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In this study, the mediating effects of perceived behavior control and attitudes toward being an entrepreneur were investigated in the relationship between family business experience and entrepreneurial intentions of university students. First, the variables of perceived behavioral control and attitude toward being an entrepreneur were defined as the mediators used in explaining the entrepreneurial intention. Then, the process of investigating the mediation effects with the structural equation modeling (SEM) approach in two cases with one and two mediating latent variables is explained.

In this study, the process of investigating the mediation effects in two situations where there is one and two mediating latent variables by SEM is presented. In addition, the decomposition of the effects for the model consisting of two mediating latent variables is given in detail with matrix notation.

It has been determined that the latent variable of perceived behavior control functions as a “full mediator” in the relationship between the family ownership story and the entrepreneurial intention. The study also revealed that students whose family's business ownership score is high and who are self-confident in the process of becoming an entrepreneur have stronger entrepreneurial intentions.

In the research, the distinction between the model used in determining the entrepreneurial intentions of university students and their mediation and indirect effects is explained in detail with matrix notations with the SEM approach.

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.

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 authors offer a new approach to segment credit card customers by classifying customers into two unobserved (latent) segments: opportunistic and needy.

The authors develop a finite mixture model to estimate customers’ tendency to borrow using the three alternatives available to them—promotional cash advances, regular cash advances and retail balances.

The results support the presence of at least two segments among credit card customers. The authors find that relative to opportunistic individuals, needy customers are typically more sensitive to interest rates. Additionally, the results indicate that offering promotional cash advances to current credit card customers increases their sensitivity to regular interest rates. Furthermore, the findings indicate that needy customers tend to have a higher stickiness in their debt. In the post-estimation analyses, the authors observe that needy customers generate more revenue than opportunistic customers. Interestingly, the bank does not perform well in targeting needy individuals and targets both groups with the same probability.

The authors argue that teaser rates attract at least two segments of borrowers—the “needy” segment, which is more likely to be cash-strapped, and the “opportunistic” segment, which looks at these teaser rates as an opportunity. However, banks do not observe segment membership. Hence, the authors offer a new approach to identifying these segments and show that understanding the behavior of these latent segments could help a bank target profitable customers more effectively.

Sports marketing and sponsorship research is located at the intersection of behavioral and design research, which means that it analyzes the current world and shapes a future world. This dual focus poses challenges for formulating and testing theories of sports marketing.

This article develops criteria for categorizing theoretical concepts as either behavioral or formed as different ways of expressing ideas of sports marketing research. It emphasizes the need for clear concept categorization for proper operationalization and applies these criteria to selected theoretical concepts of sports marketing and sponsorship research.

The study defines three criteria to categorize theoretical concepts, namely (1) the guiding idea of research, (2) the role of observed variables, and (3) the relationship among observed variables. Applying these criteria to concepts of sports marketing research manifests the relevance of categorizing theoretical concepts as either behavioral or formed to operationalize concepts correctly.

This study is the first in sports marketing to clearly categorize theoretical concepts as either behavioral or formed, and to formulate guidelines on how to differentiate behavioral concepts from formed concepts.

The ongoing business dynamics show two aspects for generating innovation: first, high-impact innovations are developed jointly by several actors, such as universities, enterprises, and governments. Second, startups are better suited to develop innovation during crises or periods of low growth as experienced at the moment. Based on these aspects and drawing on the constructs of the triple helix, this study analyzes the influence between the characteristics of the actors on the quality of the startup ecosystem from a global view.

The study examines the cross-section data of 35 countries between 2017 and 2018 and applies the partial least squares structural equation modeling (PLS-SEM) for assessing the relationships between the triple helix on the quality of the startup ecosystem on a country-level.

The findings suggest that each actor of the triple helix individually does not positively affect the quality of the startup ecosystem. Yet, when analyzing the actors jointly by creating a second-order latent variable (i.e. triple helix), the study found out that in this way, the triple helix construct has a positive effect on the quality of the startup ecosystem.

Although a large body of prior literature indicates the importance of generating interrelationships among the different entities involved in ecosystems, few studies provide empirical evidence from a global perspective of the need for these entities to act in an overlapping manner. The present study supports previous research and reinforces the importance of the triple helix for a more innovative environment.

Unsafe behavior is a major cause of safety accidents, while in most management measures for unsafe behavior, the construction workers are generally managed as a whole. Therefore, this study aims to propose group management of construction workers' unsafe behavior considering individual characteristics.

A cognitive process model with ten cognitive factors was constructed based on cognitive safety theory. The questionnaire was developed and validated based on the cognitive model, and the results showed that the questionnaire had good reliability and validity, and the cognitive model fitted well. Latent class analysis was used to classify the unsafe behaviors of construction workers.

Four categories of cognitive excellent type, cognitive failure type, no fear type and knowingly offending type were obtained. Workers of cognitive excellent type have good cognitive ability and a small tendency for unsafe behaviors. Workers of cognitive failure type have poor cognitive ability and the potential for cognitive failure in all four cognitive links. Workers of no fear type have weak cognitive ability, and cognitive failure may occur in discovering information and choosing coping links. Workers of knowingly offending type have certain cognitive abilities, but cognitive failure may occur in choosing coping link.

This study formulates targeted management measures according to the potential characteristics of these four types and provides scientific theoretical support for the personalized management of unsafe behavior.

This research theoretically proposed and empirically validated a Customer-Based Brand Equity (CBBE) scale specifically for Medical Tourism for emerging economies including recent findings from tourism theories such as gravity model and signalling theory, but more specifically accommodating political, cultural, economic, legal and social influences.

In-depth literature reviews from tourism, medical tourism, healthcare and hospitality domains are used to propose the theoretical model. The authors have used the lavaan package in R for the empirical analysis and model verification.

The research included, tested and verified the established latent variables such as “brand awareness”, “brand association”, “perceived quality” and “loyalty”, along with new observed variables for the CBBE scale from the theoretical perspectives of this research. “Infrastructure” has emerged as a new scale construct and “culture” was found to be a moderating variable for “perceived quality” in the CBBE scale, which are novel additions to the literature.

The research contributed to scale refining, latent construct assessment, and fine-tuning of the observed variables for the mentioned theoretical gaps.

This study examines opinion leadership's personal and social characteristics to see which one is more effective in opinion leadership in four different industries: fashion, travel and tourism, wellness and book and literature. The specific subject of this investigation is how largely openness, exhibitionism and competence in interpersonal relationships and status and attitude homophily affect the opinion leadership and the decision-making of opinion leaders' followers.

The proposed model was tested with the questionnaire shared via stories featured on Instagram among followers of four micro-influencers in different industries. For the purpose of testing the offered hypotheses of this study, the partial least squares method was used.

The findings show that openness, exhibitionism and competence in interpersonal relationships have a substantial effect on opinion leadership. It was also evident that status and attitude homophily impact opinion leadership. The model supports the effect of both personal and social characteristics on opinion leadership; however, based on the results, the effect of personal characteristics on opinion leadership is more remarkable, both in a direct relationship and through the mediating role of para-social interaction.

This study is novel in categorizing opinion leaders' attributes in two different extents of personal and social characteristics. The authors defined a model of the effectiveness of each personal and social characteristic on opinion leaders. The model investigates whether the personal or social characteristics have the most effect on opinion leadership, particularly with the mediating role of para-social interaction.

The study aims to investigate whether the perceived problems in exiting a business—as well as the scope of and access to external support—may impact an entrepreneur's decision regarding the exit strategy, that is, the choice between the stewardship and harvest exit strategy.

The authors have collected data from 302 entrepreneurs using a novel survey instrument and subjected the sample to structural equation modeling (SEM) and ordered logit regressions.

The results reveal that potential difficulties in implementing an exit strategy, the scope of external support anticipated by the exiting entrepreneur and access to such support influence the potential choice of an exit strategy. Furthermore, the findings indicate that the stewardship exit strategy is preferred over the harvest exit strategy by entrepreneurs who face potential difficulties in obtaining external support or foresee problems related to a potential exit strategy.

This study provides new insights into an entrepreneur's exit phenomenon by adding new elements, such as perceived problems involved in exiting a business as well as the scope of and access to external support, to the list of factors that may affect the choice of an exit strategy.