly demand rigorous methodologies and robust quantitative models to address research problems in business and management research domains (Hair et al., 2011, 2019; Ringle et al., 2023; Vaithilingam et al., 2024).

Our review bridges the gap by offering an overview of PLS-SEM, with a focus on its application, model evaluation, and presentation of results, especially for early-career researchers. Therefore, based on the best practices and PLS-SEM literature, this paper presents a comprehensive, step-by-step guide to the application of PLS-SEM, with a particular focus on critical issues and relevant metrics. The current review seeks to enhance the methodological aspects for novice researchers, equipping them with the necessary insights to produce rigorous and impactful findings when applying PLS-SEM. This contribution is expected to support the increasing demand for PLS-SEM and promote its effective application across diverse areas of business research. Moreover, it has been advocated that PLS-SEM is the most multivariate technique designed for quantitative data analysis which is utilized in papers that are published in top ranked journals with high impact factor (Memon et al., 2021). In this regard, the current guide is more likely to provide novice researchers, especially in domains of business research a chance to prepare suitable empirical papers that utilize PLS-SEM.

The rest of this paper is divided into 3 main parts. The next part presents research and methodology which provides the justifications for utilizing PLS-SEM, the most common used software tools, and analytical procedures that are essential in PLS-SEM. Part 3 presents insights about the analysis and findings which include information regarding the assessment of the measurement model and structural model. Lastly, part 4 presents the conclusion.

# Research and Methodology

## Justifications for Utilizing PLS-SEM

This part presents the common justifications for utilizing PLS-SEM. This is relevant as understanding the common reasons for using PLS-SEM ensures the choice of the technique aligns with the research objectives and nature of data. This helps in choosing the right analytical technique for valid and reliable results. Understanding its strengths prevents misuse and enhances the credibility of the research findings (Hair et al., 2024; Ringle et al., 2023). It also allows researchers to justify the choice of other methodological procedures that align with PLS-SEM. In this regard, researchers are advised to present the way PLS-SEM application contributes to the overall purpose of analysis (Ringle et al., 2023). The paper by Hair et al. (2019) reported that researchers should utilize PLS-SEM when some of the following conditions prevail:

i. Small sample sizes (due to small population or other factors depending on the nature of study);

ii. Lack of normality (non-normal data);

iii. Presence of formative measured constructs;

iv. Testing the theory from the prediction perspective;

v. Complex structural model with more latent and observed variables or indicators and interrelationships;

vi. Research that consists of financial ratios or similar types of data artifacts;

vii. Research is based on secondary/archival data;

viii. Research that focuses on exploring theoretical extensions of established theories (exploratory research for theory development); and

ix. Focusing on the latent variable scores for more analysis.

Specifically, Hanafiah (2020) advocated that PLS-SEM is particularly suitable for studies that are prediction-oriented and based on a variance-based approach, especially when dealing with complex models and limited (small) sample sizes, typically ranging from 30 to 100. On the other hand, Ringle et al. (2023) recommended to use PLS-SEM for both purposes, whether the research focuses on prediction or explanation. However, a trade-off must be made between predictive power and model fit. Therefore, novice researchers in business domains could consider the utilization of PLS-SEM, depending on the suitability of such criteria.

Most of existing studies in the research domain have quoted several reasons for applying PLS-SEM. Hair et al. (2014) emphasized that nonnormal data, small sample sizes and formatively measured constructs are the most common justifications provided by several studies on the utilization of PLS-SEM. Other recent studies such as Sharma and Sharma (2025) used PLS-SEM due to formatively measured constructs and nonnormal data. Ismail et al. (2025) and Chuwa et al. (2025) relied on the small sample sizes and achieving maximum variance as reasons for utilizing PLS-SEM. Exploratory nature of the study, small sample sizes and complexity of the model were presented as justifications for utilizing PLS-SEM in the study by Oraee et al. (2025). Some of existing studies consider predictive roles of given constructs to utilize PLS-SEM (Carrión et al., 2016; Changalima, 2024b; Ngui & Lay, 2019).

Despite the existing justifications for using PLS-SEM, researchers must take some precautions. Justifications for utilizing PLS-SEM, particularly those based on small sample sizes should not be relied upon when deviating from other requirements in business research.
The sufficient level of statistical power (recommended value is 0.8) should not be compromised when deciding the final sample size (Hair et al., 2019). Also, other researchers criticize the overuse of PLS-SEM and its model fit indices (Schuberth et al., 2023). Therefore, novice researchers should be cautious when applying PLS-SEM, considering the relevant supporting literature.

## The Most Common Used Software Tools

Software tools are essential applications that enable researchers to conduct data analysis. There are a number of software tools that support PLS-SEM such as SmartPLS, PLS-Graph, WarpPLS and ADANCO (Hanafiah, 2020; Lohmöller, 1989; Memon et al., 2021). Others have packages for PLS-SEM such as open source statistical software R (R Core Team, 2022), and cSEM (Rademaker & Schuberth, 2020). Among the available tools, SmartPLS is the most widely used for conducting PLS-SEM analysis. The current version, SmartPLS 4 (Ringle et al., 2024), has been extensively adopted in business and management-related studies due to its user friendly features for quantitative analysis.

Recent developments in the software (SmartPLS 4) have significantly enhanced its analytical capabilities, extending its utility beyond PLS-SEM to include other tools and techniques such as PROCESS, CB-SEM, GSCA, and regression analysis. With its user-friendly interface, SmartPLS 4 continues to be a relevant and valuable software tool for addressing complex analytical procedures in several research domains including business and management research. It should be noted that the free (student) version is limited in terms of features and capabilities compared to the professional (paid) version. The initial usage of professional version includes a free 30-day trial, which is primarily valuable as an entry point for novice researchers. Also, the software developers offer discounts specifically for academics, which is essential in promoting active usage of SmartPLS within the academic and research community.

Several studies employing SmartPLS analyze models involving multiple constructs with both latent and observed variables. The observed variables are measured in ratio or interval (Ringle et al., 2023; Sarstedt et al., 2022) or sometimes by using Likert-type scales (Ringle et al., 2023). In this case, it is important to report the data scale when employing PLS-SEM. SmartPLS 4 supports various types of data (continuous or categorical). Cautions should be made that categorical variables such as those with binary responses (e.g. sex which is limited to “male” or “female”) should not be mixed with metric data in the measurement model of the study’s constructs as it complicates the interpretation (Ringle et al., 2023). Thus, the nature of these variables could work more suitable as control variables or moderators in the model (Becker et al., 2023). Moreover, the software i.e. SmartPLS 4 allows for advanced analytical techniques, including PLS-SEM multigroup analysis, enhancing its flexibility for diverse range of research purposes. Several existing published articles in domain of business and management research have considered SmartPLS 4 in PLS-SEM multigroup analysis (Aggarwal & Kapoor, 2021; Changalima et al., 2024, 2025; Mambali et al., 2024; Salinas et al., 2025).

## Analytical Procedures in PLS-SEM

Figure 1 presents a generic schematic flow diagram for PLS-SEM analysis. When conducting PLS-SEM, researchers should ensure that the dataset is clean and fit for purpose, as errors and discrepancies in the data can significantly affect the overall research results. Therefore, during data preparation, issues like missing data and outliers should be well checked and handled prior to full-scale of analyzing data through PLS-SEM (Ringle et al., 2023). For instance, missing data could be handled through deletion (casewise or listwise) (Ringle et al., 2023), or through mean replacement if 5% of missing values per variable exists (Sarstedt et al., 2022). On the other hand, variables with more than 15% of missing values should be removed (Sarstedt et al., 2022). The deletion should not affect the required sample size for final data analysis. This is crucial so as to have more robust findings in analyzing data of such nature.

After being satisfied with all preliminary issues, PLS-SEM follows two basic steps; the assessment of the measurement model and assessment of the structural model (Ringle et al., 2023). These steps are relevant in studies that utilize PLS-SEM as it is vital to report the reliability and validity of measures prior to establishing of relationships and other findings from the assessment of the structural model (Hair et al., 2011, 2019; Hair & Alamer, 2022; Sarstedt et al., 2022). In some cases, researchers disregard essential evaluation metrics recommended within the PLS-SEM literature. Specifically, the omission of discriminant validity assessment or the undue emphasis placed on R² in the absence of predictive evaluation which undermines the robustness of the empirical results and, consequently, the implications and conclusions derived from them. In this regard, robustness of the findings should be well highlighted in the assessment of the measurement model and structural model. For more specific examples of how to present findings from these two stages of PLS-SEM, researchers are recommended to visit existing published articles in top journals that utilize the same approach. In most cases, the results are tabulated in a way that summarizes all the main metrics in each stage.

<figure><img src="image_4.png" /><figcaption>Figure 1: A schematic flow diagram for PLS-SEM analysis; Source: Authors</figcaption></figure>
# Analysis and Findings

## The Assessment of the Measurement Model

PLS-SEM requires researchers to report on the reliability and validity of measures. This step is more relevant for studies that utilize measures which produce data from several variables or items. The measures should be checked whether they are reliable so as not to compromise the overall research findings (Hair et al., 2020). Since, PLS-SEM allows for the analysis of the reflective and formative models (Coltman et al., 2008; Hair et al., 2021b), the novice researcher should observe whether the respective model is the reflective model, formative model or contains the combination of both reflective and formative constructs.

This is relevant given the fact that there are number of metrics that need to be reported in the measurement model depending on the type of such model. Reflective model is the model that assumes indicators (items) reflect or are caused by the underlying construct (Coltman et al., 2008; Hair et al., 2021b; Hanafiah, 2020). While, formative model is the one with indicators that form or cause the construct (Coltman et al., 2008; Hair & Alamer, 2022; Hanafiah, 2020). Therefore, the underlining theoretical nature of relationship between constructs and their items is the basis for distinction between reflective and formative model in PLS-SEM (Guenther et al., 2025). Figure 2 demonstrates a reflective construct and Figure 3 presents a formative construct that are applied in PLS-SEM. The i1-i3 denotes the *items* or *observed variables* for each type of construct.

<figure><img src="image_5.png" /><figcaption>**Figure 2:** The Reflective Construct in PLS-SEM; *Source:* Authors</figcaption></figure>

<figure><img src="image_6.png" /><figcaption>**Figure 3:** The Formative Construct in PLS-SEM; *Source:* Authors</figcaption></figure>

The measurement model clearly specifies the way constructs (latent variables) are measured by their observed variables (indicators or items). In this stage of PLS-SEM, reporting the results for the measurement model is for the purpose of establishing reliability and validity before engaging further in the results of the structural model (inner model). As explained earlier, it is important to make sure that if constructs involved in the study are considered as reflective or formative (Guenther et al., 2025). Ringle et al. (2023) emphasize on the important metrics (see Table 1) that need to be reported during the assessment of the measurement model.

In reflective measurement models, researchers should present results for the following:

### Indicator reliability

This is reported through outer loadings (standardized values) with threshold of ≥ 0.70, ideally, 0.708. In some occasions, outer loadings between 0.40 and 0.70 could be retained if AVE and CR are adequate. In this regard, researchers are advised not to automatically drop (remove) indicators with loadings below 0.70 but they should check how the removal of them affects reliability and validity. Indicators between 0.40 and 0.70 should be removed only if doing so improves internal consistency or convergent validity beyond recommended thresholds. Researchers should also consider content validity (how well the remaining items still represent the construct) which may justify keeping some weaker items. However, indicators with loadings below 0.40 should always be removed (Hair et al., 2021c).
## Internal consistency reliability

The researchers should report values of Cronbach's alpha with the desired threshold of ≥ 0.70. Also, the researchers should report values of composite reliability (CR) which are also supposed to be ≥ 0.70. Nevertheless, values of 0.95 and above are commonly considered to indicate redundancy.

## Convergent validity

This is reported through the values of average variance extracted (AVE) in the reflective model from which the threshold is supposed to be ≥ 0.50 implying that about 50% of variance is explained.

## Discriminant validity

There are various techniques that are suitable for assessing and reporting discriminant validity (Ab Hamid et al., 2017). The most common three techniques in PLS-SEM are Fornell-Larcker criterion, cross-loadings, and Heterotrait-Monotrait ratio of correlations (HTMT). The Fornell-Larcker criterion relies on the fact that the square root of AVE should be greater than inter-construct correlations while, cross loadings assume that the indicators of a given construct should load highest on their respective (own or parent) construct than in any other construct. Lastly, HTMT ratio considers values that are < 0.85 or 0.90 (Ringle et al., 2023).

In formative measurement models, researchers should present results for the following:

## Indicator validity

This is assessed in bootstrapping to check the significance and relevance of the indicators in which the indicator weights should be statistically significant and size of coefficients should be reported. Higher weights mean greater importance.

## Multicollinearity

Collinearity tests should be done in PLS-SEM from which the values of variance inflation factor (VIF) should be < 3.0. However, values which are < 5.0 are also acceptable.

## Convergent validity

As a matter of best practices, redundancy analysis could be done to check on the correlation between formative construct and a global item measure of the same construct. In this case, novice researchers may include a global item in the survey for the respective construct.

<table><caption>Table 1: Some of Essential Metrics in the Measurement Model</caption><thead><tr><td>Metrics</td><td>Model type</td><td>Threshold</td></tr></thead><tbody><tr><td>1. Outer loadings (for indicator reliability)</td><td>Reflective</td><td>≥ 0.70, ideally, 0.708. However, values between 0.40 and 0.70 may be retained provided that the AVE and CR are adequate</td></tr><tr><td>2. Cronbach's alpha (for internal consistency reliability)</td><td>Reflective</td><td>≥ 0.70</td></tr><tr><td>3. Composite reliability (CR)</td><td>Reflective</td><td>≥ 0.70</td></tr><tr><td>4. AVE (for convergent validity)</td><td>Reflective</td><td>≥ 0.50</td></tr><tr><td>5. Fornell-Larcker criterion (for discriminant validity)</td><td>Reflective</td><td>√AVE > correlations with other constructs</td></tr><tr><td>6. Cross-loadings (for discriminant validity)</td><td>Reflective</td><td>Loadings should be higher on their own construct than on any other</td></tr><tr><td>7. HTMT ratio (for discriminant validity)</td><td>Reflective</td><td>&lt; 0.85 (conservative) or &lt; 0.90 (liberal)</td></tr><tr><td>8. Convergent validity</td><td>Formative</td><td>Redundancy analysis</td></tr><tr><td>9. Indicator weights and loadings</td><td>Formative</td><td>Significant at p &lt; 0.05 (bootstrapped). Higher weights mean greater importance</td></tr><tr><td>10. VIF (for collinearity)</td><td>Formative</td><td>&lt; 5.0 (or &lt; 3.0 more favorable)</td></tr></tbody></table>

Source: Authors

## The Assessment of the Structural Model

After successfully meeting the criteria in the assessment of the measurement model for establishing reliability and validity, the novice researchers should proceed to the second step, which involves assessing the structural model. In this step, the main focus is on confirming whether the structural model supports the hypothesized relationships. A number of metrics are presented in this stage which indicate whether the relationships as hypothesized are supported or not.
In structural model assessment, PLS-SEM requires researchers to report model's predictive power (Ringle et al., 2023; Sarstedt & Danks, 2022). In this regard, procedures for PLS<sub>predict</sub> (Shmueli et al., 2016) and those for cross-validated predictive ability test (CVPAT) (Liengaard et al., 2021), should be done effectively. Regarding the findings related to model fit, Ringle et al. (2023) present the existence of two streams in PLS-SEM studies. In one aspect, researchers may report the model fit results from bootstrap-based tests, or using goodness of fit, normed fit index (NFI), and standardized root mean square residual (SRMR) (Schuberth et al., 2023). In another aspect, researcher may focus on assessment of the predictive power due to the causal-predictive nature of PLS-SEM (Sarstedt et al., 2022). Therefore, researchers should ensure that the focus and the respective stream on which their research is based are clearly presented. However, since the application of model fit indices is limited in PLS-SEM compared to CB-SEM, the model's predictive capabilities should be considered (Hair et al., 2020; Shmueli et al., 2016, 2019).

Furthermore, to avoid model misspecification in PLS-SEM, the SRMR may be reported as a measure of goodness of fit (Henseler et al., 2014). SRMR < 0.08 is considered to fall within the recommended threshold. In addition, the NFI indicates that the closer the value is to 1, the better the model fit; NFI > 0.90 is regarded as acceptable (Hair et al., 2019). The researchers may report these indices and cautiously explain the trade-offs between predictive power and model fit. Ringle et al. (2023) advise scholars to ensure compliance with the most recent PLS-SEM guidelines, in view of the streams in the PLS-SEM literature. Additionally, robustness checks including nonnormality, nonlinearity, endogeneity, unobserved heterogeneity, and heteroskedasticity become paramount for more comprehensive results.

Table 2 presents the common metrics in assessing the structural model. These metrics include VIF, bootstrap-based findings such as path coefficients (β), p-values, effect sizes for assessing the relevance of path coefficients, predictive power and model fit (considering the streams in PLS-SEM literature) and overall focus of PLS-SEM analysis (Ringle et al., 2023). The main metrics that should be considered in reporting PLS-SEM results for the structural model include β which represents the strength and direction of the relationship (hypothesis) between study's variables. In this regard, the bootstrapping procedures should be done, usually 5,000 subsamples but 10,000 option is mostly recommended (Becker et al., 2023). These procedures in the assessment of the structural model produce p-values, t-values and confidence intervals which are reported with the given thresholds.

Coefficient of determination (R²) should be reported to indicate the amount of variance in the dependent (outcome or endogenous) variable explained by the independent (predicting or exogenous) variables in the model. Hair et al. (2019) provided a threshold for the values of R² under which values ≥ 0.75 are considered to be substantial, ≥ 0.50 to be moderate and ≥ 0.25 to be weak. On the other hand, Cohen (1988) considered substantial, moderate and weak for values ranged between 0.26, 0.13 and 0.02, respectively. While, Falk and Miller (1992) considered values that are ≥ 0.10 to be adequate in explaining variance in endogenous variable.

Effect size (f²) which shows the contribution of independent variable to the dependent variable should be reported based on the recommended thresholds. Cohen (1988) recommended f² ≥ 0.35 to be large, ≥ 0.15 to be medium and ≥ 0.02 to be small. These effect sizes are produced to show the contribution effect of independent variable in explaining the dependent variable. In addition, the predictive relevance (Q²) is assessed to indicate the predictive accuracy of the model using blindfolding procedure. As a rule of thumb, Q² > 0 shows the existence of predictive accuracy. Values above 0, 0.25 and 0.50 represent small, medium and large predictive accuracy in the model (Hair et al., 2019). Therefore, in assessing the structural model, researchers should consider reporting the R², f², and Q² values. These should be done bearing in mind the focus is on prediction and/or explanation, and hence the trade-off between model fit and predictive power should be taken into consideration (Ringle et al., 2023).

<table><caption>**Table 2:** Some of Essential Metrics in the Structural model</caption><thead><tr><td>Metrics</td><td>Threshold / Recommendation</td><td>Relevance</td></tr></thead><tbody><tr><td>1. Bootstrapping option</td><td>5,000 subsamples but 10,000 is recommended</td><td>Improves accuracy of estimates</td></tr><tr><td>2. Confidence interval method</td><td>Bias-corrected and accelerated (BCa) bootstrap</td><td>Provides more accurate confidence intervals and the approach is useful when there are doubts or concerns regarding a non-normal bootstrap distribution.</td></tr><tr><td>3. P-value and path coefficients</td><td>Size of coefficient and p &lt; 0.05</td><td>Significant effect (Bootstrap-based significance testing)</td></tr><tr><td>4. R² (Coefficient of determination)</td><td>0.25 = weak, 0.50 = moderate, 0.75 = substantial ≥ 0.10 is also considered adequate</td><td>Measure the proportion of variance in a dependent variable</td></tr><tr><td>5. f² (Effect size)</td><td>0.02 = small, 0.15 = medium, 0.35 = large</td><td>The size of contribution of individual predictor (independent variable) on variance explained</td></tr><tr><td>6. Q² (Predictive relevance)</td><td>&gt; 0</td><td>Indicates predictive relevance/accuracy</td></tr><tr><td>7. SRMR</td><td>≤ 0.08</td><td>Good model fit</td></tr><tr><td>8. NFI</td><td>Closer to 1 and preferably ≥ 0.90</td><td>Acceptable model fit</td></tr></tbody></table>

Source: Authors
**Conclusion**

This paper serves as a guide for novice researchers, especially those in business research domains on the application of PLS-SEM as a second-generation multivariate technique that analyzes complex relationships. The paper presents relevant insights that enable scholars to prepare and present results from PLS-SEM analytical procedures. The relevance of the study is on the emphasis of utilizing available software tools like SmartPLS 4 for analyzing quantitative data. Researchers need to observe justifications for applying PLS-SEM which should align with the overall purpose of the study and analysis. The increasing application of PLS-SEM in some research is based on common justifications such as small sample sizes, lack of normality in data, formative nature of the model and predictive purposes.

This guide also, highlights the major two steps to be followed in PLS-SEM analysis. Firstly, the measurement model assessment needs to clearly indicate whether the assessment is based on the reflective measurement model, formative measurement model or model with both reflective and formative constructs. The review presents clearly metrics that need to be reported when assessing these measurement models. Secondly, the guide provides insights on the assessment of the structural model, from which metrics such as β, p-values, t-values, and confidence intervals are relevant for indicating the significance and relevance of path coefficients in the hypothesized relationships. Also, collinearity need to be assessed by using VIF, especially for the inner model. In addition, the need for assessing the model's predictive, explanatory power and relevance (accuracy) is crucial with values of R², f², and Q² become paramount.

The current review draws on recommended best practices from the PLS-SEM literature, with guidance specifically tailored for early-career researchers. Analytical approaches such as Necessary Condition Analysis (NCA) and the extension of results through Importance-Performance Map Analysis (IPMA) may also be clearly presented to supplement and enhance the depth of findings obtained from PLS-SEM analysis. In addition, the current review presents relevant information regarding to the presentation of the lower-order constructs and hence, researchers are recommended to observe the existing guidance for the assessing the higher-order constructs. Therefore, we recommend early-career researchers to monitor the ongoing developments in the application of PLS-SEM in business research. This is relevant as there is a growing need for more robust application of PLS-SEM in the research landscape. In this regard, recent analytical advancements must be considered. These developments can be easily tracked through the growing body of methodological articles related to PLS-SEM.

**Acknowledgement**

**Author Contributions:** I.A.C and M.P.C were all involved in conceptualization, methodology, formal analysis, writing-original draft preparation, and writing-review and editing. All authors have read and agreed to the published version of the manuscript.

**Funding:** This research did not receive any external funding.

**Informed Consent Statement:** Not applicable.

**Data Availability Statement:** Not applicable.

**Conflicts of Interest:** The authors declare no conflict of interest.

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