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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.

The purpose of this study is threefold: (1) to propose partial least squares path modeling (PLS-PM) as a way to estimate models containing composites of composites and to compare the performance of the PLS-PM approaches in this context, (2) to provide and evaluate two testing procedures to assess the overall fit of such models and (3) to introduce user-friendly step-by-step guidelines.

A simulation is conducted to examine the PLS-PM approaches and the performance of the two proposed testing procedures.

The simulation results show that the two-stage approach, its combination with the repeated indicators approach and the extended repeated indicators approach perform similarly. However, only the former is Fisher consistent. Moreover, the simulation shows that guidelines neglecting model fit assessment miss an important opportunity to detect misspecified models. Finally, the results show that both testing procedures based on the two-stage approach allow for assessment of the model fit.

Analysts who estimate and assess models containing composites of composites should use the authors’ guidelines, since the majority of existing guidelines neglect model fit assessment and thus omit a crucial step of structural equation modeling.

This study contributes to the understanding of the discussed approaches. Moreover, it highlights the importance of overall model fit assessment and provides insights about testing the fit of models containing composites of composites. Based on these findings, step-by-step guidelines are introduced to estimate and assess models containing composites of composites.

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.

Using Chiang Mai Night Safari, Thailand as a case, this research is to understand the relationship between service quality, place attachment, tourist satisfaction, and tourist loyalty. A two-stage sampling approach is used while proportionate stratified sampling is applied to determine the strata sample size. A convenient sampling approach selects the participants within each stratum that involves choosing every element after a random start. Four hundred of 450 questionnaires are usable and analyzed the study. The result suggests an effective intermediary between service quality and tourist satisfaction. This study also adds managerial implications concerning service/product differentiation and competitive advantage over competitors. Meanwhile, future studies on destination personality uniqueness of destination emotions are suggested.

This paper aims to explore the relationship between knowledge creation and intellectual capital (IC) through an empirical study in the pharmaceutical industry. In the current economy, knowledge and IC are considered as the most important organizational assets and are the key resources in gaining competitive advantage.

This paper adopts the socialization, externalization, combination and internalization (SECI) model to examine the format of knowledge creation processes (KCP) and uses a model to demonstrate the relationship between KCP and IC and its components in the pharmaceutical industry. A valid instrument was adopted to collect the required data on KCP and and IC dimensions. Structural equation modeling was used to assess the measurement model and to test the research hypotheses using the data collected from 470 completed questionnaires.

The results supported the research model and revealed that KCP has significant influence on the accumulation of human capital. The performance of human capital manifests significant impact on structural capital and relational capital.

Given the strong association between KCP and IC, managers should define their own robust operations for knowledge creation to improve their IC accumulation.

This research departs from the earlier research on KCP–IC by adopting the SECI model and a research model that facilitates the exploration of the relationship between KCP and IC dimensions in the pharmaceutical industry. The research results provided strong support for the KCP–IC relationship.

Knowledge is a key success factor in achieving competitive advantage in the current fast-paced and uncertain economic environment. Several studies in the literature have analyzed the relationship between knowledge creation (KC) and organizational success; however, the mechanisms by which KC leads to accumulation of intellectual capital (IC) and thereby affects various dimensions of organizational performance are understudied. The purpose of this paper is to examine how KC and IC and their relationship influence key dimensions of organizational performance.

A research model was developed and tested based on the literature in the areas of KC, IC and organizational performance. This study uses a survey sent to companies in an intensive knowledge-based industry. The balanced scorecard (BSC) approach was used to measure the key dimensions of organizational performance.

The results from structural equation modeling (SEM) on 470 completed questionnaires received from the pharmaceutical companies in Iran reveal that KC activities lead to the accumulation of organizational IC and IC has a crucial and positive impact on the BSC. Furthermore, the results from the path analysis indicate that IC mediates the effects of KC on the BSC.

The findings of this study contribute to the extant literature on the relationship between knowledge and organizational performance by demonstrating that knowledge and KC lead to performance when organizations utilize KC activities and leverage them to accumulate IC. Once used effectively, IC will result in a better performance in the knowledge-intensive environments.

This is the first study that investigates how KC contributes to firm performance by incorporating the mediating impact of IC on the BSC. The proposed model and results will help organizations to identify the mechanisms through which KC initiatives improve organizational performance.

Although the increase in point-of-purchase decisions heightens the communication potential of food product packaging, empirical research on understanding how visual packaging affects consumers' subsequent product and brand evaluations and perceptions is scant. This study seeks to develop a theoretical model to show the effects of consumer attitudes toward visual food packaging on perceived product quality, product value, and brand preference.

A self-administered questionnaire developed from the literature was conducted, and 315 undergraduate students participated in the study.

The empirical results show that attitudes toward visual packaging directly influence consumer-perceived food product quality and brand preference. Perceived food product quality also directly and indirectly (through product value) affects brand preference.

This paper offers directions for understanding the effects of visual packaging on positive consumer product and brand evaluations. Based on the study findings, food firms should emphasize the visual packaging design factors such as color, typeface, logo, graphics, and size to form consumers' positive perceptions and brand preference.

The aim of this study is to discuss the relationship between e‐procurement and supply chain performance.

Both interviews with practicing managers and an empirical study were conducted in the current study. Interviews with four practicing managers were conducted to gather the practical insights of the theoretical framework. Empirical data were collected from 108 Taiwanese enterprises.

The paper found that partner relationships, information sharing, and supply chain integration can represent the processes through which e‐procurement contributes to supply chain performance. Supply chain integration has the highest standardized total effect on supply chain performance.

Future studies could more systematically analyze the relationships among e‐procurement, supply chain integration and supply chain performance. Cross‐level analysis is also worthy of investigation when considering the influence of technology‐usage characteristics.

Compared to partner relationships and information sharing, supply chain integration has more influences on supply chain performance. Therefore, this study suggests that a joint‐learning practice can be implemented for properly managing supply chains (e.g. know‐how collaboration, mutual competency creation).

This paper contributes to the literature by proposing and testing the influences of partner relationships, information sharing, and supply chain integration. This allows a strategic viewpoint when implementing e‐procurement systems intended to improve supply chain performance.

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.