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Robots have been heavily utilized in many industries, including education. However, the determinants driving robot adoption for educational purposes, particularly in developing countries, are not yet well understood. Additionally, the existing literature does not examine the fitness between robot capabilities and education-related tasks. Therefore, this research integrates the extended unified theory of acceptance and use of technology (UTAUT2) and task-technology fit (TTF) to examine robot adoption in higher education. Based on 177 responses collected from university students, the proposed model is verified using a hybrid structural equation modeling and artificial neural network (SEM-ANN) approach. The findings indicated that effort expectancy is positively affected by individual technology fit and task technology fit. Further, performance expectancy is significantly driven by task technology fit, but not individual technology fit. The results also supported the role of performance expectancy, social influence, facilitating conditions, and hedonic motivation in affecting behavioral intention, with a variance of 65% in the latter. With a normalized value of 94.5%, the ANN results revealed that social influence is the most important factor affecting robot adoption. These empirical findings provide several theoretical contributions and will help higher education institutions to promote students' adoption of robots while enhancing their practical value. • We examine the factors affecting robot adoption in higher education. • UTAUT2 and TTF were adopted and integrated. • Data were collected from university students and analyzed using SEM-ANN. • Most of the hypothesized relationships were supported. • The factors explained 65% of the variance in behavioral intention. [ABSTRACT FROM AUTHOR]

Artificial intelligence (AI) products play a significant role in achieving environmental sustainability. These products can save various resources (e.g., energy, water), achieve cost savings, and manage waste better. However, understanding the determinants affecting the use of AI products and their impact on environmental sustainability is relatively low, specifically in developing countries. To fill this gap in the literature, this study develops a theoretical model by integrating two well-known theories, UTAUT and PMT, to explain the determinants influencing Generation Z use of AI products and their impact on environmental sustainability. The developed model was then evaluated using the PLS-SEM approach based on data collected from 562 respondents in Malaysia and Turkey. Although effort expectancy, performance expectancy, social influence, perceived severity, response efficacy, and response costs are significant drivers of green behavior among Malaysian individuals, effort expectancy, facilitating conditions, perceived severity, response efficacy, and response costs are essential determinants among Turkish individuals. Interestingly, there is no significant difference between the importance of coping appraisals (i.e., self-efficacy, response efficacy, and response costs) among these two populations. The outcomes provide several contributions to the literature on AI and environmental sustainability and offer valuable insights for the practitioners, policymakers, and AI product developers. • We examine the determinants affecting AI products use and their impact on environmental sustainability. • UTAUT and PMT were adopted in this research. • Data were collected from Generation Z in Malaysia and Turkey. • Green behavior significantly affects environmental sustainability in both samples. • Cultural differences were important in this study. [ABSTRACT FROM AUTHOR]