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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.

Structural equation modeling (SEM) is a well-established and frequently applied method in various disciplines. New methods in the context of SEM are being introduced in an ongoing manner. Since formal proof of statistical properties is difficult or impossible, new methods are frequently justified using Monte Carlo simulations. For SEM with covariance-based estimators, several tools are available to perform Monte Carlo simulations. Moreover, several guidelines on how to conduct a Monte Carlo simulation for SEM with these tools have been introduced. In contrast, software to estimate structural equation models with variance-based estimators such as partial least squares path modeling (PLS-PM) is limited.

As a remedy, the R package cSEM which allows researchers to estimate structural equation models and to perform Monte Carlo simulations for SEM with variance-based estimators has been introduced. This manuscript provides guidelines on how to conduct a Monte Carlo simulation for SEM with variance-based estimators using the R packages cSEM and cSEM.DGP.

The author introduces and recommends a six-step procedure to be followed in conducting each Monte Carlo simulation.

For each of the steps, common design patterns are given. Moreover, these guidelines are illustrated by an example Monte Carlo simulation with ready-to-use R code showing that PLS-PM needs the constructs to be embedded in a nomological net to yield valuable results.

This paper aims to present a model to incentivize sustainable performance (SUP) in small- and medium-sized tourism by strengthening inner relations to adapt to a complex environment.

The authors adopted the systemic approach complementing analytic, tourism, partial least squares path modeling (PLS-PM), social network analysis (SNA) and systemic approach tools as follows: frame the problem through the soft systems methodology and SNA and identify the conflicting relationships; apply PLS-PM to validate the model; and propose new interactions for small- and medium-sized enterprises conducive to SUP based on the viable system model.

Considering the results, the authors pinpointed factors and relationships managers can address to foster SUP, highlighting the need to reinforce feedback loops and reduce inconsistencies between primary operations with coordination and management mechanisms.

This work is limited to the organizational domain. Although the results apply to the Mexican context, this could be overcome using methodological complementarity to extend the ideas to other organizations.

This study invites discussing methods and viewpoints for rethinking SUP because of multiple factors. This requires adopting methodological complementarity to generate alternatives and reconfiguring inner organizational interactions.

The model captures minimum but sufficient components advising leaders about SUP. This proposal differs from previous studies because it suggests exploiting methodological complementarity to capture the insights of key operative actors to conceive the model. Hence, the authors suggest new relations among organizational factors so managers can develop strategies for adaptability.

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.

The study explored various dimensions of overconfidence bias (OB) among retail investors in Indian financial markets. Further, these dimensions were validated through formative assessments for OB.

The study applied exploratory factor analysis (EFA) to 764 respondents to explore dimensions of OB. These were validated with formative assessments on 489 respondents by the partial least square path modeling (PLS-PM) approach in SmartPLS 4.0 software.

The major findings of EFA explored four dimensions for OB, i.e. accuracy, perceived control, positive illusions and past investment success. The formative assessments revealed that positive illusions followed by past investment success among retail investors played an instrumental role in orchestrating the OBs that affect investment decisions in financial markets.

The formative index of OB has several practical implications for registered financial and investment advisors, bank advisors, business media companies and portfolio managers, besides individual investors in the domain of behavioral finance.

This research provides a novel approach to provide a formative index of OB with four dimensions. This formative index can acts as an overview for upcoming researchers to investigate the OB of retail individual investors.

1. Overconfidence bias is an important predictor of retail investors' behavior

2. Formative dimensions of the overconfidence bias index.

3. Accuracy, perceived control, positive illusions and past investment success are important dimensions of overconfidence bias.

4. Modern portfolio theory and illusion of control theory support this study.

Overconfidence bias is an important predictor of retail investors' behavior

Formative dimensions of the overconfidence bias index.

Accuracy, perceived control, positive illusions and past investment success are important dimensions of overconfidence bias.

Modern portfolio theory and illusion of control theory support this study.

Blockchain systems have been proposed as a solution for exchanging electronic health records (EHR) because they enable data sharing in decentralised networks. This paper aims to analyse the user acceptability of blockchain technology in enabling EHR exchange and to formulate practical implications for increasing user acceptability.

A technology acceptance model [extended Unified Theory of Acceptance and Use of Technology (UTAUT) model] was used as a framework to measure the effects of 13 factors. The authors conducted a survey and analysed data from 214 participants using partial least square path modelling.

The acceptance of blockchain for EHR sharing is positively influenced by performance expectancy, social influence and perceived trust. Effort expectancy and facilitating conditions do not influence acceptance. The UTAUT model explains the variance in acceptance at 58.4%. Self-efficacy influences effort expectancy, incentives influence facilitating conditions and security predicts perceived trust.

Three implications are drawn: (1) Users need to clearly understand system’s purpose, functions, security mechanism and environmental impacts. (2) Users are incentivised to share health data via a blockchain solution if the technology offers personalising options and health information. (3) Health personnel can socially impact patients to use blockchain-based solutions.

Studies have shown that blockchain technology is a valuable solution for exchanging EHR. The novelty of this work is to identify how and why patients may accept this emerging technology for EHR exchange.

This study aims to investigate the interplay between a proactive attitude towards disruptions – supply chain disruption orientation – and supply chain resilience, increasing our understanding of their influence on reducing the impact of supply chain disruptions within the B2B context.

As unexpected disruptions are closely related to a dynamic and changing perception of the environment, this research is framed under the dynamic capabilities lens, consistent with existing resilience literature. The authors used partial least squares-path modeling (PLS-PM) to empirically test the proposed research model using survey data from 216 firms.

Results show that a proactive approach to disruptions alone is insufficient in mitigating their negative impact. Instead, a firm’s disruption orientation plays a crucial role in boosting its resilience, which acts as a mediator, reducing the impact of disruptions.

This paper sheds light on the mechanisms by which firms can mitigate the effects of supply chain disruptions and offers insights into how certain capabilities are needed so that firms’ attitudes can effectively impact firm performance. This research thus suggests that dynamic capabilities, traditionally perceived as being enabled by other elements, act themselves as enablers. Consequently, they have the potential to translate strategic orientation or attitudes into tangible effects on performance, enriching our understanding of how firms combine their internal attitudes and capabilities to achieve sustained competitive advantage.

Although humanoid robots are increasingly adopted in many business settings, the dynamic effects of anthropomorphism and the functional perceptions of service robots on consumers’ responses remain unclear. This paper aims to examine the impacts of robot anthropomorphism on consumers’ trust, receptivity and the downstream effect on satisfaction. Furthermore, it examines the mediating effects of perceived ease of use (PEOU) and perceived usefulness (PU) in the relationship between anthropomorphism and consumer responses.

After conducting two separate pilot studies to help design the research materials, this research involves three sequential studies. In studies 1A and 1B, the authors used two distinct humanoid robots (i.e. Connie and Pepper) to test the direct effects of anthropomorphism on trust and receptivity and the mediated effects via PEOU and PU. Study 2 conducted a 2 (robot appearance: machine-like vs. human-like) × 2 (task complexity: low vs. high) between-subjects experimental design to further explore the boundary effects of task complexity on trust and customer satisfaction.

This research theorizes and empirically examines the mediating effects of PEOU and PU in the relationship between anthropomorphism and consumers’ responses (i.e. trust and receptivity) to service robots. Results also demonstrate a moderating role of task complexity, whereby only when the task was complex did anthropomorphism affect consumer responses and customer satisfaction. The parallel mediations of PEOU and PU were also confirmed. However, when task complexity was low, the authors observed no differences between human- and machine-like robots.

First, this research used a scenario-based method by exposing participants to different pictures or videos of service robots and measuring individuals’ responses. Consumers may respond differently upon interacting with robots in actual service contexts. Second, future research could investigate the effects of other aspects of anthropomorphism, such as robots’ voice characteristics (gender, high/low pitch), verbal communication styles and emotional expression. Finally, future research could explore other service contexts to test the generalizability of the findings.

Findings of this study also provide useful insight for companies interested in adopting service robots. First, the authors unearthed several positive outcomes of using human-like versus machine-like robots in service settings. Despite concerns about the perceived creepiness and discomfort associated with human-like robots, managers should not worry about these service agents’ potential negative effects. Second, it shows that human-like robots’ competitive advantage over machine-like robots stands out when task complexity is high. Managers should therefore carefully consider relevant service characteristics and task requirements when deciding whether to adopt robots.

This study provides original and valuable contributions to the growing literature on service robots by addressing scholarly incongruencies regarding the impact of anthropomorphism and disentangling its positive influence on consumers’ perceptions and acceptance of service robots. This study also contributes to research on technology acceptance and service robot receptivity by empirically demonstrating the mediating role of PEOU and PU. Furthermore, this research enriches the body of knowledge on task-technology fit by providing evidence that task complexity is a crucial factor to consider in service robot design.

The purpose of this study is to understand the effects of self-risk perception and health consciousness on the intention to purchase organic food. The study also explored external factors, such as social networking, culture and their impact on attitude, self-risk perception and purchasing organic food.

This study uses a quantitative research method to collect data from New Zealand and Fiji (N = 701). The data analysis used the partial least squares path modeling technique (PLS-PM) to test the proposed model.

The empirical results revealed that self-risk perception positively influenced organic food intention. The results show that the health consciousness level strengthens the relationship between self-risk perception, beliefs, values and purchase intention. The health consciousness level dampens the relationship between social networking and purchase intention.

The findings of this study provide valuable insights into the guidance and promotion of Fijian and New Zealand's organic food industry. This study enables marketers to develop health-related promotional tactics to stimulate organic food sales. It gauges organic food promoters to use social media-oriented consumer networking to spread health awareness swiftly.

This comprehensive study extends the literature by scrutinizing the profundity of self-risk perception and health consciousness in influencing and explaining consumers' purchase intentions. Aside from ample growth in the study of organic food purchase intention, which commonly replicates simple relationships, this study ascertains deeper meaning and new relationships to understand the moderating role of health consciousness levels in organic food studies, expanding the theory of planned behavior.

This study examines the relationship between green human resource management (green HRM) and employee innovative behaviour. It also investigates the mediating role of job satisfaction to explore the mechanism through which green HRM is related to employee innovative behaviour. Additionally, it examines the moderating role of inclusive leadership to determine the boundary condition of the relationship between green HRM and employee innovative behaviour.

The study used a quantitative research approach using survey and collected 508 responses from full-time employees in Australia.

The authors have found support for all the hypothesised relationships in the study. Specifically, green HRM is positively related to employee innovative behaviour. This relationship is mediated by job satisfaction and accentuated by inclusive leadership.

Green HRM promotes a green atmosphere in which employees can contribute to a safer and healthier environment. Despite the increasing attention to green HRM in the management literature, little is known about the mechanisms and boundary conditions explaining employees' responses to green HRM.