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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.

Smart wearable (SW) devices have attracted the users’ attention and their utility has been increasingly employed in different arenas of life. Of late, it is expected that wearable payments will be the norm of mobile payments soon. Recognizing the SW payments as an emerging innovation, this study investigates the consumers’ adoption of SW payments. A survey method was used to collect data from SW devices users in Saudi Arabia. For this purpose, online questionnaires were distributed and a total of 269 responses were received within that 243 operational cases were used for data analysis. Partial least squares structural equation modeling (PLS-SEM) technique was employed to analyze the data. The statistical tools employed for data analysis are SmartPLS 3.0 and SPSS23. The findings show that all hypothesized relationships were supported except the compatibility and perceived ease of use relationship which was found insignificant. Additionally, the moderating role of personal innovativeness on behavioral intention and actual use relationship was also confirmed. Although TAM is an established robust model of technology adoption, however, the integration of technological features like (perceived esthetics, compatibility, and convenience) make it a more vigorous model for adoption of the smart wearable device.

The main objective of this paper is to examine the factors influencing investor intention to adopt robo-advisory services in Saudi Arabia, with a particular focus on sustainability and platform interface quality (PIQ) within a socio-technical framework. Drawing on the Diffusion of Innovation (DOI), Technology Acceptance Model (TAM), Value-Based Adoption Model (VAM), and Trust theory, the research integrates constructs such as Knowledge about Robo-Advisors (KRA), PIQ, Green Perceived Value (GPV), and Perceived Trust (PT). Data were collected through a structured questionnaire targeting financially active individuals, with 387 valid responses analyzed using Partial Least Squares Structural Equation Modelling (PLS-SEM). The findings reveal that KRA significantly influences Intention to Use Robo-Advisors (IURA) both directly and indirectly, through GPV and Relative Advantage (RA), with only marginal support observed for Perceived Usefulness (PU). PIQ strongly influences perceived ease of use (PEOU) and PU, contributing to IURA, while PT significantly moderates the effects of KRA and PIQ. Multi-group analysis (MGA) further highlights heterogeneity across age, education, and investment groups, underscoring the contextual nature of adoption. The study highlights the critical role of PT, PIQ, and GPV alignment in investor decision-making when engaging with robo-advisory platforms. It offers theoretical contributions by extending traditional adoption models through the inclusion of green value and interface quality, and practical implications for FinTech developers and policymakers aiming to build inclusive, trustworthy, and environmentally aligned robo-advisory platforms.

The study investigates the underlying motives facilitating users’ continuance intention for digital content in academic settings. Extending the expectation confirmation model of IS continuance (ECM-ISC), the study proposes a conceptual model by incorporating personal and technological antecedents of users’ continuance intention for digital content. In addition, users’ environmental concerns and price value are considered as potential moderators in the relationship between their satisfaction and continuance intention for digital content. An online survey was used to collect data from 311 digital content users of a large public university in Saudi Arabia. Structural equation modeling was used to test the relationships in the conceptual model. The results obtained from SmartPLS 3.2 confirm that compatibility, convenience, self-efficacy, and facilitating conditions are the predictors of confirmation and usefulness of digital content. The confirmation of expectations and perceived usefulness result in greater satisfaction with the digital content, which in turn leads to users’ continuance intention. In addition, the article provides empirical evidence for the impact of environmental concerns on the satisfaction–continuance intention relationship, thus opening a novel research debate. The study is expected to offer new insights both for academicians and managers of digital content.

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.

[This corrects the article DOI: 10.1371/journal.pone.0262954.].

The study aims to investigate factors influencing Apple Pay adoption, focusing on lifestyle congruence, perceived security, perceived trust, perceived financial control, ease of use, and relative advantage. By applying the Technology Acceptance Model (TAM) and Diffusion of Innovation (DOI) theory, it seeks to understand these factors’ contributions to user perception and adoption, providing insights into user behavior and enhancing the effectiveness of digital payment solutions. A survey approach was employed, combining stratified random sampling and convenience sampling to collect data from major cities in Saudi Arabia, targeting Apple-pay users. The survey was distributed online via social media platforms. Data from 221 valid responses were analyzed using partial least squares structural equation modeling. The study revealed that the model explains 62.2% of the variance in the intention to use Apple Pay. Key findings include significant impacts of perceived financial control, relative advantage, perceived security, perceived trust, and ease of use on the intention to use Apple Pay. Lifestyle congruence and ease of use influenced usage intentions but not perceived relative advantage. These results underscore the importance of security, trust, and ease of use in driving Apple Pay adoption. The multi-group analysis findings indicate that younger users prioritize security and lifestyle congruence, while females value financial control and lifestyle fit, and males emphasize security and trust in their Apple Pay adoption decisions. This study uniquely integrates the DOI theory and TAM to examine Apple Pay adoption, highlighting the critical roles of perceived financial control, security, and trust. It expands traditional TAM by incorporating financial control, emphasizes security’s impact on trust, and introduces lifestyle congruence as a significant adoption driver. This comprehensive framework provides valuable insights for digital payment adoption.

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.

Purpose This study aims to explore the role of advanced technologies – smart building technologies (SBT), Internet of Things systems (IoTS), data analytics for resource management (DARM) and automation in facilities (AF) – in promoting green sustainable campuses (GSC) within Saudi Arabia’s higher education sector. It investigates the mediating role of technology adoption (TA) and the moderating role of digital infrastructure (DI), aligning with Saudi Vision 2030 and the diffusion of innovation theory (DOI). Design/methodology/approach A cross-sectional survey design targeted faculty, administrative staff and students from the top ten Saudi universities, yielding 429 valid responses. Data was collected using structured questionnaires and analyzed via partial least squares structural equation modeling with Smart-PLS 4.0. The study assessed direct relationships, as well as the mediating and moderating effects of TA and DI, respectively, on GSC. Findings The results revealed significant direct effects of SBT, IoTS, DARM and AF on GSC, underscoring their critical roles in achieving sustainability. TA was found to significantly mediate these relationships, enhancing the effectiveness of technology integration. DI significantly moderated the relationships for SBT, DARM and AF with GSC, but not for IoTS, suggesting differential infrastructure dependencies among the technologies. These findings highlight the necessity of robust adoption strategies and strategic investments in digital infrastructure to optimize sustainability outcomes. Originality/value This study provides a novel application of DOI within the higher education sustainability context, offering empirical insights into the varied impacts of TA and DI on technology integration in Saudi universities. It underscores the need for phased technology implementation and strategic alignment of digital capabilities with sustainability goals in emerging economies such as Saudi Arabia.

PurposeDigital 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/approachThe 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).FindingsThe 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/valueThis 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.