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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.

In their paper titled “A Miracle of Measurement or Accidental Constructivism? How PLS Subverts the Realist Search for Truth,” Cadogan and Lee (2022) cast serious doubt on PLS’s suitability for scientific studies. The purpose of this commentary is to discuss the claims of Cadogan and Lee, correct some inaccuracies, and derive recommendations for researchers using structural equation models.

This paper uses scenario analysis to show which estimators are appropriate for reflective measurement models and composite models, and formulates the statistical model that underlies PLS Mode A. It also contrasts two different perspectives: PLS as an estimator for structural equation models vs. PLS-SEM as an overarching framework with a sui generis logic.

There are different variants of PLS, which include PLS, consistent PLS, PLSe1, PLSe2, proposed ordinal PLS and robust PLS, each of which serves a particular purpose. All of these are appropriate for scientific inquiry if applied properly. It is not PLS that subverts the realist search for truth, but some proponents of a framework called “PLS-SEM.” These proponents redefine the term “reflective measurement,” argue against the assessment of model fit and suggest that researchers could obtain “confirmation” for their model.

Researchers should be more conscious, open and respectful regarding different research paradigms.

Researchers should select a statistical model that adequately represents their theory, not necessarily a common factor model, and formulate their model explicitly. Particularly for instrumentalists, pragmatists and constructivists, the composite model appears promising. Researchers should be concerned about their estimator’s properties, not about whether it is called “PLS.” Further, researchers should critically evaluate their model, not seek confirmation or blindly believe in its value.

This paper critically appraises Cadogan and Lee (2022) and reminds researchers who wish to use structural equation modeling, particularly PLS, for their statistical analysis, of some important scientific principles.

The principal research objective entails examining the nexus between green supplier selection and green performance while scrutinizing the moderating role of governance mechanisms, specifically process control and outcome control, in shaping this association.

To assess our hypotheses, this study obtained data from Chinese manufacturing sectors and utilized regression analysis on a dataset consisting of 295 samples.

This study enriches the sustainable supply chain management literature by emphasizing the influence of green supplier selection on a firm’s green performance and the moderating effects of outcome and process control, offering practical insights for industry professionals.

This study enriches the sustainable supply chain management literature by emphasizing the influence of supplier selection on a firm’s environmental performance and the moderating effects of outcome and process control, offering practical insights for industry professionals.

Past work on the role of brands in the acceptance of organic products is partial and inconclusive. Research has failed to examine the consumer sense-making process underpinning fit assessment, despite the centrality of this assessment in the acceptance of line extensions. This study reconceptualizes the fit construct, showing the relationship of the fit dimensions (noncompensatory) and contributes to the literature with a deeper understanding of the role of a brand's association in the assessment process, which has been poorly examined in the past.

Grounded theory was used to unearth the process followed by consumers to assess the fit of organic line extensions. The study was based on 14 in-depth interviews.

The findings show that the dimensions of fit that consumers consider in assessing organic line extensions depend on the schema used in the assessment process. Moreover, it demonstrates that these dimensions have disparate structural relationships with one another, depending on consumers' previous commitment to organic products. Finally, the paper identifies three possible behavioral reactions by consumers toward organic line extensions.

The main limitation of this research concerns the settings in which it was developed. Therefore, and as stated by Strauss and Corbin (1990) the model applies to the situation analyzed and not to others. Future research could study if there are cultural differences in the assessment process of an organic line extension. Moreover, the contribution presented in this paper needs further empirical testing; specifically, the configuration of dimensions needed to accept an organic line extension and the relationship among dimensions.

This paper contributes to the literature by studying the impact of brand association on assessing an organic line extension and reconceptualizing the fit construct by showing the dimensions and the relationship between them that are not additive to the overall fit, as shown in past literature. Additionally, it provides a guide to brands wishing to launch an organic product using a line extension strategy and the potential implications for the parent brand that should be considered.

With the outbreak of the COVID-19 pandemic, online assessment has become the dominant mode of examination in higher education institutions. However, there are contradictory findings on how students perceive online assessment and its impact on their academic performance. Thus, the purpose of this study is to evaluate the potential impact of online assessment on students' academic performance.

This study proposes a research model based on the task–technology fit theory and empirically validates the model using a survey from students in the UK. In addition, the study conducted four experiments based on paper-based and online assessments and analysed the data using paired sample t test and structural equation modelling.

The findings show that the use of online assessment has a positive impact on students' academic performance. Similarly, the results from the experiment also indicate that students perform better using online assessments than paper-based assessments.

The findings provide crucial evidence needed to shape policy towards institutionalising online assessment. In addition, the findings provide assurance to students, academics, administrators and policymakers that carefully designed online assessments can improve students' academic performance. Moreover, the study also provides important insights for curriculum redesign towards transitioning to online assessment in higher education institutions.

This study advances research by offering a more nuanced understanding of online assessment on students' academic performance since the majority of previous studies have offered contradictory findings. In addition, the study moves beyond existing research by complementing assessment results with the views of students in evaluating the impact of online assessment on their academic performance. Second, the study develops and validates a research model that explains how the fits between technology and assessment tasks influence students' academic performance. Lastly, the study provides evidence to support the wide use of online assessment in higher education.

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.

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.

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).

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.

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.

Firstly, this study diagnosed professional competence amongst Chinese vocational students within a broad range of the manufacturing sectors; then, the authors examined how different types of P-E fit (job, organisation and vocation) and internship quality jointly shape the newly acquired professional competences of interns.

This study utilised the COMET methodology to conduct a large-scale assessment of professional competence amongst 961 graduates from vocational colleges who had successfully completed internships. Participants actively engaged in the data collection process by responding to questionnaires that sought contextual information concurrently.

The majority of students have attained fundamental functional competencies, indicating their fulfillment of basic requirements. However, there is a tendency to overlook the cultivation of shaping competence. Three types of P-E fit and task characteristics are positively correlated with professional competence. The indirect relationship between P-E fit and professional competence mediated by task characteristics was verified through P-V fit and P-J fit except for P-O fit. Overall, the model explains 39.2% of the variance in professional competence.

“How to promote professional competence” has been highlighted as an important topic in vocational education. This paper contributes to identify the characteristics of a quality internship program for vocational colleges and firms. These insights are important in considering a student-centred approach, design internships programmes that better fit their own abilities, needs and vocations, avoiding a one-size-fits-all approach to implement internships and thus, enhance students' professional development.

Despite technological advancements to enhance patient health, the risks of not discovering the correct interactions and trends in digital health are high. Hence, a careful policy is required for health coverage tailored to needs and capacity. Therefore, this study aims to explore the adoption of a cognitive computing decision support system (CCDSS) in the assessment of health-care policymaking and validates it by extending the unified theory of acceptance and use of technology model.

A survey was conducted to collect data from different stakeholders, referred to as the 4Ps, namely, patients, providers, payors and policymakers. Structural equation modelling and one-way ANOVA were used to analyse the data.

The result reveals that the behavioural insight of policymakers towards the assessment of health-care policymaking is based on automatic and reflective systems. Investments in CCDSS for policymaking assessment have the potential to produce rational outcomes. CCDSS, built with quality procedures, can validate whether breastfeeding-supporting policies are mother-friendly.

Health-care policies are used by lawmakers to safeguard and improve public health, but it has always been a challenge. With the adoption of CCDSS, the overall goal of health-care policymaking can achieve better quality standards and improve the design of policymaking.

This study drew attention to how CCDSS as a technology enabler can drive health-care policymaking assessment for each stage and how the technology enabler can help the 4Ps of health-care gain insight into the benefits and potential value of CCDSS by demonstrating the breastfeeding supporting policy.

The emergence of digital transformation in the banking industry gives rise to the challenges of adopting technology and boosting users' confidence in the process. This study mainly explores the roles of operant resources and consumption values in the user's consumption process concerning the fintech-embedded product-service system (FPSS) that provides technologically advanced financial services in South Korea.

This study examines the research model based on users' perceived quality, assessment and recommendation of FPSS. In addition, grounded in the resource-based view (RBV) and consumption value theory (CVT), an extended model is developed to understand the impact of user consumption value on FPSS design. The research model includes both product-service system (PSS) characteristics, i.e. quality and assessment, and user-specific characteristics, i.e. conditional, utilitarian and social values (collectively referred to as user consumption value).

This study finds that information, service and security quality positively affect users' confidence through positive assessment and recommendation intention. All the elements of user consumption value play a positive role in the FPSS user confidence model. In addition, the impact of operant resources on the overall service assessment is further investigated based on the interest of user's service engagement. Notable findings include users that highly engage in FPSS services return higher service assessment when social value, information quality and service quality increase.

Overall, this study provides academic and managerial guidelines for the strategic design of fintech-embedded banking services by considering the roles of PSS and user-specific characteristics in fostering sustainable competitive advantage.