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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.

As technology in tourism and hospitality (TTH) develops technical artifacts according to visitors’ demands, it must deal with both behavioral and design constructs in the context of structural equation modeling (SEM). While behavioral constructs are typically modeled as common factors, the study at hand introduces the composite into TTH to model artifacts. To deal with both kinds of constructs, this paper aims to exploit partial least squares path modeling (PLS-PM) as a confirmatory approach to estimate models containing common factors and composites.

The study at hand presents PLS-PM in its current form, i.e. as a full-fledged approach for confirmatory purposes. By introducing the composite to model artifacts, TTH scholars can use PLS-PM to answer research questions of the type “Is artifact xyz useful?”, contributing to a further understanding of TTH. To demonstrate the composite model, an empirical example is used.

PLS-PM is a promising approach when the model contains both common factors and composites. By applying the test for overall model fit, empirical evidence can be obtained for latent variables and artifacts. In doing so, researchers can statistically test whether a developed artifact is useful.

To the best of the authors’ knowledge, this is the first study to discuss the practical application of composite and common factor models in TTH research. Besides introducing the composite to model artifacts, the study at hand also guides scholars in the assessment of PLS-PM results.

因为旅游酒店科技（TTH）根据游客需求而定制科技产品, TTH必须在结构方程模型（SEM）下结合游客行为和设计等变量。一般行为变量在模型中是常见因子, 本研究将这些变量编入TTH结构成为模块。本研究采用PLS-PM方法来预估含有隐性变量和模块的模型。.

本研究设计PLS-PM模式, 即确定性全变量方法。TTH学者们通过引进结构形成模型模块, 使用PLS-PM研究方法, 以回答研究问题“模块xyz有用吗？”, 因此对TTH进一步理解。为了展示复合模型, 本论文采用实际验证。.

PLS-PM在面对模块内存在常见因子和复合模块的结构时是有力方法。实际验证结果通过整体最佳模型参数, 得到隐性变量和模块。为此, 研究者们能够在统计方法上测量是否开发的模型模块是否有用。.

据作者所知, 本论文是首个研究在TTH领域上应用模块和常见因子模型。本研究引进显性变量在模型模块中, 以指导学者评估PLS-PM结果报告。.

People seem to function according to different models, which implies that in business and social sciences, heterogeneity is a rule rather than an exception. Researchers can investigate such heterogeneity through multigroup analysis (MGA). In the context of partial least squares path modeling (PLS-PM), MGA is currently applied to perform multiple comparisons of parameters across groups. However, this approach has significant drawbacks: first, the whole model is not considered when comparing groups, and second, the family-wise error rate is higher than the predefined significance level when the groups are indeed homogenous, leading to incorrect conclusions. Against this background, the purpose of this paper is to present and validate new MGA tests, which are applicable in the context of PLS-PM, and to compare their efficacy to existing approaches.

The authors propose two tests that adopt the squared Euclidean distance and the geodesic distance to compare the model-implied indicator correlation matrix across groups. The authors employ permutation to obtain the corresponding reference distribution to draw statistical inference about group differences. A Monte Carlo simulation provides insights into the sensitivity and specificity of both permutation tests and their performance, in comparison to existing approaches.

Both proposed tests provide a considerable degree of statistical power. However, the test based on the geodesic distance outperforms the test based on the squared Euclidean distance in this regard. Moreover, both proposed tests lead to rejection rates close to the predefined significance level in the case of no group differences. Hence, our proposed tests are more reliable than an uncontrolled repeated comparison approach.

Current guidelines on MGA in the context of PLS-PM should be extended by applying the proposed tests in an early phase of the analysis. Beyond our initial insights, more research is required to assess the performance of the proposed tests in different situations.

This paper contributes to the existing PLS-PM literature by proposing two new tests to assess multigroup differences. For the first time, this allows researchers to statistically compare a whole model across groups by applying a single statistical test.

The literature mainly concentrates on the relationships between externally oriented digital transformation (ExtDT), big data analytics capability (BDAC) and business model innovation (BMI) from an intra-organizational perspective. However, it is acknowledged that the external environment shapes the firm's strategy and affects innovation outcomes. Embracing an external environment perspective, the authors aim to fill this gap. The authors develop and test a moderated mediation model linking ExtDT to BMI. Drawing on the dynamic capabilities view, the authors' model posits that the effect of ExtDT on BMI is mediated by BDAC, while environmental hostility (EH) moderates these relationships.

The authors adopt a quantitative approach based on bootstrapped partial least square-path modeling (PLS-PM) to analyze a sample of 200 Italian data-driven SMEs.

The results highlight that ExtDT and BDAC positively affect BMI. The findings also indicate that ExtDT is an antecedent of BMI that is less disruptive than BDAC. The authors also obtain that ExtDT solely does not lead to BDAC. Interestingly, the effect of BDAC on BMI increases when EH moderates the relationship.

Analyzing the relationships between ExtDT, BDAC and BMI from an external environment perspective is an underexplored area of research. The authors contribute to this topic by evaluating how EH interacts with ExtDT and BDAC toward BMI.

This research investigates the effects that blockchain exerts on omnichannel solutions and logistics strategies with the aim of solving the last mile issues and improving performance.

Research hypotheses are developed according to the literature review and the related gaps. Then, the hypotheses are tested using structural equation modelling and adopting a partial least squares – path modelling technique on a dataset composed of 157 firms.

Blockchain technology alone is not an effective driver in solving last mile issues and improving performance. Rather, it exerts a positive contribution to both omnichannel and logistics. However, omnichannel is not effective in managing last mile problems and increasing performance without the support of other practices. Firms need to implement a strong logistics system to manage the last mile and get high performance, which can be then reinforced through blockchain and omnichannel solutions.

This research investigates the novel wave of research on blockchain and its impact on logistics management and omnichannel. It combines these ingredients to address the issues of last mile and improve the economic performance. The research provides an empirical verification of a new research stream that currently lacks empirical support.

The purpose of this study is to analyze the relationships between Entrepreneurial Orientation, Organizational Learning Capability, Service Innovation and Organizational Performance. To this end, it was sought to analyze the mediating role of organizational learning capability and service innovation within entrepreneurial orientation and organizational performance relationship in knowledge-intensive organizations.

The sample consisted of 159 architecture and urbanism companies from Santa Catarina, Brazil. The study opted to use managers as key informants since they are the ones that have general information about the organization and are a valuable source for assessing the different variables of the organization. For data analysis, the PLS-PM algorithm (Partial Least Squares Path Modeling) was used.

Results showed that entrepreneurial orientation is a strong driver of service innovation and organizational performance. Organizational learning capability acts as a facilitator of innovation and has a positive influence on organizational performance. Another theoretical contribution of this study to organizational learning capability is the confirmation of its mediation in service innovation and organizational performance. Management needs to make its organization more proactive and creative, continually promoting new ideas. Architecture and urbanism organizations should pay more attention to maintaining and promoting entrepreneurial orientation permanently. The trend toward both proactivity and risk-taking can be an inherent advantage of these knowledge-intensive business services.

Few studies have explored the mediating role of organizational learning capability and service innovations in organizational performance. In particular, the combined effects of entrepreneurial orientation and organizational learning capability have been neglected by the knowledge-intensive organizations literature. The study is justified by providing a more complete view of the relationship between entrepreneurial orientation and the performance of knowledge-intensive organizations, highlighting the role of organizational learning capability and performance in service innovation.

People are increasingly responsible for making sound financial decisions to foster their financial satisfaction and well-being, which magnifies the importance of financial literacy, and this concept and measurement is still not yet crystallized in the literature, specifically capturing different behavior perceptions. Moreover, there is not a distinction based on different classifications of behavior, such as over or underconfidence, to understand the relation between literacy and decision process. To fill this gap, this paper aims to investigate whether the financial literacy conceptual model proposed applies similarly to every group independently of their previous self-confidence perception. For this purpose and quality control, OECD (2016) data were used with a final sample of 1,487 Brazilian citizens. Quantitative analysis technique using partial least squares structural equations path modeling and differences between groups using multi-group analysis was applied. In line with general studies, when analyzing the financial literacy usual model for the group as a whole, financial knowledge construct positively influences self-confidence, and both together positively affect financial behavior. However, for individuals with low financial knowledge and low self-confidence, as well as for those with too much or too little confidence, the model did not hold. Therefore, self-confidence perception influences the way financial knowledge is used for financial decisions and should be addressed in financial education and training to be more effective.

To operationalize the variables and test the paper’s hypotheses, the authors used the methodology developed in OECD (2016), based on the research instrument’s Brazilian application adapted from the questionnaire developed in OECD (2015), with data initially used and made available by Garber and Koyama (2016). Based on the recommendations of Hair Jr et al. (2017a, 2017b), the authors used partial least squares modeling PLS-PM (SmartPLS 3.2.6) to estimate the structural models.

Concerning structural relationships, the final model showed knowledge with a positive influence on self-confidence, self-confidence with a positive effect on behavior and knowledge with a positive influence on behavior, both directly and, through its relationship with self-confidence, indirectly. This underscores that, for the total sample, the greater people’s knowledge and self-confidence, the better their behavior. The unexpected absence of attitude in the final model, even allowing for potential measurement problems, brings up an important reflection on the mediating effect that the self-control variable may exert between attitude and behavior. A person may believe that saving for the future is important (attitude) but whether they actually save (behavior) may depend on self-control, which is needed to prevent immediate gains from being prioritized in practice.

The findings reported so far concern the study’s total sample. However, as expected from the literature review that provides the basis for the sixth and the most important hypothesis, respondents were found to be heterogeneous in terms of knowledge and self-confidence levels. These differences were evaluated by means of multi-group analyses that indicated that the model does not apply to respondents with low knowledge and low self-confidence and to those who are over- and underconfident. This implies inferring that financial education programs may be of little use if they only address technical knowledge development and fail to consider behavioral aspects such as those related to self-confidence, as this paper points out, and others. This signals the importance of diagnosing people’s profiles to enable developing solutions capable of minimizing the presence of behavioral biases. This need to be studied further.

The results imply inferring that financial education programs may be of little use if they only address technical knowledge development and fail to consider behavioral aspects such as those related to self-confidence, as this paper points out, and others. Models must be reviewed in light of natural diferences of cognition and lead to customized financial education.

This signals the importance of diagnosing people’s profiles to enable developing solutions capable of minimizing the presence of behavioral biases. Therefore, not only training topics in personal finance but also a deeper education program since the kindergarden must be considered.

Its practical contribution is to suggest the development of financial education programs that also take account of the potential presence of behavioral biases, which may prevent the misallocation of (scarce) public- and private-sector funds stemming from a limited focus on developing the population’s actual financial knowledge.

This paper aims to analyze the benefits of the blockchain to the circular economy (CE), which is composed of both closed-loop supply chain (CLSC) systems and reverse omnichannel solutions. By ensuring transparency, traceability, visibility and security, the blockchain allows firms to acquire operational capabilities through a CLSC and service capabilities through a reverse omnichannel, which can boost business performance considerably. The related network of relationships can be reinforced by establishing incentives, which entail both smart contracts in the blockchain and active return approaches in CE.

After identifying the boundaries of the theoretical framework, several research hypotheses are developed according to the literature review and emerging gaps. These gaps link to the impact of the blockchain on CE systems (CLSC and reverse omnichannel), as well as the influence on business performance. The hypotheses are then tested using structural equation modeling and adopting a partial least squares-path modeling technique on a dataset composed of 157 firms. Finally, multigroup analysis is used to test the impact of incentives on the research hypotheses.

The blockchain facilitates a more efficient CE system, although reverse omnichannel solutions seldom bring any benefits to performance. The shift from a passive to an active return approach must be carefully evaluated. The CLSC network can benefit from an active return approach by developing appealing incentives for collectors and enhancing the positive effects of the blockchain. In contrast, consumer incentives can have detrimental effects on the blockchain. Various combinations of incentives can only bring a few business performance increases, while collector incentives are vital to reinforce the CE system's operational and service capabilities.

This paper takes a new approach toward the study of CE, which considers a dual circular system composed of a CLSC and a reverse omnichannel. The research explores whether the adoption of blockchain technology enables better return processes by improving the operations in CLSC and services in reverse omnichannel. Finally, this is the first empirical work to evaluate the benefits emerging from incentives, which can activate smart contracts in the blockchain and enable active return approaches in CE.

This study aims to investigate both the relationships among the on-board environment, overall satisfaction, perceived value and behavioral intentions and the moderating effects of gender, employment status, group composition and the propensity to stay on board in the context of cruise experience.

Relationships among constructs were tested on the basis of 417 surveys collected and analyzed with the structural equation modeling approach of partial least squares path modeling. A multi-group analysis was used to test the moderating effects.

The research findings suggest that on-board environment is a good predictor of behavioral intentions, but that the relationship is strongly mediated by satisfaction and perceived value. Moreover, the multi-group analysis of moderating effects indicated various differences that offer interesting insights for segmenting passengers; these insights have substantial implications for future studies and cruise line companies alike.

This study offers useful insights for managers who want to differentiate their value proposition with ship-centered elements.

This study contributes to the literature by providing a theoretical framework and empirical evidence for analyzing the role of the perceived on-board environment in passenger experience. From a managerial perspective, the moderating effects offer new insights for targeting and customizing the cruise experience value proposition.

Self-service check-out technologies (SSTs) are becoming a trend across different retail settings, allowing companies to gain efficiency and reduce costs. Nevertheless, the success of SSTs implementation is still subject to challenges and uncertainties. This study aims to provide insights for theory and managers on the necessary conditions for the successful implementation of retail SSTs.

Based on an online survey, data from 251 participants were collected to understand the factors predicting SSTs adoption and realise what conditions are mandatory for the adoption. partial Least Squares Structural Equation Modelling (PLS-SEM) and necessary condition analysis (NCA) were used to analyse the data.

According to the NCA analysis results, 12 latent variables were relevant for predicting SSTs adoption, but only seven were necessary conditions for user adoption.

The complementarity of perspectives for understanding the adoption of SSTs based on the two data analysis techniques provides novel insights into theory and support for retailers' decision-making on self-service technologies (STTs) implementation.