Factors Determining the Adoption of Artificial Intelligence in Higher Education: Insights from Accounting and Finance Education in Iraq

Factors Determining the Adoption of Artificial Intelligence in Higher Education: Insights from Accounting and Finance Education in Iraq | 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 Research Article Factors Determining the Adoption of Artificial Intelligence in Higher Education: Insights from Accounting and Finance Education in Iraq Sajead Mowafaq Alshdaifat, Huthaifa Al-Hazaima, Mushtaq Yousif Alhasnawi, and 3 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6435868/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 This study aims to investigate the factors affecting AI technology adoption (AIT) among accounting and finance educators in Iraqi universities, focusing on digital knowledge, perceived risk, technological readiness and institutional support. The study uses PLS-SEM to test the hypotheses after measuring model validation. Cross-sectional data were collected through online survey, which gathered 446 valid responses from 667 participants. Descriptive and inferential statistics were applied to analyse the data. The results show that increased digital knowledge enhances AI adoption, perceived quality and perceived value. Facilitating conditions affect perceived quality and value but did not directly affect AI adoption. Perceived risk affects AI adoption and boosts perceived quality and value. Technological readiness supports AI adoption and perceived value but had no effect on perceived quality. Mediation analysis shows that perceived risk although hinders adoption indirectly promotes it by affecting quality and value perceptions. Moreover, improved digital literacy enhances AI benefits perceptions and thus adoption. The findings suggest that AI adoption can be increased through digital knowledge and technological readiness, while institutions should address perceived risks and invest in digital literacy programs. Institutional support alone is not enough for successful AI adoption, so other factors should be considered to ensure effective implementation. These findings can guide policymakers and universities to create a more supportive environment for AI technology integration. AI adoption digital knowledge perceived risk technological readiness perceived quality perceived value accounting and finance education Figures Figure 1 1. Introduction Bringing AI technologies into Higher Education Institutions (HEIs) creates room to transform the process of teaching and learning mould management activities [ 1 ]. As awareness at large of the transformative power of AI has grown, adoption rates for those tools within higher education institutions have been uneven [ 2 ]. While some universities have got these new technologies up and running seamlessly with their own processes, others still do not know how to fit them. Automation learning, personalized tests and administration-related optimization are all ways in which AI has been common and successful at HEIs [ 3 ]. The adoption of AI in higher education institutions (HEIs) is driven by multiple factors. Successful integration relies on digital knowledge and awareness of emerging technologies. Interestingly, the higher an institution's digital maturity, the more complex rather than easier the AI adoption process becomes [ 4 ]. Additionally, technological readiness plays a crucial role, reflecting an institution's preparedness and willingness to support AI integration. However, concerns related to data privacy, security, and potential disruptions to traditional educational practices can create resistance, even within AI-ready institutions. Addressing these challenges is essential to ensuring a smooth and effective transition to AI-driven education [ 3 ]. Equally importantly, it is the development of enabling factors such as infrastructure, institutional, support and accepted culture (of technology) that will ultimately determine the success of AI and (beyond) integration [ 5 ]. Moreover, these factors form an ecosystem, in which AI technologies can flourish and provide tangible value. These perceptions of stakeholder’s administrators, faculty and students impact their own perceptions of the value and quality of AI solutions and play a significant role in the adoption of AI solutions [ 6 ]. The success of their adoption will also have greater success rate when AI technologies are used as tools that enhance educational accomplishments and institutional efficiency and are trusted for quality [ 7 ]. The purpose of this study is to explore the complex interplay of key determinants that include digital knowledge, technology readiness, perceived risk, facilitating conditions, perceived value and perceived quality, and their combined effect on AI adoption in Higher Education Institutions. The findings of this research will be compiled in a report, establishing a set of guidelines based on our understanding of the present AI landscape, drivers of AI adoption, as well as existing barriers that need to be addressed in order to fully exploit the potential of AI in higher education. This study advances the current understanding of the topic by proposing an integrated model incorporating the technological, organizational, and individual level drivers of AI adoption in higher education. Although some studies tested individual drivers of AI adoption, very few studies provide an overall look into how these factors relate to one another for Higher Education Institutions (HEIs). Additionally, by investigating the enablers and barriers to the integration of AI, this study provides actionable recommendations for policymakers and institutional leadership. These findings can help shape strategies that can allow and drive the adoption of AI and therefore help institutions cross the hurdle of technological transformation whilst in a constantly dynamically changing educational environment. 2. Literature review and hypotheses development The adoption mechanism of AI into Higher Education Institutions (HEIs), specifically within the context of Accounting and Finance education, is influenced by a convergence of individual, organizational, and environmental dynamics. This research builds upon four prevalent technology adoption theories, which include the Technology Acceptance Model (TAM), the Technology-Organization-Environment (TOE) Framework, the Diffusion of Innovation (DOI) Theory, and the Unified Theory of Acceptance and Use of Technology (UTAUT). This article will explore these different frameworks and develop each to articulate the university academics AI adoption decision-making. The Technology Acceptance Model (TAM) [ 8 ], describes the adoption of technology as arising from two factors, namely, Perceived Usefulness (PU) and Perceived Ease of Use (PEOU). In the domain of information technology education, AI-driven applications such as social media data analysis tools and virality calculators should be considered not only advantageous but also straightforward to integrate into academic workflows. Education gradually adopts AI when teachers regard AI as beneficial and able to facilitate teaching, research and decision-making and user-friendly. This model highlights how perceived value and quality are key determinants of AI adoption attitudes. A further perspective offered by the Technology-Organization-Environment (TOE) framework [ 9 ], is that technology adoption is not just affected by factors internal to an organization, but also broad institutional and environmental factors. TOE classifies these factors into three dimensions: technological (capabilities of AI, technical compatibility in financial systems), organizational (institutional support, leadership vision), and environmental (industry pressures, governmental policies). External pressures, for example, IFRS or GAAP compliance, need to be met in the field of education for accounting and finance, serving as mechanisms for the adoption of AI in education, along with the growing demand for finance graduates with AI literacy (which enables graduates to hit the ground running) all contribute to driving AI adoption in academia. This model highlights that AI adoption is not only an individual decision, but one that is heavily determined by institutional readiness and regulatory forces. The Diffusion of Innovation (DOI) theory [ 10 ], illustrates how the adoption of AI among academics is spread by considering some vital attributes like relative advantage, compatibility, complexity, trialability, and observability. AI adoption by accounting and finance instructors will depend on how AI tools improve efficiencies, decision-making and adherence to industry standards. Adoption might become difficult if AI is viewed as something complex or dangerous. Understanding this theory offers useful perspectives on the factors of knowledge availability, perceived risks, and expected benefits that influence adoption decisions, and how they contribute to the identification of desirable behavior changes. The Unified Theory of Acceptance and Use of Technology (UTAUT) [ 11 ], expands on TAM to include four constructs: performance expectancy, effort expectancy, social influence, and facilitating conditions. The adoption of AI in accounting and finance education is shaped not just by perceptions of usefulness, but by social pressures, industry expectations (e.g., CPA, ACCA certification requirements), and institutional supports (e.g., AI training workshops, digital infrastructure). Supportive Institutional Environment Inadequate resources and training are strongly correlated with AI adoption among educators. 2.1 Digital Knowledge, Perceived Value, Perceived Quality, and AI Technology Adoption According to the Technology-Organization-Environment (TOE) framework developed by [ 9 ], integrated technological, organizational, and environmental factors influence digital transformation by digital knowledge to adopt AI technology successfully. The framework demonstrates how digital knowledge functions as a fundamental enabler that provides individuals and institutions with the required competencies to efficiently utilize and manage new AI technologies. Digital knowledge transforms information creation and storage together with access methods while transforming academic research frameworks and strongly impacting social progress. Higher education institutions (HEIs) should implement digital technologies for better educational quality since the digital era is competitive [ 12 ]. The process requires HEIs to develop learning environments that match students’ digital competency to handle digital transformation difficulties [ 13 ]. Enhancing educational approaches through digital knowledge becomes essential because it builds students’ technological skills and prepares them for careers [ 14 ]. The research conducted by [ 15 ], showed that digital integration has elevated its importance as an essential element in accounting education. Scientific investigations demonstrate that the implementation of digital knowledge leads to higher perceived value. The introduction of digital service innovations presents substantial value enhancements to customers, according to [ 16 ], while [ 17 ] discovered that LearnSmart and similar interactive educational materials improve learners' perceived value and academic outcomes. Digital knowledge represents an important quality perception factor because research demonstrates its impact on customers' quality judgments throughout different industrial sectors. [ 18 ] determined that digital transformation leads to improved perceived quality among small and medium-sized enterprises (SMEs), and [ 19 ] explained that digital learning systems boost knowledge workers’ understanding of company-based career advancement resources. The adoption of AI technology in electronic manufacturing firms receives assistance from digital knowledge, which was confirmed in research by [ 20 ]. Higher education that includes digital knowledge is expected to promote AI technology adoption. Based on the context of higher education in Iraq, the authors developed the following research hypotheses: H1a: Digital knowledge is positively related to perceived value. H1b: Digital knowledge is positively related to perceived quality. H1c: Digital knowledge is positively related to AI technology adoption. 2.2 Technology Readiness, Perceived Value, Perceived Quality, and AI Technology Adoption The Technology Readiness Index (TRI) developed by [ 21 ], organized technology adoption behavior into four basic elements, which refer to optimism, innovativeness, discomfort, and insecurity. Technology adoption becomes more challenging when individuals feel discomfort and insecurity because their positive views toward innovation and optimism are deficient. Users’ adoption of AI technology depends on their perceived value and perceived quality, which technology readiness strongly impacts within this framework. The Global Competitiveness Index reveals crucial details about different countries through their technology readiness and higher education sub-indices because technological and educational advancement rates differ in modern global environments [ 22 – 24 ]. E-learning initiative implementation in higher education depends heavily on technology readiness because it serves as a fundamental digital integration factor [ 25 ]. The evaluation consists of four major aspects, which include students, teachers, technology, and environmental influences on learning. [ 26 ] also describe technology readiness as four foundational dimensions based on [ 21 ] research, which affect individual and institutional adoption and utilization of modern technology. Multiple empirical investigations demonstrate that technology readiness has a positive effect on perceived value in numerous fields. Research by [ 27 ] indicates technology readiness positively affects mobile internet services because it strengthens the value assessment of all their essential features from utility through hedonic and unique cognitive and economic dimensions. [ 28 ] studied the hospitality industry to discover optimism and innovativeness as vital value perception drivers that outweigh the negative impact of insecurity and the lack of significant influence from discomfort. Technology readiness has a positive influence related to perceived quality evaluations since airline passengers demonstrate better assessments of self-service technology when they display optimistic and innovative traits but negative perceptions when experiencing insecurity or discomfort [ 29 ]. Technology readiness is an essential factor in AI technology adoption despite its effects on perceived quality and value. The adoption of AI technology in accounting and auditing benefits from positive technology readiness, according to researchers who studied both topics [ 30 , 31 ]. The Vietnamese accounting and auditing sector exhibited no significant relationship between technology readiness and AI adoption, according to the research conducted by [ 32 ]. Thus, researchers should examine such associations in varied settings. The research establishes the following assumptions about how technology readiness affects perceived value, perceived quality, and AI technology adoption: H2a: Technology readiness is positively related to influences perceived value. H2b: Technology readiness is positively related to perceived quality. H2c: Technology readiness is positively related to AI technology adoption. 2.3 Perceived Risk, Perceived Value, Perceived Quality, and AI Technology Adoption The Expectation-Confirmation Theory (ECT) [ 33 ], suggests that AI technology adoption strengthens when users' perceived value surpasses their expectations, but high perceived risks create negative confirmation, which lessens adoption potential. Higher education institutions studied the perceived risks of Software-as-a-Service (SaaS) through the development of an integrated risk management framework, according to [ 34 ]. Student risk perception development through higher education displays clear connections because a single environmental course training enhances both student awareness and sensitivity to these matters [ 35 ]. Consumer decision-making depends heavily on perceived risk since this fundamental element consists of financial, functional, physical, temporal, psychological, and social dimensions that diminish perceived value [ 36 ]. Student evaluations of products or services decrease because of risks experienced during the buying process, according to research findings in regional wine purchasing [337]. The perceived risk, especially financial performance and physical risk, negatively affects consumer quality judgments about private-label products in retail chains. Retailers should reduce these effects by addressing particular risk dimensions as [ 38 ] demonstrated and improving their e-service quality. Following this, [ 39 ] built consumer loyalty and created a positive brand perception. The growing use of artificial intelligence technology has made perceived risk a major adoption challenge in terms of AI technology adoption. Users in financial services refrain from accepting AI technology because of perceived risk, but endorsements from respected figures and perceived security measures can help decrease this effect [ 40 ]. HEIs face challenges because students avoid using generative AI tools for assessments, mainly due to risk-related concerns, according to research by [ 41 ]. Multiple research studies show that perceived risk creates both negative influences toward perceived value and perceived quality and proves to be a significant barrier to AI technology adoption. Therefore, this study develops the following hypotheses following these discussions: H3a: Perceived risk is negatively related to perceived value. H3b: Perceived risk is negatively related to perceived quality. H3c: Perceived risk is negatively related to AI technology adoption. 2.4 Facilitating Conditions, Perceived Value, Perceived Quality, and AI Technology Adoption The Unified Theory of Acceptance and Use of Technology (UTAUT) [ 11 ], identifies facilitating conditions as fundamental adopter determinants because they include both outer resources and institutional backing together with technological foundations, which create opportunities or create barriers for user interactions with new technologies. The adoption of new technologies significantly depends on facilitating conditions as both organizations and individuals make their decisions for technological adoption. Mobile learning systems implementation by HEIs is heavily influenced by facilitating conditions, while mobile financial service usage willingness among Kenyan users depends on these conditions [ 42 , 43 ]. HEIs apply to facilitate conditions such as resource availability, training options, and technical support to improve learning processes and student engagement [ 44 ]. The essential facilitating condition of faculty supervision combined with support in university extension programs drives significant improvement in farmers' willingness toward both learning activities and new behavioral adoption [ 45 ]. Previous studies about perceived value produced conflicting results related to facilitating conditions. Research by [ 44 ], demonstrates how strong facilitating conditions shape e-human resource management usage and value creation through complex relationships; however, [ 47 ] proved that these conditions have no substantial effect on electronic document management system acceptance. Research shows that facilitating conditions do increase user experience and play a direct role in shaping perceived value, yet the specific measurements of these concepts may show some variations across studies. The effectiveness of virtual communities of practice receives positive influence from facilitating conditions while these conditions maintain a direct connection to perceived ease of use in mobile-assisted language learning, according to [ 48 , 49 ]. The assessment of technology quality by users heavily depends on perceived usefulness along with ease of use explained by facilitating conditions [ 48 ]. The adoption of AI technology throughout different domains substantially depends on the establishment of facilitating conditions. Research has shown that facilitating conditions determine how professionals in healthcare will accept AI systems for specific professional duties [ 54 ]. The agricultural sector shows a direct relationship between facilitating conditions and farmers who choose AI-based solutions to perform sustainable farming [ 51 ]. The research shows that facilitating conditions enhance students' adoption of generative AI tools in the context of higher education [ 41 ]. This research establishes the following hypotheses based on the previous study findings: H4a: Facilitating conditions are positively related to perceived value. H4b: Facilitating conditions are positively related to perceived quality. H4c: Facilitating conditions are positively related to AI technology adoption. 2.5 Perceived Value and AI Technology Adoption Perceived Value Theory (PVT) [ 52 ], emphasizes that individuals base their decisions on balancing the perceived benefits against the costs since perceived value becomes essential for adopting AI technologies in higher education. The perceived value in the higher education field proves essential for shaping student satisfaction regarding their HEIs [ 53 , 54 ]. The study by [ 55 ] proves that functional emotional and social values directly impact the purchase willingness for knowledge-based products, thus demonstrating perceived value's extensive role in decision-making processes. Therefore, based on the evaluation process of accounting firm managers' perceived value performed on their new employees by [ 56 ], it involves both quality and costs. Recent studies show how perceived value has become a fundamental aspect during the rising adoption of AI technology within higher education systems. Faculty members and educators utilize AI technology for three purposes: to assist teaching functions, develop educational methods, and provide advice for shaping future education policies [ 57 ]. Students judge the value of generative AI tools like ChatGPT in a way that stands as the leading factor determining their readiness to adopt these technologies [ 58 , 59 ]. The authors establish the following research hypothesis under consideration: H5: Perceived value is positively related to AI technology adoption. 2.6 Perceived Quality and AI Technology Adoption The study of AI adoption reveals that perceived usefulness, along with perceived ease of use and trust, serves as the fundamental antecedent in the literature, according to [ 60 ], using TAM and UTAUT as theoretical foundations. Students judge higher education quality from various standpoints. Students evaluate their service satisfaction and quality through the lens of instructional approaches, according to [ 53 , 61 , 62 ]. [ 15 ] examined through perceived quality how accounting employers assess graduate technical skills and measure their effectiveness after joining the workforce. AI integration has exposed two fundamental aspects of perceived ease of use and perceived usefulness that influence the connection between technological readiness and AI adoption [ 30 ]. The interactive aspects of perceived quality control how users feel comfortable relying on voice assistants in their systems [ 63 ]. The adoption of AI for auditing shows evidence that optimism affects this process through enhanced audit quality [ 64 ]. The research confirms that HEIs should prioritize perceived quality because it serves as a key driver for AI adoption during this period. A research hypothesis state: H6 Perceived quality is positively related to AI technology adoption. 2.7 The Mediating Role of Perceived Value PVT demonstrates how users determine their adoption decisions through a balance of perceived benefits versus perceived costs, where perceived value enables the relationship between digital knowledge, technology readiness, perceived risk, and facilitating conditions and AI technology adoption [ 52 , 65 ]. Digital knowledge combined with technology readiness at higher levels creates greater perceived value per research published by [ 16 ], in the field of marketing and [ 17 ] in educational technology, which results in increased AI technology adoption [ 58 ]. Research shows that sufficient levels of technology readiness create positive effects on perceived value, which enhances the adoption of AI technology [ 59 ]. The adoption barriers caused by perceived risk create lower perceived value because it decreases readiness to use AI technology [ 37 ]. At the same time, facilitating conditions increase perceived value, which drives AI technology adoption [ 46 ]. The research develops the following hypotheses based on these findings to examine how perceived value mediates between influencing factors and AI technology adoption. H7: Perceived value mediates the relationship between, digital knowledge, technology readiness, perceived risk, facilitating conditions and AI technology adoption. 2.8 The Mediating Role of Perceived Quality Users’ adoption decisions in AI technology systems find support from the TAM [ 8 ], and the PVT [ 52 ] through perceived quality as the intermediary factor. Users base their AI adoption intention on their perception of its usefulness and ease of use because perceived quality acts as a transitional bridge that turns digital knowledge and technology readiness with perceived risk and facilitating conditions into optimistic AI perceptions, which drive AI adoption. When users sense enough perceived quality, they will adopt AI technology after balancing the costs of perceived risk against its perceived value, according to PVT. Multiple variables, such as digital knowledge and technology readiness, together with perceived risk and facilitating conditions, determine individual and organizational decisions regarding AI adoption in the adoption process. The assessment of perceived quality acts as a key intermediary element that connects various determinants to AI adoption. AI systems deliver superior perceived quality when users combine high digital literacy with advanced readiness toward technology, according to the findings of [ 18 , 19 , 30 , 31 , 63 , 64 ] thus building enhanced trust and increasing AI deployment feasibility across every industry sector. The perception of risk creates negative impacts on quality ratings, thus prompting organizations to turn down new opportunities [ 38 , 40 ]. The adoption of AI is accelerated by strong facilitating conditions, which also enhance the perceived quality outlook [ 48 , 49 ]. Thus, the research hypotheses were proposed as follows: H8: Perceived quality mediates the relationship between, digital knowledge, technology readiness, perceived risk, facilitating conditions and AI technology adoption. Methodology Assessment and test of the hypotheses were carried out using a variance-based partial least square approach (PLS-SEM) in the SmartPLS 4 software followed by structural modeling. It was determined that PLS-SEM is a useful multivariate data analysis technique because it can analyze a complex model [ 66 ]. An electronic questionnaire was used to obtain responses, as it reduces costs and facilitates the response of participants at a time and convenience convenient for them. In addition, it permits it to be accessible to locations far from the researcher's location, which allows to collection of more data. The non-random sample was based on the convenience sample. A total of 667 questionnaires were submitted online, and participants were asked to participate in this survey. Cross-sectional data were collected using an adapted questionnaire, which was analyzed using descriptive and inferential statistics. These respondents included 667 accounting and finance educators at Iraqi universities, and 446 responses were obtained, or a response rate of %67 percent. The data collection period lasted more than six months due to the poor response at Iraqi universities [ 67 ]. The G*Power program was used to determine the required sample size, with a minimum test power of 0.80. The “10-fold rule” method [ 68 ] was followed, which stipulates that the minimum sample size requirement is 269 for a 5 percent significance level. Hence, a sample size of 446 is considered appropriate. Data were collected through an online questionnaire, which included 33 items divided into 6 variables. All latent variables were measured using items measured on a five-point Likert scale. Measurement of Variables The instrument consists of two parts: demographic questions and study items. The demographic section includes questions for respondent analysis, covering age, educational level, occupational background, and work experience duration. The research variables' measurement items were sourced from previous studies. Specifically, the AI adoption items were adapted from [ 69 ], while technology readiness items were derived from [ 30 ]. Additionally, perceived value and perceived risk were adapted from [ 70 , 71 ]. In contrast, the items for facilitating conditions, perceived quality, and knowledge of digital were adapted from [ 72 , 73 ] (see Table 1 ). The questionnaire underwent expert evaluation and refinement by five specialists, along with three academic managers responsible for quality assurance within the research context. The pilot study confirmed the internal consistency of all constructs, with Cronbach’s alpha exceeding 0.7, indicating high reliability. For linguistic consistency, a back-translation method was employed: two participants translated the survey into Arabic, followed by a reverse translation into English to ensure cross-cultural accuracy. Common method bias Research tests conducted based on Harman’s single-factor no signs of Common Method Bias contamination in the study data. At 18.673% < 40% according to Harman’s single-factor test the explained variance was below the threshold thus indicating the absence of CMB [ 74 ]. The investigated research showed no signs of common method bias (CMB) following the FC test because the variance inflation factor (VIF) variables ranged from 1.292 to 1.926 which remained under 3.33 [ 75 ]. Results Measurement Model Result Six latent constructs were examined through the measurement model to establish reliability and validity of AI Adoption, Technology Readiness, Perceived Value, Perceived Risk, Facilitating Conditions and Perceived Quality. Multiple items make up each construct which is measured through factor loadings (FL), composite reliability (CR) and average variance extracted (AVE). The research shows that all construct measurements achieve high reliability based on their CR values which exceed 0.70 thus establishing internal consistency. The AVE values exceed 0.50 throughout which demonstrates good convergent validity. The factor loadings for separate items show a strong indicator reliability between 0.717 and 0.890. The research confirms that the measurement approach succeeds in properly capturing theoretical components of AI adoption practices and associated factors that exist in academic institutions. Table 1 Measurement Model Items FL CR AVE AI adoption [ 69 ] 1. I am very familiar with the concept and applications of AI. 0.782 0.875 0.653 2. I frequently use AI applications in my accounting learning activities. 0.798 3. I believe AI can greatly support me fulfil my accounting learning 0.821 4. The key benefits of using AI in accounting education offset the drawbacks. 0.880 5. I am satisfied with the adoption of AI in accounting curricula. 0.875 Technology Readiness [ 30 ] 1. New technologies contribute to a better quality of life. 0.857 0.742 0.568 2. Technology gives me more freedom of mobility. 0.824 3. Technology gives people more control over their daily lives. 0.772 4. Technology makes me more productive in my personal life. 0.751 5. Other people come to me for advice on new technologies. 0.828 Perceived Value [ 70 ] 1. Compared to the fee I would need to pay, the AI product offers value for money 0.816 0.785 0.615 2. Compared to the effort I would need to put in, the AI product is beneficial to me 0.837 3. Compared to the time I would need to spend; the AI product is worthwhile to me 0.833 4. Overall, the AI product delivers good value 0.743 Perceived Risk [ 71 ] 1. If I decide to use AI technology in my academic work, I would be concerned that its use may not be a wise decision. 0.771 0.715 0.725 2. Adopting AI technology could result in significant financial losses for me or my institution. 0.873 3. If I decide to use AI technology in my academic work, I would be concerned that I might not obtain the expected value from it. 0.859 4. Using AI technology could lead to an inefficient use of my time. 0.797 5. Adopting AI technology could cause significant time losses in my other academic activities. 0.859 6. Given the demands of my schedule, adopting AI technology concerns me because it could impose additional time pressures that I do not need. 0.781 Facilitating Conditions [ 76 ] 1. Institutional policies encourage the use of AI technology in academic work. 0.890 0.743 0.611 2. Legal protections are available for using AI technology in academic activities. 0.762 3. Assistance is available for support with AI technology. * 0.749 4. Specialized training is available to me regarding the use of AI technology in academia. 0.799 5. Institutional training programs for AI technology are accessible to me. 0.831 6. Overall, the use of AI technology in academia is well-supported. 0.805 Perceived Quality [ 72 ] 1. Excellent overall quality of studies/teaching 0.717 0.754 0.625 2. Excellent overall quality of the institution 0.815 Knowledge of Digital [ 73 ] 1 I can use a computer and basic digital applications efficiently. 0.842 0.898 0.710 2 I can search for information online and evaluate its reliability. 0.821 3 I can easily use digital programs and applications related to my academic/professional field. 0.771 4 I have a good understanding of cybersecurity and data protection practices. 0.728 5 I quickly adapt to new digital tools and applications in my work or study environment. 0.881 Discriminant Validity Result The assessment of discriminant validity establishes separate measurements of distinct latent constructs in the model. The research used both the Heterotrait-Monotrait (HTMT) ratio and the Fornell-Larcker criterion to validate discriminant validity. The HTMT matrix shows construct inter-correlations through correlation ratios and establishes valid discriminant relationships when the ratios stay below 0.85. Research findings show that all HTMT values stay below the accepted threshold thus proving the constructs do not share excessive correlation. Similarly, the Fornell-Larcker criterion compares the square root of each construct’s AVE with its correlations to other constructs. The diagonal Fornell-Larcker matrix values surpass the construct correlations because they represent the square roots of AVE which provides additional evidence of discriminant validity. The research findings demonstrate that model constructs demonstrate meaningful theoretical separation so the theoretical framework both maintains framework reliability and shows independence when measuring academic AI adoption factors. Table 2 Discriminant validity Discriminant validity - Heterotrait-monotrait ratio (HTMT) - Matrix Constructs AIT DK FC TR PQ PR PV AI Technology Adoption (AIT) Digital Knowledge (DK) 0.365 Facilitating Conditions (FC) 0.259 0.336 Technology Readiness (TR) 0.297 0.218 0.229 Perceived Quality (PQ) 0.273 0.384 0.371 0.189 Perceived Risk (PR) 0.394 0.300 0.419 0.320 0.605 Perceived Value (PV) 0.433 0.304 0.297 0.279 0.120 0.280 Discriminant validity - Fornell-Larcker criterion AI Technology Adoption 0.861 Digital Knowledge 0.338 0.821 Facilitating Conditions 0.240 0.300 0.803 Technology Readiness 0.187 0.262 0.248 0.820 Perceived Quality 0.324 0.261 0.319 0.381 0.735 Perceived Risk 0.396 0.280 0.265 0.082 0.229 0.865 Perceived Value 0.272 0.191 0.202 0.123 0.252 0.258 0.861 Direct Relationship PLS-SEM applied to test the hypotheses after the measurement model validation process. The reporting according to [ 68 ] includes path-coefficient results, inner VIF values, coefficient of determination (R 2 ), effect sizes (f 2 ) and predictive relevance of Q 2 . AI technology adoption (AIT) (β = 0.180, p = 0.002) and PQ (β = 0.151, p = 0.003) and PV (β = 0.179, p = 0.002) improve significantly when digital knowledge increases according to the study results. The study also shows that facilitating conditions (FC) leads to positive impacts on both PQ (β = 0.105, p = 0.033) and perceived value (β = 0.147, p = 0.006) even though they fail to affect AI technology adoption (β = 0.031, p = 0.608). The perceived risk level correlates negatively with AI rates due to people showing lower adoption when they identify greater risks in AI systems (β = -0.164, p = 0.003). Organizational concerns about security and efficiency together with financial factors operate as key obstacles to the adoption of AI systems. Perceived Risk generates increased perceived quality (PQ) (β = 0.309, p = 0.000) and Perceived Value (PV) (β = 0.341, p = 0.002) in people even though it acts as a deterrent for AI Technology Adoption (AIT). The benefits of AI surpass its costs according to the respondents; this strong positive relationship between perceived value and AIT proves AI adoption likelihood increases when AI benefits exceed costs (β = 0.267, p = 0.000). AIT (β = 0.117, p = 0.004) and PV (β = 0.171, p = 0.002) receive significant support from TR however the effect on PQ (β = -0.005, p = 0.936) is insignificant showing that readiness promotes adoption without necessarily promoting positive quality perceptions (see Table 3 ). The model explains 25.3% of the variance that exists in AI Technology Adoption while demonstrating a moderate strength of explanation based on the calculated R² values. The model's predictive performance receives further support from Q² values, which were greater than zero [ 68 ]. Table 3 Direct effect Path St. Beta St. Error T-test P-v Confidence Interval VIF f 2 R 2 Q 2 Supported LB UB DK -> AIT 0.180 0.058 3.123 0.002 0.063 0.290 1.221 0.136 0.253 0.172 Yes DK -> PQ 0.151 0.051 2.932 0.003 0.047 0.248 1.150 0.024 0.174 0.157 Yes DK -> PV 0.179 0.057 3.161 0.002 0.056 0.281 1.150 0.033 0.145 0.127 Yes FC -> AIT 0.031 0.060 0.513 0.608 -0.083 0.147 1.234 0.241 Not FC -> PQ 0.105 0.049 2.139 0.033 0.004 0.197 1.192 0.155 Yes FC -> PV 0.147 0.054 2.748 0.006 0.043 0.254 1.192 0.121 Yes PQ -> AIT 0.033 0.047 0.700 0.484 -0.059 0.124 1.229 0.211 Not PR -> AIT -0.164 0.056 2.952 0.003 0.052 0.269 1.323 0.128 Yes PR -> PQ 0.309 0.060 5.173 0.000 0.191 0.425 1.191 0.098 Yes PR -> PV 0.341 0.046 7.745 0.002 0.253 0.430 1.191 0.058 Yes PV -> AIT 0.267 0.056 4.728 0.000 0.156 0.379 1.188 0.082 Yes TR -> AIT 0.117 0.038 3.608 0.004 0.018 0.250 1.134 0.117 Yes TR -> PQ -0.005 0.056 0.080 0.936 -0.11 0.112 1.099 0.212 Not TR -> PV 0.171 0.055 3.090 0.002 0.056 0.273 1.099 0.032 Yes AIT = AI Technology Adoption; PV = Perceived Value; PQ = Perceived Quality; TR = Technology Readiness; PR = Perceived Risk; FC = Facilitating Conditions; DK = digital knowledge Indirect Relationship Table 4 shows the result of the mediation analysis, which demonstrates important intermediary relationships between AIT and multiple variables of study. The perceived risks of AI lead to higher adoption through their influence on PQ (β = 0.039, p = 0.026) and PV (β = 0.040, p = 0.032). Though PR generates obstacles for acceptance it still shapes how individuals view AI's quality standards and its value potential which promotes taking up AI-based technology. AIT receives favorable outcomes from individuals with strong TR because they tend to rate AI as both high-quality PQ (β = 0.125, p = 0.000) and valuable PV (β = 0.046, p = 0.009). Better digital literacy directly leads to improved perceptions of AI benefits as well as higher quality beliefs which together boost adoption (β = 0.048, p = 0.006 for PV and β = 0.062, p = 0.001 for PQ. The research shows that institutional support measured by Facilitating Conditions (FC) produced a non-statistically significant mediation effect using PV (β = 0.010, p = 0.501) and PQ (β = 0.005, p = 0.516). Therefore, AI adoption requires additional variables beyond institutional support for successful implementation. Table 4 Mediating effect Path St. Beta St. Error T-test P-v Confidence Interval Supported LB UB PR -> PQ -> AIT 0.039 0.018 2.222 0.026 -0.018 0.042 Yes TR -> PV -> AIT 0.046 0.018 2.602 0.009 0.017 0.085 Yes TR -> PQ -> AIT 0.125 0.028 4.425 0.000 0.009 0.006 Yes DK -> PV -> AIT 0.048 0.018 2.727 0.006 -0.018 0.088 Yes FC -> PV -> AIT 0.010 0.015 0.673 0.501 0.012 0.083 NOT DK -> PQ -> AIT 0.062 0.019 3.316 0.001 0.008 0.024 Yes FC -> PQ -> AIT 0.005 0.008 0.65 0.516 -0.008 0.024 NOT PR -> PV -> AIT 0.04 0.019 2.151 0.032 0.004 0.061 Yes AIT = AI Technology Adoption; PV = Perceived Value; PQ = Perceived Quality; TR = Technology Readiness; PR = Perceived Risk; FC = Facilitating Conditions; DK = digital knowledge Discussion and Implication This study's findings make an important contribution to the understanding of factors influencing adoption of artificial intelligence (AI) technologies in organizations, particularly within higher education institutions The results indicate that digital knowledge (DK) is critical in AI adoption, with a positive influence on both perceived quality (PQ) and perceived value (PV). This is consistent with previous studies which have emphasized the importance of digital proficiency in technological adoption; DK affords one the skill to integrate an manage AI-based tools with greater efficiency in both the academic and professional settings [ 15 , 16 ]. In addition, DK has been shown to favor AI adoption: it propels growth and innovation at machine plants institutions of higher education. It was discovered that facilitation (FC) items such as institutional support, training, and digital infrastructure all had a significant positive effect on PQ and PV, further suggesting that structural or environmental preparedness is an important factor which shapes perceptions of the advantages AI can bring to a higher education institution. However, the study found no direct effect of FC on AIT, indicating that while institutional support is important it must be accompanied by other factors such as employee training and cultural readiness. In mobile learning and AI-based financial services, the same conclusions echo [ 43 , 44 ]. With AI adoption, perceived risk (PR) has a complex relationship. This is illustrated by the finding that as expected PR negatively affects AIT. It indicates that concerns about security, privacy and perceiving things to be too difficult all hinder AI adoption. However, paradoxically, but PR positively influences PQ and PV. In other words, even though they realize AI is potentially dangerous as well as rewarding There’s such a dual effect is consistent with research on financial services and education [ 40 ]. These findings highlight the need for clear policies, robust security becoming accepted practice, risk aversion and the like if AI acceptability is to develop. The study also reveals a strong positive association between PV and AIT, underlining how perception of value is linked to AI adoption. According to the Perceived Value Theory(PVT), when adopting AI, individuals compare the benefits and expenses involved, with PV being a significant determining factor for decisions on whether to adopt. Research has pointed out that AI tools are adopted when users realize their economic and functional benefits or receive awards for doing so. For example, improvements in efficiency and the quality of accounting or finance education are immediately recognized. Moreover, the technology readiness (TR) impact equally on both AIT and PV but has no direct effect on PQ. This would seem to indicate that a higher TR correlates with greater inclination to adopt AI because of its expected benefits rather than its intrinsic features. Technology Readiness Index (TRI) model [ 21 ] holds that indomitable faith and creativity are the driving forces behind AI adoption. On the contrary, fear of change and psychological discomfort bring the brakes. Studies in finance and auditing also verify this point: individuals with strong technology readiness show higher rates of AI acceptance [ 30 , 31 ] A mediation analysis can further clarify these relationships. PR is shown to have effect indirectly on AIT through PQ and PV, suggesting the importance of risk perception in AI adoption. Similarly, TR indirectly influences AIT through PV; this teaching significance of technology readiness in AI acceptance. At the same time though, no significant mediating effect for FC through PQ or PV was found. This means that when a business provides institutional support, perceived value and quality will go up but unless other assistance such as digital literacy and perceived benefits is also present, there is no direct relationship with AI acceptance. This study advances the AI adoption literature by highlighting the complex interplay among digital knowledge, perceived risk, facilitating conditions, and technology readiness. It builds on existing work like TAM, TOE, DOI, and UTAUT, demonstrating how perceived quality and value mediate relationships of adoption determinants with AI acceptance. These results validate value-based adoption strategies and demonstrate the need for a holistic AI implementation model for HEIs. For practitioners and policymakers, the findings offer key recommendations. To begin with, training programs for digital knowledge initiatives and ongoing digital literacy efforts should be a priority in order to improve AI readiness of faculty, students, and professionals. Second, organizations should take proactive measures to alleviate risk perceptions (e.g., making data policies transparent, increasing cybersecurity measures, building trust). Third, the facilitation conditions need to be structurally embedded with the training and engagement programs for maximizing the impact on AI adoption. AI solutions must also serve precise economic, functional and cognitive benefits in accordance with user expectations and institutional needs. Lastly, AI technology developers and providers need to focus on customization and make user-centric AI design the highlight, where perceived value is more than the risk and complexity perceived. This gem of a paper highlights that by addressing not only technical readiness but also perceived value and institutional-level support, an adoption strategy can play a pivotal role in effecting AI adoption at scale and in a sustainable manner not only in higher education but also beyond. Conclusion This research conducts a comprehensive examination of the factors that affect the adoption of AI technology. It particularly concentrates on five dimensions: digital knowledge, perceived risk, facilitating conditions, technology acceptance and value perception. The research results show once again that digital knowledge significantly promotes the acceptance of AI. However, risks perceived to be associated with it act as a barrier-even though these perceptions have positive effects on both quality and worth. Furthermore, facilitating conditions raise users 'perceptions of AI but leave no guarantee for its average growth. This underscore the feeling that both institutional support and involvement on part of beneficiaries are necessary is thus needed in order to finish off that split message about AI adoption. That research adds to the existing body of work by proving that the Technology Acceptance Model (TAM) and the Unified Theory of Acceptance and Use of Technology (UTAUT) are actually useful frameworks. It also shows how perceived quality and value shape the decisions people make about adopting AI. In practical terms, organizations should focus on improving digital literacy, addressing security concerns and really highlighting the benefits of AI. Policymakers should create regulatory frameworks that balance the need to mitigate risk with promoting AI readiness and value perception. Future research should explore other factors like organizational culture and the regulatory environment. By doing that, we can get a more complete understanding of how people accept technology. And by addressing those complexities, organizations can make sure AI integration is sustainable and efficient across different sectors. Declarations Not applicable. Funding: This research was funded by Middle East University, Amman, Jordan. Conflicts of interest/Competing interests: The authors declare no conflict of interest. Consent Statement: not applicable. Ethics Statement: This study was conducted according to the guidelines of the Declaration of Helsinki and was approved by the Deanship of Scientific Research Ethical Committee, Middle East University. Data Availability: Data are available on request due to privacy/ethical restrictions. Informed consent: All participants involved in this study provided informed consent to participate prior to participation . Clinical Trial Number : Not applicable Author Contributions: Conceptualization, H.A.H. and K.A; methodology, M.Y.A. S.M.A and G.H; software, S.O.A.S.; validation, S.O.A.S; formal analysis, M.Y.A and G.H; investigation A.M.H.; resources, S.O.A.S.; data curation, M.Y.A.; writing—original draft preparation, S.M.A., and M.Y.A; writing—review and editing, H.A.H, K.A and G.H; visualization, H.A.H and K.A; supervision, S.M.A.; project administration, S.M.A.; funding acquisition, S.M.A. All authors have read and agreed to the published version of the manuscript. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-6435868","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":452383373,"identity":"0ac7cb24-18a3-4d3a-9f96-7ec16a000beb","order_by":0,"name":"Sajead Mowafaq Alshdaifat","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA4ElEQVRIie3PvwuCQBTA8SdBLq9dkfJfeCLYUvmvKEJb0Njo5nK0B/0RRWDrgUNL/Q9G4JJDe0JdDUEQZ24R913uB3y4dwAq1W/WBqQegB4/T5pYgm+IC4i8EQFBjOBF5NmxXuTllGzfPIcnhEF3xeGYywhx7DtLIodZk42LMHYFiUhKANsWUqUJkloIWSjI2KgbTBDymXnYXhFu9QQ4eA8SMqOTthB4PaEMPVP8JWL7yUZsIneR1fzFTpLCKCsaJslhfSlno+58x8JcOljr/dHHDQZS8SmdNyYqlUr1190B7rtDSa8cR68AAAAASUVORK5CYII=","orcid":"","institution":"Middle East University","correspondingAuthor":true,"prefix":"","firstName":"Sajead","middleName":"Mowafaq","lastName":"Alshdaifat","suffix":""},{"id":452383374,"identity":"114348a0-3b10-4363-a202-47105dca9797","order_by":1,"name":"Huthaifa Al-Hazaima","email":"","orcid":"","institution":"The Hashemite University","correspondingAuthor":false,"prefix":"","firstName":"Huthaifa","middleName":"","lastName":"Al-Hazaima","suffix":""},{"id":452383375,"identity":"19b9305a-7ab3-43cf-81ce-a3b85f86d86d","order_by":2,"name":"Mushtaq Yousif Alhasnawi","email":"","orcid":"","institution":"University of Thi-Qar","correspondingAuthor":false,"prefix":"","firstName":"Mushtaq","middleName":"Yousif","lastName":"Alhasnawi","suffix":""},{"id":452383376,"identity":"8debbead-4673-4346-8cb4-9864136333d6","order_by":3,"name":"Guojing Hu","email":"","orcid":"","institution":"Universiti Putra Malaysia","correspondingAuthor":false,"prefix":"","firstName":"Guojing","middleName":"","lastName":"Hu","suffix":""},{"id":452383377,"identity":"e20d8e94-5cc5-4e5d-b829-119d82f93128","order_by":4,"name":"Seif Obeid Al-Shbiel","email":"","orcid":"","institution":"Al al-Bayt University","correspondingAuthor":false,"prefix":"","firstName":"Seif","middleName":"Obeid","lastName":"Al-Shbiel","suffix":""},{"id":452383379,"identity":"bcd1f63a-3cf5-4de9-b84d-ca1d29ad1555","order_by":5,"name":"Kawthar Alshdaifat","email":"","orcid":"","institution":"Middle East University","correspondingAuthor":false,"prefix":"","firstName":"Kawthar","middleName":"","lastName":"Alshdaifat","suffix":""}],"badges":[],"createdAt":"2025-04-12 17:23:14","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6435868/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6435868/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":82277382,"identity":"efca16fd-5013-45ef-94b7-96c96bc97e0d","added_by":"auto","created_at":"2025-05-08 14:51:01","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":32721,"visible":true,"origin":"","legend":"\u003cp\u003eTheoretical framework\u003c/p\u003e","description":"","filename":"Onlinefloatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-6435868/v1/535dc90eb0716154f5a317ea.png"},{"id":87496657,"identity":"8222245c-e395-444b-b3c6-dae892b935ca","added_by":"auto","created_at":"2025-07-24 13:01:54","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1372553,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6435868/v1/0b70c531-d7d4-4876-98c2-aa052e28e2d9.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Factors Determining the Adoption of Artificial Intelligence in Higher Education: Insights from Accounting and Finance Education in Iraq","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eBringing AI technologies into Higher Education Institutions (HEIs) creates room to transform the process of teaching and learning mould management activities [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e].\u0026ensp;As awareness at large of the transformative power of AI has grown, adoption rates for those tools within higher education institutions have been uneven [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. While some universities have got these new technologies up and running seamlessly with their own processes, others still do not know how to fit them. Automation learning, personalized tests and administration-related optimization are all ways in which AI has been common and successful at HEIs [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe adoption of AI in higher education institutions (HEIs) is driven by multiple factors. Successful integration relies on digital knowledge and awareness of emerging technologies. Interestingly, the higher an institution's digital maturity, the more complex rather than easier the AI adoption process becomes [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. Additionally, technological readiness plays a crucial role, reflecting an institution's preparedness and willingness to support AI integration. However, concerns related to data privacy, security, and potential disruptions to traditional educational practices can create resistance, even within AI-ready institutions. Addressing these challenges is essential to ensuring a smooth and effective transition to AI-driven education [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eEqually\u0026ensp;importantly, it is the development of enabling factors such as infrastructure, institutional, support and accepted culture (of technology) that will ultimately determine the success of AI and (beyond) integration [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. Moreover, these factors form an\u0026ensp;ecosystem, in which AI technologies can flourish and provide tangible value. These perceptions of stakeholder\u0026rsquo;s administrators, faculty and students impact their own perceptions of the value and quality of\u0026ensp;AI solutions and play a significant role in the adoption of AI solutions [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. The success\u0026ensp;of their adoption will also have greater success rate when AI technologies are used as tools that enhance educational accomplishments and institutional efficiency and are trusted for quality [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe purpose of this study is to explore the complex interplay of key determinants that include digital knowledge, technology readiness, perceived risk, facilitating conditions, perceived\u0026ensp;value and perceived quality, and their combined effect on AI adoption in Higher Education Institutions. The findings of this research will be compiled in a report, establishing a set of guidelines based on our understanding of the present AI landscape, drivers of AI adoption, as well as existing barriers that need to be addressed in order to fully exploit the potential of AI in higher education.\u003c/p\u003e \u003cp\u003eThis study advances the current understanding of the topic by proposing an integrated\u0026ensp;model incorporating the technological, organizational, and individual level drivers of AI adoption in higher education. Although some studies\u0026ensp;tested individual drivers of AI adoption, very few studies provide an overall look into how these factors relate to one another for Higher Education Institutions (HEIs). Additionally, by investigating the enablers and barriers to the integration of AI, this study provides actionable recommendations for policymakers and\u0026ensp;institutional leadership. These findings can help shape strategies that can allow and\u0026ensp;drive the adoption of AI and therefore help institutions cross the hurdle of technological transformation whilst in a constantly dynamically changing educational environment.\u003c/p\u003e"},{"header":"2. Literature review and hypotheses development","content":"\u003cp\u003eThe adoption mechanism of AI into Higher Education Institutions (HEIs), specifically within the context of Accounting and Finance education, is influenced by a convergence of individual, organizational, and environmental dynamics. This research builds upon four prevalent technology adoption theories, which include the Technology Acceptance Model (TAM), the Technology-Organization-Environment (TOE) Framework, the Diffusion of Innovation (DOI) Theory, and the Unified Theory of Acceptance and Use of Technology (UTAUT). This article will explore these different frameworks and develop each to articulate the university academics AI adoption decision-making.\u003c/p\u003e \u003cp\u003eThe Technology Acceptance Model (TAM) [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e], describes the adoption of technology as arising from two factors, namely, Perceived Usefulness (PU) and Perceived Ease of Use (PEOU). In the domain of information technology education, AI-driven applications such as social media data analysis tools and virality calculators should be considered not only advantageous but also straightforward to integrate into academic workflows. Education gradually adopts AI when teachers regard AI as beneficial and able to facilitate teaching, research and decision-making and user-friendly. This model highlights how perceived value and quality are key determinants of AI adoption attitudes.\u003c/p\u003e \u003cp\u003eA further perspective offered by the Technology-Organization-Environment (TOE) framework [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e], is that technology adoption is not just affected by factors internal to an organization, but also broad institutional and environmental factors. TOE classifies these factors into three dimensions: technological (capabilities of AI, technical compatibility in financial systems), organizational (institutional support, leadership vision), and environmental (industry pressures, governmental policies). External pressures, for example, IFRS or GAAP compliance, need to be met in the field of education for accounting and finance, serving as mechanisms for the adoption of AI in education, along with the growing demand for finance graduates with AI literacy (which enables graduates to hit the ground running) all contribute to driving AI adoption in academia. This model highlights that AI adoption is not only an individual decision, but one that is heavily determined by institutional readiness and regulatory forces.\u003c/p\u003e \u003cp\u003eThe Diffusion of Innovation (DOI) theory [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e], illustrates how the adoption of AI among academics is spread by considering some vital attributes like relative advantage, compatibility, complexity, trialability, and observability. AI adoption by accounting and finance instructors will depend on how AI tools improve efficiencies, decision-making and adherence to industry standards. Adoption might become difficult if AI is viewed as something complex or dangerous. Understanding this theory offers useful perspectives on the factors of knowledge availability, perceived risks, and expected benefits that influence adoption decisions, and how they contribute to the identification of desirable behavior changes.\u003c/p\u003e \u003cp\u003eThe Unified Theory of Acceptance and Use of Technology (UTAUT) [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e], expands on TAM to include four constructs: performance expectancy, effort expectancy, social influence, and facilitating conditions. The adoption of AI in accounting and finance education is shaped not just by perceptions of usefulness, but by social pressures, industry expectations (e.g., CPA, ACCA certification requirements), and institutional supports (e.g., AI training workshops, digital infrastructure). Supportive Institutional Environment Inadequate resources and training are strongly correlated with AI adoption among educators.\u003c/p\u003e \u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Digital Knowledge, Perceived Value, Perceived Quality, and AI Technology Adoption\u003c/h2\u003e \u003cp\u003eAccording to the Technology-Organization-Environment (TOE) framework developed by [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e], integrated technological, organizational, and environmental factors influence digital transformation by digital knowledge to adopt AI technology successfully. The framework demonstrates how digital knowledge functions as a fundamental enabler that provides individuals and institutions with the required competencies to efficiently utilize and manage new AI technologies. Digital knowledge transforms information creation and storage together with access methods while transforming academic research frameworks and strongly impacting social progress. Higher education institutions (HEIs) should implement digital technologies for better educational quality since the digital era is competitive [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. The process requires HEIs to develop learning environments that match students’ digital competency to handle digital transformation difficulties [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. Enhancing educational approaches through digital knowledge becomes essential because it builds students’ technological skills and prepares them for careers [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe research conducted by [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e], showed that digital integration has elevated its importance as an essential element in accounting education. Scientific investigations demonstrate that the implementation of digital knowledge leads to higher perceived value. The introduction of digital service innovations presents substantial value enhancements to customers, according to [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e], while [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e] discovered that LearnSmart and similar interactive educational materials improve learners' perceived value and academic outcomes. Digital knowledge represents an important quality perception factor because research demonstrates its impact on customers' quality judgments throughout different industrial sectors. [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e] determined that digital transformation leads to improved perceived quality among small and medium-sized enterprises (SMEs), and [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e] explained that digital learning systems boost knowledge workers’ understanding of company-based career advancement resources. The adoption of AI technology in electronic manufacturing firms receives assistance from digital knowledge, which was confirmed in research by [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. Higher education that includes digital knowledge is expected to promote AI technology adoption. Based on the context of higher education in Iraq, the authors developed the following research hypotheses:\u003c/p\u003e \u003cp\u003eH1a: Digital knowledge is positively related to perceived value.\u003c/p\u003e \u003cp\u003eH1b: Digital knowledge is positively related to perceived quality.\u003c/p\u003e \u003cp\u003eH1c: Digital knowledge is positively related to AI technology adoption.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2 Technology Readiness, Perceived Value, Perceived Quality, and AI Technology Adoption\u003c/h2\u003e \u003cp\u003eThe Technology Readiness Index (TRI) developed by [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e], organized technology adoption behavior into four basic elements, which refer to optimism, innovativeness, discomfort, and insecurity. Technology adoption becomes more challenging when individuals feel discomfort and insecurity because their positive views toward innovation and optimism are deficient. Users’ adoption of AI technology depends on their perceived value and perceived quality, which technology readiness strongly impacts within this framework. The Global Competitiveness Index reveals crucial details about different countries through their technology readiness and higher education sub-indices because technological and educational advancement rates differ in modern global environments [\u003cspan additionalcitationids=\"CR23\" citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e–\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. E-learning initiative implementation in higher education depends heavily on technology readiness because it serves as a fundamental digital integration factor [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. The evaluation consists of four major aspects, which include students, teachers, technology, and environmental influences on learning. [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e] also describe technology readiness as four foundational dimensions based on [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e] research, which affect individual and institutional adoption and utilization of modern technology. Multiple empirical investigations demonstrate that technology readiness has a positive effect on perceived value in numerous fields. Research by [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e] indicates technology readiness positively affects mobile internet services because it strengthens the value assessment of all their essential features from utility through hedonic and unique cognitive and economic dimensions. [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e] studied the hospitality industry to discover optimism and innovativeness as vital value perception drivers that outweigh the negative impact of insecurity and the lack of significant influence from discomfort. Technology readiness has a positive influence related to perceived quality evaluations since airline passengers demonstrate better assessments of self-service technology when they display optimistic and innovative traits but negative perceptions when experiencing insecurity or discomfort [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. Technology readiness is an essential factor in AI technology adoption despite its effects on perceived quality and value. The adoption of AI technology in accounting and auditing benefits from positive technology readiness, according to researchers who studied both topics [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. The Vietnamese accounting and auditing sector exhibited no significant relationship between technology readiness and AI adoption, according to the research conducted by [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]. Thus, researchers should examine such associations in varied settings. The research establishes the following assumptions about how technology readiness affects perceived value, perceived quality, and AI technology adoption:\u003c/p\u003e \u003cp\u003eH2a: Technology readiness is positively related to influences perceived value.\u003c/p\u003e \u003cp\u003eH2b: Technology readiness is positively related to perceived quality.\u003c/p\u003e \u003cp\u003eH2c: Technology readiness is positively related to AI technology adoption.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3 Perceived Risk, Perceived Value, Perceived Quality, and AI Technology Adoption\u003c/h2\u003e \u003cp\u003eThe Expectation-Confirmation Theory (ECT) [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e], suggests that AI technology adoption strengthens when users' perceived value surpasses their expectations, but high perceived risks create negative confirmation, which lessens adoption potential. Higher education institutions studied the perceived risks of Software-as-a-Service (SaaS) through the development of an integrated risk management framework, according to [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]. Student risk perception development through higher education displays clear connections because a single environmental course training enhances both student awareness and sensitivity to these matters [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]. Consumer decision-making depends heavily on perceived risk since this fundamental element consists of financial, functional, physical, temporal, psychological, and social dimensions that diminish perceived value [\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]. Student evaluations of products or services decrease because of risks experienced during the buying process, according to research findings in regional wine purchasing [337]. The perceived risk, especially financial performance and physical risk, negatively affects consumer quality judgments about private-label products in retail chains. Retailers should reduce these effects by addressing particular risk dimensions as [\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e] demonstrated and improving their e-service quality. Following this, [\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e] built consumer loyalty and created a positive brand perception. The growing use of artificial intelligence technology has made perceived risk a major adoption challenge in terms of AI technology adoption. Users in financial services refrain from accepting AI technology because of perceived risk, but endorsements from respected figures and perceived security measures can help decrease this effect [\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e]. HEIs face challenges because students avoid using generative AI tools for assessments, mainly due to risk-related concerns, according to research by [\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e]. Multiple research studies show that perceived risk creates both negative influences toward perceived value and perceived quality and proves to be a significant barrier to AI technology adoption. Therefore, this study develops the following hypotheses following these discussions:\u003c/p\u003e \u003cp\u003eH3a: Perceived risk is negatively related to perceived value.\u003c/p\u003e \u003cp\u003eH3b: Perceived risk is negatively related to perceived quality.\u003c/p\u003e \u003cp\u003eH3c: Perceived risk is negatively related to AI technology adoption.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e2.4 Facilitating Conditions, Perceived Value, Perceived Quality, and AI Technology Adoption\u003c/h2\u003e \u003cp\u003eThe Unified Theory of Acceptance and Use of Technology (UTAUT) [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e], identifies facilitating conditions as fundamental adopter determinants because they include both outer resources and institutional backing together with technological foundations, which create opportunities or create barriers for user interactions with new technologies. The adoption of new technologies significantly depends on facilitating conditions as both organizations and individuals make their decisions for technological adoption. Mobile learning systems implementation by HEIs is heavily influenced by facilitating conditions, while mobile financial service usage willingness among Kenyan users depends on these conditions [\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e, \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e]. HEIs apply to facilitate conditions such as resource availability, training options, and technical support to improve learning processes and student engagement [\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e]. The essential facilitating condition of faculty supervision combined with support in university extension programs drives significant improvement in farmers' willingness toward both learning activities and new behavioral adoption [\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e]. Previous studies about perceived value produced conflicting results related to facilitating conditions. Research by [\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e], demonstrates how strong facilitating conditions shape e-human resource management usage and value creation through complex relationships; however, [\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e] proved that these conditions have no substantial effect on electronic document management system acceptance. Research shows that facilitating conditions do increase user experience and play a direct role in shaping perceived value, yet the specific measurements of these concepts may show some variations across studies. The effectiveness of virtual communities of practice receives positive influence from facilitating conditions while these conditions maintain a direct connection to perceived ease of use in mobile-assisted language learning, according to [\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e, \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e]. The assessment of technology quality by users heavily depends on perceived usefulness along with ease of use explained by facilitating conditions [\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e]. The adoption of AI technology throughout different domains substantially depends on the establishment of facilitating conditions. Research has shown that facilitating conditions determine how professionals in healthcare will accept AI systems for specific professional duties [\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e]. The agricultural sector shows a direct relationship between facilitating conditions and farmers who choose AI-based solutions to perform sustainable farming [\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e]. The research shows that facilitating conditions enhance students' adoption of generative AI tools in the context of higher education [\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e]. This research establishes the following hypotheses based on the previous study findings:\u003c/p\u003e \u003cp\u003eH4a: Facilitating conditions are positively related to perceived value.\u003c/p\u003e \u003cp\u003eH4b: Facilitating conditions are positively related to perceived quality.\u003c/p\u003e \u003cp\u003eH4c: Facilitating conditions are positively related to AI technology adoption.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e2.5 Perceived Value and AI Technology Adoption\u003c/h2\u003e \u003cp\u003ePerceived Value Theory (PVT) [\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e], emphasizes that individuals base their decisions on balancing the perceived benefits against the costs since perceived value becomes essential for adopting AI technologies in higher education. The perceived value in the higher education field proves essential for shaping student satisfaction regarding their HEIs [\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e, \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e]. The study by [\u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e] proves that functional emotional and social values directly impact the purchase willingness for knowledge-based products, thus demonstrating perceived value's extensive role in decision-making processes. Therefore, based on the evaluation process of accounting firm managers' perceived value performed on their new employees by [\u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e56\u003c/span\u003e], it involves both quality and costs. Recent studies show how perceived value has become a fundamental aspect during the rising adoption of AI technology within higher education systems. Faculty members and educators utilize AI technology for three purposes: to assist teaching functions, develop educational methods, and provide advice for shaping future education policies [\u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e57\u003c/span\u003e]. Students judge the value of generative AI tools like ChatGPT in a way that stands as the leading factor determining their readiness to adopt these technologies [\u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e58\u003c/span\u003e, \u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e59\u003c/span\u003e]. The authors establish the following research hypothesis under consideration:\u003c/p\u003e \u003cp\u003eH5: Perceived value is positively related to AI technology adoption.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e2.6 Perceived Quality and AI Technology Adoption\u003c/h2\u003e \u003cp\u003eThe study of AI adoption reveals that perceived usefulness, along with perceived ease of use and trust, serves as the fundamental antecedent in the literature, according to [\u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e60\u003c/span\u003e], using TAM and UTAUT as theoretical foundations. Students judge higher education quality from various standpoints. Students evaluate their service satisfaction and quality through the lens of instructional approaches, according to [\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e, \u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e61\u003c/span\u003e, \u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e62\u003c/span\u003e]. [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e] examined through perceived quality how accounting employers assess graduate technical skills and measure their effectiveness after joining the workforce. AI integration has exposed two fundamental aspects of perceived ease of use and perceived usefulness that influence the connection between technological readiness and AI adoption [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. The interactive aspects of perceived quality control how users feel comfortable relying on voice assistants in their systems [\u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e63\u003c/span\u003e]. The adoption of AI for auditing shows evidence that optimism affects this process through enhanced audit quality [\u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e64\u003c/span\u003e]. The research confirms that HEIs should prioritize perceived quality because it serves as a key driver for AI adoption during this period. A research hypothesis state:\u003c/p\u003e \u003cp\u003e \u003cstrong\u003eH6\u003c/strong\u003e \u003c/p\u003e\u003cp\u003ePerceived quality is positively related to AI technology adoption.\u003c/p\u003e \u003cp\u003e\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e2.7 The Mediating Role of Perceived Value\u003c/h2\u003e \u003cp\u003ePVT demonstrates how users determine their adoption decisions through a balance of perceived benefits versus perceived costs, where perceived value enables the relationship between digital knowledge, technology readiness, perceived risk, and facilitating conditions and AI technology adoption [\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e, \u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e65\u003c/span\u003e]. Digital knowledge combined with technology readiness at higher levels creates greater perceived value per research published by [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e], in the field of marketing and [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e] in educational technology, which results in increased AI technology adoption [\u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e58\u003c/span\u003e]. Research shows that sufficient levels of technology readiness create positive effects on perceived value, which enhances the adoption of AI technology [\u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e59\u003c/span\u003e]. The adoption barriers caused by perceived risk create lower perceived value because it decreases readiness to use AI technology [\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e]. At the same time, facilitating conditions increase perceived value, which drives AI technology adoption [\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e]. The research develops the following hypotheses based on these findings to examine how perceived value mediates between influencing factors and AI technology adoption.\u003c/p\u003e \u003cp\u003eH7: Perceived value mediates the relationship between, digital knowledge, technology readiness, perceived risk, facilitating conditions and AI technology adoption.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003e2.8 The Mediating Role of Perceived Quality\u003c/h2\u003e \u003cp\u003eUsers’ adoption decisions in AI technology systems find support from the TAM [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e], and the PVT [\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e] through perceived quality as the intermediary factor. Users base their AI adoption intention on their perception of its usefulness and ease of use because perceived quality acts as a transitional bridge that turns digital knowledge and technology readiness with perceived risk and facilitating conditions into optimistic AI perceptions, which drive AI adoption. When users sense enough perceived quality, they will adopt AI technology after balancing the costs of perceived risk against its perceived value, according to PVT. Multiple variables, such as digital knowledge and technology readiness, together with perceived risk and facilitating conditions, determine individual and organizational decisions regarding AI adoption in the adoption process. The assessment of perceived quality acts as a key intermediary element that connects various determinants to AI adoption. AI systems deliver superior perceived quality when users combine high digital literacy with advanced readiness toward technology, according to the findings of [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e, \u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e63\u003c/span\u003e, \u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e64\u003c/span\u003e] thus building enhanced trust and increasing AI deployment feasibility across every industry sector. The perception of risk creates negative impacts on quality ratings, thus prompting organizations to turn down new opportunities [\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e, \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e]. The adoption of AI is accelerated by strong facilitating conditions, which also enhance the perceived quality outlook [\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e, \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e]. Thus, the research hypotheses were proposed as follows:\u003c/p\u003e \u003cp\u003eH8: Perceived quality mediates the relationship between, digital knowledge, technology readiness, perceived risk, facilitating conditions and AI technology adoption.\u003c/p\u003e "},{"header":"Methodology","content":"\u003cp\u003eAssessment and test of the hypotheses were carried out using a variance-based partial least square approach (PLS-SEM) in the SmartPLS 4 software followed by structural modeling. It was determined that PLS-SEM is a useful multivariate data analysis technique because it can analyze a complex model [\u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e66\u003c/span\u003e]. An electronic questionnaire was used to obtain responses, as it reduces costs and facilitates the response of participants at a time and convenience convenient for them. In addition, it permits it to be accessible to locations far from the researcher's location, which allows to collection of more data. The non-random sample was based on the convenience sample. A total of 667 questionnaires were submitted online, and participants were asked to participate in this survey. Cross-sectional data were collected using an adapted questionnaire, which was analyzed using descriptive and inferential statistics. These respondents included 667 accounting and finance educators at Iraqi universities, and 446 responses were obtained, or a response rate of %67 percent. The data collection period lasted more than six months due to the poor response at Iraqi universities [\u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e67\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eThe G*Power program was used to determine the required sample size, with a minimum test power of 0.80. The “10-fold rule” method [\u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e68\u003c/span\u003e] was followed, which stipulates that the minimum sample size requirement is 269 for a 5 percent significance level. Hence, a sample size of 446 is considered appropriate. Data were collected through an online questionnaire, which included 33 items divided into 6 variables. All latent variables were measured using items measured on a five-point Likert scale.\u003c/p\u003e\u003cp\u003e \u003cb\u003eMeasurement of Variables\u003c/b\u003e \u003c/p\u003e\u003cp\u003eThe instrument consists of two parts: demographic questions and study items. The demographic section includes questions for respondent analysis, covering age, educational level, occupational background, and work experience duration. The research variables' measurement items were sourced from previous studies. Specifically, the AI adoption items were adapted from [\u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e69\u003c/span\u003e], while technology readiness items were derived from [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. Additionally, perceived value and perceived risk were adapted from [\u003cspan citationid=\"CR70\" class=\"CitationRef\"\u003e70\u003c/span\u003e, \u003cspan citationid=\"CR71\" class=\"CitationRef\"\u003e71\u003c/span\u003e]. In contrast, the items for facilitating conditions, perceived quality, and knowledge of digital were adapted from [\u003cspan citationid=\"CR72\" class=\"CitationRef\"\u003e72\u003c/span\u003e, \u003cspan citationid=\"CR73\" class=\"CitationRef\"\u003e73\u003c/span\u003e] (see Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). The questionnaire underwent expert evaluation and refinement by five specialists, along with three academic managers responsible for quality assurance within the research context. The pilot study confirmed the internal consistency of all constructs, with Cronbach’s alpha exceeding 0.7, indicating high reliability. For linguistic consistency, a back-translation method was employed: two participants translated the survey into Arabic, followed by a reverse translation into English to ensure cross-cultural accuracy.\u003c/p\u003e\u003cp\u003e \u003cb\u003eCommon method bias\u003c/b\u003e \u003c/p\u003e\u003cp\u003eResearch tests conducted based on Harman’s single-factor no signs of Common Method Bias contamination in the study data. At 18.673% \u0026lt; 40% according to Harman’s single-factor test the explained variance was below the threshold thus indicating the absence of CMB [\u003cspan citationid=\"CR74\" class=\"CitationRef\"\u003e74\u003c/span\u003e]. The investigated research showed no signs of common method bias (CMB) following the FC test because the variance inflation factor (VIF) variables ranged from 1.292 to 1.926 which remained under 3.33 [\u003cspan citationid=\"CR75\" class=\"CitationRef\"\u003e75\u003c/span\u003e].\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003e \u003cb\u003eMeasurement Model Result\u003c/b\u003e \u003c/p\u003e\u003cp\u003eSix latent constructs were examined through the measurement model to establish reliability and validity of AI Adoption, Technology Readiness, Perceived Value, Perceived Risk, Facilitating Conditions and Perceived Quality. Multiple items make up each construct which is measured through factor loadings (FL), composite reliability (CR) and average variance extracted (AVE). The research shows that all construct measurements achieve high reliability based on their CR values which exceed 0.70 thus establishing internal consistency. The AVE values exceed 0.50 throughout which demonstrates good convergent validity. The factor loadings for separate items show a strong indicator reliability between 0.717 and 0.890. The research confirms that the measurement approach succeeds in properly capturing theoretical components of AI adoption practices and associated factors that exist in academic institutions.\u003c/p\u003e\u003cdiv class=\"gridtable\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eMeasurement Model\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e\u003ccolgroup cols=\"5\"\u003e\u003c/colgroup\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eItems\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eFL\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eCR\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eAVE\u003c/p\u003e \u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAI adoption\u003c/b\u003e [\u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e69\u003c/span\u003e]\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1.\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eI am very familiar with the concept and applications of AI.\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.782\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.875\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.653\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2.\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eI frequently use AI applications in my accounting learning activities.\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.798\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3.\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eI believe AI can greatly support me fulfil my accounting learning\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.821\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e4.\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eThe key benefits of using AI in accounting education offset the drawbacks.\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.880\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e5.\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eI am satisfied with the adoption of AI in accounting curricula.\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.875\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eTechnology Readiness\u003c/b\u003e [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1.\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNew technologies contribute to a better quality of life.\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.857\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.742\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.568\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2.\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTechnology gives me more freedom of mobility.\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.824\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3.\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTechnology gives people more control over their daily lives.\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.772\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e4.\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTechnology makes me more productive in my personal life.\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.751\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e5.\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOther people come to me for advice on new technologies.\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.828\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003ePerceived Value\u003c/b\u003e [\u003cspan citationid=\"CR70\" class=\"CitationRef\"\u003e70\u003c/span\u003e]\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1.\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCompared to the fee I would need to pay, the AI product offers value for money\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.816\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.785\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.615\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2.\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCompared to the effort I would need to put in, the AI product is beneficial to me\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.837\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3.\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCompared to the time I would need to spend; the AI product is worthwhile to me\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.833\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e4.\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOverall, the AI product delivers good value\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.743\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003ePerceived Risk\u003c/b\u003e [\u003cspan citationid=\"CR71\" class=\"CitationRef\"\u003e71\u003c/span\u003e]\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1.\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eIf I decide to use AI technology in my academic work, I would be concerned that its use may not be a wise decision.\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.771\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.715\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.725\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2.\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAdopting AI technology could result in significant financial losses for me or my institution.\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.873\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3.\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eIf I decide to use AI technology in my academic work, I would be concerned that I might not obtain the expected value from it.\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.859\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e4.\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eUsing AI technology could lead to an inefficient use of my time.\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.797\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e5.\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAdopting AI technology could cause significant time losses in my other academic activities.\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.859\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e6.\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGiven the demands of my schedule, adopting AI technology concerns me because it could impose additional time pressures that I do not need.\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.781\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eFacilitating Conditions\u003c/b\u003e [\u003cspan citationid=\"CR76\" class=\"CitationRef\"\u003e76\u003c/span\u003e]\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1.\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eInstitutional policies encourage the use of AI technology in academic work.\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.890\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.743\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.611\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2.\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLegal protections are available for using AI technology in academic activities.\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.762\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3.\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAssistance is available for support with AI technology. *\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.749\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e4.\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSpecialized training is available to me regarding the use of AI technology in academia.\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.799\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e5.\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eInstitutional training programs for AI technology are accessible to me.\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.831\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e6.\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOverall, the use of AI technology in academia is well-supported.\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.805\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003ePerceived Quality\u003c/b\u003e [\u003cspan citationid=\"CR72\" class=\"CitationRef\"\u003e72\u003c/span\u003e]\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1.\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eExcellent overall quality of studies/teaching\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.717\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.754\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.625\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2.\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eExcellent overall quality of the institution\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.815\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eKnowledge of Digital\u003c/b\u003e [\u003cspan citationid=\"CR73\" class=\"CitationRef\"\u003e73\u003c/span\u003e]\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eI can use a computer and basic digital applications efficiently.\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.842\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.898\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.710\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eI can search for information online and evaluate its reliability.\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.821\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eI can easily use digital programs and applications related to my academic/professional field.\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.771\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eI have a good understanding of cybersecurity and data protection practices.\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.728\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eI quickly adapt to new digital tools and applications in my work or study environment.\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.881\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/table\u003e\u003c/div\u003e\u003cp\u003e \u003cb\u003eDiscriminant Validity Result\u003c/b\u003e \u003c/p\u003e\u003cp\u003eThe assessment of discriminant validity establishes separate measurements of distinct latent constructs in the model. The research used both the Heterotrait-Monotrait (HTMT) ratio and the Fornell-Larcker criterion to validate discriminant validity. The HTMT matrix shows construct inter-correlations through correlation ratios and establishes valid discriminant relationships when the ratios stay below 0.85. Research findings show that all HTMT values stay below the accepted threshold thus proving the constructs do not share excessive correlation. Similarly, the Fornell-Larcker criterion compares the square root of each construct’s AVE with its correlations to other constructs. The diagonal Fornell-Larcker matrix values surpass the construct correlations because they represent the square roots of AVE which provides additional evidence of discriminant validity. The research findings demonstrate that model constructs demonstrate meaningful theoretical separation so the theoretical framework both maintains framework reliability and shows independence when measuring academic AI adoption factors.\u003c/p\u003e\u003cdiv class=\"gridtable\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eDiscriminant validity\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e\u003ccolgroup cols=\"8\"\u003e\u003c/colgroup\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colspan=\"8\" nameend=\"c8\" namest=\"c1\"\u003e \u003cp\u003eDiscriminant validity - Heterotrait-monotrait ratio (HTMT) - Matrix\u003c/p\u003e \u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eConstructs\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAIT\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eDK\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eFC\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eTR\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003ePQ\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003ePR\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003ePV\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAI Technology Adoption (AIT)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDigital Knowledge (DK)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.365\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFacilitating Conditions (FC)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.259\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.336\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTechnology Readiness (TR)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.297\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.218\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.229\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePerceived Quality (PQ)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.273\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.384\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.371\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.189\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePerceived Risk (PR)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.394\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.300\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.419\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.320\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.605\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePerceived Value (PV)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.433\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.304\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.297\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.279\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.120\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.280\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"8\" nameend=\"c8\" namest=\"c1\"\u003e \u003cp\u003eDiscriminant validity - Fornell-Larcker criterion\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAI Technology Adoption\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.861\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDigital Knowledge\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.338\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.821\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFacilitating Conditions\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.240\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.300\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.803\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTechnology Readiness\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.187\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.262\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.248\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.820\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePerceived Quality\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.324\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.261\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.319\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.381\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.735\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePerceived Risk\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.396\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.280\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.265\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.082\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.229\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.865\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePerceived Value\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.272\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.191\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.202\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.123\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.252\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.258\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.861\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/table\u003e\u003c/div\u003e\u003cp\u003e \u003cb\u003eDirect Relationship\u003c/b\u003e \u003c/p\u003e\u003cp\u003ePLS-SEM applied to test the hypotheses after the measurement model validation process. The reporting according to [\u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e68\u003c/span\u003e] includes path-coefficient results, inner VIF values, coefficient of determination (R\u003csup\u003e2\u003c/sup\u003e), effect sizes (f\u003csup\u003e2\u003c/sup\u003e) and predictive relevance of Q\u003csup\u003e2\u003c/sup\u003e. AI technology adoption (AIT) (β = 0.180, p = 0.002) and PQ (β = 0.151, p = 0.003) and PV (β = 0.179, p = 0.002) improve significantly when digital knowledge increases according to the study results. The study also shows that facilitating conditions (FC) leads to positive impacts on both PQ (β = 0.105, p = 0.033) and perceived value (β = 0.147, p = 0.006) even though they fail to affect AI technology adoption (β = 0.031, p = 0.608). The perceived risk level correlates negatively with AI rates due to people showing lower adoption when they identify greater risks in AI systems (β = -0.164, p = 0.003). Organizational concerns about security and efficiency together with financial factors operate as key obstacles to the adoption of AI systems. Perceived Risk generates increased perceived quality (PQ) (β = 0.309, p = 0.000) and Perceived Value (PV) (β = 0.341, p = 0.002) in people even though it acts as a deterrent for AI Technology Adoption (AIT). The benefits of AI surpass its costs according to the respondents; this strong positive relationship between perceived value and AIT proves AI adoption likelihood increases when AI benefits exceed costs (β = 0.267, p = 0.000). AIT (β = 0.117, p = 0.004) and PV (β = 0.171, p = 0.002) receive significant support from TR however the effect on PQ (β = -0.005, p = 0.936) is insignificant showing that readiness promotes adoption without necessarily promoting positive quality perceptions (see Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). The model explains 25.3% of the variance that exists in AI Technology Adoption while demonstrating a moderate strength of explanation based on the calculated R² values. The model's predictive performance receives further support from Q² values, which were greater than zero [\u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e68\u003c/span\u003e].\u003c/p\u003e\u003cdiv class=\"gridtable\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c12\" colnum=\"12\"\u003e\u003c/div\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eDirect effect\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e\u003ccolgroup cols=\"12\"\u003e\u003c/colgroup\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003ePath\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eSt. Beta\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c3\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eSt. Error\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c4\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eT-test\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eP-v\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003eConfidence Interval\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c8\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eVIF\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c9\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003ef\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c10\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eR\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c11\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eQ\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c12\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eSupported\u003c/p\u003e \u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003eLB\u003c/b\u003e\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003eUB\u003c/b\u003e\u003c/p\u003e \u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDK -\u0026gt; AIT\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.180\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.058\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.123\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.063\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.290\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.221\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.136\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.253\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.172\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDK -\u0026gt; PQ\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.151\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.051\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.932\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.003\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.047\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.248\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.150\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.024\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.174\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.157\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDK -\u0026gt; PV\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.179\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.057\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.161\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.056\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.281\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.150\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.033\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.145\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.127\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFC -\u0026gt; AIT\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.031\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.060\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.513\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.608\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.083\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.147\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.234\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.241\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e\u003cb\u003eNot\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFC -\u0026gt; PQ\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.105\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.049\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.139\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.033\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.004\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.197\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.192\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.155\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFC -\u0026gt; PV\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.147\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.054\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.748\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.006\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.043\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.254\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.192\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.121\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePQ -\u0026gt; AIT\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.033\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.047\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.700\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.484\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.059\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.124\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.229\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.211\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e\u003cb\u003eNot\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePR -\u0026gt; AIT\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.164\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.056\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.952\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.003\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.052\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.269\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.323\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.128\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePR -\u0026gt; PQ\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.309\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.060\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5.173\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.191\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.425\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.191\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.098\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePR -\u0026gt; PV\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.341\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.046\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e7.745\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.253\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.430\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.191\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.058\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePV -\u0026gt; AIT\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.267\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.056\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4.728\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.156\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.379\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.188\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.082\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTR -\u0026gt; AIT\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.117\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.038\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.608\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.004\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.018\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.250\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.134\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.117\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTR -\u0026gt; PQ\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.005\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.056\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.080\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.936\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.11\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.112\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.099\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.212\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e\u003cb\u003eNot\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTR -\u0026gt; PV\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.171\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.055\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.090\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.056\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.273\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.099\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.032\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"12\" nameend=\"c12\" namest=\"c1\"\u003e \u003cp\u003eAIT = AI Technology Adoption; PV = Perceived Value; PQ = Perceived Quality; TR = Technology Readiness; PR = Perceived Risk; FC = Facilitating Conditions; DK = digital knowledge\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/table\u003e\u003c/div\u003e\u003cp\u003e\u003cb\u003eIndirect Relationship\u003c/b\u003e\u003c/p\u003e\u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e shows the result of the mediation analysis, which demonstrates important intermediary relationships between AIT and multiple variables of study. The perceived risks of AI lead to higher adoption through their influence on PQ (β = 0.039, p = 0.026) and PV (β = 0.040, p = 0.032). Though PR generates obstacles for acceptance it still shapes how individuals view AI's quality standards and its value potential which promotes taking up AI-based technology. AIT receives favorable outcomes from individuals with strong TR because they tend to rate AI as both high-quality PQ (β = 0.125, p = 0.000) and valuable PV (β = 0.046, p = 0.009). Better digital literacy directly leads to improved perceptions of AI benefits as well as higher quality beliefs which together boost adoption (β = 0.048, p = 0.006 for PV and β = 0.062, p = 0.001 for PQ. The research shows that institutional support measured by Facilitating Conditions (FC) produced a non-statistically significant mediation effect using PV (β = 0.010, p = 0.501) and PQ (β = 0.005, p = 0.516). Therefore, AI adoption requires additional variables beyond institutional support for successful implementation.\u003c/p\u003e\u003cdiv class=\"gridtable\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eMediating effect\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e\u003ccolgroup cols=\"10\"\u003e\u003c/colgroup\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003ePath\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eSt. Beta\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c3\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eSt. Error\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c4\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eT-test\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eP-v\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colspan=\"4\" nameend=\"c9\" namest=\"c6\"\u003e \u003cp\u003eConfidence Interval\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c10\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eSupported\u003c/p\u003e \u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003eLB\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003eUB\u003c/p\u003e \u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePR -\u0026gt; PQ -\u0026gt; AIT\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.039\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.018\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.222\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.026\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e-0.018\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003e0.042\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTR -\u0026gt; PV -\u0026gt; AIT\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.046\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.018\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.602\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.009\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e0.017\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003e0.085\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTR -\u0026gt; PQ -\u0026gt; AIT\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.125\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.028\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4.425\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e0.009\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003e0.006\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDK -\u0026gt; PV -\u0026gt; AIT\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.048\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.018\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.727\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.006\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e-0.018\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003e0.088\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFC -\u0026gt; PV -\u0026gt; AIT\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.010\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.015\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.673\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.501\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e0.012\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003e0.083\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u003cb\u003eNOT\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDK -\u0026gt; PQ -\u0026gt; AIT\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.062\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.019\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.316\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e0.008\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003e0.024\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFC -\u0026gt; PQ -\u0026gt; AIT\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.005\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.008\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.65\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.516\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.008\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e \u003cp\u003e0.024\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c10\" namest=\"c9\"\u003e \u003cp\u003e\u003cb\u003eNOT\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePR -\u0026gt; PV -\u0026gt; AIT\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.04\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.019\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.151\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.032\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.004\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e \u003cp\u003e0.061\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c10\" namest=\"c9\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"10\" nameend=\"c10\" namest=\"c1\"\u003e \u003cp\u003eAIT = AI Technology Adoption; PV = Perceived Value; PQ = Perceived Quality; TR = Technology Readiness; PR = Perceived Risk; FC = Facilitating Conditions; DK = digital knowledge\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/table\u003e\u003c/div\u003e"},{"header":"Discussion and Implication","content":"\u003cp\u003eThis study's findings make an important contribution to the understanding of factors influencing adoption of artificial intelligence (AI) technologies in organizations, particularly within higher education institutions The results indicate that digital knowledge (DK) is critical in AI adoption, with a positive influence on both perceived quality (PQ) and perceived value (PV). This is consistent with previous studies which have emphasized the importance of digital proficiency in technological adoption; DK affords one the skill to integrate an manage AI-based tools with greater efficiency in both the academic and professional settings [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. In addition, DK has been shown to favor AI adoption: it propels growth and innovation at machine plants institutions of higher education.\u003c/p\u003e\u003cp\u003eIt was discovered that facilitation (FC) items such as institutional support, training, and digital infrastructure all had a significant positive effect on PQ and PV, further suggesting that structural or environmental preparedness is an important factor which shapes perceptions of the advantages AI can bring to a higher education institution. However, the study found no direct effect of FC on AIT, indicating that while institutional support is important it must be accompanied by other factors such as employee training and cultural readiness. In mobile learning and AI-based financial services, the same conclusions echo [\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e, \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eWith AI adoption, perceived risk (PR) has a complex relationship. This is illustrated by the finding that as expected PR negatively affects AIT. It indicates that concerns about security, privacy and perceiving things to be too difficult all hinder AI adoption. However, paradoxically, but PR positively influences PQ and PV. In other words, even though they realize AI is potentially dangerous as well as rewarding There’s such a dual effect is consistent with research on financial services and education [\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e]. These findings highlight the need for clear policies, robust security becoming accepted practice, risk aversion and the like if AI acceptability is to develop.\u003c/p\u003e\u003cp\u003eThe study also reveals a strong positive association between PV and AIT, underlining how perception of value is linked to AI adoption. According to the Perceived Value Theory(PVT), when adopting AI, individuals compare the benefits and expenses involved, with PV being a significant determining factor for decisions on whether to adopt. Research has pointed out that AI tools are adopted when users realize their economic and functional benefits or receive awards for doing so. For example, improvements in efficiency and the quality of accounting or finance education are immediately recognized.\u003c/p\u003e\u003cp\u003eMoreover, the technology readiness (TR) impact equally on both AIT and PV but has no direct effect on PQ. This would seem to indicate that a higher TR correlates with greater inclination to adopt AI because of its expected benefits rather than its intrinsic features. Technology Readiness Index (TRI) model [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e] holds that indomitable faith and creativity are the driving forces behind AI adoption. On the contrary, fear of change and psychological discomfort bring the brakes. Studies in finance and auditing also verify this point: individuals with strong technology readiness show higher rates of AI acceptance [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]\u003c/p\u003e\u003cp\u003eA mediation analysis can further clarify these relationships. PR is shown to have effect indirectly on AIT through PQ and PV, suggesting the importance of risk perception in AI adoption. Similarly, TR indirectly influences AIT through PV; this teaching significance of technology readiness in AI acceptance. At the same time though, no significant mediating effect for FC through PQ or PV was found. This means that when a business provides institutional support, perceived value and quality will go up but unless other assistance such as digital literacy and perceived benefits is also present, there is no direct relationship with AI acceptance.\u003c/p\u003e\u003cp\u003eThis study advances the AI adoption literature by highlighting the complex interplay among digital knowledge, perceived risk, facilitating conditions, and technology readiness. It builds on existing work like TAM, TOE, DOI, and UTAUT, demonstrating how perceived quality and value mediate relationships of adoption determinants with AI acceptance. These results validate value-based adoption strategies and demonstrate the need for a holistic AI implementation model for HEIs. For practitioners and policymakers, the findings offer key recommendations. To begin with, training programs for digital knowledge initiatives and ongoing digital literacy efforts should be a priority in order to improve AI readiness of faculty, students, and professionals. Second, organizations should take proactive measures to alleviate risk perceptions (e.g., making data policies transparent, increasing cybersecurity measures, building trust). Third, the facilitation conditions need to be structurally embedded with the training and engagement programs for maximizing the impact on AI adoption. AI solutions must also serve precise economic, functional and cognitive benefits in accordance with user expectations and institutional needs. Lastly, AI technology developers and providers need to focus on customization and make user-centric AI design the highlight, where perceived value is more than the risk and complexity perceived. This gem of a paper highlights that by addressing not only technical readiness but also perceived value and institutional-level support, an adoption strategy can play a pivotal role in effecting AI adoption at scale and in a sustainable manner not only in higher education but also beyond.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThis research conducts a comprehensive examination of the factors that affect the adoption of AI technology. It particularly concentrates on five dimensions: digital knowledge, perceived risk, facilitating conditions, technology acceptance and value perception. The research results show once again that digital knowledge significantly promotes the acceptance of AI. However, risks perceived to be associated with it act as a barrier-even though these perceptions have positive effects on both quality and worth. Furthermore, facilitating conditions raise users 'perceptions of AI but leave no guarantee for its average growth. This underscore the feeling that both institutional support and involvement on part of beneficiaries are necessary is thus needed in order to finish off that split message about AI adoption.\u003c/p\u003e\u003cp\u003eThat research adds to the existing body of work by proving that the Technology Acceptance Model (TAM) and the Unified Theory of Acceptance and Use of Technology (UTAUT) are actually useful frameworks. It also shows how perceived quality and value shape the decisions people make about adopting AI. In practical terms, organizations should focus on improving digital literacy, addressing security concerns and really highlighting the benefits of AI. Policymakers should create regulatory frameworks that balance the need to mitigate risk with promoting AI readiness and value perception. Future research should explore other factors like organizational culture and the regulatory environment. By doing that, we can get a more complete understanding of how people accept technology. And by addressing those complexities, organizations can make sure AI integration is sustainable and efficient across different sectors.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding:\u0026nbsp;\u003c/strong\u003eThis research was funded by Middle East University, Amman, Jordan.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflicts of interest/Competing interests:\u003c/strong\u003e The authors declare no conflict of interest.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent Statement:\u003c/strong\u003e not applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics Statement:\u0026nbsp;\u003c/strong\u003eThis study was conducted according to the guidelines of the Declaration of Helsinki and was approved by the Deanship of Scientific Research Ethical Committee, Middle East University.\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData Availability:\u003c/strong\u003e Data are available on request due to privacy/ethical restrictions.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eInformed consent:\u0026nbsp;\u003c/strong\u003eAll participants involved in this study provided informed consent to participate prior to participation\u003cstrong\u003e.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eClinical Trial Number\u003c/strong\u003e: Not applicable\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor Contributions:\u003c/strong\u003e Conceptualization, H.A.H. and K.A; methodology, M.Y.A. S.M.A and G.H; software, S.O.A.S.; validation, S.O.A.S; formal analysis, M.Y.A and G.H; investigation A.M.H.; resources, S.O.A.S.; data curation, M.Y.A.; writing\u0026mdash;original draft preparation, S.M.A., and M.Y.A; writing\u0026mdash;review and editing,\u0026nbsp;H.A.H, K.A and G.H; visualization, H.A.H and K.A; supervision, S.M.A.; project administration, S.M.A.; funding acquisition, S.M.A. All authors have read and agreed to the published version of the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSupplementary Materials:\u0026nbsp;\u003c/strong\u003eNone.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgments:\u003c/strong\u003e The authors are grateful to Middle East University, Amman, Jordan, for the financial support to cover this article\u0026rsquo;s publishing fee.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eKuleto, V., Ilić, M., Dumangiu, M., Ranković, M., Martins, O.M., Păun, D. \u0026amp; Mihoreanu, L. 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Journal of Computer Information Systems, 46(1), 17\u0026ndash;24 (2005).\u003c/li\u003e\n\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":"AI adoption, digital knowledge, perceived risk, technological readiness, perceived quality, perceived value, accounting and finance education","lastPublishedDoi":"10.21203/rs.3.rs-6435868/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6435868/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThis study aims to investigate the factors affecting AI technology adoption (AIT) among accounting and finance educators in Iraqi universities, focusing on digital knowledge, perceived risk, technological readiness and institutional support. The study uses PLS-SEM to test the hypotheses after measuring model validation. Cross-sectional data were collected through online survey, which gathered 446 valid responses from 667 participants. Descriptive and inferential statistics were applied to analyse the data. The results show that increased digital knowledge enhances AI adoption, perceived quality and perceived value. Facilitating conditions affect perceived quality and value but did not directly affect AI adoption. Perceived risk affects AI adoption and boosts perceived quality and value. Technological readiness supports AI adoption and perceived value but had no effect on perceived quality. Mediation analysis shows that perceived risk although hinders adoption indirectly promotes it by affecting quality and value perceptions. Moreover, improved digital literacy enhances AI benefits perceptions and thus adoption. The findings suggest that AI adoption can be increased through digital knowledge and technological readiness, while institutions should address perceived risks and invest in digital literacy programs. Institutional support alone is not enough for successful AI adoption, so other factors should be considered to ensure effective implementation. These findings can guide policymakers and universities to create a more supportive environment for AI technology integration.\u003c/p\u003e","manuscriptTitle":"Factors Determining the Adoption of Artificial Intelligence in Higher Education: Insights from Accounting and Finance Education in Iraq","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-05-08 14:50:57","doi":"10.21203/rs.3.rs-6435868/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":"d5810e68-cc4f-4709-9e39-4d5d3c9d403b","owner":[],"postedDate":"May 8th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-07-24T12:53:45+00:00","versionOfRecord":[],"versionCreatedAt":"2025-05-08 14:50:57","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6435868","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6435868","identity":"rs-6435868","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}