As e-learning platforms gain traction globally, understanding students’ perceptions and intentions towards these platforms is paramount, especially within the context of Saudi universities, where e-learning is rapidly emerging as a transformative educational tool for sustainable development. This study examined the influence of different AI-based social learning networks, personal learning portfolios, and personal learning environments on Saudi university students’ perceived usefulness and ease of use regarding AI-driven platforms (Blackboard, Moodle, Edmodo, Coursera and edX). Furthermore, the study explored the direct effects of these perceptions on students’ satisfaction and intentions to use e-learning. The study also delved into the moderating effects of individual characteristics like readiness for self-directed e-learning, self-efficacy, and personal innovativeness on students’ e-learning intentions. A cross-sectional design was employed, collecting self-reported data from a strong sample of Saudi university students using stratified random sampling. The study targeted 500 students from different universities in Saudi Arabia. Results underscored the significant influence of AI-based social learning networks, personal learning portfolios, and personal learning environments on perceived usefulness and ease of use. Both perceived usefulness and ease of use also significantly and positively influenced satisfaction, influencing students’ attitudes toward e-learning but not their intention to use it. Student characteristics, especially self-efficacy, showed notable impacts on e-learning intentions. However, their interaction with satisfaction yielded insignificant effects on intentions.

Background: Healthcare chatbots have emerged as a promising application of artificial intelligence in healthcare, offering potential benefits in accessibility, efficiency, and patient engagement. However, despite their growing adoption, limited research has examined the factors that determine their success from the user’s perspective. Objective: This study aimed to evaluate the success of a health chatbot service by applying the updated DeLone and McLean Information Systems Success Model augmented with a trust construct, examining the effects of information quality, system quality, service quality, and trust on intention to use, user satisfaction, and net benefits. Methods: An online survey design was employed, utilizing a structured questionnaire with 28 items measuring seven constructs on a seven-point Likert scale. Data were collected electronically from residents of Saudi Arabia between July and September 2024 using convenience sampling. Eligible participants were adults aged 18 years or older who had previously used the health chatbot service. A total of 321 valid responses were obtained. Partial Least Squares Structural Equation (PLS-SEM) was conducted using SmartPLS 3.3 software for measurement and structural model analysis. Results: The measurement model demonstrated acceptable reliability and validity, with composite reliability values exceeding 0.90 and average variance extracted values above 0.70 for all constructs. Structural model analysis supported eight of ten hypotheses. Trust exhibited the strongest effect on intention to use (β = 0.359, p < 0.001), followed by system quality (β = 0.234, p < 0.001) and information quality (β = 0.147, p < 0.01). Intention to use significantly predicted user satisfaction (β = 0.620, p < 0.001) and net benefits (β = 0.278, p < 0.001). User satisfaction demonstrated a strong positive effect on net benefits (β = 0.610, p < 0.001). The model explained 67.6% of the variance in intention to use, 72.7% in user satisfaction, and 71.4% in net benefits. Conclusions: Trust emerged as the most influential factor affecting intention to use the healthcare chatbot service, underscoring its critical role in user acceptance of health chatbot services. Information quality, system quality, and service quality exerted small to moderate effects on behavioral outcomes. These findings suggest that healthcare organizations deploying chatbot services should prioritize building user trust alongside ensuring high system and information quality to maximize user satisfaction and realized net benefits.

This article presents a taxonomy that is grounded in the findings of a Strategy-as-Practice (SaP) study undertaken within the public higher education sector. Two cases, operating under the auspices of Ireland’s Institutes of Technology (IIT), were chosen to explore how the alignment of business and information systems (IS) strategies is practiced by IS managers in the public higher education sector. We applied a methodology within the interpretive paradigm that integrates three research pillars comprising integrative framework for SaP, constructivist grounded theory coding and case research methodology. The taxonomy we develop reveals that the IS manager is a functional manager tasked with reducing costs and gaining efficiencies, while being excluded from direct involvement in the development of IS strategy and prevented from liaising directly with the organization’s principal IS services provider. The IS manager depends largely on being able to successfully lay down some mechanisms and practices in his/her efforts to align business and IS strategies. Our study makes a theoretical, practical and methodological contribution to the strategic alignment body of knowledge.