Purpose: The global emphasis on sustainability is driving organizations to embrace financial technology (Fintech) solutions as a means of enhancing their sustainable performance. This study seeks to unveil the intermediary role played by green finance and competitiveness, along with the moderating impact of digital transformation (DT), in the intricate relationship between Fintech adoption and sustainable performance. Design/methodology/approach: Drawing on existing literature, we construct a comprehensive conceptual framework to thoroughly analyse these interconnected variables. To empirical validate of our model, a dual structural equation modelling–artificial neural network) SEM–ANN approach was employed, adding a robust layer of validation to our study’s proposed framework. A sample of 438 banking employees in Pakistan was collected using a simple random sampling technique, with 411 samples deemed suitable for subsequent analysis. Initially, data scrutiny and hypothesis testing were carried out using Smart-PLS 4.0 and SPSS-23. Subsequently, the ANN technique was utilized to assess the importance of exogenous factors in forecasting endogenous factors. Findings: The findings from this research underscore the direct and significant influence of Fintech adoption and DT on the sustainable performance of banks. Notably, green finance and competitiveness emerge as pivotal mediators, bridging the gap between Fintech adoption and sustainable performance. Moreover, DT emerges as a critical moderator, shaping the relationships between Fintech adoption and both green finance and competitiveness. The integration of the ANN approach enhances the SEM analysis, providing deeper insights and a more comprehensive understanding of the subject matter. Originality/value: This study contributes to the enhanced comprehension of Fintech, green finance, competitiveness, DT and the sustainable performance of banks. Recognizing the importance of amalgamating Fintech adoption, green finance and transformational leadership becomes essential for elevating the sustainable performance of banks. The insights garnered from this study hold valuable implications for policymakers, practitioners and scholars aiming to enhance the sustainable performance of banks within the competitive business landscape.

Purpose: Digital technology's integration into education has transformed learning frameworks, necessitating the exploration of factors influencing students’ engagement in digital informal settings. This study, grounded in self-determination theory (SDT), proposes a model comprising artificial intelligence (AI) competence, chatbot usage, perceived autonomy (PA), digital informal learning (DIL) and students’ engagement.Design/methodology/approach: The study collected survey data from 409 participants at Saudi Arabian universities, ultimately using 387 valid responses for analysis. This dataset was subjected to a thorough examination to confirm the validity of our proposed model. To decipher the complex interactions within our model, we utilized partial least squares structural equation modeling (PLS-SEM). The study adopted a disjoint two-stage method to formulate a reflective-formative higher-order construct (HOC).Findings: The study's findings showed that cognitive learning (CL), metacognitive learning (MCL) and social and motivational learning (SML) are the essential components of DIL. Significantly, the study determined that AI competence, chatbot usage, PA and DIL markedly affect students’ engagement. Moreover, the R² value of 0.592 for student engagement indicates the model's robustness in explaining 59.2% of the variance, highlighting its effectiveness in identifying key drivers of student engagement in DIL contexts.Originality/value: This research enhances understanding by detailing the intricate relationships among AI competence, chatbot usage, and students’ engagement in informal digital learning. It extends SDT to emphasize intrinsic motivations and AI capabilities, introducing reflective-formative HOCs for comprehending educational intricacies. It provides practical strategies for enhancing AI abilities and chatbot use in education, promoting personalized, engaging and autonomous digital learning spaces, thereby advancing educational theory and practice.

M-wallets are comparatively more advantageous and convenient than conventional payment systems as m-wallets allow users to avoid cash. The present research uses the diffusion of innovation theory as the base theory to propose a research model by incorporating constructs like convenience, perceived security, personal innovativeness, and perceived trust to investigate the determinants of consumers’ intention-to-use m-wallets. A twofold approach comprising of Structural Equation Modelling—Artificial Neural Network (SEM-ANN) was used: First, partial least squares structural equation modelling (PLS-SEM) was employed to determine the significant determinants of intention-to-use. Second, the ANN approach was applied as robustness to corroborate the outcomes of PLS-SEM and to estimate the relative importance of the SEM-based significant determinants. Our findings confirmed that compatibility, ease of use, observability, convenience, relative advantage, personal innovativeness, perceived trust, and perceived security are the key elements that influence the intention-to-use m-wallets. Moreover, we ascertained that perceived security is the most influential predictor of intention-to-use. The outcomes of ANN have complemented the findings of PLS-SEM, but some differences were also exhibited in the order of influential factors. The study brings to fore significant insights and a set of suggestions for the companies carrying out the development, execution, and marketing of M-wallet services.

Worldwide governments are launching e-government (E-Gov) systems to ensure the superior delivery of services to their citizens. Ensuring the success of government-to-citizen (G2C), E-Gov systems is still challenging for various developing countries. To overcome this challenge, investigating the success of G2C E-Gov systems is important. This paper assesses the success of E-Gov systems from the perspective of a developing country. For this purpose, this research incorporates perceived-usefulness and perceived-trust variables into DeLone and McLean’s IS success model. A survey-based method was employed to collect data from Pakistani citizens. The PLS-SEM method was used for the validation of the conceptual model. Using the non-probability sampling technique, a sample of 264 usable cases was collected and used to analyze our model. It is evident from the outcomes of the study that information-quality has significant effects on the use of E-Gov and user-satisfaction, while service-quality, system-quality, and perceived-trust have significant effects on the use of E-Gov, user-satisfaction and perceived-usefulness. Further, the use of E-Gov, perceived-usefulness, and user-satisfaction have direct, significant impacts on the perceived-net-benefits. The practitioners are recommended to concentrate on the three quality dimensions i.e. information quality, system quality, and service quality while preparing E-Gov applications. The role of governments is also important to launch awareness campaigns and motivate their citizens to use E-Gov services.

Advances in information technology have included the development of smart wearable healthcare (SWH) devices that have potential benefits for consumer health. The adoption of SWH devices is limited, however, compared with other established digital technologies. This study examines the determinants of consumers’ adoption of SWH devices. A conceptual model is proposed that incorporates health (health beliefs and health information accuracy), and technology (compatibility and functional congruence) attributes into the technology acceptance model framework. The proposed model was tested in two steps. Structural equation modelling (SEM) was performed with 473 usable responses to test the hypothesized relationships. The artificial neural network (ANN) approach was then applied to validate the outcomes of Step 1. The SEM analysis indicates that all the hypothesized relationships are supported. The ANN analysis further validates the outcomes of the SEM. The findings of this study and the dual-stage SEM-ANN methodology will have a strong impact on the existing literature regarding SWH devices.