Ai Practices, Trust, And Cloud Accounting Adoption: A Moderation Analysis In Resource-Constrained Environments

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.

Micro Small and Medium Enterprises (MSMEs) have a very significant role in the economy, can even be said to be the backbone of the economy. However, MSMEs are precisely the sector hardest hit by the Covid-19 pandemic. Digitalization creates new opportunities for MSMEs in the pandemic. FinTech, one of the implementations of digitalization should be pursued to be used by MSMEs. Using path analysis processed using Partial Least Square (PLS), this research aims to identify how Unified Theory of Acceptance and Use of Technology (UTAUT) works on Adopting FinTech by MSMEs. The Outer Model and Inner Model reflect that the model work. The outer loading value of ? 0.7, AVE value ? of 0.5, Cronbach's Alpha ? 0,70, a correlation value ? of 0.50; and Composite Reliability ? 0.6, and R-Square 0.716 reflect it. However, regarding the factors that influence intention to use, it is different from previous research that became a reference. There are only four hypotheses proven. Those are Context had a significant positive effect on Perceived Ease of Use and Perceived Usefulness, and Perceived Ease of Use and Perceived Usefulness had a significant positive effect on the intention to use fintech. Keywords: FinTech, MSMEs, Digitalization, UTAUT, Inclusive

Artificial intelligence (AI) is rapidly expanding in myriad industries and systems. This study sought to investigate public trust in using AI in the criminal court process. While previous research has identified factors that influence trust in AI, such as perceived accuracy and transparency of algorithms, less is known about the role of influential leaders—such as judges—in shaping public trust in new technology. This study examined the relationship between locus of control, anthropomorphism, cultural values, and perceived trust in AI. Participants completed a survey assessing their perceptions of trust in AI in determining bail eligibility, bail fines and fees, sentencing length, sentencing fines and fees, and writing legal documents (e.g., findings and disposition). Participants were more likely to trust AI performing financial calculations rather than determining bail eligibility, sentence length, or drafting legal documents. Participants’ comfort with AI in decision-making also depended on their perceptions of judges’ trust in AI, and they expressed concerns about AI perpetuating bias and the need for extensive testing to ensure accuracy. Interestingly, no significant association was found with other participant characteristics (e.g., locus of control, anthropomorphism, or cultural values). This study contributes to the literature by highlighting the role of judges as influential leaders in shaping public trust in AI and examining the influence of individual differences on trust in AI. The findings also help inform the development of recommended practices and ethical guidelines for the responsible use of AI in the courts.

SMEs and artificial intelligence (AI): Antecedents and consequences of AI-based B2B practices

The Meaning of “Explainability Fosters Trust in AI”

As artificial intelligence (AI) becomes increasingly integral to our lives, ensuring these systems are trustworthy and transparent is paramount. The concept of explainability has emerged as a crucial element in fostering trust within AI systems. Nevertheless, the dynamics between explainability and trust in AI are intricate and not fully comprehended. This paper delves into the nexus between explainability and trust in AI, offering perspectives on crafting AI systems that users can rely on. Through an examination of existing literature, we investigate how transparency, accountability, and human oversight influence trust in AI systems and assess how various explainability approaches contribute to trust enhancement. Utilizing a set of experiments, our research examines how different explanatory models impact users' trust in AI systems, revealing that the nature and quality of explanations have a significant influence on trust levels. Additionally, we scrutinize the balance between explainability and accuracy in AI systems, discussing its implications for the development of reliable AI. This study underscores the critical role of explainability in engendering trust in AI systems, providing guidance on the development of AI systems that are both transparent and trustworthy, thereby fostering confidence among users.

Patient Perspectives on Artificial Intelligence in Radiology

This systematic review examines how traditional Technology Acceptance Models (TAMs) can be adapted to better understand user acceptance of AI technologies. The study analyzed 80 peer-reviewed articles published between 2010 and 2025, identified through a comprehensive search of EBSCOhost, Embase, Inspec, Scopus, and Web of Science databases using keywords related to AI acceptance factors and technology acceptance models. Our analysis employed descriptive statistics, thematic analysis, and narrative synthesis to identify key factors influencing AI acceptance, including perceived usefulness, perceived ease of use, trust, transparency, explainability, and ethical considerations. We found that trust in AI systems' reliability, fairness, and privacy protection plays a crucial role in user acceptance, with low trust leading to resistance. Transparency and explainability of AI decision-making processes were identified as critical for building user trust. The review also highlights limitations of current research, including inconsistent definitions of AI technologies, inadequate exploration of cultural and contextual differences in AI acceptance, and the predominance of cross-sectional research designs. By addressing these areas, future research can provide a more comprehensive theoretical foundation for understanding user acceptance of AI technologies, guiding the development and ethical application of AI systems. Keywords: Artificial Intelligence (AI), Trust and Explainability in AI, Technology Acceptance Models (TAM).

Trust in artificial intelligence (AI) has emerged as a critical factor influencing students' interactions with AI-supported learning environments. However, validated instruments for measuring student trust in AI within educational contexts remain limited. This study developed and validated a multidimensional scale to assess student trust in AI systems in online distance education. Scale development followed a sequential multi-stage design, including item generation based on literature review, expert evaluation for content validity, and psychometric validation through exploratory and confirmatory factor analyses. A total of 837 distance learning students participated in the study (EFA = 412; CFA = 307; validation sample = 118). Exploratory factor analysis supported a five-factor structure explaining 63.20% of the total variance. Confirmatory factor analysis with an independent sample indicated good model fit ([Formula: see text]/df = 2.17; CFI = .95; TLI = .94; RMSEA = .06). The scale demonstrated high internal consistency (α = .94; ω = .94). Multi-group confirmatory factor analysis supported metric invariance across gender, indicating comparable measurement across groups. No significant gender differences were observed, and trust in AI was not significantly associated with grade point average or system engagement frequency. These results suggest that trust represents a distinct perception that may influence how students engage with AI-supported learning environments. The resulting 21-item scale provides a reliable instrument for investigating student trust in AI and offers practical implications for the design and implementation of AI-supported learning in higher education.

This study investigated the behavioral adoption of electronic commerce (EC) among small and medium enterprises (SMEs) in China. This was undertaken by integrating the Technological, Organizational, and Environmental (TOE) framework into the Unified Theory of Acceptance and Use of Technology (UTAUT). The data generated from 349 SMEs were analyzed with SPSS and SmartPLs 3.0 via the utilization of the structural equation modeling (SEM) procedure. The results reveal that while the environmental factor was a positive predictor of the performance expectancy of EC among SMEs, it does not, however, encourage the adoption of EC. Again, while the knowledge factor was not significant in driving the performance expectancy of EC, it was a significant determinant of the adoption of EC among SMEs. Also, the study found that organizational factor was a positive predictor of both performance expectancy and the adoption of EC by SMEs. The technological factor was not a significant predictor of the intention to use EC but was significant in determining the performance expectancy of EC. Finally, the study demonstrated that the performance expectancy of EC was a significant predictor of the behavioral adoption of EC. The managerial and research implications of these findings are deliberated.

Artificial intelligence (AI) is spreading rapidly in organizational settings, yet limited research examines how culture shapes leaders' readiness to adopt these technologies. The current study addresses that gap by exploring cross‐cultural differences in AI acceptance among 434 leaders from Western and Eastern regions, guided by the unified theory of acceptance and use of technology (UTAUT). A cross‐sectional, quantitative design, supplemented by short, open‐ended responses, assessed five UTAUT constructs: performance expectancy, effort expectancy, social influence, facilitating conditions, and behavioral intention. Results showed that Western leaders report significantly higher average effort expectancy, social influence, facilitating conditions, and behavioral intention than Eastern leaders, although both groups find AI beneficial. Multiple regression analyses reveal that Western leaders' intention to adopt AI is primarily related to ease of use, whereas Eastern leaders intention is related to organizational support and peer encouragement. Open‐ended responses demonstrate that leaders across regions share ethical and privacy concerns, but Western participants emphasize security and training, while Eastern leaders highlight transparency and real‐time insights. These results imply that AI implementation strategies require cultural adaptation, such as prioritizing the quality of user interfaces and training for Western leaders and ensuring organizational endorsements for Eastern contexts. By identifying how leaders evaluate and integrate AI, the current research delivers practical insights for multinational organizations and deepens theoretical dialogues on leadership and technology acceptance. These findings also address current leadership journal calls by spotlighting AI bias, inclusivity, and ethical governance in distinct regional settings.

The rise of Artificial Intelligence (AI) in our society is becoming ubiquitous and undoubtedly holds much promise. However, AI has also been implicated in high profile breaches of trust or ethical standards, and concerns have been raised over the use of AI in initiatives and technologies that could be inimical to society. Public trust and perceptions of AI trustworthiness underpin AI systems’ social licence to operate, and a myriad of company, industry, governmental and intergovernmental reports have set out principles for ethical and trustworthy AI. To guide the responsible stewardship of AI into our society, a firm foundation of research on trust in AI to enable evidence-based policy and practice is required. However, in order to inform and guide future research, it is imperative to first take stock and understand what is already know about human trust in AI. As such, we undertake a review of 100 papers examining the relationship between trust and AI. We found a fragmented, disjointed and siloed literature with an empirical emphasis on experimentation and surveys relating to specific AI technologies. While findings suggest some convergence on the importance of explainability as a determinant of trust in AI technologies, there are still gaps between conceptual arguments and what has been examined empirically. We urge future research to take a more holistic approach and investigate how trust in different referents impacts on attitudinal and behavioural intentions. Doing so will facilitate a more nuanced understanding of what it means to develop trustworthy AI.

The rapid advancement of Artificial Intelligence (AI) has transformed business operations across the globe, with increasing relevance for Small and Medium Enterprises (SMEs) in emerging economies like India. This study investigates the impact of AI implementation on the performance, productivity, and competitiveness of Indian SMEs. By analyzing primary data collected through surveys and interviews with SME owners and managers across various sectors, and secondary data from industry reports, the research identifies key areas where AI technologies—such as machine learning, predictive analytics, and intelligent automation—are being adopted. The study reveals that AI adoption enhances operational efficiency, customer engagement, and decision-making processes in SMEs. However, it also highlights several challenges including high implementation costs, lack of technical expertise, and resistance to change. The findings underscore the critical role of government support, digital infrastructure, and upskilling initiatives in facilitating effective AI integration within SMEs. This research contributes to the understanding of AI's transformative potential for small businesses in developing nations and offers strategic recommendations for stakeholders to foster an inclusive and innovation-driven SME ecosystem in India..

This chapter discusses the complicated interplay between trust and distrust in artificial intelligence. It argues against the prevalent view of trust and distrust as two contrary states, providing theoretical foundations for the existence of overlapping states in decision-making processes. This revolutionary view is corroborated with both theoretical analysis and methodological novelties by applying a probabilistic model based on the Beta distribution. Thus, it becomes clear that trust in artificial intelligence has to go much beyond just the parameters of credibility and benevolence, which is comparable to human relationships, and include ethical, social, and technical components that are interlinked in the collaboration between humans and machines.

The application of cloud accounting technology is an important solution in improving the efficiency and transparency of financial management for Micro, Small and Medium Enterprises (MSMEs). However, the readiness of MSMEs in Solo Raya to adopt this technology is still a challenge. This study aims to analyze the factors that influence the readiness and intention of MSMEs in using cloud accounting with the Unified Theory of Acceptance and Use of Technology (UTAUT) approach. This study uses quantitative data with a survey method through a questionnaire distributed to 150 MSMEs in Solo Raya and analyzed using SmartPLS 3.0. The results show that the main variables such as performance expectancy, effort expectancy, social influence, and facilitating conditions have a positive and significant effect on behavioral intention. In addition, perceived trust also has positive and significant effect, while perceived risk has a significant negative effect on behavioral intention. Behavioral intention then has a positive and significant effect on cloud accounting use behavior. The findings of this study are expected to provide useful insights for future researchers as a basis for evaluation and further study.