This study investigates the impact of human-like features and student perceptions on the intention to adopt Artificial Intelligence (AI)-based teaching assistants (AITAs) in higher education. Grounded in the Technology Acceptance Model, the study explores the roles of anthropomorphism, social language use, engagement quality, perceived usefulness, and ease of communication in shaping students' attitudes toward AITAs and their subsequent intention to adopt these technologies. Data were collected from 385 students in Ha'il Province, Saudi Arabia, using a structured questionnaire. The data were analyzed through Partial Least Squares Structural Equation Modeling to assess both direct and mediating effects. The findings reveal that all proposed factors have a positive influence on students' attitudes, which, in turn, significantly predict their intention to adopt AITAs. Furthermore, students' attitudes mediate the relationships between human-like features and adoption intentions. This research offers both theoretical insights and practical implications for integrating AITAs in educational contexts, highlighting the importance of designing AI systems that facilitate human-like interactions and promote positive student perceptions.

The current research aimed to ascertain the impact of the in-store factors on the impulse purchasing behavior of Pakistani’ footwear consumer’. Do the in-store factors, store environment, store promotions, friendly employees, hedonic motivation, good mood, and impulse buying tendency influence the consumers when they visit the store or not? The research design took a deductive approach and survey strategy as methodological tools. The consumers of the footwear industry were approached and asked to complete the questionnaires. The study took a total sample size of 250. The recollected questionnaires were 218 out of 250, making it a response rate of 87 percent. The structural equation modeling technique (SEM) was used with AMOS’s help to analyze the data. The finding elaborated on the positive and significant impact of all the in-store factors, like the atmosphere,friendly staff, sales, etc., on consumers’ impulse buying behavior. The urge to buy impulsively plays a pivotal part in the in-store factors and impulse buying behavior. This study took the in-store factors of impulse buying behavior in Pakistan’s footwear industry, which no researcher has studied until now.

In an era where Financial technology (FinTech) firms are increasingly dependent on blockchain technology, FinTech employees are confronted with information overload that can affect organizational performance. Grounded in socio-technical systems theory (STS), this study explores the role of blockchain financial communication adequacy in driving employee-perceived economic outcomes with occupational well-being and employee's workplace resilience as two mediating variables while employee perceived chatbot assistance in blockchain finance plays the moderating role. Data from 436 employees from a total of 3 FinTech firms with a blockchain native to Saudi Arabia were analyzed using SmartPLS. Results show that communication adequacy directly increases perceived economic outcomes and works through improved well-being and resilience. Moreover, every mediation channel is further enhanced with AI-chatbot support. These insights shed light on the synergies between humans and machines that are required to bring ledger transparency to financial success and provide a blueprint for practitioners looking to optimize socio-technical interfaces in digital finance.

Introduction: Generative artificial intelligence (AI) tutors rarely disagree. Drawing on cognitive offloading theory, this study examines how perceived AI sycophancy (PAS) and perceived AI intellectual stimulation (PAIS) relate to learner autonomy through the serial mediators of metacognitive laziness (MCL) and AI dependence (AIDEP). Methods: Responses from 542 university students using generative AI for coursework were analyzed using partial least squares structural equation modeling (PLS-SEM), necessary condition analysis (NCA), and fuzzy-set qualitative comparative analysis (fsQCA). Results: Perceived sycophancy was associated with greater MCL ( β = 0.38), whereas intellectual stimulation was associated with lower levels of MCL ( β = −0.29); laziness predicted AIDEP ( β = 0.45), and dependence predicted lower autonomy ( β = −0.30). Both serial paths were significant and opposite in sign, explaining 46% of autonomy variance. NCA identified intellectual stimulation as the tightest necessary condition, and fsQCA showed that no high-autonomy configuration tolerated sycophancy Discussion: Because data are cross-sectional and self-reported, findings indicate associations rather than causation. Perceived interaction style, not usage intensity, correlated most with autonomous learning.