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This paper aims to create and evaluate a model for cryptocurrency adoption by investigating how age, education, and gender impact Behavioural Intention. A hybrid approach that combined partial least squares structural equation modeling (PLS-SEM) and artificial neural network (ANN) was used for the purpose.

This study uses a multi-analytical hybrid approach, combining PLS-SEM and ANN to illustrate the impact of various identified variables on behavioral intention toward using cryptocurrency. Multi-group analysis (MGA) is applied to determine whether different data groups of age, gender and education have significant differences in the parameter estimates that are specific to each group.

The findings indicate that Social Influence (SI) has the greatest impact on Behavioral Intention (BI), which suggests that the viewpoints and recommendations of influential and well-known individuals can serve as a motivating factor to invest in cryptocurrencies. Furthermore, education was found to be a moderating factor in the relationship found between behavioral intention and design.

Prior studies on technology adoption have utilized superficial SEM and ANN methods, whereas a more effective outcome has been suggested by implementing a dual-stage PLS-SEM and ANN approach utilizing a deep neural network architecture. This methodology can enhance the accuracy of nonlinear connections in the model and augment the deep learning capacity.

The research is based on the Unified Theory of Acceptance and Use of Technology (UTAUT2) and expands upon this model by integrating elements of design and trust. This is an important addition, as design can influence individuals' willingness to try new technologies, while trust is a critical factor in determining whether individuals will adopt and use new technology.

Cryptocurrencies are a relatively new phenomenon in India, and their use and adoption have grown significantly in recent years. However, this development has not been without controversy, as the implications of cryptocurrencies for society, the economy and governance remain uncertain. The results reveal that social influence is an important predictor for the adoption of cryptocurrency in India, and this can help financial institutions and regulators in making policy decisions accordingly.

Given the emerging nature of cryptocurrency adoption in India, there is certainly a need for further empirical research in this area. The current study aims to address this research gap and achieve the following objectives: (a) to determine if a dual-stage PLS-SEM and ANN analysis utilizing deep learning techniques can yield more comprehensive research findings than a PLS-SEM approach and (b) to identify variables that can forecast the intention to adopt cryptocurrency.

This study aims to explore and ranks the factors that might determine attitudes and intentions toward using autonomous vehicles (AVs).

The “technology acceptance model” (TAM) was extended by assessing the moderating influences of personal-related factors. Data were collected from 378 Vietnamese and analysed using a combination of “partial least squares” and the “adaptive neuro-fuzzy inference system” (ANFIS) technique.

The findings demonstrated the power of TAM in explaining the attitude and intention to use AVs. ANFIS enables ranking the importance of determinants and predicting the outcomes. Perceived ease of use and attitude were the most crucial drivers of attitude and intention to use AVs, respectively. Personal innovativeness negatively moderates the influence of perceived ease of use on attitude. Data privacy concerns moderate positively the impact of perceived usefulness on attitude. The moderating effect of price sensitivity was not supported.

These findings provide insights for policymakers and automobile companies' managers, designers and marketers on driving factors in making decisions to adopt AVs.

The study extends the AVs literature by illustrating the importance of personal-related factors, ranking the determinants of attitude and intention, illustrating the inter-relationships among AVs adoption factors and predicting individuals' attitudes and behaviours towards using AVs.

This study aims to determine whether knowledge creation processes (KCPs) – knowledge exchange and knowledge integration affect digital innovation (DI), including information technology (IT)-enabled capabilities (ITECs) as a mediator and absorptive capacity (AC) as a moderator.

With a survey data set of 390 employees from Pakistani software small- and medium-sized enterprises (SMEs), the current study employed Structural Equation Modeling (SEM) using Smart Partial Least Squares to estimate the structural relationships in the conceptual model.

The results confirm that KCPs – knowledge exchange and knowledge integration positively enhance software SME's DI; ITECs play a partial mediating role in the linkage between KCPs and DI; AC positively moderates the relationship between knowledge integration and ITECs, and ITECs and DI, while AC doesn’t moderate the relationship between knowledge exchange and ITECs. The AC positively moderates the mediating role of ITECs amongst KCPs (knowledge exchange and knowledge integration) and DI, respectively.

This research uniquely integrates the knowledge-based view and dynamic capability theory to present a comprehensive framework that explains the interdependencies between knowledge process, ITECs and AC in driving DI. This approach advances the understanding of how software SMEs can strengthen internal knowledge and IT resources to achieve superior innovation outcomes.

The healthcare sector relies on knowledge management systems to improve knowledge flows and effectively capture, leverage and share knowledge with several organizational stakeholders. However, knowledge as a resource represents a social construct that involves additional managerial complexities and challenges, including undesirable knowledge behaviours. The aim of the current study is to provide insight on how knowledge management systems, knowledge hoarding, knowledge hiding and task conflict shape the quality of care provided by hospitals. We propose and test an original revealing model.

We follow a quantitative approach to address the structural relationship between variables using a combination of factor analysis and multiple regression analysis. The model is tested adopting a structural equation modelling approach and using survey data conducted to 318 healthcare professionals working in Portuguese hospitals.

The main findings suggest that knowledge hiding is positively related to task conflict in hospitals, and task conflict negatively influences quality of care. Knowledge management systems directly and indirectly (via knowledge hoarding) promote quality of care. Moreover, knowledge management systems also mitigate the negative influence of task conflict over quality of care. We propose a final corollary on the relevant role of HRM as the backstage for the model.

Our research offers a novel insight into an overlap of organizational behaviour and healthcare management research. It provides an original framework on knowledge management systems, counterproductive knowledge behaviours and task conflict in hospital settings.

Our research offers a novel insight into an overlap of organizational behaviour and healthcare management research. It provides an original framework on knowledge management systems, counterproductive knowledge behaviours and task conflict in hospital settings.

This study utilised Mehrabian and Russel's stimulus-organism-response (SOR) model to investigate the inter-relationship between perceived social media information qualities (IQ) of online UGC, destination image, perceived travel risk and behavioural intentions.

A total of 255 responses from international tourists were collected. The data collection via an online survey was performed from October 2020 to February 2021. The study model and hypotheses were examined using partial least squares-structural equation modelling (PLS-SEM).

The result indicated that social media intrinsic, representational, and social IQ significantly influenced tourists perceived cognitive image, while only contextual and social IQ significantly predicted the perceived affective image. This study also confirms the significant effect of cognitive image on the affective image. In addition, the authors found that both destination image components, cognitive and affective, significantly affect tourists' behavioural intentions. However, surprisingly, tourists' perceived travel risk did not moderate the effect of destination image components on behavioural intentions.

This study contributes to the body of knowledge by extending Mehrabian and Russel's SOR model in the tourism behaviour context.

From a practical perspective, this study proves that UGC significantly affects destination image and plays an integral part in luring tourists to visit a destination.

Previous research in this area is limited, making this study particularly novel. This study represents one of the initial attempts to evaluate the dimensions of information quality in UGC on social media and online review platforms, particularly within the field of tourism. Treating online travel UGC seriously could assist organisations in leveraging tourist behaviour and enhancing destination image.

Drawing on the theory of planned behavior and value-belief norms theory, this study aims to explore the causes of pro-environmental behavioral intentions in the context of Sri Lanka.

Data were garnered from 503 subjects with the aid of self-reported questionnaires. The formulated hypotheses were examined using a variance-based statistical approach (PLS-SEM).

The findings of the study revealed that personal values, particularly altruistic and biosphere values, significantly strengthen the psychological drivers of pro-environmental behavioral intentions. The study further found that religiosity enhances the association between personal norms and pro-environmental behavioral intentions and the place attachment augments the relationship between attitude and pro-environmental behavioral intentions.

This study suggests that instilling altruistic and biosphere values in individuals can boost pro-environmental behavioral intentions. Campaigns and educational programs could focus on instilling these values to foster responsibility and empathy. Recognizing the influence of religiosity on pro-environmental behaviors and attitudes, this study suggests collaborative efforts between organizations and religious institutions. Religious leaders can integrate environmental stewardship into teachings, emphasizing ethical responsibilities.

The study pushes back the frontiers of environmental consciousness literature by highlighting the importance of personal values, psychological factors and contextual variables such as religiosity and place attachment in fostering pro-environmental behavior within the specific context of an emerging country – Sri Lanka.

The purpose of this paper is to identify the factors determining students’ attitude toward using newly emerged artificial intelligence (AI) tool, Chat Generative Pre-Trained Transformer (ChatGPT), for educational and learning purpose based on technology acceptance model.

The recommended model was empirically tested with partial least squares structural equation modeling using 375 student survey responses.

The study revealed that students have a favorable view of the instructional use of ChatGPT. Usefulness, social presence and legitimacy of the tool, as well as enjoyment and motivation, contribute to a favorable attitude toward using this tool in a learning environment. However, perceived ease of use was not found to be a significant determinant in the adoption and utilization of ChatGPT by the students.

This research is intended to benefit enterprises, academic institutions and the global community by offering light on how students perceive the ChatGPT service in an educational setting. Furthermore, the application enhances confidence and interest among learners, leading to improved literacy and general awareness. Eventually, the outcome of this research will help AI developers to improve their product and service delivery, as well as benefit regulators in regulating the usage of AI-based bots.

Due to its novelty, the current research on AI-based ChatGPT usage in the education sector is rather restricted. This study provides the adoption aspects of ChatGPT, a new AI-based technology for students, thereby contributing significantly to the existing research on the adoption of advanced education technologies. In addition, the literature lacks research on the adoption of ChatGPT by students for educational purposes; this study addresses this gap by identifying adoption determinants of ChatGPT in education.

This study delves into the challenges obstructing the integration of blockchain-enabled smart contracts (BESC) in the construction industry. Its primary objective is to identify these barriers and propose a roadmap to streamline BESC adoption, thereby promoting sustainability and resilience in building engineering.

Employing a unique approach, this study combines the Technology-Organization-Environment-Social (TOE + S) framework with the IF-Delphi-HF-DEMATEL-IFISM methodology. Data is collected through surveys and expert interviews, enabling a comprehensive analysis of BESC implementation barriers.

The analysis reveals significant hindrances in the construction industry’s adoption of BESC. Key obstacles include economic and market conditions, insufficient awareness and education about blockchain technology among stakeholders, and limited digital technology integration in specific cultural and societal contexts. These findings shed light on the complexities faced by the industry in embracing blockchain solutions.

The research makes a significant contribution by combining the TOE + S framework with the IF-Delphi-HF-DEMATEL-IFISM methodology, resulting in a comprehensive roadmap to address barriers in implementing BESC in Sustainable Construction Projects. Noteworthy for its practicality, this roadmap provides valuable guidance for construction stakeholders. Its impact extends beyond the industry, influencing both academic discourse and practical applications.

This paper aims to discuss recent criticism related to partial least squares structural equation modeling (PLS-SEM).

Using a combination of literature reviews, empirical examples, and simulation evidence, this research demonstrates that critical accounts of PLS-SEM paint an overly negative picture of PLS-SEM’s capabilities.

Criticisms of PLS-SEM often generalize from boundary conditions with little practical relevance to the method’s general performance, and disregard the metrics and analyses (e.g., Type I error assessment) that are important when assessing the method’s efficacy.

We believe the alleged “fallacies” and “untold facts” have already been addressed in prior research and that the discussion should shift toward constructive avenues by exploring future research areas that are relevant to PLS-SEM applications.

All statistical methods, including PLS-SEM, have strengths and weaknesses. Researchers need to consider established guidelines and recent advancements when using the method, especially given the fast pace of developments in the field.

This research addresses criticisms of PLS-SEM and offers researchers, reviewers, and journal editors a more constructive view of its capabilities.

The increasing prevalence of mobile phones and the ease of cheaper Internet access have accelerated the growth of mobile-based payments in India. The study aims to identify must-have and should-have factors affecting consumers’ usage intention toward “Near-Field Communication (NFC)” based mobile payments.

The research integrates diffusion innovation theory and the technology acceptance model. Employing a mixed-methods approach, combining partial least square structural equation modeling and Necessary Condition Analysis (NCA). Thus, the study draws from qualitative interviews and a quantitative survey of 218 respondents.

The study results indicate that the relative advantage of NFC-based mobile payments over conventional payment modes and security concerns directly affect the intention to use it. However, complexity, transaction speed and ease of use do not affect the intention to use. Moreover, perceived usefulness mediates the relationship between complexity, relative advantage and transaction speed with the intention to use NFC-based mobile payments. Less complexity and ease of use, despite being insignificant determinants as per PLS-SEM, must be focused on as the NCA results indicate its necessity for future market growth.

This research addresses a significant gap by focusing on Indian consumers' intention to use NFC-based mobile payments, an area largely unexplored despite the global and availability of such services. The study distinguishes itself by examining factors influencing adoption within the nascent stage of mobile-based NFC payments in India, where digital growth and technology uptake are gradually unfolding.