Attitudes, Risks and Regulation: The Social Foundations of AI Adoption in Croatia

Human-Technology Relationship: AI Adoption in Manufacturing

Purpose This study investigates the individual-level determinants influencing the adoption of Artificial Intelligence (AI) in the accounting profession within an emerging economy context. It explores how perceptions such as usefulness, ease of use, trust, threat, and susceptibility shape AI adoption, and examines the mediating role of accounting profit. The research is grounded in an extended Technology Acceptance Model and contextualized for Bangladesh to address the gap in AI adoption literature in developing countries. Design/methodology/approach A structured online survey was administered to 478 accounting professionals across various sectors in Bangladesh. The questionnaire included established measures adapted from prior studies and was analyzed using Partial Least Squares Structural Equation Modelling (PLS-SEM). The model assessed both direct and mediated paths from individual perceptions to AI adoption, with accounting profit as the mediating variable. Bias testing, robustness checks, and validity assessments were also conducted to ensure reliability of the findings. Findings Perceived usefulness, trust in AI, and perceived threat significantly influenced AI adoption. Perceived ease of use and perceived susceptibility did not show direct significance. Accounting profit was found to mediate the relationships between perceived usefulness, perceived threat, and trust with AI adoption. These results indicate that while individual perceptions matter, financial feasibility plays a critical role in actual adoption decisions within accounting contexts in emerging economies. Practical implications Accounting firms and policymakers in emerging markets must prioritize financial readiness alongside technology training. To enhance AI adoption, strategies should focus on building trust, communicating the tangible value of AI, and ensuring that AI investments align with profit goals. The findings also suggest firms should promote perceived usefulness and address risk concerns when introducing AI into accounting systems. Social implications The study highlights the socio-economic barriers that affect technology diffusion in emerging economies. As accounting professionals face trust and threat perceptions, along with resource limitations, the integration of AI requires institutional support, inclusive digital training, and clear cost-benefit communication. Broader AI adoption could enhance transparency, efficiency, and job transformation in financial services if these conditions are addressed. Originality/value This study offers a novel integration of individual-level behavioural constructs and financial performance (accounting profit) to explain AI adoption in accounting. By focussing on Bangladesh, it extends Technology Acceptance Model literature into a highly relevant yet underexplored emerging market context. The research also contributes to accounting technology literature by showing how strategic perceptions and profitability jointly shape innovation decisions in the profession.

IntroductionDespite the potential of artificial intelligence (AI) to transform healthcare delivery and reduce costs, adoption remains uneven across populations. Understanding the demographic, behavioral, and cognitive factors influencing public willingness to use AI-powered health tools is critical for equitable implementation. This study examined determinants of AI adoption in healthcare among adults in the United States (U.S.).MethodsA cross-sectional survey was conducted between March and June 2024 using convenience sampling across the U.S. The study included 568 adult respondents recruited via Qualtrics. The survey assessed demographic characteristics, digital health behaviors, self-reported health status, cognitive and attitudinal factors, and behavioral intentions related to AI use in healthcare. Logistic regression models were used to examine associations between predictors and willingness to adopt AI, with z-tests for subgroup comparisons and Bonferroni correction applied for multiple hypothesis testing.ResultsThe sample was predominantly female (66.7%) and Hispanic/Latino (50.7%), with moderate income and education levels. Older age was negatively associated with AI adoption (β = −0.029), males were less likely to use AI than females (β = −0.388), and income was positively correlated with AI adoption (β = 0.096). Trust in AI was substantially lower than trust in physicians: 14.6% trusted ChatGPT's diagnosis for serious illness compared with 92.3% trusting physicians, and 17.1% versus 96.4% for specialist referrals. Telehealth use strongly predicted AI adoption (β = 1.012), while lower self-rated mental health was associated with higher AI use (β = −0.254). Uninsured participants reported higher trust in AI diagnostic capabilities than insured participants (57% vs. 43%, p < 0.05). Ethnic differences were observed, with Asian participants reporting higher AI usage rates than Hispanic participants (16.49% vs. 5.56%, p < 0.05).DiscussionAI adoption in healthcare is shaped by the interaction of demographic, socioeconomic, and cultural factors. While AI has the potential to expand healthcare access, adoption patterns reflect existing disparities in healthcare access and trust. Trust emerged as a central determinant, with AI functioning as a compensatory tool when traditional healthcare access is limited. Given the U.S.-specific context, findings should be interpreted as exploratory and may not generalize to other healthcare systems. These results highlight the need for future research on transparency, digital literacy, and structural barriers to support equitable implementation of healthcare AI.

Purpose This study investigates the interplay between artificial intelligence (AI) adoption, ethical capitalistic orientation and the moderating role of human–AI synergy, a new construct developed via grounded theory. This study aims to understand how these variables influence organizational decision-making in the AI era, specifically promoting ethical practices and mitigating negative outcomes such as employee displacement. Design/methodology/approach Using both qualitative and quantitative regression analysis, this study examines the relationships among AI adoption, ethical capitalistic orientation and human–AI synergy. Statistical tools were used to test hypotheses, identify AI adoption’s impact on organizational ethics and assess human–AI synergy’s moderating role. This research also explores how combining AI and human collaboration can foster a more ethical, socially responsible business model. Findings Findings show AI adoption positively impacts ethical capitalistic orientation (β1 = 0.45, p = 0.0001). Human–AI synergy significantly enhances this effect (β2 = 0.30, p = 0.0005) and moderates the relationship between AI adoption and ethical practices (β4 = 0.15, p = 0.0030). The study emphasizes focusing on human–AI collaboration over workforce displacement to maintain ethical practices and achieve efficiency. These results highlight the importance of ethical AI adoption and social responsibility. Originality/value This study introduces human–AI synergy as a novel construct, demonstrating its critical moderating role in the relationship between AI adoption and ethical capitalistic orientation. It offers new insights into how businesses can leverage AI without workforce reduction or unethical practices. This research emphasizes building ethical frameworks for AI adoption, presenting a novel perspective integrating technology with human-centered decision-making.

In the context of the rapid integration of artificial intelligence (AI) into daily life, this study explores underexamined factors influencing adolescents’ intentions to adopt AI technology. Based on the stimulus-organism-response (SOR) framework, the study uses a structural equation model to investigate the relationships between parental mediation, AI literacy, trust in AI, perceived creepiness of AI, and intention to use AI. A sample of 853 adolescents participated in this study. The findings reveal that active parental mediation significantly enhances AI literacy, fosters trust in AI, and mitigates perceived creepiness of AI, thereby increasing adolescents’ intention to use AI technology. In contrast, restrictive parental mediation does not exhibit any significant effects on AI literacy or usage intentions. The findings highlight the critical role of family influence in adolescents’ adoption of AI, advocating for parents to take an active parental mediation to help adolescents benefit in the AI era.

Purpose Artificial intelligence (AI) is transforming diabetes management in India, yet its adoption remains limited beyond metropolitan areas. This study aims to explore AI’s role across different phases of diabetes care, focusing on healthcare access, behavioral change and patient engagement in non-metropolitan regions of Maharashtra and Karnataka. Design/methodology/approach A qualitative study was conducted using semi-structured interviews with healthcare professionals and their patients exclusively from 15 hospitals and super-specialty clinics in Maharashtra and Karnataka. Thematic analysis identified key trends in AI adoption, particularly in diagnosis, treatment and patient engagement. Findings AI-powered platforms enhance early access to healthcare, risk assessment and patient-clinician interactions. AI-driven insights support personalized treatment, real-time monitoring and predictive healthcare interventions. In addition, AI fosters behavioral change through continuous engagement and lifestyle recommendations. However, challenges such as infrastructure limitations, data security concerns and lack of AI literacy among healthcare providers hinder widespread adoption. Research limitations/implications This study’s findings are specific to 15 hospitals in Maharashtra and Karnataka, which may limit broader applicability. The perspectives of frontline healthcare workers and patients require deeper exploration. Expanding research to a larger, more diverse sample and conducting longitudinal studies will strengthen insights into AI’s long-term impact on diabetes care. Addressing AI literacy, data security and infrastructure gaps is essential for widespread adoption. Policymakers must establish robust frameworks ensuring algorithmic transparency and equitable access, reinforcing AI’s effectiveness in healthcare. Practical implications Integrating AI in diabetes care enhances early diagnosis, personalized treatment and continuous monitoring, improving patient outcomes. Hospitals must invest in AI literacy programs to equip healthcare professionals with the necessary skills for effective adoption. Policymakers should establish regulatory frameworks to ensure data security, ethical AI use and interoperability with existing healthcare systems. AI developers must focus on user-friendly interfaces to increase patient trust and engagement. Expanding AI adoption in non-metropolitan areas requires infrastructure improvements and public–private partnerships. Strengthening these areas will accelerate AI-driven healthcare transformation, making diabetes management more efficient, accessible and patient-centric. Social implications AI-driven diabetes care can bridge healthcare accessibility gaps, particularly in underserved regions, by enabling early diagnosis and remote monitoring. Increased AI adoption fosters health equity, reducing disparities between urban and rural populations. However, digital literacy and trust in AI remain challenges, necessitating awareness campaigns and patient education initiatives. Ethical AI implementation must prioritize data privacy and algorithmic transparency to maintain public confidence. In addition, AI-driven healthcare can empower individuals with personalized health insights, promoting proactive disease management and healthier lifestyles. By fostering collaboration between healthcare providers, policymakers and technology developers, AI can contribute to a more inclusive and patient-centric healthcare system. Originality/value This study fills a critical research gap by evaluating AI’s impact on diabetes care beyond metropolitan India. With Maharashtra and Karnataka facing high diabetes prevalence and limited AI adoption, understanding its regional healthcare applications is essential. The study highlights practical strategies to overcome adoption barriers, advocating collaborative efforts between hospitals, policymakers and AI developers to maximize AI’s potential in healthcare.

Artificial intelligence (AI) is increasingly integrated into healthcare decision-making. Public trust in AI remains a critical determinant of its acceptance and effective use. Evidence on the factors shaping trust in AI within Middle Eastern contexts, particularly Saudi Arabia, remains limited. Therefore, we aimed to identify the determinants of trust in AI for health-related decision-making and to examine a theory-informed mediation pathway in which patient satisfaction mediates the association between patient-doctor relationships and trust in AI. We conducted a cross-sectional, facility-based survey of adults in Saudi Arabia, using an electronic questionnaire distributed in four primary healthcare centers. We performed multiple linear regression to assess the association of trust in AI for health-related decision-making with patient satisfaction, patient-doctor relationships, sociodemographic characteristics, and healthcare-related factors. A mediation analysis was also employed to evaluate the indirect and direct association linking patient-doctor relationships, patient satisfaction, and trust in AI. Our findings showed that patient satisfaction was positively associated with trust in AI (β = 0.54, 95% CI: 0.18-0.90), while patient-doctor relationships showed an inverse association (β = -0.34, 95% CI: -0.48 to -0.20), possibly reflecting a greater reliance on physicians' clinical judgment and a reduced perceived need for AI-supported decision-making. Trust in AI varied across age groups, with a lower trust observed in older age categories compared with younger adults. No strong associations were observed for sex, education, body mass index, or healthcare-related factors. Patient-doctor relationship quality was indirectly associated with trust in AI via patient satisfaction (ACME = 0.138, 95% CI: 0.043-0.246), alongside a direct association with trust in AI (ADE = -0.313, 95% CI: -0.456 to -0.160). This means that patient-doctor relationships influenced trust in AI both directly and indirectly through patient satisfaction, suggesting that, while interpersonal care may reduce the reliance on AI (direct effect), enhancing patient satisfaction can partially offset this effect and promote trust in AI (indirect effect). These findings highlight that fostering patient-centered care and satisfaction may be crucial for promoting public trust in AI, which has important implications for AI governance, ethical deployment, and the design of AI-supported healthcare systems.

Purpose The purpose of this study is to examine how AI adoption enhances operational performance in manufacturing firms and to investigate how variations in firms’ strategic focus moderate this relationship. Specifically, this study explores how AI adoption functions as a dynamic capability that enhances operational performance in manufacturing firms and investigates how different strategic orientations—exploration, exploitation, or ambidexterity—moderate this effect from the perspective of the Attention-Based View (ABV) of the firm. Design/methodology/approach This study employs a quantitative research method, using a sample of 426 Chinese manufacturing firms to examine the impact of artificial intelligence (AI) adoption on operational performance and to analyze the moderating role of firms’ strategic focus (exploration, exploitation, and ambidexterity). Data were collected through a structured questionnaire survey. To ensure the robustness of the findings and address potential endogeneity issues, hierarchical regression analysis and the two-stage least squares (2SLS) method were employed. Findings This study reveals that AI adoption significantly enhances the operational performance of manufacturing enterprises, but this effect is moderated by firms’ strategic focus. A high exploration tendency weakens the performance-enhancing effect of AI adoption due to implementation instability caused by excessive experimentation. A high exploitation tendency also reduces the positive impact of AI adoption, as over-reliance on existing processes constrains AI’s transformative potential. Furthermore, an ambidextrous strategy (coexistence of high exploration and high exploitation) further diminishes the positive effect of AI adoption, indicating that resource dispersion and increased coordination costs may offset its benefits. Originality/value From the perspective of Dynamic Capabilities Theory, this study empirically examines the impact of AI adoption—as a dynamic capability—on operational performance. Additionally, drawing on the Attention-Based View (ABV) of the firm, it uncovers the moderating role of firms’ strategic focus, addressing an existing research gap concerning strategic-level decision-making in AI adoption. The findings offer theoretical insights that guide enterprise managers in optimizing AI adoption strategies, helping them strike a balance between innovation and efficiency to maximize the benefits of digital transformation.

The AI-environment paradox: Unraveling the impact of artificial intelligence (AI) adoption on pro-environmental behavior through work overload and self-efficacy in AI learning.

Artificial Intelligence (AI) transforms Education by enhancing personalized learning, automating administrative tasks, and improving student engagement. Despite its growing presence, AI adoption among university students in Malaysia remains limited. This study investigates the level of AI application usage, identifies key factors influencing adoption, and examines the relationship between these factors and actual AI usage. Using the Technology Acceptance Model (TAM), the study focuses on perceived usefulness (PU), perceived ease of use (PEOU), and technological skills as primary determinants of AI adoption. A quantitative research approach was employed, with data collected through a structured survey from 115 undergraduate students at a university in Kedah, Malaysia. After data screening, 105 valid responses were analyzed using descriptive statistics, correlation analysis, and multiple regression analysis. The findings reveal that AI-powered chatbots and academic support tools are the most frequently used applications due to their perceived usefulness and ease of use. However, AI adoption remains moderate, primarily due to limited technological skills and concerns about AI complexity and ethical implications. The study highlights the need for AI literacy programs, improved user-friendly AI designs, and clear ethical policies on AI-assisted learning. Universities should integrate AI training into curricula, and developers should ensure AI accessibility for students with varying technical proficiencies. Future research should explore cultural attitudes, ethical concerns, and trust in AI within educational settings. By addressing these challenges, institutions can enhance AI adoption, ultimately improving student learning outcomes and academic experiences.

Background: The role of AI-based predictive analytics in general surgery. The use of artificial intelligence (AI) tools in surgical environments is influenced by several factors including perceived usefulness, usability, and trust between professionals. This study focused on the core factors of acceptance and use of artificial intelligence-24 predictive analytics in surgery with the use of the technology acceptance model (TAM). Methods: A cross-sectional quantitative survey was conducted on 273 healthcare providers (general surgeons, surgical residents, anesthesiologists, operating room nurses, and hospital administrators). A questionnaire was designed to measure the Perceived Usefulness (PU), Perceived Ease of Use (PEU), Trust, and Behavioral Intention (BI) to use AI. We measured the survey on a 5-point Likert scale and data analysis was performed using descriptive statistics, reliability testing (Cronbach’s Alpha), correlation analysis, and multiple regression modeling to find the relationship between the TAM factors and AI adoption. Results: The results demonstrated that trust in AI (Trust_Q14) was the only significant predictor variable when considering the behavioral intention to use AI (p = 0.023), while perceived usefulness and ease of use did not significantly affect AI adoption. The Cronbach’s Alpha score (0.087) was low, which means the internal consistency of the survey instrument should be improved. The regression analysis found a low R-squared (0.029), which indicates that TAM is not the only factor that drives AI adoption; other factors like regulatory policies, ethical considerations, and institutional support systems may also be very important. Similarly, the Shapiro-Wilk normality test substantiated the non-normal distribution of the dataset (all variables: p &lt; 0.05), necessitating the use of alternative analytical methods in forthcoming studies. Conclusion: We identify trust as the dominant driver of AI adoption in surgical contexts and provide evidence of limitations in using TAM as a standalone framework for predicting adoption behavior. The results indicate that a commitment to support AI transparency, development of training programs, and setting up ethical regulatory frameworks will help build trust to adopt the AI [dimensional] X-ray imaging diagnosis solution. Marked limitations of the current model involved using TAM only, incorporating other external influencing factors, and using advanced statistical techniques in future research to provide further insights into AI adoption trends in surgery. While there are challenges to be overcome, AI-powered predictive analytics has the potential to improve patient safety, surgical procedure optimization, and healthcare decision-making

Purpose The purpose of this paper is to identify and explain the organizational conditions under which artificial intelligence adoption in universities leads to structural change rather than incremental adaptation. By integrating Luhmann’s theory of decision premises with Argyris and Schön’s concept of organizational learning loops, the study conceptualizes artificial intelligence (AI) adoption as a process mediated by institutional structures and mechanisms of invisibilization and proposes strategies to foster double-loop learning that enable universities to surface and address organizational paradoxes, thereby creating the conditions for meaningful transformation in teaching, research and governance. Design/methodology/approach This conceptual study develops an analytical framework combining Luhmann’s theory of decision premises (programs, communication channels and personnel) with Argyris and Schön’s distinction between single-loop and double-loop learning to examine how universities process AI adoption. The approach synthesizes literature from organizational sociology, higher education studies and paradox theory to explain how contradictions are mediated by institutional structures and managed through mechanisms of invisibilization. The framework is applied analytically to the context of AI in teaching, research and governance, identifying conditions under which contradictions escalate into paradoxes that destabilize decision premises and create opportunities for structural change. Findings The study shows that universities often integrate AI within existing decision premises, containing contradictions through mechanisms of invisibilization, reframing contradictions as technical adjustments, recasting innovation as continuity and suspending role redefinitions, sustaining single-loop learning and organizational stability. Structural change through double-loop learning occurs when external pressures, such as regulatory mandates and funding constraints, converge with internal tensions in academic culture, governance and faculty roles, escalating contradictions into paradoxes that destabilize decision premises. The analysis posits that transformation depends on reconfiguring program premises toward reflexivity, redesigning communication channels for deliberative governance and redefining personnel premises to integrate AI-related expertise into formal authority structures. Research limitations/implications As a conceptual analysis, the study does not include empirical testing, which limits the ability to generalize findings across institutional contexts. Future research should apply and refine the proposed framework through comparative and longitudinal studies of AI adoption in universities, examining variations across governance models, regulatory environments and disciplinary cultures. The framework offers a basis for analyzing how decision premises mediate technological change, highlighting the need for research that investigates the interaction between external pressures, internal tensions and invisibilization mechanisms. Such work can inform both theory development in organizational change and the design of policies that foster reflexive, transformative AI integration. Practical implications The framework offers university leaders and policymakers strategies to foster transformative AI adoption by making organizational contradictions visible and actionable. Institutions can reconfigure program premises to align AI initiatives with mission and values, redesign communication channels to integrate AI within participatory governance and redefine personnel premises to incorporate AI-related expertise into formal authority structures. These interventions can help balance efficiency gains with academic autonomy, transparency and epistemic diversity. Policymakers can use the framework to design regulatory and funding mechanisms that incentivize reflexive adaptation rather than superficial compliance, thereby creating conditions for sustainable organizational change in teaching, research and governance. Social implications By framing AI adoption in universities as an organizational learning challenge, the study highlights its potential societal impact beyond technical efficiency. Universities play a central role in shaping knowledge production, professional formation, and public trust in expertise. AI integration that prioritizes reflexivity, inclusivity and participatory governance can strengthen these societal functions, fostering equitable access to high-quality education and preserving epistemic diversity. Conversely, uncritical adoption risks reinforcing managerial logics that marginalize academic voices and narrow the social purposes of higher education. The framework encourages institutions to engage with AI in ways that support democratic accountability and socially responsive knowledge systems. Originality/value This paper offers a novel conceptual framework linking Luhmann’s theory of decision premises with Argyris and Schön’s organizational learning loops to explain how AI adoption in universities is mediated by institutional structures. By introducing the concept of invisibilization mechanisms, reframing contradictions as technical adjustments, recasting innovation as continuity and suspending role redefinitions, the study advances understanding of why AI often reinforces stability rather than triggering structural change. It also extends organizational change theory in higher education by specifying conditions under which contradictions escalate into paradoxes and by proposing targeted strategies to foster double-loop learning that enable transformative, reflexive integration of AI technologies.

This paper investigates drivers of Artificial Intelligence (AI) adoption, specifically in rural small and medium-sized enterprises (SMEs). Research on AI has gained traction in recent times, however, remains an area in need of further investigation, notably the adoption of AI by SMEs, and particularly among rural SMEs. The focus on SMEs is important as they account for the majority of businesses worldwide, playing an important role in job creation and economic development. The research uses secondary data from the Longitudinal Survey for Small Business (LSBS), a large UK panel survey of SMEs, which provides a broad range of variables on a range of SMEs. Probit regression, using a series of environment, firm and network engagement factors as predictive variables, identifies drivers of AI adoption for rural SMEs. Among the numerous drivers of AI adoption in rural SMEs, networking, is identified as a key variable associated with adoption. This research contributes to the limited knowledge on this subject and more broadly to technology adoption in organisations. This leads to policy recommendations in promoting AI adoption among rural SMEs through better communication of the advantages of adoption among SMEs and network development. • A rural location is not a barrier to AI adoption for SMEs, with numerous rural SMEs adopting AI technology in their operations. • There are numerous antecedents to AI adoption for rural SMEs, including networking, prior innovative activity influence, and strategic planning. • Rural SMEs that adopt AI technology are more likely to have employees, tend to be larger, have more sites, and have a greater turnover than non-adopters. • Engagement in networks can facilitate the adoption of AI technology among rural SMEs, as well as enhance understanding of the benefits of AI technology to SMEs' operations. • Policy should seek to encourage increased levels of AI adoption among rural SMEs to support growth, and ensure that appropriate infrastructure exists to support the use of AI technology.

This study investigates the relationship between artificial intelligence (AI) adoption, knowledge sharing, and employee performance in Malaysian small and medium-sized enterprises (SMEs), with knowledge sharing examined as a mediating mechanism. SMEs represent a vital component of Malaysia’s economy, yet many face resource limitations that affect their readiness to fully harness AI technologies. While AI is recognized for its potential to enhance efficiency and innovation, its impact on employee performance is not always straightforward. This research therefore, explores whether knowledge sharing acts as the bridge through which AI adoption translates into performance outcomes. Data were collected through a survey of SME employees across service and manufacturing sectors, and the responses were analysed using partial least squares structural equation modelling (PLS-SEM). Measurement model results confirmed strong reliability and validity for the constructs of AI adoption, knowledge sharing, and employee performance. Structural model assessment revealed that AI adoption significantly and positively influences knowledge sharing but does not directly affect employee performance. Meanwhile, knowledge sharing revealed a strong and significant relationship with employee performance and was also found to partially mediate the relationship between AI adoption and performance. The study findings highlight that AI’s value in SMEs lies not in the technology itself but in its ability to foster knowledge exchange, learning, and collaboration. In addition, employee performance improves when AI is embedded into organizational practices that encourage knowledge sharing, thereby complementing human creativity and expertise. Theoretically, this study integrated the Knowledge-Based View (KBV) and the Technology–Organization–Environment (TOE) framework to explain how AI adoption and knowledge sharing practices together influence employee performance. Practically, the results underscore the need for SME leaders to move beyond technology acquisition and focus on building collaborative cultures that enable knowledge sharing. Overall, this research contributes both theoretical and practical insights into how SMEs can strategically leverage AI adoption to enhance employee performance through the mediating mechanism of knowledge sharing.

With the widespread application of artificial intelligence (AI) in fields like online shopping, digital education, communication, and entertainment, it has greatly facilitated users' decision-making. However, it also brings social and ethical challenges, among which consumer trust in AI is a prominent issueconsumers are concerned about privacy protection, algorithm normal operation, and the fairness and transparency of AI outputs. This study adopts a combination of theoretical and empirical methods to explore the factors influencing consumers' trust in AI. An anonymous survey was conducted with 80 predominantly young, educated, and AI-familiar respondents. The results show that consumers' trust in AI is multi-factorial, with privacy, personalization, and reliability being the core influencing factors. Additionally, consumer spending habits (e.g., higher trust among frequent digital service users than offline-dependent groups) and AI technical attributes (e.g., authoritative professionalism and recommendation authenticity) also impact trust. Younger and more technology-educated users tend to have higher trust in AI, while the lack of privacy, personalization, or reliability will lead to low consumer trust, restricting AI's acceptance and market success. This study aims to contribute to discussions on the ethical application of AI, provide references for enterprises to develop trusted AI products, and support the formulation of user rights protection policies.