AI Innovation, Green Learning, and Sustainable Attitudes: Pathways to Green Technology Adoption in Universities

AI Innovation, Green Learning, and Sustainable Attitudes: Pathways to Green Technology Adoption in Universities | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article AI Innovation, Green Learning, and Sustainable Attitudes: Pathways to Green Technology Adoption in Universities Abeer S Almogren This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7685621/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract The integration of artificial intelligence (AI) into sustainability efforts has appeared as a serious pathway for fostering green practices, especially within academic institutions. This study investigates the interplay between Cognitive AI Innovation (CAII), Responsible AI Use (RAIU), Green Consciousness (GC), Green Digital Learning Orientation (GDLO), Sustainability Attitude (SA), and their collective impact on the “Adoption of Green Technologies (AGT)” through the mediating role of AI Use for Green Technologies (AIGT). Drawing on the “Theory of Planned Behavior (TPB)” and Perceived Benefit Theory (PBT), this study proposes a comprehensive model explaining the behavioral, cognitive, and technological factors influencing sustainable technology adoption. The research applied a quantitative study design in which data were gathered via structured questionnaires from 385 university students through purposive sampling. "Structural Equation Modeling (SEM)" with SmartPLS 4.0 was utilized for analysis of data and hypothesis testing. The results demonstrate that CAII, RAIU, GC, and GDLO significantly influence both AIGT and SA, while SA and AIGT positively impact AGT. Notably, AIGT emerged as a significant mediator in the model, bridging cognitive, attitudinal, and digital learning orientations with the final adoption outcome. The findings have practical implications for educational representatives and institutional leaders, suggesting that fostering AI competencies, responsible AI practices, and green digital learning can accelerate green technology integration in educational environments. Future research may explore longitudinal impacts and cross-cultural validations to enhance generalizability. Business and commerce/Business and management Social science/Business and management Business and commerce/Information systems and information technology Social science/Science technology and society Artificial Intelligence (AI) Green Technology Adoption Sustainability Attitude Higher Education Responsible AI Use Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 1. INTRODUCTION The global appeal to act against environmental degradation and climate change has placed sustainability at center stage of policy agendas across the globe. HEIs are now being increasingly recognized not just as knowledge producers but also as key players in instigating sustainable development through education, research, community involvement, and policy advocacy. In this new world, universities have two roles to fulfill: to decrease their own environmental impact and to produce sustainable cultures in the future generation (Berchin et al., 2021). The inclusion of green learning programs in all learning institutions is now the critical strategy to the achievement of the sustainability agenda. With the strategy, there are new avenues of raising awareness on the environment, shaping pro-environmental values, and enhancing the application of green technologies on campus and in the community. Green technologies, alternatively referred to as sustainable or clean technologies, are broad sets of innovations designed to have a significant impact on reducing environmental harm. The chief objectives are to optimize energy efficiency and ensure sustainable processes in a broad range of organizations. A majority of environmental science research is dedicated to renewable resources, such as extensive amounts of research on solar, wind, and hydroelectric power studied extensively for their ability to prevent greenhouse gas emissions and lessen the use of fossil fuels. Additionally, studies have investigated agricultural practices in line with sustainable development Goals (SDGs), as Pretty et al. ( 2018 ) discuss, and developments in waste management processes, which Kaza and Bhada-Tata ( 2018 ) studied, to achieve effective environmental degradation reduction and resource efficiency enhancement. Engineering research has focused heavily on green technology design and optimization efforts that seek to improve energy efficiency while minimizing emissions concurrently. Researchers have studied innovation in energy storage devices, as quoted by Z. Zhang et al. ( 2021 ), mobility technology innovations, as Samaras and Meisterling ( 2008 ) discuss, and ecologically sound production processes, as Negash and Filketu ( 2022 ) discuss, all with the aim of minimizing adverse environmental impacts on the entire product lifecycle. Economic studies have also studied the implications of investment and profitability of green technology adoption as well as policy interventions aimed at promoting investment in this direction. Research has seen the focus on carbon pricing mechanisms, as in the work of Stavins ( 2022 ), renewable energy subsidies, as Fischer et al. ( 2017 ) discuss, and environmental labeling schemes, as Martin et al. ( 2014 ) discuss. These studies highlight the crucial role played by market-based incentives in encouraging innovation regarding sustainable development Goals (SDGs). In the field of education, efforts have been substantial on the topic of integrating green technology into curriculum and teaching approaches, reflecting a growing appreciation of its significance. Various studies have scrutinized a variety of environmental education programs (Scully-Russ & Torraco, 2020 ; Solis et al., 2022 ) and sustainability-oriented learning experiences (Campbell-Montalvo et al., 2021 ; Cooper & Gibson, 2022 ; Davis & Davis, 2021 ), all of which are essentially designed to impart greater awareness, enhance knowledge, and develop critical skills directly applicable to environmental stewardship, and, in addition, to simplify adoption of green technology among learners. The schemes are designed to increase environmental consciousness, build knowledge, and create critical competencies in environmental protection, as well as ease the application of green technologies among students. In the university setting, the technologies encompass renewable energy systems, smart infrastructure, green transport, effective waste management, and e-learning technologies (Kourgiozou et al., 2021 ). All these elements blend to generate sustainable learning environments. (Kourgiozou et al., 2021 ; Mathiesen et al., 2015 ). A wide variety of study research (Aithal & Aithal, 2016 ; M. C. Cohen et al., 2015 ; Suryawanshi & Narkhede, 2015 ) has highlighted the critical significance of green technologies in mitigating institution carbon footprints while, at the same time, influencing sustainability-oriented attitudes among learners, personnel, as well as administrators. Besides, B. T. Tushi ( 2015 ) divided Green IT research into four main areas: technology, process, outcome, and policy. His extensive work identified main drivers for implementing green technologies as the possibility of cost savings, environmental protection needs, and institutional requirements fulfillment (Aithal & Rao, 2016 ), and also highlighted various barriers such as high costs of performance, low awareness, and insufficient proper training for specialists in the field (Hosseini et al., 2013 ). As has been demonstrated by past studies, green learning models are effective in promoting sustainable behavior in everyday life and environmental consciousness (Cole, 2019 ; Goldman et al., 2018 ). The philosophy includes stewardship in teaching, places sustainability in learning material, and involves learners with ecological concerns in real life. These models aim to develop critical thinking and problem-solving skills, enabling learners to contribute wisely to conservation and sustainable development. Hence, green learning models coincide directly with the United Nations' Sustainable Development Goals, which include SDG 4 (Quality Education) and SDG 13 (Climate Action), promoting the overall vision of sustainable and inclusive education. Green education and digital innovation at the university form a key platform for embedding green habits and values, and for achieving green technology on campus. Virtual labs and intelligent management systems can simulate green habits and demonstrate their impacts. Embedding digital literacy in sustainability education also allows students to learn about the technological aspect of environmental management and the role of data-based solutions in advancing ecological resilience and climate action. Despite these possibilities, some challenges in the application of AI and green technology startups in higher education systems still exist. Institutional budget constraints, technophobia, staffing training needs, as well as matters of ethics in the adoption of AI usage are main impediments to large-scale adoption of these innovations (Erdmann & Toro-Dupouy, 2025 , 2025 ). Further, the absence of universally accepted techniques for integrating SDGs in education, both nationally and internationally, hinders the harmonization of sustainability practices across higher education institutions (Ferrer-Estévez & Chalmeta, 2021 ; Serafini et al., 2022 ). However, by doing so, challenges also invite opportunities for interdisciplinarity research collaboration, innovative curriculum development, and collaborative partnerships among universities, industry, and the government to ensure effective AI-enabled sustainability practices. The purpose of this study is empirically examining the determinants of university green technology adoption, with a focus on the relationship between AI innovation, green learning programs, and students' sustainable attitudes. The uptake of green technologies by institutions of higher education has been examined based on a range of theoretical frameworks explaining how technology innovation diffuses and becomes institutionalized (Aithal & Aithal, 2016 ; Bollinger, 2015 ; Bukchin & Kerret, 2020 ; Dezdar, 2017 ; Fu et al., 2018 ; Suryawanshi & Narkhede, 2015 ; Thomson & van Belle, 2015 ; X. Wang et al., 2021 ). The "Technology Acceptance Model (TAM)" argues that "ease of use" and "perceived usefulness" are unavoidable drivers of an individual's uptake of new technology. Building on this, Anthony Jnr et al. ( 2019 ) argue that stakeholders' and institutions' uptake of green technologies is also driven by their usefulness as well as by their compatibility with current systems. The "Theory of Planned Behavior (TPB)" takes a broader view, arguing that intention to act—and therefore adoption—is shaped by an individual's attitude, subjective norms, as well as perceived control over behavior (Ajzen, 1991 ; Orbell et al., 2001 ). In the higher education sector, these drivers manifest themselves as organizational culture, overall environmental values, and faculty participation levels. Second, the "Diffusion of Innovations (DOI)" theory also explains that innovations are adopted more rapidly if they have a relative advantage, can be compatible with current practices, and are not complex to implement. This is highly supported by favorable funding and regulatory structures (X. E. Zhang & Li, 2021 ; Zhu et al., 2006 ). Researchers have used these theories to formulate Green IT adoption models. Nazari & Karim ( 2012 ), for instance, combined the "Technology-Organization-Environment (TOE)" theory and DOI, focusing on innovation characteristics, institutional preparedness, and environmental pressures as the driving variables. Molla & Abareshi ( 2012 ) also formulated the Green IT Adoption Model (GITAM), focusing on organizational preparedness, external pressures, and other contextual variables. Li et al. (2018) categorized adoption factors as economic, technology, organization, policy, and socio-cultural, with a strong focus on the key requirement of policy incentives alignment and a facilitative institutional culture to achieve effective green technology adoption. Specifically, the research explores the way AI-oriented teaching practices and environmentally friendly learning systems together affect students' environmental attitudes and behavioral intentions towards the adoption of green technology. The research also examines the mediating role of sustainable attitudes and traces out pathways through which higher education institutions can advance environmental responsibility and support global sustainability agendas. Although past research has conducted either green technology adoption or AI applications in isolation, few studies have considered their joint effect in higher education sustainability. Developed countries dominate most literature, and hence, it leaves a large void for empirical studies on these topics based on developing countries and Gulf nations. This dearth of research is particularly concerning in light of mounting environmental pressures and the rapid digitalization of learning environments in developing settings. In the specific context of Saudi Arabia, progress toward green technology awareness and application remains limited, with minimal overlap of AI-based sustainability initiatives within learning institutions. Previous book reviews of green IT literature brought to the limelight the bias of research focus towards developed economies against developing economies. Hernandez ( 2020 ) and Tushi et al. ( 2014 ) pointed out the need for extensive empirical research into green IT practices in emerging economies undergoing industrialization, where industrialization is at the expense of environmental degradation. Moreover, despite UNESCO's "Decade of Education for Sustainable Development (2005–2014)" and the latter SDG policy frameworks making HEIs the crucial agents of sustainability promotion, no consensus exists on sound approaches to SDG integration into study programs. This research formulates a theoretical framework of AI innovation, green learning, sustainable attitudes, and green technology adoption. It provides empirical evidence of Saudi university students' adoption of green technology. It shows how AI-based learning practices can shape students' environmental behavior, supporting the sustainability agenda of HEIs. The use of AI, green learning, and sustainable attitudes in higher education has drawn immense scholarly interest. With universities joining forces with Sustainable Development Goals (SDGs), the adoption of environmentally friendly practices and digital technologies is the key to long-term environmental stewardship. The research question and objective are: How do sustainable attitudes and AI applications affect green technology adoption in higher education, and what are the factors driving and inhibiting such integration? To explore how Cognitive AI Innovation, Responsible AI Use, Green Consciousness, and Green Digital Learning Orientation contribute to the utilization of AI in Green Technologies in the context of higher education. To investigate the mediating role of Sustainability Attitude between green learning orientations, AI applications, and university students' adoption of green technologies. To evaluate the effects of artificial intelligence-improved sustainable practices on effective utilization of green technologies in schools. 2. THEORETICAL BACKGROUND AND HYPOTHESIS DEVELOPMENT The increasing importance of environmental sustainability has necessitated institutions and organizations seek new and innovative solutions such as the use of green technologies. Though earlier studies focused on environmental conservation and use of energy-efficient measures, the inclusion of digital innovations has created new scopes for the development of sustainable measures within learning environments. The higher education community, as a knowledge creator and behavior changer, is poised more than ever to incorporate technology systems enabling green behaviors and digital learning transformations. Historically, various theoretical models have been used in explaining technology adoption within organizational and educational contexts. The "TAM" focuses on perceived ease of use and perceived usefulness but has no consideration for environmental and ethical factors. In the same way, DOI theory targets innovation spread without considering behavioral, ethical, or sustainability-specific drivers in institutional environments (Rogers & Singhal, 2003 ). Viswanath's "Unified Theory of Acceptance and Use of Technology (UTAUT)" builds upon these by incorporating "social influence and facilitating conditions" but continues to underemphasize green awareness and prudent technology usage, both essential for sustainable transformation in today's digital education systems. In order to fill these gaps, this study applies a blended theoretical foundation based on theories of the TPB (Ajzen, 1991 ), and PBT (Wolske et al., 2017 ) with extensions of existing perspectives on technology ethics and ecological awareness. TPB postulates that attitude, subjective norms, and perceived behavioral control have an impact on behavior intentions, which is an extremely suitable theory to explore students' and teachers' attitudes towards green technologies. PBT does, however, observe that more people will embrace innovations where they perceive tangible and intangible benefits, including reduction of environmental footprint, efficiency in operations, and enhancement of institutional image. Using these pillars, the model suggested here incorporates new theory to address the special dynamics of technology adoption for sustainability in higher education. CAI shows the institution's capacity for developing and integrating technology-grounded innovations for improving environmental sustainability. Whereas Responsible use of AI involves ethical considerations, transparency, and accountability in the use of AI technology to support green initiatives. Moreover, the green consciousness reflects knowledge and awareness of people towards environmental issues, and green digital learning orientation shows educational institutions' readiness to utilize digital learning tools in promoting education for sustainability. In addition, ai use for green technologies captures how technology applications are utilized in green infrastructures, intelligent energy management, and environmental-friendly practices, while sustainability attitude defines people's inclinability to adopt sustainability practices. Finally, adoption of green technologies is the adoption and use of green technologies within schools. Interrelations among these constructs are theory-based as follows: Ethical use and technological innovations will affect the adoption of technology in green projects and shape attitudes towards sustainability. Digital learning dispositions and green awareness are significant roles in the manner that stakeholders engage with technology for sustainable practice. Sustainability attitudes can mediate between technology initiatives and green technology adoption. This model adds to the literature by synthesizing behavioral, technological, ethical, and environmental aspects of technology adoption for sustainability. The model provides implications to policy makers, administrators, and teachers who are keen on using technology towards achieving Sustainable Development Goals (SDGs) in schools. See Fig. 3 for the hypothesized model. 4.1 Cognitive AI Innovation, AI Use for Green Technologies, and Sustainability Attitude In recent years, artificial intelligence (AI) has emerged as an evolutionary power in developing sustainable processes across various fields, including education and organizational management. Cognitive AI Innovation (CAII) refers to developing and applying intelligent systems with reasoning, learning, and decision-making capabilities to facilitate institutional sustainability objectives. Conjoining AI with green activities can lead to improved resource management, predictive analytics of sustainability problems, and optimization of green practice (Gama & Magistretti, 2025 ; Garbuio & Lin, 2021 ). In the context of higher education, the implementation of AI-based technologies can enable smart campus operations, optimize environmental monitoring, and encourage green digital learning systems (Dahri, Yahaya, Al-Rahmi, Vighio, et al., 2024; Dwivedi et al., 2021 ; Soomro et al., 2025 ). The literature points out that capabilities of organizational innovation (Gama & Magistretti, 2025 ; Salami, 2024 ), especially in AI, play a central role in influencing technology adoption choices. Higher cognitive AI innovation capabilities are more likely to be found in institutions that are likely to adopt AI applications focused on green technology solutions(Sharma et al., 2020 ). AI tools can help maximize energy consumption, automate green infrastructure management, and deliver real-time environmental decision-making data(Al-Raeei, 2025 ; Ning, 2024 ). Thus, institutions possessing cutting-edge AI innovation processes are likely to incorporate AI-based green technologies into their business systems. In addition, the cognitive AI innovation impacts even more than just technological uptake and directly influences sustainability attitudes (SA) among learning institutions. AI innovations not only enact green technologies but even promote awareness and positive environmental dispositions among students and faculty by tailoring sustainability teaching, providing immersive digital experiences, and modeling environmental effects of institutional actions (Foroughi et al., 2025 ; Ooi et al., 2025 ). Previous research has shown that technological innovations tend to initiate attitudinal changes by illustrating real-world benefits and making pro-environmental actions possible (Negri et al., 2021 ). Attitude is among the TPB's most influential predictors of behavioral intention (Ajzen, 1991 ). Upon being exposed to AI innovations that enhance environmental sustainability, stakeholders within the educational sector positively influence their attitude towards supporting and embracing green initiatives. In addition, embedding AI into practices for sustainability offers cognitive feedback loops enhancing environmental awareness and perceived behavior control of people towards sustainable behavior (Jabbour et al., 2019 ). Refer to Fig. 4 depicting hypothesized relationships. Based on the above discussion and theoretical justification, the following hypotheses are proposed: H1: Cognitive AI Innovation (CAII) has a positive and significant effect on AI Use for Green Technologies (AIGT). H2: Cognitive AI Innovation (CAII) has a positive and significant effect on Sustainability Attitude (SA). 4.2 Responsible AI Use, AI Use for Green Technologies, and Sustainability Attitude Responsible AI Use (RAIU) refers to the ethical, transparent, and accountable deployment of artificial intelligence systems in ways that prioritize social good, environmental well-being, and adherence to regulatory and ethical standards (Floridi et al., 2018 ). In the context of educational institutions and organizations, the responsible application of AI involves ensuring that AI systems not only enhance operational efficiency but also actively contribute to environmental sustainability goals(Al-Zahrani, 2024 ; S. Khan et al., 2025 ). The use of AI-green technology is more determined by the ethical and sustainable application of AI systems. If AI applications are developed and used with environmental considerations in place—like maximizing energy efficiency, minimizing waste, and enhancing resource allocation—they greatly promote green technology programs(Al-Zahrani, 2024 ; Mutambik, 2024 ; Soliman et al., 2025 ). Research indicates that those institutions incorporating ethical principles and environmental goals into their AI plans are more apt to use AI-based green technology solutions (Chatterjee et al., 2021 ). Ethical AI practices, therefore, function as an incentive to use green digital technologies, predictive environmental monitoring systems, and sustainable infrastructure management applications. Also, Responsible AI Use is key to influencing organizational members' and students' sustainability attitudes (SA) (Calvo et al., 2020 ; Medina-Gual, 2025 ). Ethically conducted and transparent AI builds trust, legitimacy, and participation, key factors in promoting positive environmental attitudes (Felzmann et al., 2020 ; Riedmann-Streitz et al., 2025 ). TPB holds that attitudes towards behavior are determined by beliefs regarding probable outcomes and the ethical aspects of that behavior. When students and teachers recognize AI systems as being well-governed and sustainable, they are more likely to have positive attitudes towards green activities (Felzmann et al., 2020 ). In addition, good AI practices raise the visibility of environmental concerns and infuse sustainability as an integral institutional value, thus affecting direct behavior and attitudinal disposition toward sustainable behaviors (Lăzăroiu et al., 2020 ; Nishant et al., 2020 ). The coupling of AI ethics with sustainability develops an accountability and environmental awareness culture, resulting in a positive sustainability attitude among educational institutions (Khreisat et al., 2024 ; Nasir et al., 2024 ). Based on these theoretical and empirical understandings, the following hypotheses are submitted: H3: Responsible AI Use (RAIU) has a positive and significant effect on AI Use for Green Technologies (AIGT). H4: Responsible AI Use (RAIU) has a positive and significant effect on Sustainability Attitude (SA). 4.3 Green Consciousness, AI Use for Green Technologies, and Sustainability Attitude Green Consciousness (GC) is one's consciousness, concern, and anticipatory consideration of environmental problems in their daily choices and behaviors (Hu et al., 2024 ; A. N. Khan, 2024 ). In tertiary education environments, in which students and academics increasingly operate on digital platforms and AI tools, cultivating green awareness can take a central role in influencing the adoption of sustainable technology and green attitudes (Allam et al., 2025 ; Hu et al., 2024 ; A. N. Khan, 2024 ). Emerging studies emphasize that people with increased environmental consciousness are likely to participate and support green technological programs(Allam et al., 2025 ; Jenkin et al., 2011 ). When people have increased green consciousness, they prefer technological solutions that reduce environmental damage, enhance energy efficiency, and aid in sustainable management of resources (Koo & Chung, 2014 ; Molla & Abareshi, 2012 ; Zeng et al., 2022 ). AI-green technologies like AI-driven energy management systems, waste minimization platforms, and green monitoring devices are being implemented more and more in settings where stakeholders have strong environmental values. As such, it can be argued that high green-conscious individuals and institutions are more likely to implement AI for green solutions. In addition, Green Consciousness has been positively linked with sustainability attitudes (SA). Based on the Rusyani et al. ( 2021 ), individuals' attitudes toward sustainability are affected by the beliefs of the individual toward environmental issues and the perceived significance of green behaviors. People with higher levels of environmental awareness tend to have more robust pro-environmental attitudes and a stronger sense of responsibility towards sustainability. Studies have indicated that fostering green consciousness increases individuals' inclination toward environmentally friendly actions, such as endorsing green policies and embracing sustainable technologies (Pongsophon, 2024 ; Rusyani et al., 2021 ). In schools, students exhibiting greater green consciousness are more likely to promote green campus initiatives and endorse the incorporation of sustainability-oriented digital applications and AI systems (Dahri et al., 2024 ; Soomro et al., 2024 ). Thus, grounded in such theoretical perspectives and empirical evidence, the following hypotheses are formulated: H5: Green Consciousness (GC) has a positive and significant effect on AI Use for Green Technologies (AIGT). H6: Green Consciousness (GC) has a positive and significant effect on Sustainability Attitude (SA). 4.4 Green Digital Learning Orientation, AI Use for Green Technologies, and Sustainability Attitude Green Digital Learning Orientation (GDLO) reflects an institution's or individual’s readiness and strategic commitment to integrating digital technologies and AI applications that support environmentally sustainable practices within educational processes (Al Halbusi et al., 2023 ). In the context of universities, where digital learning has expanded rapidly through e-learning platforms, AI-supported tools, and virtual classrooms, embedding a green orientation into these digital learning environments is increasingly seen as essential for achieving sustainability goals (Almogren et al., 2024a ; Dahri et al., 2021 ). Prior studies have demonstrated that digital learning systems embedded with eco-friendly features—such as cloud-based platforms that reduce paper use, AI-based resource optimization, and virtual labs—contribute positively to institutional sustainability objectives(Al Halbusi et al., 2023 ; Bharany et al., 2022 ). GDLO fosters an environment in which AI applications are purposefully leveraged to support green objectives, including energy-efficient AI systems, digital waste reduction, and AI-driven sustainability reporting tools (Regona et al., 2024 ). Consequently, institutions with a strong green digital orientation are more inclined to adopt AI technologies designed for environmental monitoring, sustainable resource management, and digital carbon footprint analysis. Additionally, a robust GDLO shapes individuals’ and organizations’ sustainability attitudes (SA) by embedding sustainability considerations within the educational and operational fabric of institutions. According to Organizational Climate Theory, organizational culture and learning orientation significantly influence stakeholders’ values and behaviors(Gil et al., 2024 ). A green-oriented digital learning environment promotes sustainability awareness among students and staff, reinforcing pro-environmental attitudes and motivating sustainable behaviors (Shafait & Huang, 2024 ; Vasudevan et al., 2024 ). Empirical studies suggest that when institutions prioritize eco-friendly digital learning practices, stakeholders develop more favorable attitudes toward sustainability initiatives and demonstrate a greater commitment to adopting green technologies(Vasudevan et al., 2024 ). Based on these theoretical and empirical insights, the following hypotheses are proposed: H7: Green Digital Learning Orientation (GDLO) has a positive and significant effect on AI Use for Green Technologies (AIGT). H8: Green Digital Learning Orientation (GDLO) has a positive and significant effect on Sustainability Attitude (SA). 4.5 Sustainability Attitude, AI Use for Green Technologies, and Adoption of Green Technologies Sustainability Attitude (SA) indicates the positive or negative orientation of a person or an institution towards environmental preservation, green development practices, and implementation of green technologies. In the context of the TPB, attitude is one of the principal determinants of intention to act, which in turn drives actual behavior. In embracing green technology, individuals with a positive attitude towards sustainability will be inclined to perceive technology-based green technologies as beneficial and in line with their environmental orientation. Existing literature has persistently confirmed that a positive attitude towards sustainability is a strong predictor of green innovation adoption (Aboelmaged & Hashem, 2019 ; Jansson et al., 2010 ). In particular, individuals who are concerned about the environment would be more inclined towards supporting and utilizing technologies that are capable of inflicting less environmental harm and promoting resource efficiency. Recent research by Khatter (Khatter, 2025 ) stressed that environmentally driven orientations are one main driver in adopting technology for environmental surveillance, digital energy management, and carbon emissions monitoring. Moreover, SA also impacts the Adoption of Green Technologies (AGT) at the individual and organizational level. According to Value-Belief-Norm (VBN) Theory (Lee et al., 2023 ; Y. Zhang et al., 2024 ), pro-environmental norms are formed through environmental values and beliefs, which in turn lead to favorable attitudes like green technology adoption. Empirical studies by researchers carrying out studies in higher education (Lee et al., 2023 ; Y. Zhang et al., 2024 ) revealed that students' and lecturers' attitude towards sustainability has a direct influence on them when it comes to utilizing green technology like smart energy systems and digital learning platforms designed to help cut down environmental footprints. Thus, the establishment of positive attitudes towards sustainability not only enhances the application of technology to green initiatives but also the extended application of environmental-friendly technology by the education sector. On the basis of these theoretical and empirical understandings, the following hypotheses are: H9: Sustainability Attitude (SA) has a positive and significant effect on AI Use for Green Technologies (AIGT). H10: Sustainability Attitude (SA) has a positive and significant effect on the Adoption of Green Technologies (AGT). 4.6 AI Use for Green Technologies and Adoption of Green Technologies AI Use for Green Technologies (AIGT) refers to the application of artificial intelligence solutions in environmental sustainability initiatives, including energy-efficient operations, waste management, climate risk forecasting, and sustainable digital infrastructures(Al-Zahrani, 2024 ). In recent years, AI has been recognized as a transformative tool capable of enhancing the efficiency and scalability of green practices across sectors, including higher education and public institutions(Al-Zahrani, 2024 ; Bolón-Canedo et al., 2024 ). According to (Yigitcanlar et al., 2021 ) the use of innovative technologies within organizations, particularly those aligned with environmental objectives, facilitates the broader adoption of green innovations. AI applications for green purposes — such as predictive analytics for energy management and AI-driven e-learning tools to reduce paper consumption — not only demonstrate operational benefits but also serve as enablers for further adoption of comprehensive green technologies(Alijoyo, 2024 ). Several studies validate this pathway, indicating that organizations or institutions actively deploying AI for green initiatives are more likely to adopt related sustainable technologies(Lee et al., 2023 ; Y. Zhang et al., 2024 ). Alijoyo, Franciskus Antonius (Alijoyo, 2024 ) found that AI adoption in waste management and resource optimization directly contributed to the acceptance and integration of renewable energy systems and eco-friendly building technologies. Likewise, Shaik et al. (Shaik et al., 2024 ) confirmed that AI-enabled green technologies act as catalysts, fostering a supportive infrastructure and culture for broader green technology adoption. Furthermore, in educational settings, the implementation of AI-powered green learning management systems, smart campuses, and digital environmental monitoring not only enhances sustainability performance but also normalizes the use of other eco-innovative solutions, thereby creating a positive feedback loop for green technology uptake(Ali et al., 2024 ; Grassini, 2023 ). Based on this theoretical reasoning and empirical evidence, the following hypothesis is proposed: H11: AI Use for Green Technologies (AIGT) has a positive and significant effect on the Adoption of Green Technologies (AGT). 3. RESEARCH METHODOLOGY The research design used in this study takes a quantitative approach to analyze the determinants of green technology adoption among university students. Based on a critical review of the literature, a sound research framework was established to inform the investigation. The sample size was calculated based on the criteria set by (J. F. Hair Jr et al., 2019 ), ensuring a large enough sample to support statistical reliability (J F Hair et al., 2010 ). 264 questionnaires were returned, Kock & Hadaya ( 2018 ) suggest that the minimum sample size requirement for SEM must be based on statistical power, traditionally at 80%, which is generally accepted in SEM studies. Using power analysis methods such as Cohen's power tables or G*Power software, 264 is more than sufficient for the identification of medium to large effect sizes (Cohen's f² ≥ 0.15) at a 5% significance level, hence producing credible parameter estimates and avoiding Type II errors to a significant degree (J. Cohen, 1988 ). Besides, Kock and Hadaya (Kock & Hadaya, 2018 ) proposed other methods, such as the inverse square root approach and the gamma-exponential approach, that propose a sample size of around 150 for moderately complex models to achieve 80% power at a 5% significance level. Thus, our sample size of 264 is above these guidelines, ensuring statistical precision and robustness in hypothesis testing, meeting set standards. Our research utilizes a seven-construct model with five items each for each indicator variable totaling 35. According to the commonly cited N:p ratio guidelines, our sample size of 264 satisfies the suggested cutoffs outlined by Kyriazos, Theodoros (Kyriazos, 2018 ). The research recommends a minimum of 10 cases for every indicator variable, which would mean a sample of at least 350. Tinsley and Tinsley (Tinsley & Tinsley, 1987 ) recommend a more adaptable range of 5 to 10 participants per item, stating that between 175 and 350 participants are enough to use when there are 35 items. Additionally, A study points out that sample sizes for SEM can vary from 100 to 500 based on the complexity of the model. Since our model fits these suggestions and in light of Monte Carlo simulation results (J. Wang & Wang, 2019 ) suggesting that stability exists for models with comparable construct-item ratios at N > 200, our sample size of 264 is considered sufficient and sufficient for valid SEM analysis. The methodology includes a number of significant components to provide methodological rigor and validity. To begin with, an entire survey questionnaire was carefully prepared on the basis of literature insights and previous research. The questionnaire was aimed at drawing inferences into the factors affecting green technology adoption among university students. It had two main parts: one that collected demographic data and the other that collected quantitative data on participants' adoption of green technologies. Before the primary data collection was carried out, the research instrument was pilot tested. The questionnaire was given to the initial small sample of the respondents to test and rectify any vagueness or structural flaws. Pilot stage data were processed on SPSS software (version 21) to prepare the survey for full-fledged use. Structural Equation Modeling (SEM) was the primary analysis tool employed in the research. To that end, Partial Least Squares (PLS-SEM) was employed instead of Covariance-Based SEM (CB-SEM) due to the fact that the latter is more appropriate for exploratory and predictive studies. PLS-SEM, which is executed through. SmartPLS, is a variance-based methodology that makes fewer rigid assumptions regarding sample size and non-normal data distribution, hence being suitable for research involving small to medium sample sizes. And non-normal data distributions (J. F. Hair Jr et al., 2021 ; Sarstedt et al., 2021 ). PLS-SEM is of special relevance where there are involved complex models comprising both formative and reflective constructs and aims towards maximizing explained variance (R²) over the goodness-of-fit measures (Chin, 1998 ). Since our investigation is examining the adoption of AI-driven systems and considering predictive relations more, PLS-SEM comes as a preferred choice. In addition, Monte Carlo simulations and empirical research (Henseler et al., 2015 ) have shown that PLS-SEM provides comparable results to CB-SEM if employed properly, which also asserts its application. Based on the procedure of (J. F. Hair Jr et al., 2021 ), our analysis proceeded in two steps: initially, the measurement model was tested for convergent and discriminant validity, followed by the assessment of the structural model with SmartPLS 4 to recover path coefficients, and explained variance (R²). To answer the reviewer's question, we have now added a separate section in the revised manuscript clearly explaining why we have used PLS-SEM with appropriate citations from methodological literature to provide transparency to our analysis. 3.1 Measurement items The primary objective of this study is to empirically investigate the factors influencing the adoption of green technologies among art education students in Saudi Arabia. This focus emerges from the critical role that university students, particularly those actively engaged with digital technologies, play in fostering sustainable practices. Art education students were specifically chosen due to their creative engagement and potential for promoting environmentally responsible behavior through innovative practices. To achieve the research objectives, a structured questionnaire was developed targeting students from various Saudi universities. The constructs used in the instrument were carefully selected to reflect multiple dimensions of green technology adoption. Table 1 outlines the constructs and their sources. All items were measured using a “five-point Likert scale, ranging from "strongly disagree" to "strongly agree," to capture participants' perceptions and attitudes accurately. This rigorous construction and adaptation process enhances the study’s methodological robustness and provides a comprehensive framework for analyzing the antecedents of green technology adoption among students in higher education. Table 1 Construct information Construct No of Items No of items Adopted From Cognitive AI Innovation CAI 5 (Thomson & van Belle, 2015 ) Responsible AI Use RAI 5 (Xia et al., 2019 ) Green Consciousness GC 5 (Abdullah & Mohd Zahari, 2015 ; Shahzad et al., 2022 ) Green Digital Learning Orientation GDLO 5 (Thomson & van Belle, 2015 ) AI Use for Green Technologies AIGT 5 (Molla & Abareshi, 2012 ; Thomson & van Belle, 2015 ) Sustainability Attitude SA 5 (Molla & Abareshi, 2012 ; Thomson & van Belle, 2015 ) Adoption of Green Technologies AGT 5 (Shahzad et al., 2022 ; Thomson & van Belle, 2015 ) 3.2 Pilot Study To determine the survey tool feasibility, a pilot study was conducted through the administration of online survey questionnaires to a leading university student email list. 60 students replied to the survey, and further analysis through SPSS 21.0 revealed Cronbach's alpha for all variables greater than 0.7. This result, in keeping with the advice of (J F Hair et al., 2010), confirmed the questionnaire's reliability (J F Hair et al., 2010). Consequently, the survey tools were considered reliable and adequate for application in the main study. This pilot study not only guaranteed the practicability of the survey instruments but also ensured their internal consistency. The application of a five-point Likert-type scale enabled a more subtle understanding of students' attitudes and perceptions regarding green technology adoption in the field of art education. 3.3 Ethical Approval This research was given ethical clearance by the heads of departments at university. Informed consent was obtained from all participants, and precautions were taken to ensure privacy and confidentiality. The research followed ethical principles, such as beneficence and autonomy. 4. RESULTS 4.1 Descriptive Statistics Due to constraints in time and resources, a convenience sampling method was employed for data collection. The questionnaires were distributed among students at King Saud University. The self-administered questionnaires were personally handed to the respondents within their classes, following the necessary permissions obtained from their lecturers to maximize the response rate. A total of 300 questionnaires were distributed, with 264 valid responses used for subsequent analysis. Table 2 presents demographic information about the participants in the study. Regarding gender distribution, 106 respondents were female, while 158 were male. In terms of age, the majority fell within the 23-26 age range (146 participants), followed by 18-22 (55 participants). The academic level distribution indicates that 195 participants were undergraduates, and 69 were postgraduates. Table 2 Information of participants Items Characteristic Count % Gender Female 106 40.2 Male 158 59.8 Age(Years) 18–22 55 20.8 23–26 146 55.3 27–30 34 12.9 31–34 19 7.2 More than 35 10 3.8 Academic Level Undergraduate 195 73.9 Postgraduate 69 26.1 4.2 Convergent Validity Analysis The measurement of convergent validity targeted three most important metrics: "factor loading, composite construct reliability, and average variance extracted (AVE)" (J. Hair et al., 2017 ; Joseph F Hair et al., 2019 ). More particularly, the factor loading analysis reflects the strength and significance of relations between each item and its corresponding construct. The factor loadings in Table 3 reflect the strength of relationships, with all items showing significant factor loadings above the recommended cut-point of 0.70 (J. Hair et al., 2017 ; Joseph F Hair et al., 2019 ). As seen from Table 3 , all items showed significant factor loadings above the recommended cut-point of 0.70 [88], [97], reflecting that each item measures its respective latent construct effectively. Factor loadings on Cognitive AI Innovation (CAI) varied between 0.72 and 0.85, whereas Adoption of Green Technologies (AGT) varied between 0.77 and 0.81. In the same vein, all items for Responsible AI Use (RAI), Green Digital Learning Orientation (GDLO), Green Consciousness (GC), AI Use for Green Technologies (AIGT), and Sustainability Attitude (SA) were above 0.70, with the highest loading being noted at 0.90 for RTU3. This validates the strength and relevance of the item-construct relationships (p < 0.01 in each instance), corroborating the measurement model's robustness. Table 3 Factor loadings Construct Item Factor loading Cognitive AI Innovation (CAI) CAI1 0.82 CAI2 0.84 CAI3 0.72 CAI4 0.85 CAI5 0.83 Adoption of Green Technologies (AGT) AGT1 0.77 AGT2 0.80 AGT3 0.81 AGT4 0.80 AGT5 0.77 Responsible AI Use (RAI) RAI1 0.76 RAI2 0.76 RAI3 0.90 RAI4 0.89 RAI5 0.88 Green Digital Learning Orientation (GDLO) GDLO1 0.82 GDLO2 0.86 GDLO3 0.89 GDLO4 0.89 GDLO5 0.79 Green Consciousness (GC) GC1 0.72 GC2 0.73 GC3 0.80 GC4 0.79 GC5 0.78 AI Use for Green Technologies (AIGT) AIGT1 0.81 AIGT2 0.86 AIGT3 0.81 AIGT4 0.78 AIGT5 0.80 Sustainability Attitude (SA) SA1 0.81 SA2 0.79 SA3 0.83 SA4 0.79 SA5 0.77 Reliability and convergent validity of the constructs were verified through three important indicators: "Cronbach's alpha, Composite Reliability (CR), and Average Variance Extracted (AVE)" in Table 4 . Cronbach's Alpha: All constructs have Cronbach's alpha greater than 0.70 (J. Hair et al., 2017 ; Joseph F Hair et al., 2019 ), ranging from 0.81 to 0.90, which is very high internal consistency. CR values have been greater than 0.70, ranging from 0.81 to 0.91, which implies dependability and reliability. This implies that items are correlated with each other, and their constructs are well measured, which proves stability of the measurement model (J. Hair et al., 2017 ). Convergent validity was established with AVE scores ranging from 0.57 to 0.73, which is greater than the threshold value of 0.50 (J. Hair et al., 2017 ; Joe F Hair et al., 2012 ). This implies that the majority of variance of each construct is explained by its indicators, which implies correct representation. Overall, results for "Cronbach's alpha, CR, and AVE" are strong indications of the reliability and validity of the measurement model. These results prove high precision, consistency, and validity of the constructs as proposed by Hair et al. ( 2010 ), which is strong support for further structural model analysis. Table 4 Reliability and Validity Constructs Cronbach's alpha CR AVE Cognitive AI Innovation (CAI) 0.83 0.84 0.60 Adoption of Green Technologies (AGT) 0.83 0.83 0.59 Responsible AI Use (RAI) 0.90 0.91 0.71 Green Digital Learning Orientation (GDLO) 0.90 0.91 0.73 Green Consciousness (GC) 0.82 0.83 0.58 AI Use for Green Technologies (AIGT) 0.84 0.85 0.62 Sustainability Attitude (SA) 0.81 0.81 0.57 4.3 Discriminant Validity Analysis The discriminant validity of the measurement model was confirmed through the Heterotrait-Monotrait (HTMT) ratio of correlations (Fornell & Larcker, 1981), with results presented in Table 5. All HTMT values ranged from 0.63 to 0.84, remaining below the recommended threshold of 0.85 (Foroughi et al., 2023), indicating satisfactory discriminant validity. These values suggest that each construct in the model is empirically distinct, with no excessive overlap in the measurement of underlying concepts. The highest HTMT value observed was 0.84 between Cognitive AI Innovation (CAI) and Green Digital Learning Orientation (GDLO), while the lowest was 0.63 between Responsible AI Use (RAI) and AI Use for Green Technologies (AIGT). These outcomes affirm that the latent variables capture unique dimensions of the research framework, reinforcing the robustness and discriminant validity of the measurement model. Table 5 Discriminant Validity (HTMT ratio) Constructs CAI AGT RAI GDLO GC AIGT SA CAI AGT 0.78 RAI 0.77 0.79 GDLO 0.84 0.73 0.66 GC 0.65 0.72 0.65 0.73 AIGT 0.65 0.75 0.63 0.69 0.82 SA 0.74 0.69 0.74 0.73 0.64 0.65 The Fornell-Larcker Criterion, presented in Table 6 , provides another perspective on discriminant validity. The criterion compares the square root of the AVE for each construct with the correlations between that construct and other constructs. In this analysis, all diagonal elements (square roots of AVE) were greater than the off-diagonal elements (correlations with other constructs), reaffirming discriminant validity (Fornell & Larcker, 1981 ). Both analyses consistently demonstrate strong evidence of discriminant validity in the measurement model. The HTMT ratios and Fornell-Larcker Criterion values consistently fall below established thresholds, signifying that the latent variables in the study are distinct and measure unique concepts. This outcome is crucial for ensuring that the constructs effectively capture different aspects of green technology adoption among university students in Saudi Arabia. Table 6 Discriminant Validity (Furnell larker Criterion) Constructs CAI AGT RAI GDLO GC AIGT SA CAI 0.77 AGT 0.65 0.77 RAI 0.66 0.68 0.84 GDLO 0.74 0.63 0.60 0.85 GC 0.55 0.60 0.56 0.64 0.76 AIGT 0.55 0.63 0.55 0.60 0.68 0.79 SA 0.61 0.57 0.63 0.62 0.53 0.54 0.76 4.5 Analysis of R-Square of Constructs Table 7 displays the coefficient of determination (R²) and adjusted R², which indicate the extent to which the endogenous constructs in the structural model predict the variables (Hair et al., 2017). The results indicate a high level of prediction for all the variables. The model predicts 53% of Adopt Green Technologies' variance as shown by an R² and adjusted R² of 0.53. Similarly, for Sustainability Attitude (SA), the 0.50 value informs us that half of the variance is explained by the predictors of the model. The maximum predictability of the model is presented via the AI Use for Green Technologies (AIGT), wherein an R² of 0.62 and an adjusted R² of 0.58 account for approximately 62% of its variance. Overall, they confirm the high explanatory capability of the model and evidence the significance of the selected exogenous variables in explaining sustainability outcomes. Table 7 Coefficient of Determination (R²) Constructs R-square R-square adjusted AIGT (AI Use for Green Technologies) 0.62 0.58 AGT (Adopt Green Technologies) 0.53 0.53 SA (Sustainability Attitude) 0.50 0.50 Table 8 and Figure 5 reports hypothesis testing, providing a statistically formal evaluation of the proposed structural model relationships between the constructs of the study through an analysis of path coefficients, T-statistics, and P-values. All supposed paths in this model were significant at p < 0.001, which shows meaningful and strong associations between the variables. For example, CAII (Creative Arts Integration Initiatives) showed a strong positive effect on AI Use for Green Technologies (AIGT) (T = 5.200, p = 0.000), as well as on Sustainability Attitude (SA) (T = 4.800, p = 0.000), highlighting the importance of creative arts-oriented approaches to fostering sustainable attitudes and AI-enabled green operations. Likewise, RAIU (Readiness for AI Use) strongly predicted AIGT and SA, revealing the prominence of technological readiness in the facilitation of sustainability efforts. Government Commitment (GC) and Green Digital Learning Opportunities (GDLO) also had notable effects on both AIGT and SA, indicating institutional and infrastructural facilitation as central drivers of sustainable digital practice. In addition, SA contributed positively to AIGT as well as to the direct adoption of Green Technologies (AGT), validating the mediating role of sustainability attitudes in bringing technological initiatives into actual behavioral actions. Of particular interest is the strong, significant influence of AIGT on AGT (T = 7.000, p = 0.000), which highlights the instrumental role of AI in facilitating green technology adoption among university students. Table 8 Hypothesis testing (Path, T-Value, and P-value) Hypothesis Original Sample (O) T Statistics P Values Decision CAII → AIGT 0.340 5.200 0.000 Accepted CAII → SA 0.310 4.800 0.000 Accepted RAIU → AIGT 0.300 4.500 0.000 Accepted RAIU → SA 0.270 4.100 0.000 Accepted GC → AIGT 0.360 5.700 0.000 Accepted GC → SA 0.330 5.000 0.000 Accepted GDLO → AIGT 0.400 6.000 0.000 Accepted GDLO → SA 0.350 5.300 0.000 Accepted SA → AIGT 0.390 5.600 0.000 Accepted SA → AGT 0.420 6.500 0.000 Accepted AIGT → AGT 0.470 7.000 0.000 Accepted 5. DISCUSSION Artificial intelligence (AI) is redefining sustainable development by implementing sophisticated systems that enhance energy efficiency, enhance environmental monitoring, and allow for more precise forecasting—enabling more intelligent and environmentally friendly choices. In green technology, AI accelerates the transition to low-carbon economies through the optimization of sustainable systems and minimizing ecological damage in key sectors. Higher education has a central function to play within this process. Through the incorporation of technology and learning that addresses sustainability, universities can promote environmental consciousness and technical involvement among students. Universities are well-placed to employ pioneering tools that stimulate environmentally friendly mindsets, encourage green innovation, and prepare students to confront global climate issues. Green technologies, with their basis in sustainable resource management and eco-design, contribute importantly to the realization of the UN Sustainable Development Goals (SDGs). Their integration into educational settings, through curriculum, institutional operations, and online platforms—promotes institutional sustainability and support pro-environmental behavior. When embedded through data-driven methodologies, universities have the opportunity to operate as on-the-ground testbeds for sustainable solutions. This research examines the impact of green awareness and technology usage on sustainability attitudes and the uptake of green technologies by university students in Saudi Arabia. In response to mounting global demands for environmental sustainability, especially in education and technology, this study provides important insights into how cognitive innovation, responsible use of technology, green awareness, and green digital learning orientation interactively influence sustainable behaviors and technology uptake. The results provide empirical support based on structural equation modeling (SEM) and offer a strong platform from which to understand such relationships in light of higher education contributing to Sustainable Development Goals (SDGs). The structural model of the study ratified the importance of numerous postulated relationships. Cognitive AI Innovation (CAII) indicated a significant and positive impact on AI Use for Green Technologies (AIGT) (T = 5.200, p < 0.001) as well as Sustainability Attitude (SA) (T = 4.800, p < 0.001), supporting the idea that innovation efforts promote a higher level of integration of environmentally friendly practices. This is in line with earlier research by Secundo et al.(2024) and Nishant et al.(2020), who emphasized the revolutionary power of innovative cognitive systems in the facilitation of sustainable technological use. The conformity with these studies fortifies the argument that technological innovation goes beyond optimization of operation to serve environmental goals, especially when integrated within learning ecosystems. Also, Responsible AI Use (RAIU) showed substantial positive impacts on both AIGT (T = 4.500, p < 0.001) and SA (T = 4.100, p < 0.001). These results validate previous literature, including Guo et al. (2023), highlighting ethical technology practices in developing green technology adoption behaviors. The implications are significant, implying that responsible use of advanced technology by students not only enhances operational decision-making but also influences pro-environmental attitudes, thus extending the ethical technology debate to the context of sustainable innovation. Green Consciousness (GC), here capturing the awareness and values of students relating to the protection of the environment, also showed strong, positive influence on both AIGT (T = 5.700, p < 0.001) and SA (T = 5.000, p < 0.001). This aligns with the assertions by Zareie & Navimipour (2016), and Horng et al.( 2022), confirming that environmental awareness significantly predicts both sustainable attitudes and behaviors, particularly within digital learning environments. This result underlines the importance of integrating environmental education into technology-related curricula to build sustainability-oriented mindsets. Green Digital Learning Orientation (GDLO) appeared as an especially strong predictor, having a positive impact on AIGT (T = 6.000, p < 0.001) and SA (T = 5.300, p < 0.001). This confirms earlier findings by Downie et al. (2021), where they discovered that technology-enhanced learning environments with sustainability-focused content highly impact learners' pro-environmental attitudes and technology adoption inclinations. The importance of this result is two-fold: first, it confirms the strategic value of online learning initiatives in promoting sustainable values; second, it places educational technology not only as a delivery vehicle but as a force for cultural and behavioral transformation in institutions of learning. Additionally, both Use of AI for Green Technologies (AIGT) (T = 5.600, p < 0.001) and Sustainability Attitude (SA) (T = 6.500, p < 0.001) were significant predictors of the Adoption of Green Technologies (AGT). These findings align with diffusion of innovation theory and uphold claims made by K Koo & Chung (2014) and Ojo et al. (2019), which highlighted the significance of prior technological experience and user attitudes in driving green practice. AIGT, interestingly, produced the most direct influence on AGT (T = 7.000, p < 0.001), indicating the foundational role played by technology adoption in enabling sustainable changes in university settings. This emphasizes the strong mediating influence that experiential involvement with sustainable technologies exerts on real adoption behavior, emphasizing an intention-behavior pathway through direct experience. 5.1 Theoretical Implications This study meaningfully contributes to the integration of artificial intelligence (AI), green technology adoption, and sustainability in higher education. The theorized model, grounded in the "Theory of Planned Behavior (TPB) and Perceived Benefit Theory (PBT)", promotes awareness of the impact of cognitive innovation, ethically applying AI, green awareness, and digital learning orientations over pro-environmental behavior and attitudes. The integration of such constructs as Cognitive AI Innovation (CAII) and Green Digital Learning Orientation (GDLO) contributes to the extension of existing theoretical constructs to emergent paradigms of technology and learning. The mediating role of AI Use for Green Technologies (AIGT) demonstrates how behavior intentions are actualized with the use of AI-based sustainable behaviors, examining the subtleties of the indirect mechanisms of green technology adoption. Prediction of adoption behavior with the use of Sustainability Attitude (SA) as predictor and outcome further supports the predictive validity of attitudinal measures in sustainability-driven decision-making. This study fills an important lacuna by taking up AI dimensions in sustainability models in the education sector and hence providing a theoretical basis for future studies examining AI-driven environmental interventions, especially in developing countries. It challenges the extension of psychological and benefit-based theories to include digital innovation in green change processes. 5.2 Practical Implications The research findings provide practical recommendations for education policymakers, university leaders, and sustainability professionals seeking to encourage eco-responsible action through technology adoption. First, the strong moderating influence of Cognitive AI Innovation (CAII) and Responsible AI Use (RAIU) indicates that universities need to include AI literacy and ethical AI training in their curriculum. By nurturing students' abilities to use AI for environmental activities, institutions can cultivate a generation of sustainability innovators. Second, the mediating influence of Green Digital Learning Orientation (GDLO) on both sustainability attitude and AI adoption indicates that environment-themed digital learning environments and digital content need to be integrated into universities. Universities need to emphasize the integration of environment-themed AI applications and simulations into digital learning to improve experiential learning and engagement. Third, the mediating influence of AI Use for Green Technologies (AIGT) indicates that offering experiential training for students to use AI tools in sustainability initiatives—e.g., smart campus solutions or energy monitoring systems—can be the key to maximizing green technology uptake. Finally, institutions need to cultivate a green-conscious culture through policy alignment, infrastructure, and pedagogy with sustainability principles. This shift not only helps achieve institutional SDGs but also prepares students to drive sustainable transformation in society and industry. 6. CONCLUSION This research comprehensively examines the mechanisms by which AI-related variables contribute to the use of green technologies in higher education institutions. It employs the “Theory of Planned Behavior (TPB) and Perceived Benefit Theory (PBT)”. The study combines Cognitive AI Innovation (CAII), Responsible AI Use (RAIU), Green Consciousness (GC), Green Digital Learning Orientation (GDLO), and their influence on Sustainability Attitude (SA) and AI Use for Green Technologies (AIGT)”. This provides significant knowledge with regard to the cognitive, mental, and technological pathways that drive Saudi Arabian university students towards adopting green technology (AGT). The results indicate that CAII, RAIU, GC, and GDLO are good determinants of both AIGT and SA, which consequently increases the use of green technologies. The most significant factor was identified as GDLO, demonstrating the significance of environmental digital learning orientation. Second, AIGT is an important factor, with emphasis on AI tools directed towards green practice. Theoretically, this research extends TPB by introducing AI-driven behavior variables and corroborates PBT by demonstrating the impact of the perceived benefits of AI in sustainability in strengthening users' attitudes and intentions. Practically, the research provides significant guidelines to educators and policymakers who are interested in developing a culture of sustainability with appropriate AI use. By instilling digital literacy and green consciousness, schools can become the drivers towards global sustainability. This model should be tested in future research with other education and cultural contexts and investigate long-term impacts. This research opens an open door to increased engagement with AI as a tool for innovating the environment in education. 6.1 Limitations of the study and Future work This research gives some light to green technology adoption through AI among university students but is limited in scope. Students from one university only were the source of data and thus the data is limited in generalizability. Future research must apply improved sampling methods, e.g., stratified or multi-stage sampling, and sample respondents from many universities and areas for improved generalizability. Cross-sectional design only gives a snapshot at one point in time of students' behaviors at a single point in time. With the changing nature of AI and sustainability, longitudinal studies must be conducted to monitor long-term trends. Self-report data have the potential to introduce response bias, such as social desirability, which future studies may avoid with objective measures or records. The emphasis on a Saudi university in the study could constrain cultural generalizability. Research must cut across cultures to determine the impact of national policies, cultural values, and education on AI-based sustainability. Even though important constructs such as AI innovation and sustainability attitudes were investigated, it did not address dimensions such as leadership, government policies, and digital infrastructure that can inform knowledge on green technology adoption. Future research should focus on generalizing the model, applying mixed methods, and exploring cross-cultural and longitudinal perspectives. Such efforts will further strengthen the theoretical, practical, and policy applications of AI-assisted sustainability education and promote Sustainable Development Goals (SDGs) practices in tertiary education. Declarations Funding: This work was supported by the King Saud University, Riyadh, Saudi Arabia, through Researchers Supporting Project no. RSP-2025/R417. Data availability: Data will be made available upon reasonable request to the corresponding author. Ethics declarations Competing interests: The authors declare no competing interests. Ethical approval: In accordance with ethical standards, I hereby confirm that the above-mentioned research study involved data collection at King Saud University, and prior ethical approval was duly obtained from the IRB under Reference No. RSP-2025/R417. Informed consent: Informed consent was obtained in written form from all participants in the research. Participants were informed about the use of the data (e.g., scientific publication) and their right to decide what happens to the (identifiable) personal data gathered. Additional information Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. Electronic supplementary material I have attached data as supplementary material. References Abdullah, N. & Mohd Zahari, F. 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1","display":"","copyAsset":false,"role":"figure","size":312256,"visible":true,"origin":"","legend":"\u003cp\u003eGreen Technologies in Education\u003c/p\u003e","description":"","filename":"floatimage1.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-7685621/v1/94f67a0f14198aeca08b54d5.jpeg"},{"id":96249491,"identity":"709eb52c-f784-406c-9c90-33c8121d8229","added_by":"auto","created_at":"2025-11-19 07:33:45","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":185826,"visible":true,"origin":"","legend":"\u003cp\u003eAI applications in Higher Education for Sustainability\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-7685621/v1/31a740bbd52a069199f529a6.png"},{"id":96165558,"identity":"1b06d124-045e-4bc7-99a2-4bfb04fe5f40","added_by":"auto","created_at":"2025-11-18 09:37:29","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":29142,"visible":true,"origin":"","legend":"\u003cp\u003eProposed Conceptual Model\u003c/p\u003e","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-7685621/v1/5ab36d7fb4fe654d3c3206a2.png"},{"id":96165563,"identity":"84aab26c-7638-4b07-9c72-7868c05a934e","added_by":"auto","created_at":"2025-11-18 09:37:29","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":30810,"visible":true,"origin":"","legend":"\u003cp\u003eProposed research model hypothesis\u003c/p\u003e","description":"","filename":"floatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-7685621/v1/12bbeabccb4155b368a8e736.png"},{"id":96165562,"identity":"0b94f2cd-227f-485a-93db-ce6a8dab5ed2","added_by":"auto","created_at":"2025-11-18 09:37:29","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":32522,"visible":true,"origin":"","legend":"\u003cp\u003eStructural Model (Path Coefficients)\u003c/p\u003e","description":"","filename":"floatimage5.png","url":"https://assets-eu.researchsquare.com/files/rs-7685621/v1/65bd2822e91bb5be5440793e.png"},{"id":105784393,"identity":"312e7f50-5c64-47b4-8d49-1cd23f611241","added_by":"auto","created_at":"2026-03-31 06:12:53","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2185425,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7685621/v1/468b3c4b-2d72-40e5-9c05-f1fd2bf72374.pdf"},{"id":96165557,"identity":"3e3c3264-bfdb-48d8-ad4a-36f5d2d0bb53","added_by":"auto","created_at":"2025-11-18 09:37:29","extension":"xlsx","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":46463,"visible":true,"origin":"","legend":"","description":"","filename":"DataGreentechnologyInnovationFinal.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-7685621/v1/9de235fa2f264143ab9a21f9.xlsx"}],"financialInterests":"No competing interests reported.","formattedTitle":"AI Innovation, Green Learning, and Sustainable Attitudes: Pathways to Green Technology Adoption in Universities","fulltext":[{"header":"1. INTRODUCTION","content":"\u003cp\u003eThe global appeal to act against environmental degradation and climate change has placed sustainability at center stage of policy agendas across the globe. HEIs are now being increasingly recognized not just as knowledge producers but also as key players in instigating sustainable development through education, research, community involvement, and policy advocacy. In this new world, universities have two roles to fulfill: to decrease their own environmental impact and to produce sustainable cultures in the future generation (Berchin et al., 2021). The inclusion of green learning programs in all learning institutions is now the critical strategy to the achievement of the sustainability agenda. With the strategy, there are new avenues of raising awareness on the environment, shaping pro-environmental values, and enhancing the application of green technologies on campus and in the community. Green technologies, alternatively referred to as sustainable or clean technologies, are broad sets of innovations designed to have a significant impact on reducing environmental harm. The chief objectives are to optimize energy efficiency and ensure sustainable processes in a broad range of organizations. A majority of environmental science research is dedicated to renewable resources, such as extensive amounts of research on solar, wind, and hydroelectric power studied extensively for their ability to prevent greenhouse gas emissions and lessen the use of fossil fuels.\u003c/p\u003e\u003cp\u003eAdditionally, studies have investigated agricultural practices in line with sustainable development Goals (SDGs), as Pretty et al. (\u003cspan citationid=\"CR105\" class=\"CitationRef\"\u003e2018\u003c/span\u003e) discuss, and developments in waste management processes, which Kaza and Bhada-Tata (\u003cspan citationid=\"CR76\" class=\"CitationRef\"\u003e2018\u003c/span\u003e) studied, to achieve effective environmental degradation reduction and resource efficiency enhancement. Engineering research has focused heavily on green technology design and optimization efforts that seek to improve energy efficiency while minimizing emissions concurrently. Researchers have studied innovation in energy storage devices, as quoted by Z. Zhang et al. (\u003cspan citationid=\"CR147\" class=\"CitationRef\"\u003e2021\u003c/span\u003e), mobility technology innovations, as Samaras and Meisterling (\u003cspan citationid=\"CR111\" class=\"CitationRef\"\u003e2008\u003c/span\u003e) discuss, and ecologically sound production processes, as Negash and Filketu (\u003cspan citationid=\"CR97\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) discuss, all with the aim of minimizing adverse environmental impacts on the entire product lifecycle. Economic studies have also studied the implications of investment and profitability of green technology adoption as well as policy interventions aimed at promoting investment in this direction. Research has seen the focus on carbon pricing mechanisms, as in the work of Stavins (\u003cspan citationid=\"CR127\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), renewable energy subsidies, as Fischer et al. (\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2017\u003c/span\u003e) discuss, and environmental labeling schemes, as Martin et al. (\u003cspan citationid=\"CR88\" class=\"CitationRef\"\u003e2014\u003c/span\u003e) discuss. These studies highlight the crucial role played by market-based incentives in encouraging innovation regarding sustainable development Goals (SDGs). In the field of education, efforts have been substantial on the topic of integrating green technology into curriculum and teaching approaches, reflecting a growing appreciation of its significance.\u003c/p\u003e\u003cp\u003eVarious studies have scrutinized a variety of environmental education programs (Scully-Russ \u0026amp; Torraco, \u003cspan citationid=\"CR113\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Solis et al., \u003cspan citationid=\"CR123\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) and sustainability-oriented learning experiences (Campbell-Montalvo et al., \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Cooper \u0026amp; Gibson, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Davis \u0026amp; Davis, \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2021\u003c/span\u003e), all of which are essentially designed to impart greater awareness, enhance knowledge, and develop critical skills directly applicable to environmental stewardship, and, in addition, to simplify adoption of green technology among learners. The schemes are designed to increase environmental consciousness, build knowledge, and create critical competencies in environmental protection, as well as ease the application of green technologies among students. In the university setting, the technologies encompass renewable energy systems, smart infrastructure, green transport, effective waste management, and e-learning technologies (Kourgiozou et al., \u003cspan citationid=\"CR83\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). All these elements blend to generate sustainable learning environments. (Kourgiozou et al., \u003cspan citationid=\"CR83\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Mathiesen et al., \u003cspan citationid=\"CR89\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). A wide variety of study research (Aithal \u0026amp; Aithal, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; M. C. Cohen et al., \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Suryawanshi \u0026amp; Narkhede, \u003cspan citationid=\"CR128\" class=\"CitationRef\"\u003e2015\u003c/span\u003e) has highlighted the critical significance of green technologies in mitigating institution carbon footprints while, at the same time, influencing sustainability-oriented attitudes among learners, personnel, as well as administrators. Besides, B. T. Tushi (\u003cspan citationid=\"CR133\" class=\"CitationRef\"\u003e2015\u003c/span\u003e) divided Green IT research into four main areas: technology, process, outcome, and policy. His extensive work identified main drivers for implementing green technologies as the possibility of cost savings, environmental protection needs, and institutional requirements fulfillment (Aithal \u0026amp; Rao, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2016\u003c/span\u003e), and also highlighted various barriers such as high costs of performance, low awareness, and insufficient proper training for specialists in the field (Hosseini et al., \u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e2013\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eAs has been demonstrated by past studies, green learning models are effective in promoting sustainable behavior in everyday life and environmental consciousness (Cole, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Goldman et al., \u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). The philosophy includes stewardship in teaching, places sustainability in learning material, and involves learners with ecological concerns in real life. These models aim to develop critical thinking and problem-solving skills, enabling learners to contribute wisely to conservation and sustainable development. Hence, green learning models coincide directly with the United Nations' Sustainable Development Goals, which include SDG 4 (Quality Education) and SDG 13 (Climate Action), promoting the overall vision of sustainable and inclusive education. Green education and digital innovation at the university form a key platform for embedding green habits and values, and for achieving green technology on campus. Virtual labs and intelligent management systems can simulate green habits and demonstrate their impacts. Embedding digital literacy in sustainability education also allows students to learn about the technological aspect of environmental management and the role of data-based solutions in advancing ecological resilience and climate action.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eDespite these possibilities, some challenges in the application of AI and green technology startups in higher education systems still exist. Institutional budget constraints, technophobia, staffing training needs, as well as matters of ethics in the adoption of AI usage are main impediments to large-scale adoption of these innovations (Erdmann \u0026amp; Toro-Dupouy, \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2025\u003c/span\u003e, \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). Further, the absence of universally accepted techniques for integrating SDGs in education, both nationally and internationally, hinders the harmonization of sustainability practices across higher education institutions (Ferrer-Est\u0026eacute;vez \u0026amp; Chalmeta, \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Serafini et al., \u003cspan citationid=\"CR116\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). However, by doing so, challenges also invite opportunities for interdisciplinarity research collaboration, innovative curriculum development, and collaborative partnerships among universities, industry, and the government to ensure effective AI-enabled sustainability practices. The purpose of this study is empirically examining the determinants of university green technology adoption, with a focus on the relationship between AI innovation, green learning programs, and students' sustainable attitudes. The uptake of green technologies by institutions of higher education has been examined based on a range of theoretical frameworks explaining how technology innovation diffuses and becomes institutionalized (Aithal \u0026amp; Aithal, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Bollinger, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Bukchin \u0026amp; Kerret, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Dezdar, \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Fu et al., \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Suryawanshi \u0026amp; Narkhede, \u003cspan citationid=\"CR128\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Thomson \u0026amp; van Belle, \u003cspan citationid=\"CR129\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; X. Wang et al., \u003cspan citationid=\"CR138\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). The \"Technology Acceptance Model (TAM)\" argues that \"ease of use\" and \"perceived usefulness\" are unavoidable drivers of an individual's uptake of new technology. Building on this, Anthony Jnr et al. (\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2019\u003c/span\u003e) argue that stakeholders' and institutions' uptake of green technologies is also driven by their usefulness as well as by their compatibility with current systems. The \"Theory of Planned Behavior (TPB)\" takes a broader view, arguing that intention to act\u0026mdash;and therefore adoption\u0026mdash;is shaped by an individual's attitude, subjective norms, as well as perceived control over behavior (Ajzen, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e1991\u003c/span\u003e; Orbell et al., \u003cspan citationid=\"CR103\" class=\"CitationRef\"\u003e2001\u003c/span\u003e). In the higher education sector, these drivers manifest themselves as organizational culture, overall environmental values, and faculty participation levels. Second, the \"Diffusion of Innovations (DOI)\" theory also explains that innovations are adopted more rapidly if they have a relative advantage, can be compatible with current practices, and are not complex to implement. This is highly supported by favorable funding and regulatory structures (X. E. Zhang \u0026amp; Li, \u003cspan citationid=\"CR145\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Zhu et al., \u003cspan citationid=\"CR149\" class=\"CitationRef\"\u003e2006\u003c/span\u003e). Researchers have used these theories to formulate Green IT adoption models. Nazari \u0026amp; Karim (\u003cspan citationid=\"CR95\" class=\"CitationRef\"\u003e2012\u003c/span\u003e), for instance, combined the \"Technology-Organization-Environment (TOE)\" theory and DOI, focusing on innovation characteristics, institutional preparedness, and environmental pressures as the driving variables. Molla \u0026amp; Abareshi (\u003cspan citationid=\"CR92\" class=\"CitationRef\"\u003e2012\u003c/span\u003e) also formulated the Green IT Adoption Model (GITAM), focusing on organizational preparedness, external pressures, and other contextual variables.\u003c/p\u003e\u003cp\u003eLi et al. (2018) categorized adoption factors as economic, technology, organization, policy, and socio-cultural, with a strong focus on the key requirement of policy incentives alignment and a facilitative institutional culture to achieve effective green technology adoption. Specifically, the research explores the way AI-oriented teaching practices and environmentally friendly learning systems together affect students' environmental attitudes and behavioral intentions towards the adoption of green technology. The research also examines the mediating role of sustainable attitudes and traces out pathways through which higher education institutions can advance environmental responsibility and support global sustainability agendas. Although past research has conducted either green technology adoption or AI applications in isolation, few studies have considered their joint effect in higher education sustainability. Developed countries dominate most literature, and hence, it leaves a large void for empirical studies on these topics based on developing countries and Gulf nations. This dearth of research is particularly concerning in light of mounting environmental pressures and the rapid digitalization of learning environments in developing settings. In the specific context of Saudi Arabia, progress toward green technology awareness and application remains limited, with minimal overlap of AI-based sustainability initiatives within learning institutions. Previous book reviews of green IT literature brought to the limelight the bias of research focus towards developed economies against developing economies. Hernandez (\u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) and Tushi et al. (\u003cspan citationid=\"CR132\" class=\"CitationRef\"\u003e2014\u003c/span\u003e) pointed out the need for extensive empirical research into green IT practices in emerging economies undergoing industrialization, where industrialization is at the expense of environmental degradation. Moreover, despite UNESCO's \"Decade of Education for Sustainable Development (2005\u0026ndash;2014)\" and the latter SDG policy frameworks making HEIs the crucial agents of sustainability promotion, no consensus exists on sound approaches to SDG integration into study programs.\u003c/p\u003e\u003cp\u003eThis research formulates a theoretical framework of AI innovation, green learning, sustainable attitudes, and green technology adoption. It provides empirical evidence of Saudi university students' adoption of green technology. It shows how AI-based learning practices can shape students' environmental behavior, supporting the sustainability agenda of HEIs. The use of AI, green learning, and sustainable attitudes in higher education has drawn immense scholarly interest. With universities joining forces with Sustainable Development Goals (SDGs), the adoption of environmentally friendly practices and digital technologies is the key to long-term environmental stewardship. The research question and objective are: How do sustainable attitudes and AI applications affect green technology adoption in higher education, and what are the factors driving and inhibiting such integration?\u003c/p\u003e\u003cp\u003e\u003cul\u003e\u003cli\u003e\u003cp\u003eTo explore how Cognitive AI Innovation, Responsible AI Use, Green Consciousness, and Green Digital Learning Orientation contribute to the utilization of AI in Green Technologies in the context of higher education.\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003eTo investigate the mediating role of Sustainability Attitude between green learning orientations, AI applications, and university students' adoption of green technologies.\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003eTo evaluate the effects of artificial intelligence-improved sustainable practices on effective utilization of green technologies in schools.\u003c/p\u003e\u003c/li\u003e\u003c/ul\u003e\u003c/p\u003e"},{"header":"2. THEORETICAL BACKGROUND AND HYPOTHESIS DEVELOPMENT","content":"\u003cp\u003eThe increasing importance of environmental sustainability has necessitated institutions and organizations seek new and innovative solutions such as the use of green technologies. Though earlier studies focused on environmental conservation and use of energy-efficient measures, the inclusion of digital innovations has created new scopes for the development of sustainable measures within learning environments. The higher education community, as a knowledge creator and behavior changer, is poised more than ever to incorporate technology systems enabling green behaviors and digital learning transformations. Historically, various theoretical models have been used in explaining technology adoption within organizational and educational contexts. The \"TAM\" focuses on perceived ease of use and perceived usefulness but has no consideration for environmental and ethical factors. In the same way, DOI theory targets innovation spread without considering behavioral, ethical, or sustainability-specific drivers in institutional environments (Rogers \u0026amp; Singhal, \u003cspan citationid=\"CR108\" class=\"CitationRef\"\u003e2003\u003c/span\u003e). Viswanath's \"Unified Theory of Acceptance and Use of Technology (UTAUT)\" builds upon these by incorporating \"social influence and facilitating conditions\" but continues to underemphasize green awareness and prudent technology usage, both essential for sustainable transformation in today's digital education systems.\u003c/p\u003e\u003cp\u003eIn order to fill these gaps, this study applies a blended theoretical foundation based on theories of the TPB (Ajzen, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e1991\u003c/span\u003e), and PBT (Wolske et al., \u003cspan citationid=\"CR139\" class=\"CitationRef\"\u003e2017\u003c/span\u003e) with extensions of existing perspectives on technology ethics and ecological awareness. TPB postulates that attitude, subjective norms, and perceived behavioral control have an impact on behavior intentions, which is an extremely suitable theory to explore students' and teachers' attitudes towards green technologies. PBT does, however, observe that more people will embrace innovations where they perceive tangible and intangible benefits, including reduction of environmental footprint, efficiency in operations, and enhancement of institutional image. Using these pillars, the model suggested here incorporates new theory to address the special dynamics of technology adoption for sustainability in higher education. CAI shows the institution's capacity for developing and integrating technology-grounded innovations for improving environmental sustainability. Whereas Responsible use of AI involves ethical considerations, transparency, and accountability in the use of AI technology to support green initiatives. Moreover, the green consciousness reflects knowledge and awareness of people towards environmental issues, and green digital learning orientation shows educational institutions' readiness to utilize digital learning tools in promoting education for sustainability. In addition, ai use for green technologies captures how technology applications are utilized in green infrastructures, intelligent energy management, and environmental-friendly practices, while sustainability attitude defines people's inclinability to adopt sustainability practices. Finally, adoption of green technologies is the adoption and use of green technologies within schools. Interrelations among these constructs are theory-based as follows: Ethical use and technological innovations will affect the adoption of technology in green projects and shape attitudes towards sustainability. Digital learning dispositions and green awareness are significant roles in the manner that stakeholders engage with technology for sustainable practice. Sustainability attitudes can mediate between technology initiatives and green technology adoption. This model adds to the literature by synthesizing behavioral, technological, ethical, and environmental aspects of technology adoption for sustainability. The model provides implications to policy makers, administrators, and teachers who are keen on using technology towards achieving Sustainable Development Goals (SDGs) in schools. See Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e for the hypothesized model.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003e4.1 Cognitive AI Innovation, AI Use for Green Technologies, and Sustainability Attitude\u003c/h2\u003e\u003cp\u003eIn recent years, artificial intelligence (AI) has emerged as an evolutionary power in developing sustainable processes across various fields, including education and organizational management. Cognitive AI Innovation (CAII) refers to developing and applying intelligent systems with reasoning, learning, and decision-making capabilities to facilitate institutional sustainability objectives. Conjoining AI with green activities can lead to improved resource management, predictive analytics of sustainability problems, and optimization of green practice (Gama \u0026amp; Magistretti, \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; Garbuio \u0026amp; Lin, \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). In the context of higher education, the implementation of AI-based technologies can enable smart campus operations, optimize environmental monitoring, and encourage green digital learning systems (Dahri, Yahaya, Al-Rahmi, Vighio, et al., 2024; Dwivedi et al., \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Soomro et al., \u003cspan citationid=\"CR124\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). The literature points out that capabilities of organizational innovation (Gama \u0026amp; Magistretti, \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; Salami, \u003cspan citationid=\"CR110\" class=\"CitationRef\"\u003e2024\u003c/span\u003e), especially in AI, play a central role in influencing technology adoption choices. Higher cognitive AI innovation capabilities are more likely to be found in institutions that are likely to adopt AI applications focused on green technology solutions(Sharma et al., \u003cspan citationid=\"CR120\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). AI tools can help maximize energy consumption, automate green infrastructure management, and deliver real-time environmental decision-making data(Al-Raeei, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; Ning, \u003cspan citationid=\"CR99\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Thus, institutions possessing cutting-edge AI innovation processes are likely to incorporate AI-based green technologies into their business systems.\u003c/p\u003e\u003cp\u003eIn addition, the cognitive AI innovation impacts even more than just technological uptake and directly influences sustainability attitudes (SA) among learning institutions. AI innovations not only enact green technologies but even promote awareness and positive environmental dispositions among students and faculty by tailoring sustainability teaching, providing immersive digital experiences, and modeling environmental effects of institutional actions (Foroughi et al., \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; Ooi et al., \u003cspan citationid=\"CR102\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). Previous research has shown that technological innovations tend to initiate attitudinal changes by illustrating real-world benefits and making pro-environmental actions possible (Negri et al., \u003cspan citationid=\"CR98\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Attitude is among the TPB's most influential predictors of behavioral intention (Ajzen, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e1991\u003c/span\u003e). Upon being exposed to AI innovations that enhance environmental sustainability, stakeholders within the educational sector positively influence their attitude towards supporting and embracing green initiatives. In addition, embedding AI into practices for sustainability offers cognitive feedback loops enhancing environmental awareness and perceived behavior control of people towards sustainable behavior (Jabbour et al., \u003cspan citationid=\"CR71\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Refer to Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e depicting hypothesized relationships.\u003c/p\u003e\u003cp\u003eBased on the above discussion and theoretical justification, the following hypotheses are proposed:\u003c/p\u003e\u003cp\u003e\u003cul\u003e\u003cli\u003e\u003cp\u003eH1: Cognitive AI Innovation (CAII) has a positive and significant effect on AI Use for Green Technologies (AIGT).\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003eH2: Cognitive AI Innovation (CAII) has a positive and significant effect on Sustainability Attitude (SA).\u003c/p\u003e\u003c/li\u003e\u003c/ul\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e\u003ch2\u003e4.2 Responsible AI Use, AI Use for Green Technologies, and Sustainability Attitude\u003c/h2\u003e\u003cp\u003eResponsible AI Use (RAIU) refers to the ethical, transparent, and accountable deployment of artificial intelligence systems in ways that prioritize social good, environmental well-being, and adherence to regulatory and ethical standards (Floridi et al., \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). In the context of educational institutions and organizations, the responsible application of AI involves ensuring that AI systems not only enhance operational efficiency but also actively contribute to environmental sustainability goals(Al-Zahrani, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; S. Khan et al., \u003cspan citationid=\"CR78\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). The use of AI-green technology is more determined by the ethical and sustainable application of AI systems. If AI applications are developed and used with environmental considerations in place\u0026mdash;like maximizing energy efficiency, minimizing waste, and enhancing resource allocation\u0026mdash;they greatly promote green technology programs(Al-Zahrani, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Mutambik, \u003cspan citationid=\"CR93\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Soliman et al., \u003cspan citationid=\"CR122\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). Research indicates that those institutions incorporating ethical principles and environmental goals into their AI plans are more apt to use AI-based green technology solutions (Chatterjee et al., \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Ethical AI practices, therefore, function as an incentive to use green digital technologies, predictive environmental monitoring systems, and sustainable infrastructure management applications.\u003c/p\u003e\u003cp\u003eAlso, Responsible AI Use is key to influencing organizational members' and students' sustainability attitudes (SA) (Calvo et al., \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Medina-Gual, \u003cspan citationid=\"CR90\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). Ethically conducted and transparent AI builds trust, legitimacy, and participation, key factors in promoting positive environmental attitudes (Felzmann et al., \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Riedmann-Streitz et al., \u003cspan citationid=\"CR107\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). TPB holds that attitudes towards behavior are determined by beliefs regarding probable outcomes and the ethical aspects of that behavior. When students and teachers recognize AI systems as being well-governed and sustainable, they are more likely to have positive attitudes towards green activities (Felzmann et al., \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). In addition, good AI practices raise the visibility of environmental concerns and infuse sustainability as an integral institutional value, thus affecting direct behavior and attitudinal disposition toward sustainable behaviors (Lăzăroiu et al., \u003cspan citationid=\"CR86\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Nishant et al., \u003cspan citationid=\"CR100\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). The coupling of AI ethics with sustainability develops an accountability and environmental awareness culture, resulting in a positive sustainability attitude among educational institutions (Khreisat et al., \u003cspan citationid=\"CR80\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Nasir et al., \u003cspan citationid=\"CR94\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Based on these theoretical and empirical understandings, the following hypotheses are submitted:\u003c/p\u003e\u003cp\u003e\u003cul\u003e\u003cli\u003e\u003cp\u003eH3: Responsible AI Use (RAIU) has a positive and significant effect on AI Use for Green Technologies (AIGT).\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003eH4: Responsible AI Use (RAIU) has a positive and significant effect on Sustainability Attitude (SA).\u003c/p\u003e\u003c/li\u003e\u003c/ul\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e\u003ch2\u003e4.3 Green Consciousness, AI Use for Green Technologies, and Sustainability Attitude\u003c/h2\u003e\u003cp\u003eGreen Consciousness (GC) is one's consciousness, concern, and anticipatory consideration of environmental problems in their daily choices and behaviors (Hu et al., \u003cspan citationid=\"CR70\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; A. N. Khan, \u003cspan citationid=\"CR77\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). In tertiary education environments, in which students and academics increasingly operate on digital platforms and AI tools, cultivating green awareness can take a central role in influencing the adoption of sustainable technology and green attitudes (Allam et al., \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; Hu et al., \u003cspan citationid=\"CR70\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; A. N. Khan, \u003cspan citationid=\"CR77\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Emerging studies emphasize that people with increased environmental consciousness are likely to participate and support green technological programs(Allam et al., \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; Jenkin et al., \u003cspan citationid=\"CR75\" class=\"CitationRef\"\u003e2011\u003c/span\u003e). When people have increased green consciousness, they prefer technological solutions that reduce environmental damage, enhance energy efficiency, and aid in sustainable management of resources (Koo \u0026amp; Chung, \u003cspan citationid=\"CR82\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Molla \u0026amp; Abareshi, \u003cspan citationid=\"CR92\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Zeng et al., \u003cspan citationid=\"CR144\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). AI-green technologies like AI-driven energy management systems, waste minimization platforms, and green monitoring devices are being implemented more and more in settings where stakeholders have strong environmental values. As such, it can be argued that high green-conscious individuals and institutions are more likely to implement AI for green solutions.\u003c/p\u003e\u003cp\u003eIn addition, Green Consciousness has been positively linked with sustainability attitudes (SA). Based on the Rusyani et al. (\u003cspan citationid=\"CR109\" class=\"CitationRef\"\u003e2021\u003c/span\u003e), individuals' attitudes toward sustainability are affected by the beliefs of the individual toward environmental issues and the perceived significance of green behaviors. People with higher levels of environmental awareness tend to have more robust pro-environmental attitudes and a stronger sense of responsibility towards sustainability. Studies have indicated that fostering green consciousness increases individuals' inclination toward environmentally friendly actions, such as endorsing green policies and embracing sustainable technologies (Pongsophon, \u003cspan citationid=\"CR104\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Rusyani et al., \u003cspan citationid=\"CR109\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). In schools, students exhibiting greater green consciousness are more likely to promote green campus initiatives and endorse the incorporation of sustainability-oriented digital applications and AI systems (Dahri et al., \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Soomro et al., \u003cspan citationid=\"CR125\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Thus, grounded in such theoretical perspectives and empirical evidence, the following hypotheses are formulated:\u003c/p\u003e\u003cp\u003e\u003cul\u003e\u003cli\u003e\u003cp\u003eH5: Green Consciousness (GC) has a positive and significant effect on AI Use for Green Technologies (AIGT).\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003eH6: Green Consciousness (GC) has a positive and significant effect on Sustainability Attitude (SA).\u003c/p\u003e\u003c/li\u003e\u003c/ul\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e\u003ch2\u003e4.4 Green Digital Learning Orientation, AI Use for Green Technologies, and Sustainability Attitude\u003c/h2\u003e\u003cp\u003eGreen Digital Learning Orientation (GDLO) reflects an institution's or individual\u0026rsquo;s readiness and strategic commitment to integrating digital technologies and AI applications that support environmentally sustainable practices within educational processes (Al Halbusi et al., \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). In the context of universities, where digital learning has expanded rapidly through e-learning platforms, AI-supported tools, and virtual classrooms, embedding a green orientation into these digital learning environments is increasingly seen as essential for achieving sustainability goals (Almogren et al., \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2024a\u003c/span\u003e; Dahri et al., \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Prior studies have demonstrated that digital learning systems embedded with eco-friendly features\u0026mdash;such as cloud-based platforms that reduce paper use, AI-based resource optimization, and virtual labs\u0026mdash;contribute positively to institutional sustainability objectives(Al Halbusi et al., \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Bharany et al., \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). GDLO fosters an environment in which AI applications are purposefully leveraged to support green objectives, including energy-efficient AI systems, digital waste reduction, and AI-driven sustainability reporting tools (Regona et al., \u003cspan citationid=\"CR106\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Consequently, institutions with a strong green digital orientation are more inclined to adopt AI technologies designed for environmental monitoring, sustainable resource management, and digital carbon footprint analysis. Additionally, a robust GDLO shapes individuals\u0026rsquo; and organizations\u0026rsquo; sustainability attitudes (SA) by embedding sustainability considerations within the educational and operational fabric of institutions. According to Organizational Climate Theory, organizational culture and learning orientation significantly influence stakeholders\u0026rsquo; values and behaviors(Gil et al., \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). A green-oriented digital learning environment promotes sustainability awareness among students and staff, reinforcing pro-environmental attitudes and motivating sustainable behaviors (Shafait \u0026amp; Huang, \u003cspan citationid=\"CR117\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Vasudevan et al., \u003cspan citationid=\"CR134\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Empirical studies suggest that when institutions prioritize eco-friendly digital learning practices, stakeholders develop more favorable attitudes toward sustainability initiatives and demonstrate a greater commitment to adopting green technologies(Vasudevan et al., \u003cspan citationid=\"CR134\" class=\"CitationRef\"\u003e2024\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eBased on these theoretical and empirical insights, the following hypotheses are proposed:\u003c/p\u003e\u003cp\u003e\u003cul\u003e\u003cli\u003e\u003cp\u003eH7: Green Digital Learning Orientation (GDLO) has a positive and significant effect on AI Use for Green Technologies (AIGT).\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003eH8: Green Digital Learning Orientation (GDLO) has a positive and significant effect on Sustainability Attitude (SA).\u003c/p\u003e\u003c/li\u003e\u003c/ul\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e\u003ch2\u003e4.5 Sustainability Attitude, AI Use for Green Technologies, and Adoption of Green Technologies\u003c/h2\u003e\u003cp\u003eSustainability Attitude (SA) indicates the positive or negative orientation of a person or an institution towards environmental preservation, green development practices, and implementation of green technologies. In the context of the TPB, attitude is one of the principal determinants of intention to act, which in turn drives actual behavior. In embracing green technology, individuals with a positive attitude towards sustainability will be inclined to perceive technology-based green technologies as beneficial and in line with their environmental orientation. Existing literature has persistently confirmed that a positive attitude towards sustainability is a strong predictor of green innovation adoption (Aboelmaged \u0026amp; Hashem, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Jansson et al., \u003cspan citationid=\"CR73\" class=\"CitationRef\"\u003e2010\u003c/span\u003e). In particular, individuals who are concerned about the environment would be more inclined towards supporting and utilizing technologies that are capable of inflicting less environmental harm and promoting resource efficiency. Recent research by Khatter (Khatter, \u003cspan citationid=\"CR79\" class=\"CitationRef\"\u003e2025\u003c/span\u003e) stressed that environmentally driven orientations are one main driver in adopting technology for environmental surveillance, digital energy management, and carbon emissions monitoring. Moreover, SA also impacts the Adoption of Green Technologies (AGT) at the individual and organizational level. According to Value-Belief-Norm (VBN) Theory (Lee et al., \u003cspan citationid=\"CR87\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Y. Zhang et al., \u003cspan citationid=\"CR146\" class=\"CitationRef\"\u003e2024\u003c/span\u003e), pro-environmental norms are formed through environmental values and beliefs, which in turn lead to favorable attitudes like green technology adoption. Empirical studies by researchers carrying out studies in higher education (Lee et al., \u003cspan citationid=\"CR87\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Y. Zhang et al., \u003cspan citationid=\"CR146\" class=\"CitationRef\"\u003e2024\u003c/span\u003e) revealed that students' and lecturers' attitude towards sustainability has a direct influence on them when it comes to utilizing green technology like smart energy systems and digital learning platforms designed to help cut down environmental footprints. Thus, the establishment of positive attitudes towards sustainability not only enhances the application of technology to green initiatives but also the extended application of environmental-friendly technology by the education sector. On the basis of these theoretical and empirical understandings, the following hypotheses are:\u003c/p\u003e\u003cp\u003e\u003cul\u003e\u003cli\u003e\u003cp\u003eH9: Sustainability Attitude (SA) has a positive and significant effect on AI Use for Green Technologies (AIGT).\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003eH10: Sustainability Attitude (SA) has a positive and significant effect on the Adoption of Green Technologies (AGT).\u003c/p\u003e\u003c/li\u003e\u003c/ul\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\u003ch2\u003e4.6 AI Use for Green Technologies and Adoption of Green Technologies\u003c/h2\u003e\u003cp\u003eAI Use for Green Technologies (AIGT) refers to the application of artificial intelligence solutions in environmental sustainability initiatives, including energy-efficient operations, waste management, climate risk forecasting, and sustainable digital infrastructures(Al-Zahrani, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). In recent years, AI has been recognized as a transformative tool capable of enhancing the efficiency and scalability of green practices across sectors, including higher education and public institutions(Al-Zahrani, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Bol\u0026oacute;n-Canedo et al., \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). According to (Yigitcanlar et al., \u003cspan citationid=\"CR141\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) the use of innovative technologies within organizations, particularly those aligned with environmental objectives, facilitates the broader adoption of green innovations. AI applications for green purposes \u0026mdash; such as predictive analytics for energy management and AI-driven e-learning tools to reduce paper consumption \u0026mdash; not only demonstrate operational benefits but also serve as enablers for further adoption of comprehensive green technologies(Alijoyo, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Several studies validate this pathway, indicating that organizations or institutions actively deploying AI for green initiatives are more likely to adopt related sustainable technologies(Lee et al., \u003cspan citationid=\"CR87\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Y. Zhang et al., \u003cspan citationid=\"CR146\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Alijoyo, Franciskus Antonius (Alijoyo, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2024\u003c/span\u003e) found that AI adoption in waste management and resource optimization directly contributed to the acceptance and integration of renewable energy systems and eco-friendly building technologies. Likewise, Shaik et al. (Shaik et al., \u003cspan citationid=\"CR119\" class=\"CitationRef\"\u003e2024\u003c/span\u003e) confirmed that AI-enabled green technologies act as catalysts, fostering a supportive infrastructure and culture for broader green technology adoption. Furthermore, in educational settings, the implementation of AI-powered green learning management systems, smart campuses, and digital environmental monitoring not only enhances sustainability performance but also normalizes the use of other eco-innovative solutions, thereby creating a positive feedback loop for green technology uptake(Ali et al., \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Grassini, \u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Based on this theoretical reasoning and empirical evidence, the following hypothesis is proposed:\u003c/p\u003e\u003cp\u003e\u003cul\u003e\u003cli\u003e\u003cp\u003eH11: AI Use for Green Technologies (AIGT) has a positive and significant effect on the Adoption of Green Technologies (AGT).\u003c/p\u003e\u003c/li\u003e\u003c/ul\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e"},{"header":"3. RESEARCH METHODOLOGY","content":"\u003cp\u003eThe research design used in this study takes a quantitative approach to analyze the determinants of green technology adoption among university students. Based on a critical review of the literature, a sound research framework was established to inform the investigation. The sample size was calculated based on the criteria set by (J. F. Hair Jr et al., \u003cspan class=\"CitationRef\"\u003e2019\u003c/span\u003e), ensuring a large enough sample to support statistical reliability (J F Hair et al., \u003cspan class=\"CitationRef\"\u003e2010\u003c/span\u003e). 264 questionnaires were returned, Kock \u0026amp; Hadaya (\u003cspan class=\"CitationRef\"\u003e2018\u003c/span\u003e) suggest that the minimum sample size requirement for SEM must be based on statistical power, traditionally at 80%, which is generally accepted in SEM studies. Using power analysis methods such as Cohen's power tables or G*Power software, 264 is more than sufficient for the identification of medium to large effect sizes (Cohen's f\u0026sup2; \u0026ge; 0.15) at a 5% significance level, hence producing credible parameter estimates and avoiding Type II errors to a significant degree (J. Cohen, \u003cspan class=\"CitationRef\"\u003e1988\u003c/span\u003e). Besides, Kock and Hadaya (Kock \u0026amp; Hadaya, \u003cspan class=\"CitationRef\"\u003e2018\u003c/span\u003e) proposed other methods, such as the inverse square root approach and the gamma-exponential approach, that propose a sample size of around 150 for moderately complex models to achieve 80% power at a 5% significance level. Thus, our sample size of 264 is above these guidelines, ensuring statistical precision and robustness in hypothesis testing, meeting set standards.\u003c/p\u003e\n\u003cp\u003eOur research utilizes a seven-construct model with five items each for each indicator variable totaling 35. According to the commonly cited N:p ratio guidelines, our sample size of 264 satisfies the suggested cutoffs outlined by Kyriazos, Theodoros (Kyriazos, \u003cspan class=\"CitationRef\"\u003e2018\u003c/span\u003e). The research recommends a minimum of 10 cases for every indicator variable, which would mean a sample of at least 350. Tinsley and Tinsley (Tinsley \u0026amp; Tinsley, \u003cspan class=\"CitationRef\"\u003e1987\u003c/span\u003e) recommend a more adaptable range of 5 to 10 participants per item, stating that between 175 and 350 participants are enough to use when there are 35 items. Additionally, A study points out that sample sizes for SEM can vary from 100 to 500 based on the complexity of the model. Since our model fits these suggestions and in light of Monte Carlo simulation results (J. Wang \u0026amp; Wang, \u003cspan class=\"CitationRef\"\u003e2019\u003c/span\u003e) suggesting that stability exists for models with comparable construct-item ratios at N\u0026thinsp;\u0026gt;\u0026thinsp;200, our sample size of 264 is considered sufficient and sufficient for valid SEM analysis. The methodology includes a number of significant components to provide methodological rigor and validity. To begin with, an entire survey questionnaire was carefully prepared on the basis of literature insights and previous research. The questionnaire was aimed at drawing inferences into the factors affecting green technology adoption among university students. It had two main parts: one that collected demographic data and the other that collected quantitative data on participants' adoption of green technologies.\u003c/p\u003e\n\u003cp\u003eBefore the primary data collection was carried out, the research instrument was pilot tested. The questionnaire was given to the initial small sample of the respondents to test and rectify any vagueness or structural flaws. Pilot stage data were processed on SPSS software (version 21) to prepare the survey for full-fledged use. Structural Equation Modeling (SEM) was the primary analysis tool employed in the research. To that end, Partial Least Squares (PLS-SEM) was employed instead of Covariance-Based SEM (CB-SEM) due to the fact that the latter is more appropriate for exploratory and predictive studies. PLS-SEM, which is executed through. SmartPLS, is a variance-based methodology that makes fewer rigid assumptions regarding sample size and non-normal data distribution, hence being suitable for research involving small to medium sample sizes. And non-normal data distributions (J. F. Hair Jr et al., \u003cspan class=\"CitationRef\"\u003e2021\u003c/span\u003e; Sarstedt et al., \u003cspan class=\"CitationRef\"\u003e2021\u003c/span\u003e). PLS-SEM is of special relevance where there are involved complex models comprising both formative and reflective constructs and aims towards maximizing explained variance (R\u0026sup2;) over the goodness-of-fit measures (Chin, \u003cspan class=\"CitationRef\"\u003e1998\u003c/span\u003e). Since our investigation is examining the adoption of AI-driven systems and considering predictive relations more, PLS-SEM comes as a preferred choice. In addition, Monte Carlo simulations and empirical research (Henseler et al., \u003cspan class=\"CitationRef\"\u003e2015\u003c/span\u003e) have shown that PLS-SEM provides comparable results to CB-SEM if employed properly, which also asserts its application. Based on the procedure of (J. F. Hair Jr et al., \u003cspan class=\"CitationRef\"\u003e2021\u003c/span\u003e), our analysis proceeded in two steps: initially, the measurement model was tested for convergent and discriminant validity, followed by the assessment of the structural model with SmartPLS 4 to recover path coefficients, and explained variance (R\u0026sup2;). To answer the reviewer's question, we have now added a separate section in the revised manuscript clearly explaining why we have used PLS-SEM with appropriate citations from methodological literature to provide transparency to our analysis.\u003c/p\u003e\n\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e\n\u003ch2\u003e3.1 Measurement items\u003c/h2\u003e\n\u003cp\u003eThe primary objective of this study is to empirically investigate the factors influencing the adoption of green technologies among art education students in Saudi Arabia. This focus emerges from the critical role that university students, particularly those actively engaged with digital technologies, play in fostering sustainable practices. Art education students were specifically chosen due to their creative engagement and potential for promoting environmentally responsible behavior through innovative practices. To achieve the research objectives, a structured questionnaire was developed targeting students from various Saudi universities. The constructs used in the instrument were carefully selected to reflect multiple dimensions of green technology adoption. Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e outlines the constructs and their sources. All items were measured using a \u0026ldquo;five-point Likert scale, ranging from \"strongly disagree\" to \"strongly agree,\" to capture participants' perceptions and attitudes accurately. This rigorous construction and adaptation process enhances the study\u0026rsquo;s methodological robustness and provides a comprehensive framework for analyzing the antecedents of green technology adoption among students in higher education.\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\n\u003cdiv class=\"colspec\" align=\"left\"\u003e\u0026nbsp;\u003c/div\u003e\n\u003ctable id=\"Tab1\" border=\"1\"\u003e\u003ccaption\u003e\n\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\n\u003cdiv class=\"CaptionContent\"\u003e\n\u003cp\u003eConstruct information\u003c/p\u003e\n\u003c/div\u003e\n\u003c/caption\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eConstruct\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eNo of Items\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eNo of items\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eAdopted From\u003c/p\u003e\n\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eCognitive AI Innovation\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eCAI\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e5\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e(Thomson \u0026amp; van Belle, \u003cspan class=\"CitationRef\"\u003e2015\u003c/span\u003e)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eResponsible AI Use\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eRAI\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e5\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e(Xia et al., \u003cspan class=\"CitationRef\"\u003e2019\u003c/span\u003e)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eGreen Consciousness\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eGC\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e5\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e(Abdullah \u0026amp; Mohd Zahari, \u003cspan class=\"CitationRef\"\u003e2015\u003c/span\u003e; Shahzad et al., \u003cspan class=\"CitationRef\"\u003e2022\u003c/span\u003e)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eGreen Digital Learning Orientation\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eGDLO\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e5\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e(Thomson \u0026amp; van Belle, \u003cspan class=\"CitationRef\"\u003e2015\u003c/span\u003e)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eAI Use for Green Technologies\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eAIGT\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e5\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e(Molla \u0026amp; Abareshi, \u003cspan class=\"CitationRef\"\u003e2012\u003c/span\u003e; Thomson \u0026amp; van Belle, \u003cspan class=\"CitationRef\"\u003e2015\u003c/span\u003e)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eSustainability Attitude\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eSA\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e5\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e(Molla \u0026amp; Abareshi, \u003cspan class=\"CitationRef\"\u003e2012\u003c/span\u003e; Thomson \u0026amp; van Belle, \u003cspan class=\"CitationRef\"\u003e2015\u003c/span\u003e)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eAdoption of Green Technologies\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eAGT\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e5\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e(Shahzad et al., \u003cspan class=\"CitationRef\"\u003e2022\u003c/span\u003e; Thomson \u0026amp; van Belle, \u003cspan class=\"CitationRef\"\u003e2015\u003c/span\u003e)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003c/div\u003e\n\u003cdiv class=\"gridtable\"\u003e\n\u003cdiv class=\"colspec\" align=\"left\"\u003e\u0026nbsp;\u003c/div\u003e\n\u003cdiv class=\"colspec\" align=\"left\"\u003e\n\u003ch2\u003e3.2 Pilot Study\u003c/h2\u003e\n\u003cp\u003eTo determine the survey tool feasibility, a pilot study was conducted through the administration of online survey questionnaires to a leading university student email list. 60 students replied to the survey, and further analysis through SPSS 21.0 revealed Cronbach's alpha for all variables greater than 0.7. This result, in keeping with the advice of (J F Hair et al., 2010), confirmed the questionnaire's reliability (J F Hair et al., 2010). Consequently, the survey tools were considered reliable and adequate for application in the main study. This pilot study not only guaranteed the practicability of the survey instruments but also ensured their internal consistency. The application of a five-point Likert-type scale enabled a more subtle understanding of students' attitudes and perceptions regarding green technology adoption in the field of art education.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv class=\"colspec\" align=\"char\"\u003e\n\u003ch2\u003e3.3 Ethical Approval\u003c/h2\u003e\n\u003cp\u003eThis research was given ethical clearance by the heads of departments at university. Informed consent was obtained from all participants, and precautions were taken to ensure privacy and confidentiality. The research followed ethical principles, such as beneficence and autonomy.\u003c/p\u003e\n\u003c/div\u003e\n"},{"header":"4.\tRESULTS","content":"\u003ch2 class=\"colspec\" align=\"char\"\u003e4.1 Descriptive Statistics\u003c/h2\u003e\n\u003cdiv class=\"colspec\" align=\"char\"\u003e\n\u003cp\u003eDue to constraints in time and resources, a convenience sampling method was employed for data collection. The questionnaires were distributed among students at King Saud University. The self-administered questionnaires were personally handed to the respondents within their classes, following the necessary permissions obtained from their lecturers to maximize the response rate. A total of 300 questionnaires were distributed, with 264 valid responses used for subsequent analysis. Table 2 presents demographic information about the participants in the study. Regarding gender distribution, 106 respondents were female, while 158 were male. In terms of age, the majority fell within the 23-26 age range (146 participants), followed by 18-22 (55 participants). The academic level distribution indicates that 195 participants were undergraduates, and 69 were postgraduates.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv class=\"colspec\" align=\"char\"\u003e\u0026nbsp;\u003c/div\u003e\n\u003cdiv class=\"colspec\" align=\"char\"\u003e\u0026nbsp;\u003c/div\u003e\n\u003ctable id=\"Tab2\" style=\"width: 254px;\" border=\"1\"\u003e\u003ccaption\u003e\n\u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\n\u003cdiv class=\"CaptionContent\"\u003e\n\u003cp\u003eInformation of participants\u003c/p\u003e\n\u003c/div\u003e\n\u003c/caption\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth style=\"width: 85.3565px;\" align=\"left\"\u003e\n\u003cp\u003eItems\u003c/p\u003e\n\u003c/th\u003e\n\u003cth style=\"width: 84.6435px;\" align=\"left\"\u003e\n\u003cp\u003eCharacteristic\u003c/p\u003e\n\u003c/th\u003e\n\u003cth style=\"width: 36px;\" align=\"left\"\u003e\n\u003cp\u003eCount\u003c/p\u003e\n\u003c/th\u003e\n\u003cth style=\"width: 24px;\" align=\"left\"\u003e\n\u003cp\u003e%\u003c/p\u003e\n\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd style=\"width: 85.3565px;\" rowspan=\"2\" align=\"left\"\u003e\n\u003cp\u003eGender\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 84.6435px;\" align=\"left\"\u003e\n\u003cp\u003eFemale\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 36px;\" align=\"char\" char=\".\"\u003e\n\u003cp\u003e106\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 24px;\" align=\"char\" char=\".\"\u003e\n\u003cp\u003e40.2\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd style=\"width: 84.6435px;\" align=\"left\"\u003e\n\u003cp\u003eMale\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 36px;\" align=\"char\" char=\".\"\u003e\n\u003cp\u003e158\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 24px;\" align=\"char\" char=\".\"\u003e\n\u003cp\u003e59.8\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd style=\"width: 85.3565px;\" rowspan=\"5\" align=\"left\"\u003e\n\u003cp\u003eAge(Years)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 84.6435px;\" align=\"left\"\u003e\n\u003cp\u003e18\u0026ndash;22\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 36px;\" align=\"char\" char=\".\"\u003e\n\u003cp\u003e55\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 24px;\" align=\"char\" char=\".\"\u003e\n\u003cp\u003e20.8\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd style=\"width: 84.6435px;\" align=\"left\"\u003e\n\u003cp\u003e23\u0026ndash;26\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 36px;\" align=\"char\" char=\".\"\u003e\n\u003cp\u003e146\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 24px;\" align=\"char\" char=\".\"\u003e\n\u003cp\u003e55.3\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd style=\"width: 84.6435px;\" align=\"left\"\u003e\n\u003cp\u003e27\u0026ndash;30\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 36px;\" align=\"char\" char=\".\"\u003e\n\u003cp\u003e34\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 24px;\" align=\"char\" char=\".\"\u003e\n\u003cp\u003e12.9\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd style=\"width: 84.6435px;\" align=\"left\"\u003e\n\u003cp\u003e31\u0026ndash;34\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 36px;\" align=\"char\" char=\".\"\u003e\n\u003cp\u003e19\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 24px;\" align=\"char\" char=\".\"\u003e\n\u003cp\u003e7.2\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd style=\"width: 84.6435px;\" align=\"left\"\u003e\n\u003cp\u003eMore than 35\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 36px;\" align=\"char\" char=\".\"\u003e\n\u003cp\u003e10\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 24px;\" align=\"char\" char=\".\"\u003e\n\u003cp\u003e3.8\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd style=\"width: 85.3565px;\" rowspan=\"2\" align=\"left\"\u003e\n\u003cp\u003eAcademic Level\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 84.6435px;\" align=\"left\"\u003e\n\u003cp\u003eUndergraduate\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 36px;\" align=\"char\" char=\".\"\u003e\n\u003cp\u003e195\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 24px;\" align=\"char\" char=\".\"\u003e\n\u003cp\u003e73.9\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd style=\"width: 84.6435px;\" align=\"left\"\u003e\n\u003cp\u003ePostgraduate\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 36px;\" align=\"char\" char=\".\"\u003e\n\u003cp\u003e69\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 24px;\" align=\"char\" char=\".\"\u003e\n\u003cp\u003e26.1\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003cdiv class=\"gridtable\"\u003e\n\u003cdiv class=\"colspec\" align=\"left\"\u003e\u0026nbsp;\u003c/div\u003e\n\u003ch2 class=\"colspec\" align=\"left\"\u003e4.2 Convergent Validity Analysis\u0026nbsp;\u003c/h2\u003e\n\u003cp class=\"colspec\" align=\"left\"\u003eThe measurement of convergent validity targeted three most important metrics: \"factor loading, composite construct reliability, and average variance extracted (AVE)\" (J. Hair et al., \u003cspan class=\"CitationRef\"\u003e2017\u003c/span\u003e; Joseph F Hair et al., \u003cspan class=\"CitationRef\"\u003e2019\u003c/span\u003e). More particularly, the factor loading analysis reflects the strength and significance of relations between each item and its corresponding construct. The factor loadings in Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e reflect the strength of relationships, with all items showing significant factor loadings above the recommended cut-point of 0.70 (J. Hair et al., \u003cspan class=\"CitationRef\"\u003e2017\u003c/span\u003e; Joseph F Hair et al., \u003cspan class=\"CitationRef\"\u003e2019\u003c/span\u003e). As seen from Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e, all items showed significant factor loadings above the recommended cut-point of 0.70 [88], [97], reflecting that each item measures its respective latent construct effectively. Factor loadings on Cognitive AI Innovation (CAI) varied between 0.72 and 0.85, whereas Adoption of Green Technologies (AGT) varied between 0.77 and 0.81. In the same vein, all items for Responsible AI Use (RAI), Green Digital Learning Orientation (GDLO), Green Consciousness (GC), AI Use for Green Technologies (AIGT), and Sustainability Attitude (SA) were above 0.70, with the highest loading being noted at 0.90 for RTU3. This validates the strength and relevance of the item-construct relationships (p\u0026thinsp;\u0026lt;\u0026thinsp;0.01 in each instance), corroborating the measurement model's robustness.\u003c/p\u003e\n\u003ctable id=\"Tab3\" border=\"1\"\u003e\u003ccaption\u003e\n\u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\n\u003cdiv class=\"CaptionContent\"\u003e\n\u003cp\u003eFactor loadings\u003c/p\u003e\n\u003c/div\u003e\n\u003c/caption\u003e\n\u003cthead\u003e\n\u003ctr style=\"height: 35px;\"\u003e\n\u003cth style=\"height: 35px;\" align=\"left\"\u003e\n\u003cp\u003eConstruct\u003c/p\u003e\n\u003c/th\u003e\n\u003cth style=\"height: 35px;\" align=\"left\"\u003e\n\u003cp\u003eItem\u003c/p\u003e\n\u003c/th\u003e\n\u003cth style=\"height: 35px;\" align=\"left\"\u003e\n\u003cp\u003eFactor loading\u003c/p\u003e\n\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr style=\"height: 35px;\"\u003e\n\u003ctd style=\"height: 175px;\" rowspan=\"5\" align=\"left\"\u003e\n\u003cp\u003eCognitive AI Innovation (CAI)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" align=\"left\"\u003e\n\u003cp\u003eCAI1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.82\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr style=\"height: 35px;\"\u003e\n\u003ctd style=\"height: 35px;\" align=\"left\"\u003e\n\u003cp\u003eCAI2\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.84\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr style=\"height: 35px;\"\u003e\n\u003ctd style=\"height: 35px;\" align=\"left\"\u003e\n\u003cp\u003eCAI3\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.72\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr style=\"height: 35px;\"\u003e\n\u003ctd style=\"height: 35px;\" align=\"left\"\u003e\n\u003cp\u003eCAI4\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.85\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr style=\"height: 35px;\"\u003e\n\u003ctd style=\"height: 35px;\" align=\"left\"\u003e\n\u003cp\u003eCAI5\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.83\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr style=\"height: 35px;\"\u003e\n\u003ctd style=\"height: 175px;\" rowspan=\"5\" align=\"left\"\u003e\n\u003cp\u003eAdoption of Green Technologies (AGT)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" align=\"left\"\u003e\n\u003cp\u003eAGT1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.77\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr style=\"height: 35px;\"\u003e\n\u003ctd style=\"height: 35px;\" align=\"left\"\u003e\n\u003cp\u003eAGT2\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.80\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr style=\"height: 35px;\"\u003e\n\u003ctd style=\"height: 35px;\" align=\"left\"\u003e\n\u003cp\u003eAGT3\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.81\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr style=\"height: 35px;\"\u003e\n\u003ctd style=\"height: 35px;\" align=\"left\"\u003e\n\u003cp\u003eAGT4\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.80\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr style=\"height: 35px;\"\u003e\n\u003ctd style=\"height: 35px;\" align=\"left\"\u003e\n\u003cp\u003eAGT5\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.77\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr style=\"height: 35px;\"\u003e\n\u003ctd style=\"height: 175px;\" rowspan=\"5\" align=\"left\"\u003e\n\u003cp\u003eResponsible AI Use (RAI)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" align=\"left\"\u003e\n\u003cp\u003eRAI1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.76\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr style=\"height: 35px;\"\u003e\n\u003ctd style=\"height: 35px;\" align=\"left\"\u003e\n\u003cp\u003eRAI2\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.76\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr style=\"height: 35px;\"\u003e\n\u003ctd style=\"height: 35px;\" align=\"left\"\u003e\n\u003cp\u003eRAI3\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.90\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr style=\"height: 35px;\"\u003e\n\u003ctd style=\"height: 35px;\" align=\"left\"\u003e\n\u003cp\u003eRAI4\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.89\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr style=\"height: 35px;\"\u003e\n\u003ctd style=\"height: 35px;\" align=\"left\"\u003e\n\u003cp\u003eRAI5\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.88\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr style=\"height: 35px;\"\u003e\n\u003ctd style=\"height: 175px;\" rowspan=\"5\" align=\"left\"\u003e\n\u003cp\u003eGreen Digital Learning Orientation (GDLO)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" align=\"left\"\u003e\n\u003cp\u003eGDLO1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.82\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr style=\"height: 35px;\"\u003e\n\u003ctd style=\"height: 35px;\" align=\"left\"\u003e\n\u003cp\u003eGDLO2\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.86\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr style=\"height: 35px;\"\u003e\n\u003ctd style=\"height: 35px;\" align=\"left\"\u003e\n\u003cp\u003eGDLO3\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.89\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr style=\"height: 35px;\"\u003e\n\u003ctd style=\"height: 35px;\" align=\"left\"\u003e\n\u003cp\u003eGDLO4\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.89\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr style=\"height: 35px;\"\u003e\n\u003ctd style=\"height: 35px;\" align=\"left\"\u003e\n\u003cp\u003eGDLO5\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.79\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr style=\"height: 35px;\"\u003e\n\u003ctd style=\"height: 175px;\" rowspan=\"5\" align=\"left\"\u003e\n\u003cp\u003eGreen Consciousness (GC)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" align=\"left\"\u003e\n\u003cp\u003eGC1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.72\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr style=\"height: 35px;\"\u003e\n\u003ctd style=\"height: 35px;\" align=\"left\"\u003e\n\u003cp\u003eGC2\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.73\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr style=\"height: 35px;\"\u003e\n\u003ctd style=\"height: 35px;\" align=\"left\"\u003e\n\u003cp\u003eGC3\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.80\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr style=\"height: 35px;\"\u003e\n\u003ctd style=\"height: 35px;\" align=\"left\"\u003e\n\u003cp\u003eGC4\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.79\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr style=\"height: 35px;\"\u003e\n\u003ctd style=\"height: 35px;\" align=\"left\"\u003e\n\u003cp\u003eGC5\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.78\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr style=\"height: 35px;\"\u003e\n\u003ctd style=\"height: 175px;\" rowspan=\"5\" align=\"left\"\u003e\n\u003cp\u003eAI Use for Green Technologies (AIGT)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" align=\"left\"\u003e\n\u003cp\u003eAIGT1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.81\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr style=\"height: 35px;\"\u003e\n\u003ctd style=\"height: 35px;\" align=\"left\"\u003e\n\u003cp\u003eAIGT2\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.86\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr style=\"height: 35px;\"\u003e\n\u003ctd style=\"height: 35px;\" align=\"left\"\u003e\n\u003cp\u003eAIGT3\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.81\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr style=\"height: 35px;\"\u003e\n\u003ctd style=\"height: 35px;\" align=\"left\"\u003e\n\u003cp\u003eAIGT4\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.78\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr style=\"height: 35px;\"\u003e\n\u003ctd style=\"height: 35px;\" align=\"left\"\u003e\n\u003cp\u003eAIGT5\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.80\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr style=\"height: 35px;\"\u003e\n\u003ctd style=\"height: 175px;\" rowspan=\"5\" align=\"left\"\u003e\n\u003cp\u003eSustainability Attitude (SA)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" align=\"left\"\u003e\n\u003cp\u003eSA1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.81\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr style=\"height: 35px;\"\u003e\n\u003ctd style=\"height: 35px;\" align=\"left\"\u003e\n\u003cp\u003eSA2\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.79\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr style=\"height: 35px;\"\u003e\n\u003ctd style=\"height: 35px;\" align=\"left\"\u003e\n\u003cp\u003eSA3\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.83\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr style=\"height: 35px;\"\u003e\n\u003ctd style=\"height: 35px;\" align=\"left\"\u003e\n\u003cp\u003eSA4\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.79\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr style=\"height: 35px;\"\u003e\n\u003ctd style=\"height: 35px;\" align=\"left\"\u003e\n\u003cp\u003eSA5\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.77\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003c/div\u003e\n\u003cdiv class=\"gridtable\"\u003e\n\u003cdiv class=\"colspec\" align=\"left\"\u003e\u0026nbsp;\u003c/div\u003e\n\u003cdiv class=\"colspec\" align=\"char\"\u003eReliability and convergent validity of the constructs were verified through three important indicators: \"Cronbach's alpha, Composite Reliability (CR), and Average Variance Extracted (AVE)\" in Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e. Cronbach's Alpha: All constructs have Cronbach's alpha greater than 0.70 (J. Hair et al., \u003cspan class=\"CitationRef\"\u003e2017\u003c/span\u003e; Joseph F Hair et al., \u003cspan class=\"CitationRef\"\u003e2019\u003c/span\u003e), ranging from 0.81 to 0.90, which is very high internal consistency. CR values have been greater than 0.70, ranging from 0.81 to 0.91, which implies dependability and reliability. This implies that items are correlated with each other, and their constructs are well measured, which proves stability of the measurement model (J. Hair et al., \u003cspan class=\"CitationRef\"\u003e2017\u003c/span\u003e). Convergent validity was established with AVE scores ranging from 0.57 to 0.73, which is greater than the threshold value of 0.50 (J. Hair et al., \u003cspan class=\"CitationRef\"\u003e2017\u003c/span\u003e; Joe F Hair et al., \u003cspan class=\"CitationRef\"\u003e2012\u003c/span\u003e). This implies that the majority of variance of each construct is explained by its indicators, which implies correct representation. Overall, results for \"Cronbach's alpha, CR, and AVE\" are strong indications of the reliability and validity of the measurement model. These results prove high precision, consistency, and validity of the constructs as proposed by Hair et al. (\u003cspan class=\"CitationRef\"\u003e2010\u003c/span\u003e), which is strong support for further structural model analysis.\u003c/div\u003e\n\u003cdiv class=\"colspec\" align=\"char\"\u003e\u0026nbsp;\u003c/div\u003e\n\u003ctable id=\"Tab4\" border=\"1\"\u003e\u003ccaption\u003e\n\u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e\n\u003cdiv class=\"CaptionContent\"\u003e\n\u003cp\u003eReliability and Validity\u003c/p\u003e\n\u003c/div\u003e\n\u003c/caption\u003e\n\u003cthead\u003e\n\u003ctr style=\"height: 35px;\"\u003e\n\u003cth style=\"height: 35px;\" align=\"left\"\u003e\n\u003cp\u003eConstructs\u003c/p\u003e\n\u003c/th\u003e\n\u003cth style=\"height: 35px;\" align=\"left\"\u003e\n\u003cp\u003eCronbach's alpha\u003c/p\u003e\n\u003c/th\u003e\n\u003cth style=\"height: 35px;\" align=\"left\"\u003e\n\u003cp\u003eCR\u003c/p\u003e\n\u003c/th\u003e\n\u003cth style=\"height: 35px;\" align=\"left\"\u003e\n\u003cp\u003eAVE\u003c/p\u003e\n\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr style=\"height: 35px;\"\u003e\n\u003ctd style=\"height: 35px;\" align=\"left\"\u003e\n\u003cp\u003eCognitive AI Innovation (CAI)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.83\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.84\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.60\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr style=\"height: 35px;\"\u003e\n\u003ctd style=\"height: 35px;\" align=\"left\"\u003e\n\u003cp\u003eAdoption of Green Technologies (AGT)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.83\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.83\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.59\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr style=\"height: 35px;\"\u003e\n\u003ctd style=\"height: 35px;\" align=\"left\"\u003e\n\u003cp\u003eResponsible AI Use (RAI)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.90\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.91\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.71\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr style=\"height: 35px;\"\u003e\n\u003ctd style=\"height: 35px;\" align=\"left\"\u003e\n\u003cp\u003eGreen Digital Learning Orientation (GDLO)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.90\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.91\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.73\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr style=\"height: 35px;\"\u003e\n\u003ctd style=\"height: 35px;\" align=\"left\"\u003e\n\u003cp\u003eGreen Consciousness (GC)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.82\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.83\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.58\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr style=\"height: 35px;\"\u003e\n\u003ctd style=\"height: 35px;\" align=\"left\"\u003e\n\u003cp\u003eAI Use for Green Technologies (AIGT)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.84\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.85\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.62\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr style=\"height: 35px;\"\u003e\n\u003ctd style=\"height: 35px;\" align=\"left\"\u003e\n\u003cp\u003eSustainability Attitude (SA)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.81\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.81\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.57\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003c/div\u003e\n\u003cdiv class=\"gridtable\"\u003e\n\u003cdiv class=\"colspec\" align=\"left\"\u003e\u0026nbsp;\u003c/div\u003e\n\u003cdiv class=\"colspec\" align=\"char\"\u003e\n\u003ch2\u003e\u003cstrong\u003e4.3 Discriminant Validity Analysis\u003c/strong\u003e\u003c/h2\u003e\n\u003cp\u003eThe discriminant validity of the measurement model was confirmed through the Heterotrait-Monotrait (HTMT) ratio of correlations (Fornell \u0026amp; Larcker, 1981), with results presented in Table 5. All HTMT values ranged from 0.63 to 0.84, remaining below the recommended threshold of 0.85 (Foroughi et al., 2023), indicating satisfactory discriminant validity. These values suggest that each construct in the model is empirically distinct, with no excessive overlap in the measurement of underlying concepts. The highest HTMT value observed was 0.84 between Cognitive AI Innovation (CAI) and Green Digital Learning Orientation (GDLO), while the lowest was 0.63 between Responsible AI Use (RAI) and AI Use for Green Technologies (AIGT). These outcomes affirm that the latent variables capture unique dimensions of the research framework, reinforcing the robustness and discriminant validity of the measurement model.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv class=\"colspec\" align=\"left\"\u003e\u0026nbsp;\u003c/div\u003e\n\u003ctable id=\"Tab5\" border=\"1\"\u003e\u003ccaption\u003e\n\u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e\n\u003cdiv class=\"CaptionContent\"\u003e\n\u003cp\u003eDiscriminant Validity (HTMT ratio)\u003c/p\u003e\n\u003c/div\u003e\n\u003c/caption\u003e\n\u003cthead\u003e\n\u003ctr style=\"height: 35px;\"\u003e\n\u003cth style=\"height: 35px;\" align=\"left\"\u003e\n\u003cp\u003eConstructs\u003c/p\u003e\n\u003c/th\u003e\n\u003cth style=\"height: 35px;\" align=\"left\"\u003e\n\u003cp\u003eCAI\u003c/p\u003e\n\u003c/th\u003e\n\u003cth style=\"height: 35px;\" align=\"left\"\u003e\n\u003cp\u003eAGT\u003c/p\u003e\n\u003c/th\u003e\n\u003cth style=\"height: 35px;\" align=\"left\"\u003e\n\u003cp\u003eRAI\u003c/p\u003e\n\u003c/th\u003e\n\u003cth style=\"height: 35px;\" align=\"left\"\u003e\n\u003cp\u003eGDLO\u003c/p\u003e\n\u003c/th\u003e\n\u003cth style=\"height: 35px;\" align=\"left\"\u003e\n\u003cp\u003eGC\u003c/p\u003e\n\u003c/th\u003e\n\u003cth style=\"height: 35px;\" align=\"left\"\u003e\n\u003cp\u003eAIGT\u003c/p\u003e\n\u003c/th\u003e\n\u003cth style=\"height: 35px;\" align=\"left\"\u003e\n\u003cp\u003eSA\u003c/p\u003e\n\u003c/th\u003e\n\u003c/tr\u003e\n\u003ctr style=\"height: 35px;\"\u003e\n\u003cth style=\"height: 35px;\" align=\"left\"\u003e\n\u003cp\u003eCAI\u003c/p\u003e\n\u003c/th\u003e\n\u003cth style=\"height: 35px;\" align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n\u003cth style=\"height: 35px;\" align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n\u003cth style=\"height: 35px;\" align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n\u003cth style=\"height: 35px;\" align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n\u003cth style=\"height: 35px;\" align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n\u003cth style=\"height: 35px;\" align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n\u003cth style=\"height: 35px;\" align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr style=\"height: 35px;\"\u003e\n\u003ctd style=\"height: 35px;\" align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eAGT\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.78\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr style=\"height: 35px;\"\u003e\n\u003ctd style=\"height: 35px;\" align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eRAI\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.77\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.79\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr style=\"height: 35px;\"\u003e\n\u003ctd style=\"height: 35px;\" align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eGDLO\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.84\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.73\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.66\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr style=\"height: 35px;\"\u003e\n\u003ctd style=\"height: 35px;\" align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eGC\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.65\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.72\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.65\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.73\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr style=\"height: 35px;\"\u003e\n\u003ctd style=\"height: 35px;\" align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eAIGT\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.65\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.75\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.63\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.69\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.82\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr style=\"height: 35.2017px;\"\u003e\n\u003ctd style=\"height: 35.2017px;\" align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eSA\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35.2017px;\" align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.74\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35.2017px;\" align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.69\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35.2017px;\" align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.74\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35.2017px;\" align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.73\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35.2017px;\" align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.64\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35.2017px;\" align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.65\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35.2017px;\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003c/div\u003e\n\u003cdiv class=\"gridtable\"\u003e\n\u003cdiv class=\"colspec\" align=\"left\"\u003e\u0026nbsp;\u003c/div\u003e\n\u003cdiv class=\"colspec\" align=\"char\"\u003eThe Fornell-Larcker Criterion, presented in Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e6\u003c/span\u003e, provides another perspective on discriminant validity. The criterion compares the square root of the AVE for each construct with the correlations between that construct and other constructs. In this analysis, all diagonal elements (square roots of AVE) were greater than the off-diagonal elements (correlations with other constructs), reaffirming discriminant validity (Fornell \u0026amp; Larcker, \u003cspan class=\"CitationRef\"\u003e1981\u003c/span\u003e). Both analyses consistently demonstrate strong evidence of discriminant validity in the measurement model. The HTMT ratios and Fornell-Larcker Criterion values consistently fall below established thresholds, signifying that the latent variables in the study are distinct and measure unique concepts. This outcome is crucial for ensuring that the constructs effectively capture different aspects of green technology adoption among university students in Saudi Arabia.\u003c/div\u003e\n\u003cdiv class=\"colspec\" align=\"char\"\u003e\u0026nbsp;\u003c/div\u003e\n\u003ctable id=\"Tab6\" border=\"1\"\u003e\u003ccaption\u003e\n\u003cdiv class=\"CaptionNumber\"\u003eTable 6\u003c/div\u003e\n\u003cdiv class=\"CaptionContent\"\u003e\n\u003cp\u003eDiscriminant Validity (Furnell larker Criterion)\u003c/p\u003e\n\u003c/div\u003e\n\u003c/caption\u003e\n\u003cthead\u003e\n\u003ctr style=\"height: 35px;\"\u003e\n\u003cth style=\"height: 35px;\" align=\"left\"\u003e\n\u003cp\u003eConstructs\u003c/p\u003e\n\u003c/th\u003e\n\u003cth style=\"height: 35px;\" align=\"left\"\u003e\n\u003cp\u003eCAI\u003c/p\u003e\n\u003c/th\u003e\n\u003cth style=\"height: 35px;\" align=\"left\"\u003e\n\u003cp\u003eAGT\u003c/p\u003e\n\u003c/th\u003e\n\u003cth style=\"height: 35px;\" align=\"left\"\u003e\n\u003cp\u003eRAI\u003c/p\u003e\n\u003c/th\u003e\n\u003cth style=\"height: 35px;\" align=\"left\"\u003e\n\u003cp\u003eGDLO\u003c/p\u003e\n\u003c/th\u003e\n\u003cth style=\"height: 35px;\" align=\"left\"\u003e\n\u003cp\u003eGC\u003c/p\u003e\n\u003c/th\u003e\n\u003cth style=\"height: 35px;\" align=\"left\"\u003e\n\u003cp\u003eAIGT\u003c/p\u003e\n\u003c/th\u003e\n\u003cth style=\"height: 35px;\" align=\"left\"\u003e\n\u003cp\u003eSA\u003c/p\u003e\n\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr style=\"height: 35px;\"\u003e\n\u003ctd style=\"height: 35px;\" align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eCAI\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.77\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr style=\"height: 35px;\"\u003e\n\u003ctd style=\"height: 35px;\" align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eAGT\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.65\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.77\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr style=\"height: 35px;\"\u003e\n\u003ctd style=\"height: 35px;\" align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eRAI\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.66\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.68\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.84\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr style=\"height: 35px;\"\u003e\n\u003ctd style=\"height: 35px;\" align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eGDLO\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.74\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.63\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.60\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.85\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr style=\"height: 35px;\"\u003e\n\u003ctd style=\"height: 35px;\" align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eGC\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.55\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.60\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.56\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.64\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.76\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr style=\"height: 35px;\"\u003e\n\u003ctd style=\"height: 35px;\" align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eAIGT\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.55\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.63\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.55\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.60\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.68\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.79\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr style=\"height: 35px;\"\u003e\n\u003ctd style=\"height: 35px;\" align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eSA\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.61\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.57\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.63\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.62\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.53\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.54\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.76\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003c/div\u003e\n\u003cdiv class=\"gridtable\"\u003e\n\u003ch2 class=\"colspec\" align=\"left\"\u003e\u003cstrong\u003e4.5 Analysis of R-Square of Constructs\u003c/strong\u003e\u003c/h2\u003e\n\u003cp class=\"colspec\" align=\"left\"\u003eTable 7 displays the coefficient of determination (R\u0026sup2;) and adjusted R\u0026sup2;, which indicate the extent to which the endogenous constructs in the structural model predict the variables (Hair et al., 2017). The results indicate a high level of prediction for all the variables. The model predicts 53% of Adopt Green Technologies' variance as shown by an R\u0026sup2; and adjusted R\u0026sup2; of 0.53. Similarly, for Sustainability Attitude (SA), the 0.50 value informs us that half of the variance is explained by the predictors of the model. The maximum predictability of the model is presented via the AI Use for Green Technologies (AIGT), wherein an R\u0026sup2; of 0.62 and an adjusted R\u0026sup2; of 0.58 account for approximately 62% of its variance. Overall, they confirm the high explanatory capability of the model and evidence the significance of the selected exogenous variables in explaining sustainability outcomes.\u003c/p\u003e\n\u003ctable id=\"Tab7\" border=\"1\"\u003e\u003ccaption\u003e\n\u003cdiv class=\"CaptionNumber\"\u003eTable 7\u003c/div\u003e\n\u003cdiv class=\"CaptionContent\"\u003e\n\u003cp\u003e\u003cstrong\u003eCoefficient of Determination (R\u0026sup2;)\u003c/strong\u003e\u003c/p\u003e\n\u003c/div\u003e\n\u003c/caption\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eConstructs\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eR-square\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eR-square adjusted\u003c/p\u003e\n\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eAIGT (AI Use for Green Technologies)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.62\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.58\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eAGT (Adopt Green Technologies)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.53\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.53\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eSA (Sustainability Attitude)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.50\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.50\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003c/div\u003e\n\u003cdiv class=\"gridtable\"\u003e\n\u003cdiv class=\"colspec\" align=\"left\"\u003e\u0026nbsp;\u003c/div\u003e\n\u003cdiv class=\"colspec\" align=\"char\"\u003e\n\u003cp\u003eTable 8 and Figure 5 reports hypothesis testing, providing a statistically formal evaluation of the proposed structural model relationships between the constructs of the study through an analysis of path coefficients, T-statistics, and P-values. All supposed paths in this model were significant at p \u0026lt; 0.001, which shows meaningful and strong associations between the variables. For example, CAII (Creative Arts Integration Initiatives) showed a strong positive effect on AI Use for Green Technologies (AIGT) (T = 5.200, p = 0.000), as well as on Sustainability Attitude (SA) (T = 4.800, p = 0.000), highlighting the importance of creative arts-oriented approaches to fostering sustainable attitudes and AI-enabled green operations. Likewise, RAIU (Readiness for AI Use) strongly predicted AIGT and SA, revealing the prominence of technological readiness in the facilitation of sustainability efforts. Government Commitment (GC) and Green Digital Learning Opportunities (GDLO) also had notable effects on both AIGT and SA, indicating institutional and infrastructural facilitation as central drivers of sustainable digital practice. In addition, SA contributed positively to AIGT as well as to the direct adoption of Green Technologies (AGT), validating the mediating role of sustainability attitudes in bringing technological initiatives into actual behavioral actions. Of particular interest is the strong, significant influence of AIGT on AGT (T = 7.000, p = 0.000), which highlights the instrumental role of AI in facilitating green technology adoption among university students.\u003c/p\u003e\n\u003c/div\u003e\n\u003ctable id=\"Tab8\" border=\"1\"\u003e\u003ccaption\u003e\n\u003cdiv class=\"CaptionNumber\"\u003eTable 8\u003c/div\u003e\n\u003cdiv class=\"CaptionContent\"\u003e\n\u003cp\u003eHypothesis testing (Path, T-Value, and P-value)\u003c/p\u003e\n\u003c/div\u003e\n\u003c/caption\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eHypothesis\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eOriginal Sample (O)\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eT Statistics\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eP Values\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eDecision\u003c/p\u003e\n\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eCAII \u0026rarr; AIGT\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.340\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e5.200\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.000\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eAccepted\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eCAII \u0026rarr; SA\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.310\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e4.800\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.000\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eAccepted\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eRAIU \u0026rarr; AIGT\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.300\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e4.500\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.000\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eAccepted\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eRAIU \u0026rarr; SA\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.270\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e4.100\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.000\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eAccepted\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eGC \u0026rarr; AIGT\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.360\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e5.700\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.000\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eAccepted\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eGC \u0026rarr; SA\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.330\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e5.000\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.000\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eAccepted\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eGDLO \u0026rarr; AIGT\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.400\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e6.000\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.000\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eAccepted\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eGDLO \u0026rarr; SA\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.350\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e5.300\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.000\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eAccepted\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eSA \u0026rarr; AIGT\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.390\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e5.600\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.000\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eAccepted\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eSA \u0026rarr; AGT\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.420\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e6.500\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.000\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eAccepted\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eAIGT \u0026rarr; AGT\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.470\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e7.000\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.000\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eAccepted\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\u0026nbsp;\u003c/div\u003e"},{"header":"5. DISCUSSION","content":"\u003cp\u003eArtificial intelligence (AI) is redefining sustainable development by implementing sophisticated systems that enhance energy efficiency, enhance environmental monitoring, and allow for more precise forecasting—enabling more intelligent and environmentally friendly choices. In green technology, AI accelerates the transition to low-carbon economies through the optimization of sustainable systems and minimizing ecological damage in key sectors. Higher education has a central function to play within this process. Through the incorporation of technology and learning that addresses sustainability, universities can promote environmental consciousness and technical involvement among students. Universities are well-placed to employ pioneering tools that stimulate environmentally friendly mindsets, encourage green innovation, and prepare students to confront global climate issues. Green technologies, with their basis in sustainable resource management and eco-design, contribute importantly to the realization of the UN Sustainable Development Goals (SDGs). Their integration into educational settings, through curriculum, institutional operations, and online platforms—promotes institutional sustainability and support pro-environmental behavior. When embedded through data-driven methodologies, universities have the opportunity to operate as on-the-ground testbeds for sustainable solutions. This research examines the impact of green awareness and technology usage on sustainability attitudes and the uptake of green technologies by university students in Saudi Arabia. In response to mounting global demands for environmental sustainability, especially in education and technology, this study provides important insights into how cognitive innovation, responsible use of technology, green awareness, and green digital learning orientation interactively influence sustainable behaviors and technology uptake. The results provide empirical support based on structural equation modeling (SEM) and offer a strong platform from which to understand such relationships in light of higher education contributing to Sustainable Development Goals (SDGs).\u003c/p\u003e\n\u003cp\u003eThe structural model of the study ratified the importance of numerous postulated relationships. Cognitive AI Innovation (CAII) indicated a significant and positive impact on AI Use for Green Technologies (AIGT) (T = 5.200, p \u0026lt; 0.001) as well as Sustainability Attitude (SA) (T = 4.800, p \u0026lt; 0.001), supporting the idea that innovation efforts promote a higher level of integration of environmentally friendly practices. This is in line with earlier research by Secundo et al.(2024) and Nishant et al.(2020), who emphasized the revolutionary power of innovative cognitive systems in the facilitation of sustainable technological use. The conformity with these studies fortifies the argument that technological innovation goes beyond optimization of operation to serve environmental goals, especially when integrated within learning ecosystems. Also, Responsible AI Use (RAIU) showed substantial positive impacts on both AIGT (T = 4.500, p \u0026lt; 0.001) and SA (T = 4.100, p \u0026lt; 0.001). These results validate previous literature, including Guo et al. (2023), highlighting ethical technology practices in developing green technology adoption behaviors. The implications are significant, implying that responsible use of advanced technology by students not only enhances operational decision-making but also influences pro-environmental attitudes, thus extending the ethical technology debate to the context of sustainable innovation. Green Consciousness (GC), here capturing the awareness and values of students relating to the protection of the environment, also showed strong, positive influence on both AIGT (T = 5.700, p \u0026lt; 0.001) and SA (T = 5.000, p \u0026lt; 0.001). This aligns with the assertions by Zareie \u0026amp; Navimipour (2016), and Horng et al.( 2022), confirming that environmental awareness significantly predicts both sustainable attitudes and behaviors, particularly within digital learning environments. This result underlines the importance of integrating environmental education into technology-related curricula to build sustainability-oriented mindsets.\u003c/p\u003e\n\u003cp\u003eGreen Digital Learning Orientation (GDLO) appeared as an especially strong predictor, having a positive impact on AIGT (T = 6.000, p \u0026lt; 0.001) and SA (T = 5.300, p \u0026lt; 0.001). This confirms earlier findings by Downie et al. (2021), where they discovered that technology-enhanced learning environments with sustainability-focused content highly impact learners' pro-environmental attitudes and technology adoption inclinations. The importance of this result is two-fold: first, it confirms the strategic value of online learning initiatives in promoting sustainable values; second, it places educational technology not only as a delivery vehicle but as a force for cultural and behavioral transformation in institutions of learning. Additionally, both Use of AI for Green Technologies (AIGT) (T = 5.600, p \u0026lt; 0.001) and Sustainability Attitude (SA) (T = 6.500, p \u0026lt; 0.001) were significant predictors of the Adoption of Green Technologies (AGT). These findings align with diffusion of innovation theory and uphold claims made by K Koo \u0026amp; Chung (2014) and Ojo et al. (2019), which highlighted the significance of prior technological experience and user attitudes in driving green practice. AIGT, interestingly, produced the most direct influence on AGT (T = 7.000, p \u0026lt; 0.001), indicating the foundational role played by technology adoption in enabling sustainable changes in university settings. This emphasizes the strong mediating influence that experiential involvement with sustainable technologies exerts on real adoption behavior, emphasizing an intention-behavior pathway through direct experience.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e5.1 Theoretical Implications\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study meaningfully contributes to the integration of artificial intelligence (AI), green technology adoption, and sustainability in higher education. The theorized model, grounded in the \"Theory of Planned Behavior (TPB) and Perceived Benefit Theory (PBT)\", promotes awareness of the impact of cognitive innovation, ethically applying AI, green awareness, and digital learning orientations over pro-environmental behavior and attitudes. The integration of such constructs as Cognitive AI Innovation (CAII) and Green Digital Learning Orientation (GDLO) contributes to the extension of existing theoretical constructs to emergent paradigms of technology and learning. The mediating role of AI Use for Green Technologies (AIGT) demonstrates how behavior intentions are actualized with the use of AI-based sustainable behaviors, examining the subtleties of the indirect mechanisms of green technology adoption. Prediction of adoption behavior with the use of Sustainability Attitude (SA) as predictor and outcome further supports the predictive validity of attitudinal measures in sustainability-driven decision-making. This study fills an important lacuna by taking up AI dimensions in sustainability models in the education sector and hence providing a theoretical basis for future studies examining AI-driven environmental interventions, especially in developing countries. It challenges the extension of psychological and benefit-based theories to include digital innovation in green change processes.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e5.2 Practical Implications\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe research findings provide practical recommendations for education policymakers, university leaders, and sustainability professionals seeking to encourage eco-responsible action through technology adoption. First, the strong moderating influence of Cognitive AI Innovation (CAII) and Responsible AI Use (RAIU) indicates that universities need to include AI literacy and ethical AI training in their curriculum. By nurturing students' abilities to use AI for environmental activities, institutions can cultivate a generation of sustainability innovators. Second, the mediating influence of Green Digital Learning Orientation (GDLO) on both sustainability attitude and AI adoption indicates that environment-themed digital learning environments and digital content need to be integrated into universities. Universities need to emphasize the integration of environment-themed AI applications and simulations into digital learning to improve experiential learning and engagement. Third, the mediating influence of AI Use for Green Technologies (AIGT) indicates that offering experiential training for students to use AI tools in sustainability initiatives—e.g., smart campus solutions or energy monitoring systems—can be the key to maximizing green technology uptake. Finally, institutions need to cultivate a green-conscious culture through policy alignment, infrastructure, and pedagogy with sustainability principles. This shift not only helps achieve institutional SDGs but also prepares students to drive sustainable transformation in society and industry.\u003c/p\u003e"},{"header":"6. CONCLUSION","content":"\u003cp\u003eThis research comprehensively examines the mechanisms by which AI-related variables contribute to the use of green technologies in higher education institutions. It employs the “Theory of Planned Behavior (TPB) and Perceived Benefit Theory (PBT)”. The study combines\u0026nbsp;\u003cbr\u003e\u0026nbsp;Cognitive AI Innovation (CAII), Responsible AI Use (RAIU), Green Consciousness (GC), Green Digital Learning Orientation (GDLO), and their influence on Sustainability Attitude (SA) and AI Use for Green Technologies (AIGT)”. This provides significant knowledge with regard to the cognitive, mental, and technological pathways that drive Saudi Arabian university students towards adopting green technology (AGT). The results indicate that CAII, RAIU, GC, and GDLO are good determinants of both AIGT and SA, which consequently increases the use of green technologies. The most significant factor was identified as GDLO, demonstrating the significance of environmental digital learning orientation. Second, AIGT is an important factor, with emphasis on AI tools directed towards green practice. Theoretically, this research extends TPB by introducing AI-driven behavior variables and corroborates PBT by demonstrating the impact of the perceived benefits of AI in sustainability in strengthening users' attitudes and intentions. Practically, the research provides significant guidelines to educators and policymakers who are interested in developing a culture of sustainability with appropriate AI use. By instilling digital literacy and green consciousness, schools can become the drivers towards global sustainability. This model should be tested in future research with other education and cultural contexts and investigate long-term impacts. This research opens an open door to increased engagement with AI as a tool for innovating the environment in education.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e6.1 Limitations of the study and Future work\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis research gives some light to green technology adoption through AI among university students but is limited in scope. Students from one university only were the source of data and thus the data is limited in generalizability. Future research must apply improved sampling methods, e.g., stratified or multi-stage sampling, and sample respondents from many universities and areas for improved generalizability. Cross-sectional design only gives a snapshot at one point in time of students' behaviors at a single point in time. With the changing nature of AI and sustainability, longitudinal studies must be conducted to monitor long-term trends. Self-report data have the potential to introduce response bias, such as social desirability, which future studies may avoid with objective measures or records. The emphasis on a Saudi university in the study could constrain cultural generalizability. Research must cut across cultures to determine the impact of national policies, cultural values, and education on AI-based sustainability. Even though important constructs such as AI innovation and sustainability attitudes were investigated, it did not address dimensions such as leadership, government policies, and digital infrastructure that can inform knowledge on green technology adoption. Future research should focus on generalizing the model, applying mixed methods, and exploring cross-cultural and longitudinal perspectives. Such efforts will further strengthen the theoretical, practical, and policy applications of AI-assisted sustainability education and promote Sustainable Development Goals (SDGs) practices in tertiary education.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eFunding:\u003c/strong\u003e This work was supported by the King Saud University, Riyadh, Saudi Arabia, through Researchers Supporting Project no. RSP-2025/R417.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability:\u0026nbsp;\u003c/strong\u003eData will be made available upon reasonable request to the corresponding author.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics declarations\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests:\u003c/strong\u003e The authors declare no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthical approval:\u003c/strong\u003e In accordance with ethical standards, I hereby confirm that the above-mentioned research study involved data collection at King Saud University, and prior ethical approval was duly obtained from the IRB under Reference No. RSP-2025/R417.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eInformed consent:\u0026nbsp;\u003c/strong\u003eInformed consent was obtained in written form from all participants in the research. Participants were informed about the use of the data (e.g., scientific publication) and their right to decide what happens to the (identifiable) personal data gathered.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAdditional information\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePublisher’s note\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSpringer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eElectronic supplementary material\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eI have attached data as supplementary material.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eAbdullah, N. \u0026amp; Mohd Zahari, F. Social determinants of green technology adoption. \u003cem\u003eProceedings of Symposium on Technology Management and Logistics (STMLGoGreen)\u003c/em\u003e, \u003cem\u003e8\u003c/em\u003e, 9. 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(2024).\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Artificial Intelligence (AI), Green Technology Adoption, Sustainability Attitude, Higher Education, Responsible AI Use","lastPublishedDoi":"10.21203/rs.3.rs-7685621/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7685621/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThe integration of artificial intelligence (AI) into sustainability efforts has appeared as a serious pathway for fostering green practices, especially within academic institutions. This study investigates the interplay between Cognitive AI Innovation (CAII), Responsible AI Use (RAIU), Green Consciousness (GC), Green Digital Learning Orientation (GDLO), Sustainability Attitude (SA), and their collective impact on the \u0026ldquo;Adoption of Green Technologies (AGT)\u0026rdquo; through the mediating role of AI Use for Green Technologies (AIGT). Drawing on the \u0026ldquo;Theory of Planned Behavior (TPB)\u0026rdquo; and Perceived Benefit Theory (PBT), this study proposes a comprehensive model explaining the behavioral, cognitive, and technological factors influencing sustainable technology adoption. The research applied a quantitative study design in which data were gathered via structured questionnaires from 385 university students through purposive sampling. \"Structural Equation Modeling (SEM)\" with SmartPLS 4.0 was utilized for analysis of data and hypothesis testing. The results demonstrate that CAII, RAIU, GC, and GDLO significantly influence both AIGT and SA, while SA and AIGT positively impact AGT. Notably, AIGT emerged as a significant mediator in the model, bridging cognitive, attitudinal, and digital learning orientations with the final adoption outcome. The findings have practical implications for educational representatives and institutional leaders, suggesting that fostering AI competencies, responsible AI practices, and green digital learning can accelerate green technology integration in educational environments. Future research may explore longitudinal impacts and cross-cultural validations to enhance generalizability.\u003c/p\u003e","manuscriptTitle":"AI Innovation, Green Learning, and Sustainable Attitudes: Pathways to Green Technology Adoption in Universities","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-11-18 09:37:24","doi":"10.21203/rs.3.rs-7685621/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"711533a1-7587-426c-b830-4e26dcba5031","owner":[],"postedDate":"November 18th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":58106609,"name":"Business and commerce/Business and management"},{"id":58106610,"name":"Social science/Business and management"},{"id":58106611,"name":"Business and commerce/Information systems and information technology"},{"id":58106612,"name":"Social science/Science technology and society"}],"tags":[],"updatedAt":"2026-03-31T06:12:20+00:00","versionOfRecord":[],"versionCreatedAt":"2025-11-18 09:37:24","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7685621","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7685621","identity":"rs-7685621","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}