The Role of Technology Acceptance, AI Anxiety, and Demographic Factors in Jordanian Healthcare Decision Makers' Attitudes Toward Artificial Intelligence

The Role of Technology Acceptance, AI Anxiety, and Demographic Factors in Jordanian Healthcare Decision Makers' Attitudes Toward Artificial Intelligence | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article The Role of Technology Acceptance, AI Anxiety, and Demographic Factors in Jordanian Healthcare Decision Makers' Attitudes Toward Artificial Intelligence Sara Al-Ajlouny, Hindya O. Al-Maqableh, Ala’a F. Al-Shaikh, Yousef Khader, and 4 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7338088/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 15 You are reading this latest preprint version Abstract Artificial intelligence (AI) is increasingly acknowledged as a transformational influence in healthcare, including early diagnostic responses, decision support, and therapeutic enhancement. Successful implementation of AI technologies is dependent on the attitudes of healthcare decision-makers (HDMs) who have a central role in influencing institutional and national adoption plans. This research aimed to explore the impact of technology acceptance, AI anxiety, and demographic factors on Jordanian HDMs' attitudes towards AI in healthcare institutions A cross-sectional approach was used, focusing on 152 healthcare decision-makers from governmental, NGO, and academic institutions in Jordan. Data was collected using a structured online questionnaire utilizing three validated instruments: the General AI Attitudes Scale, the Artificial Intelligence Anxiety Scale, and a modified Technology Acceptance Model. Hierarchical multiple linear regression was used to identify determinants of favorable and negative views about AI. The results indicated the most positive feelings toward AI among participants, shown by a mean positive attitude score of 3.94 (SD = 0.59) and a somewhat lower mean negative attitude score of 2.60 (SD = 0.71). Junior HDMs and those employed in academic institutions, NGOs, and governmental organizations had greater positive views compared to their private sector counterparts. Heightened anxiety over AI, especially linked to its learning capacities and sociotechnical ramifications, was strongly associated with negative perceptions. Furthermore, perceived value and ease of use were identified as significant factors influencing positive attitudes towards AI. These results emphasize the need for specialized training and intuitive AI solutions to enhance acceptance and enable the efficient use of AI in healthcare systems. Health sciences/Health care Humanities/Health humanities Humanities/Medical humanities Artificial Intelligence Technology Acceptance AI Anxiety Healthcare Decision-Makers Jordan Attitudes toward AI Introduction Artificial intelligence (AI) adoption in healthcare is a promising new path that has revolutionized the practice of medicine and improved outcomes for patients. Given this opportunity, AI is a topic of debate in global healthcare systems. (Koo et al., 2024). AI technologies may assist in better and early diagnosis, decision support systems, and treatment plans, which may serve as a framework to enhance access, quality and healthcare outcomes (Kaya et al., 2024; Yu et al., 2018; Topol, 2019). However, healthcare decision-makers’ (HDM) attitudes about these innovations are important if AI is to be effectively implemented (Jiang et al., 2017; Ye et al., 2019). Certain psychological factors make the acceptance of AI in healthcare feature pronounced barriers, such as AI-related anxiety. AI anxiety is defined as the discomfort that people feel when they use or even contemplate AI systems (Kaya et al., 2024). Some of these are job insecurity, perceived loss of personal control over some part of their lives, matters that are ethical, or AI-related bias. Ye et al., (2019) conducted a cross-sectional study in China based in which they found that psychosocial factors such as anxiety, influence the implementation of AI in the health sector. HDMs who are more anxious about the integration of AI software may be rated higher in negative AI attitudes and hence, be resistant to or slower at implementing it. Cruz et al. (2023) have also described the effects of the AI anxiety trend among medical students; they found out that students who are less trusting of AI are those who do not seem to be eager to benefit from the use of technology, and this is normally so since such people have higher levels of anxiety. This means that AI anxiety can influence the implementation of novel health technologies. The Technology Acceptance Model (TAM), widely used to predict individuals' acceptance of new technologies, highlights two key determinants: perceived ease of use and perceived usefulness (Şahin & Yıldırım, 2024). In the context of healthcare, these determinants are critical because decision-makers are more motivated to implement and utilize AI if they view it in a positive light and within the context of easily incorporated into working practices (Babiker et al., 2024). While knowledge regarding the application of AI to healthcare exists, concerns regarding its use as well as its complexity continue to be factors that influence the adoption of such technology. Therefore, exploring the attitudes related to perceived usefulness and ease of use formed by HDMs, will help to evaluate how AI could be implemented in healthcare systems. Several other factors including age, gender, level of education, and the level of technological literacy also contribute to the specific attitude towards the use of AI in health care. Early career professionals had more positive attitudes toward AI than their older colleagues (Morales-García et al., 2024). In contrast, older employees may be less ready to accept modern technology because of an informed or uniformed fear of change (Bergdahl et al., 2023). Furthermore, the ease with which AI systems are utilized–how well they can perform a task, plays a role in attitude formation. In a study conducted by Arishi et al. (2023) on nurses in Saudi Arabia, attitudes toward AI capabilities were closely linked to comfort levels in using AI systems. Those who believed AI systems were capable and reliable reported more positive attitudes and greater comfort in adopting the technology. Jordan's healthcare sector has evolved significantly; the government has made efforts to improve healthcare delivery through both public and private initiatives with significant investments in facilities. (AlMomani et al., 2019). As Jordan embraces technological advancements to address these challenges, the adoption of AI in healthcare emerges as a promising step. The adoption of advanced healthcare technologies in Jordan, however, faces challenges, including varying levels of technological literacy among healthcare professionals and the need for comprehensive training programs. (Hamad et al., 2024) This work aimed to explore the effect that technology acceptance, AI anxiety, and demographic characteristics on the attitudes of Jordanian healthcare decision-makers towards AI. Since Jordan is seeking ways to upgrade its healthcare system by integrating smart technologies, exploring relevant factors is of critical importance for effective adoption and implementation. The results of this research will therefore be useful in giving healthcare policymakers in Jordan a guideline on how to address the needs and concerns of health decision-makers. This research also fills a gap in the literature regarding AI adoption in the region. It also contributes to the global conversation on implications and concerns regarding integrating AI into healthcare. Methods 2.1 Study Design This study employed a descriptive cross-sectional methodology to evaluate the association between AI attitudes and AI anxiety as well as AI technology acceptance among HDMs. 2.2 study population The study targeted healthcare decision-makers (HDMs) in Jordan, defined as individuals in leadership roles within governmental, private, NGO, or academic healthcare sectors. Participants held leadership roles at different administrative levels, including senior managers (e.g., department directors), middle managers (e.g., unit supervisors), executive committee members, and board representatives involved in healthcare policy and governance (Huber, 2017) . Inclusion criteria were current employment in Jordan. Individuals not involved in healthcare leadership or working outside Jordan were excluded. 2.3Sampling The estimated population of healthcare decision-makers (HDMs) in Jordan was set at 250, based on the total national health workforce of approximately 70,000 professionals (High Health Council, 2018) and international benchmarks indicating that HDMs typically represent 1–3% of the healthcare workforce (World Health Organization, 2016; OECD, 2016). Using the Raosoft sample size calculator (Raosoft, 2004) with a 95% confidence level, 5% margin of error, and 50% response distribution, the required sample size was calculated to be 152. A total of 152 HDMs participated, resulting in a 100% response rate relative to the calculated sample size. 2.4 Questionnaire The main instrument for data acquisition consisted of a structured questionnaire which assessed the variables across three domains including General AI Attitudes Scale (GAAIS) (Schepman and Rodway, 2020; Kaya et al., 2022), AI Anxiety Scale (AIAS) (Wang and Wang, 2022), and Technology Acceptance Model (TAM) (Gagnon et al., 2012). A The TAM was modified to ensure item wording reflected AI use within healthcare decision-making contexts Demographics The survey collected demographic data which included age, administrative roles, and occupational sectors (Private Sector, NGOs, Universities, Government). It is noteworthy that despite literature indicating that gender does factor in attitudes regarding AI adoption and acceptance, female representation comprises approximately 27% of management and leadership positions across all levels in Jordan, and fewer than 10% occupy higher-level administrative roles. (Human Resources for Health 2030, 2018). Given this significant underrepresentation, we opted out of including gender as a variable because of the concern of it not yielding meaningful insights due to the limited sample size. GAAIS The questionnaire was modified from previous studies (Schepman and Rodway, 2020; Kaya et al., 2022) to evaluate HDMs attitudes towards the utilization of AI technology. It comprises 20 items rated on a five-point Likert scale ranging from “strongly disagree” (1) to “strongly agree” (5)." Respondents evaluate 12 positive and 8 negative statements, with higher scores reflecting more favorable attitudes. Reverse scoring was applied to the negative items to ensure consistency in interpretation. AIAS The anxiety measure was derived from the Artificial Intelligence Anxiety Scale by Wang and Wang, 2022. Anxieties about AI learning, job replacement, sociotechnical blindness, and AI configuration are the four dimensions. The scale consists of 21 items rated on a 7-point Likert scale, ranging from 1 (“never”) to 7 (“always”). Higher scores indicate greater anxiety regarding the implementation and impact of AI technologies. TAM model In the context of the Technology Acceptance Model, we quantified the Perceived usefulness Perceived ease of use of AI. Participants evaluated remarks concerning the simplicity and perceived advantages of AI technology in their daily professional tasks using a five-point Likert scale ranging from “strongly disagree” (1) to “strongly agree” (5) (Gagnon, Orruño, Asua, Abdeljelil, & Emparanza, 2012). Previous research on AI in healthcare scales has confirmed their validity, with GAAIS having a Cronbach's alpha of 0.85, the AI Anxiety Scale at 0.88, and the TAM at 0.87, ensuring reliability of measurement. 2.5 Data collection Data was collected during a period of three months, from May 2024 to July 2024. A Google Form online survey was generated, and HDMs received the survey link via email. The email = included a consent form to participate in the study. On occasion, participants were given reminders to finalize the survey. The utilization of online surveys via Google Forms facilitates efficient dissemination, effortless participant interaction, and safe data preservation. (Radhaswati et al., 2022). A pilot run was performed involving (n=15) experts to assess the readability and understanding levels of the questionnaire items. Pilot participants were urged to provide feedback on any items they found ambiguous or challenging, facilitating enhancements to the questionnaire's overall clarity through necessary adjustments. Pilot participants and their responses were eliminated from the final sample to mitigate any potential bias or influence on the results. 2.6 Data Analysis Data were analyzed using IBM SPSS Statistics (Version 27). Descriptive statistics summarized participant characteristics and key variables. Hierarchical multiple linear regression was used to examine predictors of AI attitudes and anxiety, entering demographic and professional variables first, followed by AI-related factors. Logistic regression assessed determinants of high versus low AI acceptance based on median TAM scores. All models were tested for statistical assumptions, with significance set at p < 0.05. Results A total of 152 health decision-makers in Jordanian healthcare setting participated. The Private sector constitutes the largest share, making up over half of the participants (n=79, 52.0%), while NGOs represent the smallest proportion at (n=19, 12.5%). The participants are classified into senior management (n=59, 38.8%), middle management (n=52, 34.2%) and executive committee members (n=26, 17.1%), and board members (n=15, 9.9%). The mean (SD) age of participants was 44.23 (10.36) years. Table 1 shows the socio-demographic characteristics of the study participants, including their distribution across work sectors (private, governmental, NGO, and academic), administrative levels (senior and middle management, executive committee members, and board members), and age Table (1) Participants’ socio-demographic characteristics Variables Categories Frequency Percent Mean (SD) Work sectors Private Sector NGOs Universities Governmental 79 19 20 34 52.0 12.5 13.2 22.4 Administrative levels Senior Management Middle Management Excusive committee members Board members 59 52 26 15 38.8 34.2 17.1 9.9 Age /years 44.23 (10.36) NGOs: Non-Governmental Organization Levels of general attitudes, anxiety and technology acceptance model toward artificial intelligence . Our results demonstrated that the participants had a mean score of positive GAAIS 3.94, SD=0.59 denoting agreeableness toward AI. On other hand negative domain had a mean score of 2.60, SD=0.71 indicating to below neutral level. The highest scored domain was sociotechnical blindness (M=4.42, SD=1.77) followed by Job replacement anxiety (M=4.31, SD=1.75) while the lowest one was AI learning anxiety (M=3.38, SD=1.58). These results suggest the participants had a moderate to high level of anxiety toward AI Considering technology acceptance, the perceived usefulness had a mean score of 3.55 (1.02), while the perceived ease of use had a mean score of 3.51 (0.93) pointing their perception above neutral level regarding accepting of technology. Table 2 shows the Levels of general attitudes, anxiety and technology acceptance model toward artificial intelligence Table (2) Levels of general attitudes, anxiety and technology acceptance model toward artificial intelligence Study instruments Mean SD General attitudes towards AI Positive general attitudes towards AI Negative general attitudes towards AI 3.94 2.60 0.59 0.71 Artificial intelligence anxiety AI learning anxiety Job replacement anxiety Sociotechnical blindness AI configuration anxiety 3.38 4.31 4.42 3.96 1.58 1.75 1.77 1.86 Technology acceptance model Perceived usefulness Perceived ease of use 3.55 3.51 1.02 0.93 Predicting the positive GAAIS Hierarchical multiple linear regression test was carried out to predict positive GAAIS as a function of participants’ demographics, AI anxiety subscales and technology acceptance model subscales. In the first model the participants’ work sectors, administrative level namely ( middle management and excusive committee members and age explained 20.0% of the variance in positive attitudes toward AI ( F=6.33, P<0.001 ).It was found that those working in universities ( B =0.403, P =0.002) , NGOs ( B =0.325, P =0.016) and governmental work sector ( B =0.219, P =0.034) reported significantly higher positive attitudes score toward AI compared to those work in a private sector. On other hand the participants’ age was significantly inversely correlated with attitudes toward AI ( B = -0.100, P =0.025). Then, the artificial intelligence anxiety subscales were added to the model (Model2) and it was demonstrated that the additional factors explained a further 21.8% variance in positive attitudes score toward AI (ΔR change = 21.8%, F change 12.25, P <0.001). Artificial intelligence anxiety subscales were inversely correlated with positive attitudes score toward AI, the highest predictor was AI anxiety learning ( B =0.090, P =0.012), followed by sociotechnical blindness ( B =0.085, P =0.032), then job replacement ( B =0.064, P =0.039) and the lowest one was AI configuration ( B =0.071, P =0.046). When it comes to model (3) individual factors and artificial intelligence anxiety were controlled, the technology acceptance subscales were added to the model, an additional 3.7% of variance was explained (ΔR change = 3.7%, F change 8.84, P <0.001). Perceived usefulness demonstrates the strongest positive predictor for positive attitudes score toward AI ( B =0.09, P =0.014) followed by perceived ease of use ( B =0.094, P =0.034). Table (3) Predicting the negative GAAIS Another separate Hierarchical multiple linear regression was utilized to predict negative GAAIS. Findings showed that in the first model, the participants’ work sectors, administrative level namely ( middle management and excusive committee members and age explained 16.0% of the variance in negative attitudes toward AI ( F =4.98, P < 0.001).It was found that those working in universities ( B =0.376, P =0.016) , NGOs ( B =0.359, P =0.026) and governmental work sector ( B =0.246, P =0.040) reported significantly higher forgiving about the negative attitudes score toward AI compared to those work in a private sector. On other hand the participants’ age was significantly inversely correlated with forgiving about the negative attitudes toward AI ( B = -0.11, P = 0.045). Meaning older people had lower forgiving scores about the negative attitudes toward AI In the model (2) where the artificial intelligence anxiety subscales were added, it was noted that the additional factors explained a further 16.0% of variance in negative attitudes score toward AI (ΔR change = 16.0%, F change 4.98, P <0.001). Artificial intelligence anxiety subscales were inversely correlated with negative attitudes score toward AI. Among the subscales, the strongest predictor was AI anxiety learning ( B =-0.127, P =0.007), followed by sociotechnical blindness ( B = -0.120, P =0.011), then job replacement ( B = -0.104, P =0.012) and the lowest one was AI configuration ( B = -0.108, P =0.015). The negative relationship in this context reveals as the artificial intelligence anxiety subscales score increase, participants are less likely to hold forgiving or lenient about the negative attitudes toward AI drawbacks When it comes to model (3) individual factors and artificial intelligence anxiety were controlled, the technology acceptance subscales were added to the model, an additional 6.5% of variance was explained (ΔR change = 6.5%, F change 9.25, P <0.001). Perceived usefulness demonstrates the strongest positive predictor for negative attitudes score toward AI ( B =0.120, P =0.031) followed by perceived ease of use ( B =0.130, P =0.036). Suggesting higher technology perception associated with higher forgiving score about the negative attitudes toward AI Table (4) Table (3) Hierarchical multiple linear regression analysis results to predict positive attitudes toward artificial intelligence. Variables Model 1 Model II Model III B SE p B SE p B SE P Demographic Work sectors NGOs Universities Governmental 0.32 0.40 0.22 0.13 0.12 0.10 0.016 0.002 0.034 0.25 0.34 0.255 0.10 0.12 0.09 0.016 0.007 0.005 0.15 0.17 0.17 0.11 0.08 0.09 0.18 0.01 0.05 Administrative levels Middle Management Excusive committee members Board members 0.22 0.31 0.16 0.10 0.12 0.15 0.035 0.014 0.294 0.26 0.26 0.27 0.10 0.12 1.5 0.011 0.033 0.137 0.18 0.18 0.22 0.09 0.09 0.13 0.04 0.04 0.08 Age -0.10 0.00 0.025 -0.00 0.00 0.11 -0.006 0.00 0.11 AI anxiety AI learning Job replacement Sociotechnical blindness AI configuration -0.09 -0.06 -0.08 -0.07 0.03 0.03 0.04 0.03 0.01 0.04 0.03 0.04 -0.1 -0.09 -0.07 -0.07 0.03 0.03 0.03 0.03 0.00 0.02 0.01 0.03 Technology acceptance model Perceived usefulness Perceived ease of use 0.09 0.09 0.03 0.04 0.01 0.03 R 2 0.238 0.39 0.50 Adjusted R 2 0.200 0.418 0.455 Adjusted R 2 change --- 21.8 3.7 F for Change in R 2 6.33** 12.25** 8.84** *Private sector and senior management are reference group Table (4) Hierarchical multiple linear regression analysis results to predict negative attitudes toward artificial intelligence. Variables Model 1 Model II Model III B* SE Β* T p B* SE β* t p B* SE β* t p Demographic Work sectors NGOs Universities Governmental 0.359 0.376 0.246 0.160 0.154 0.122 0.178 0.197 0.156 2.249 2.445 2.012 0.026 0.016 0.040 0.246 0.295 0.265 0.122 0.143 0.122 0.156 0.155 0.167 2.012 2.006 2.352 0.046 0.041 0.020 0.059 0.176 0.374 0.104 0.178 0.146 0.041 0.077 0.200 0.561 0.988 2.558 0.571 0.325 0.012 Administrative levels Middle Management Excusive committee members Board members 0.282 0.444 0.066 0.122 0.165 0.132 0.179 0.220 0.040 2.310 2.691 0.501 0.022 0.008 0.617 0.278 0.264 0.047 0.139 0.110 0.123 0.109 0.167 0.029 1.995 2.402 0.380 0.048 0.018 0.704 0.098 0.367 0.240 0.116 0.159 0.123 0.069 0.182 0.152 0.842 2.312 1.940 0.401 0.022 0.054 Age -0.11 0.005 -0.164 2.202 0.045 -0.007 0.005 -0.109 1.444 0.152 -0.006 0.005 -0.09 1.367 0.174 AI anxiety AI learning Job replacement Sociotechnical blindness AI configuration -0.127 -0.104 -0.120 -0.108 0.046 0.041 0.046 0.044 -0.270 -0.262 -0.256 -0.233 2.754 2.524 2.588 2.475 0.007 0.012 0.011 0.015 -0.105 -0.113 -0.102 -0.023 0.043 0.041 0.402 0.042 -0.226 -0.285 -0.218 -0.055 2.445 2.759 2.409 0.532 0.016 0.007 0.017 0.596 Technology acceptance model Perceived usefulness Perceived ease of use 0.120 0.130 0.055 0.062 0.185 0.173 2.183 2.114 0.031 0.036 R 2 0.200 0.38 0.448 Adjusted R 2 0.160 0.329 0.394 Adjusted R 2 change --- 0.169 0.065 F for Change in R 2 4.98** 9.75** 9.25** Private sector and senior management are reference groups. * Positive sign indicates to have more forgiving about the negative attitudes due to reversing score Discussion The findings of this study suggest that HDMs in Jordan generally hold favorable attitudes toward AI, as indicated by a mean positive attitude score of 3.94 (SD = 0.59). In contrast, negative attitudes were comparatively lower, with a mean score of 2.60 (SD = 0.59). These results reflect an overall inclination to acknowledge AI's potential to enhance efficiency, accuracy, and decision-making in healthcare, an observation consistent with prior research (Busch et al., 2024). Significantly more positive attitudes were reported among HDMs affiliated with academic institutions, governmental bodies, NGOs, and corporates, compared to those in the private sector. This disparity may be attributed to greater exposure to AI-related policies, research initiatives, and institutional investments in these sectors (Al-Dmour et al., 2025). Familiarity with and engagement in AI technologies have been identified as critical factors in shaping positive perceptions (Issa et al., 2024; Al-Qerem et al., 2023), particularly in well-resourced environments where decision-makers are more likely to interact with AI systems. The broader regional context supports these findings, as cultural, governmental, and institutional frameworks across the Middle East and North Africa region influence AI adoption trajectories. Although countries like Saudi Arabia have demonstrated substantial progress in AI healthcare applications—driven by robust governmental investment and policy support (Saeed et al., 2023)—Jordan’s advancement may require more strategic outreach and education to foster wider acceptance among its healthcare workforces. While growing interest in AI is evident, concerns surrounding its ethical use, data privacy, and practical implementation remain substantial. The lack of region-specific ethical frameworks for AI implementation in healthcare contributes to unease among stakeholders, particularly around patient data confidentiality, transparency, and accountability (Hasan et al., 2024; Amann et al., 2023). This study also found that younger HDMs expressed more positive attitudes toward AI, consistent with literature suggesting that younger professionals are more comfortable with emerging technologies (Smith et al., 2022; Hoffman et al., 2024, Li et al., 2024). These findings highlight the importance of tailored AI literacy and professional development programs aimed at older cohorts, helping bridge generational gaps in technology adoption. Anxiety surrounding AI emerged as a key predictor of negative attitudes. Specifically, AI learning anxiety, more so than concerns about automation or sociotechnical complexity, was most strongly associated with lower acceptance. This aligns with research showing that job security concerns and perceived difficulty in learning AI tools hinder adoption (Wen et al., 2024; Terzi, 2020). However, structured training programs have been shown to mitigate these anxieties and enhance openness to AI integration (Patel et al., 2020; Green et al., 2019). The user-centered design of AI tools remains critical. Evidence suggests that intuitive, user-friendly interfaces reduce implementation barriers and foster positive engagement (Kimiafar et al., 2023; Lambert et al., 2023). Moreover, open discussion and transparency about AI's role can alleviate uncertainty and build trust among HDMs (Cho et al., 2024). The Technology Acceptance Model (TAM) offers valuable insights into these dynamics. Perceived usefulness (PU) was strongly associated with positive attitudes toward AI, while perceived ease of use (PEOU) showed more mixed influence. These findings reinforce the necessity for AI systems that are both effective and easy to navigate (Ibrahim et al., 2025). Limitations Several limitations need to be acknowledged as cross-sectional design limits our causal inferences, and several longitudinal studies will be required which track the development of attitudes over time as AI increasingly embeds within healthcare settings. Furthermore, the relatively small sample size may restrict the generalizability of findings. HDMs represent a niche and often inaccessible group, making large-scale recruitment challenging. Additionally, the study's cross-sectional design poses the risk of potential self-selection bias which may have skewed participation toward individuals with preexisting interest in AI. Our sample contained many key decision-makers, but future research also needs to move to include perspectives from frontline health professionals and from patients themselves to fully understand how AI is likely to be accepted in healthcare. Second, ethical and legal issues related to AI in Jordan's healthcare sector need more detailed attention. Due to the challenges in accessing the cohort in a swift manner, during this study, policymakers may have developed regulatory frameworks that address issues related to transparency, accountability, and equal access to healthcare solutions driven by AI. Conclusion This is the first study in Jordan to explore the perspectives of healthcare decision-makers—rather than students, nurses, or clinicians—on AI adoption. This offers a unique contribution to literature, considering the pivotal role HDMs play in guiding health technology policy and institutional decision-making. The present study provides critical insight into the enablers and barriers shaping AI adoption among an influential cohort in Jordanian healthcare. Our results demonstrate the necessity for user-friendly AI tools, structured training programs to guide AI-literacy, and a growing need for region-specific ethical standards. Future research should aim to incorporate comparative, multi-country analyses, longitudinal designs, and larger samples to further inform effective, context-specific AI integration strategies. Abbreviations AI Artificial Intelligence HDM Healthcare Decision-Maker TAM Technology Acceptance Model TAM Technology Acceptance Model GAIAS General AI Attitudes Scale AIAS Artificial Intelligence Anxiety Scale NGO Non-Governmental Organization SPSS Statistical Package for Social Sciences SD Standard Deviation Declarations Ethical Considerations Ethical approval (2023/11/15/16436) was obtained from the Jordan Ministry of Health’s Institutional Review Board (IRB), and the study was conducted in accordance with the Declaration of Helsinki (World Medical Association, 2013). All participants provided written informed consent. They were fully informed about the voluntary nature of their participation, the anonymity and confidentiality of their responses, and their right to withdraw at any time without any consequences. Competing interests The authors declare no competing interests Funding This work was supported by a competitive grant (8671) from the University of Sharjah to Sara Al-Ajlouny and Hindya Al-Maqableh. This research work was also supported by Abu Dhabi National Oil Company (ADNOC), Emirates NBD, Sharjah Electricity Water & Gas Authority (SEWA), Technology Innovation Institute (TII), and GSK as the sponsors of the 4 th Forum for Women in Research (QUWA): Sustaining Women’s Empowerment in Research & Innovation at the University of Sharjah. Author contributions S.A. and H.A. contributed equally to this work. S.A. and H.A. conceptualized and designed the study. S.A., H.A., A.A. and M.A. participated in data acquisition. Y.K., T.S., Y.A., and T.L.M. critically reviewed and revised the manuscript. All authors approved the final version of the manuscript. Acknowledgements We sincerely express our gratitude to Mr. Anees Hjazeen, Biostatistician at the Royal Medical Services, for his invaluable support in conducting the data analysis. His expertise and dedication ensured the accuracy and rigor of our results, contributing significantly to the quality of this study. References Koo T. , Zakaria A. , Ng J. , & Leong X.. Systematic review of the application of artificial intelligence in healthcare and nursing care. Malaysian Journal of Medical Sciences 2024;31(5):135-142. https://doi.org/10.21315/mjms2024.31.5.9 Kaya, F. et al. (2022) ‘The Roles of Personality Traits, AI Anxiety, and Demographic Factors in Attitudes toward Artificial Intelligence’, International Journal of Human–Computer Interaction, 40(2), pp. 497–514. doi: 10.1080/10447318.2022.2151730. Yu KH, Beam AL, Kohane IS. Artificial intelligence in healthcare. Nat Biomed Eng. 2018 Oct;2(10):719-731. doi: 10.1038/s41551-018-0305-z. Epub 2018 Oct 10. PMID: 31015651. Topol, E.J. High-performance medicine: the convergence of human and artificial intelligence. Nat Med 25, 44–56 (2019). https://doi.org/10.1038/s41591-018-0300-7 Jiang F, Jiang Y, Zhi H, Dong Y, Li H, Ma S, Wang Y, Dong Q, Shen H, Wang Y. Artificial intelligence in healthcare: past, present and future. Stroke Vasc Neurol. 2017 Jun 21;2(4):230-243. doi: 10.1136/svn-2017-000101. PMID: 29507784; PMCID: PMC5829945. Ye T, Xue J, He M, Gu J, Lin H, Xu B, Cheng Y. Psychosocial Factors Affecting Artificial Intelligence Adoption in Health Care in China: Cross-Sectional Study, J Med Internet Res 2019;21(10):e14316. doi: 10.2196/14316PMID: 31625950PMCID: 6913088 Cruz JP, Sembekova A, Omirzakova D, Bolla SR, Balay-odao EM. General Attitudes Towards and Readiness for Medical Artificial Intelligence among Medical and Health Sciences Students in Kazakhstan JMIR Preprints. 01/06/2023:49536 DOI: 10.2196/preprints.49536 Şahin, M. G., & Yıldırım, Y. (2024). The general attitudes towards artificial intelligence (GAAIS): A meta-analytic reliability generalization study. International Journal of Assessment Tools in Education, 11(2), 303-319. https://doi.org/10.21449/ijate.1369023 Babiker, A., Alshakhsi, S., Al-Thani, D., Montag, C., & Ali, R. (2024). Attitude Towards AI: Potential Influence of Conspiracy Belief, XAI Experience and Locus of Control. International Journal of Human–Computer Interaction, 41(13), 7939–7951. https://doi.org/10.1080/10447318.2024.2401249 Morales-García WC, Sairitupa-Sanchez LZ, Morales-García SB, Morales-García M. Adaptation and psychometric properties of a brief version of the general self-efficacy scale for use with artificial intelligence (GSE-6AI) among university students. InFrontiers in Education 2024 Mar 8 (Vol. 9, p. 1293437). Frontiers Media SA. Bergdahl J, Latikka R, Celuch M, Savolainen I, Mantere ES, Savela N, Oksanen A. Self-determination and attitudes toward artificial intelligence: Cross-national and longitudinal perspectives. Telematics and Informatics. 2023 Aug 1; 82:102013. Arishi, D. Y. O., Elseesy, N., & Al-Abdullah, N. (2023). Nurses’ general attitudes, comfortableness, and perceived capabilities toward using artificial intelligence systems among nurses at Makkah city. Bioscience Research, 20(3), 682–695. Almomani, H., Obaidat, M., Khazaleh, A., Muneizel, O., Afyouni, N. M., & Fayyad, S. M. (2019). Review of medical waste management in jordanian health care organisations. British Journal of Healthcare Management, 25(8), 1-8. https://doi.org/10.12968/bjhc.2019.0041 Hamad, M., Qtaishat, F. A., Mhairat, E., AL-Qunbar, A., Jaradat, M., Mousa, A., … & Alkhaldi, S. M. (2024). Artificial intelligence readiness among jordanian medical students: using medical artificial intelligence readiness scale for medical students (mairs-ms). Journal of Medical Education and Curricular Development, 11. https://doi.org/10.1177/23821205241281648 Raosoft, Inc. (2004). Sample size calculator. http://www.raosoft.com/samplesize.html HRH2030. (2018). Women in health management: A case study in Jordan. Chemonics International Inc. https://haqqi.s3.eu-north-1.amazonaws.com/2018-10/HRH2030%20Jordan%20Women%20in%20Health%20Mana.pdf Schepman A, Rodway P. Initial validation of the general attitudes towards Artificial Intelligence Scale. Comput Hum Behav Rep. 2020 Jan-Jul; 1:100014. doi: 10.1016/j.chbr.2020.100014. Epub 2020 May 18. PMID: 34235291; PMCID: PMC7231759. Wang, Y.-Y., & Wang, Y.-S. (2022). Development and validation of an artificial intelligence anxiety scale: An initial application in predicting motivated learning behavior. Interactive Learning Environments, 30(4), 619–634. https://doi.org/10.1080/10494820.2019.1674887 Gagnon MP, Orruño E, Asua J, Abdeljelil AB, Emparanza J. Using a modified technology acceptance model to evaluate healthcare professionals' adoption of a new telemonitoring system. Telemed J E Health. 2012 Jan-Feb;18(1):54-9. doi: 10.1089/tmj.2011.0066. Epub 2011 Nov 14. PMID: 22082108; PMCID: PMC3270047. Radhaswati, I. D. A. A. and Santosa, M. H. (2022). Teachers’ perceptions: the use of google form as a media to assess primary school students. EDUTEC : Journal of Education and Technology, 5(4), 910-924. https://doi.org/10.29062/edu.v5i4.308 IBM Corp. (2021). IBM SPSS Statistics for Windows (Version 25.0). IBM Corp Busch, F., Hoffmann, L., Xu, L., Zhang, L., Hu, B., García-Juàrez, I., ... & COMFORT consortium. (2024). Multinational attitudes towards AI in healthcare and diagnostics among hospital patients. medRxiv , 2024-09. Al-Dmour R, Al-Dmour H, Basheer Amin E, Al-Dmour A. Impact of AI and big data analytics on healthcare outcomes: An empirical study in Jordanian healthcare institutions. DIGITAL HEALTH. 2025;11. doi:10.1177/20552076241311051 Issa, W. B., Shorbagi, A., Alshorman, A., Rababa, M., Majed, K. A., Radwan, H., … & Fakhry, R. (2024). Shaping the future: perspectives on the integration of artificial intelligence in health profession education: a multi-country survey.. https://doi.org/10.21203/rs.3.rs-4396289/v1 Al‐Qerem, W., Eberhardt, J., Jarab, A. S., Bawab, A. Q. A., Hammad, A. M., Alasmari, F., … & Al-Beool, S. (2023). Exploring knowledge, attitudes, and practices towards artificial intelligence among health professions’ students in jordan. BMC Medical Informatics and Decision Making, 23(1). https://doi.org/10.1186/s12911-023-02403-0 Saeed A, Bin Saeed A, AlAhmri FA. Saudi Arabia Health Systems: Challenging and Future Transformations With Artificial Intelligence. Cureus. 2023 Apr 19;15(4):e37826. doi: 10.7759/cureus.37826. PMID: 37214025; PMCID: PMC10197987. Hasan HE, Jaber D, Khabour OF, Alzoubi KH. Ethical considerations and concerns in the implementation of AI in pharmacy practice: a cross-sectional study. BMC Med Ethics. 2024 May 16;25(1):55. doi: 10.1186/s12910-024-01062-8. PMID: 38750441; PMCID: PMC11096093. Amann J, Vayena E, Ormond KE, Frey D, Madai VI, Blasimme A. Expectations and attitudes towards medical artificial intelligence: A qualitative study in the field of stroke. PLoS One. 2023 Jan 11;18(1):e0279088. doi: 10.1371/journal.pone.0279088. PMID: 36630325; PMCID: PMC9833517. Smith, J. and Johnson, P. (2022) The Impact of AI on Medical Data Analysis: A Case Study of IBM Watson Health. Journal of Health Informatics, 45, 145-156. Lee, H.S. and Lee, J. (2021) Applying Artificial Intelligence in Physical Education and Future Perspectives. Sustainability, 13, Article 351. https://doi.org/10.3390/su13010351 Hoffman J, Hattingh L, Shinners L, Angus R, Richards B, Hughes I, Wenke R, Allied Health Professionals’ Perceptions of Artificial Intelligence in the Clinical Setting: Cross-Sectional Survey, JMIR Form Res 2024;8:e57204. URL: https://formative.jmir.org/2024/1/e5720 DOI: 10.2196/57204 Li M, Xiong X and Xu B (2024) Attitudes and perceptions of Chinese oncologists towards artificial intelligence in healthcare: a cross-sectional survey. Front. Digit. Health 6 :1371302. doi: 10.3389/fdgth.2024.1371302 Wen, F., Li, Y., Zhou, Y., An, X., & Zou, Q. (2024). A study on the relationship between AI anxiety and AI behavioral intention of secondary school students learning English as a foreign language. Journal of Educational Technology Development and Exchange (JETDE) , 17 (1), 130-154. Terzi, R. (2020). An Adaptation of Artificial Intelligence Anxiety Scale into Turkish: Reliability and Validity Study. International Online Journal of Education and Teaching , 7 (4), 1501-1515. Patel V, Khan MN, Shrivastava A, Sadiq K, Ali SA, Moore SR, Brown DE, Syed S. Artificial Intelligence Applied to Gastrointestinal Diagnostics: A Review. J Pediatr Gastroenterol Nutr. 2020 Jan;70(1):4-11. doi: 10.1097/MPG.0000000000002507. PMID: 31567886; PMCID: PMC6934912. Green, C. S., Bavelier, D., Kramer, A. F., Vinogradov, S., Ansorge, U., Ball, K. K., & Witt, C. M. (2019). Improving methodological standards in behavioral interventions for cognitive enhancement. Journal of Cognitive Enhancement, 3 (1), 2–29. (Publisher: Springer Science and Business Media LLC) Kimiafar, K., Sarbaz, M., Tabatabaei, S. M., Ghaddaripouri, K., Mousavi, A. S., Mehneh, M. R., & Baigi, S. F. M. (2023). Artificial intelligence literacy among healthcare professionals and students: a systematic review. Frontiers in Health Informatics , 12 , 168. Lambert, S.I., Madi, M., Sopka, S. et al. An integrative review on the acceptance of artificial intelligence among healthcare professionals in hospitals. npj Digit. Med. 6 , 111 (2023). https://doi.org/10.1038/s41746-023-00852-5 Cho KA, Seo YH. Dual mediating effects of anxiety to use and acceptance attitude of artificial intelligence technology on the relationship between nursing students' perception of and intention to use them: a descriptive study. BMC Nurs. 2024 Mar 28;23(1):212. doi: 10.1186/s12912-024-01887-z. PMID: 38539198; PMCID: PMC10976840. Ibrahim F, Münscher JC, Daseking M, Telle NT. The technology acceptance model and adopter type analysis in the context of artificial intelligence. Front Artif Intell. 2025 Jan 16; 7:1496518. doi: 10.3389/frai.2024.1496518. PMID: 39886023; PMCID: PMC11780378. High Health Council. (2018). National Human Resources for Health Strategy for Jordan 2018–2022 . Amman, Jordan. Organisation for Economic Co-operation and Development (OECD). (2016). Health workforce policies in OECD countries: Right jobs, right skills, right places . OECD Publishing. https://doi.org/10.1787/9789264239517-en Huber, D. (2017). Leadership and Nursing Care Management (6th ed.). Elsevier Health Sciences. World Medical Association. (2013). World Medical Association Declaration of Helsinki: Ethical Principles for Medical Research Involving Human Subjects. JAMA , 310(20), 2191–2194. https://doi.org/10.1001/jama.2013.281053 Additional Declarations No competing interests reported. Supplementary Files AIqestionniare.docx Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Revision requested 23 Apr, 2026 Reviews received at journal 17 Apr, 2026 Reviews received at journal 14 Apr, 2026 Reviewers agreed at journal 12 Apr, 2026 Reviewers agreed at journal 09 Apr, 2026 Reviews received at journal 14 Dec, 2025 Reviews received at journal 12 Dec, 2025 Reviewers agreed at journal 14 Nov, 2025 Reviewers agreed at journal 14 Nov, 2025 Reviewers agreed at journal 14 Nov, 2025 Reviewers invited by journal 29 Sep, 2025 Editor assigned by journal 22 Sep, 2025 Editor invited by journal 18 Aug, 2025 Submission checks completed at journal 13 Aug, 2025 First submitted to journal 13 Aug, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. 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-7338088","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":527044229,"identity":"1a8b6cdc-e87d-456c-8955-c3ca17f3c787","order_by":0,"name":"Sara Al-Ajlouny","email":"","orcid":"","institution":"Yarmouk University","correspondingAuthor":false,"prefix":"","firstName":"Sara","middleName":"","lastName":"Al-Ajlouny","suffix":""},{"id":527044230,"identity":"45f492ef-6649-4b04-bd19-cba81723b8a9","order_by":1,"name":"Hindya O. Al-Maqableh","email":"data:image/png;base64,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","orcid":"","institution":"Jordan University of Science \u0026 Technology","correspondingAuthor":true,"prefix":"","firstName":"Hindya","middleName":"O.","lastName":"Al-Maqableh","suffix":""},{"id":527044231,"identity":"c89313f4-1d6d-43e9-b376-ae4c09a7ca06","order_by":2,"name":"Ala’a F. Al-Shaikh","email":"","orcid":"","institution":"Ministry of Health","correspondingAuthor":false,"prefix":"","firstName":"Ala’a","middleName":"F.","lastName":"Al-Shaikh","suffix":""},{"id":527044232,"identity":"097f135f-05ec-4367-a66a-a9908308c996","order_by":3,"name":"Yousef Khader","email":"","orcid":"","institution":"Jordan University of Science and Technology","correspondingAuthor":false,"prefix":"","firstName":"Yousef","middleName":"","lastName":"Khader","suffix":""},{"id":527044233,"identity":"7a906dfd-76c0-4024-8a57-d94264b53b6c","order_by":4,"name":"Taghrid S. Suifan","email":"","orcid":"","institution":"the University of Jordan","correspondingAuthor":false,"prefix":"","firstName":"Taghrid","middleName":"S.","lastName":"Suifan","suffix":""},{"id":527044234,"identity":"da2b1194-82aa-4c85-839c-dc6bfcd832ef","order_by":5,"name":"Yazan Alsoud","email":"","orcid":"","institution":"University of Jordan","correspondingAuthor":false,"prefix":"","firstName":"Yazan","middleName":"","lastName":"Alsoud","suffix":""},{"id":527044235,"identity":"8ac2df64-0fc8-46cb-89a0-fa3991429f81","order_by":6,"name":"Tareq L. Mukattash","email":"","orcid":"","institution":"Jordan University of Science \u0026 Technology","correspondingAuthor":false,"prefix":"","firstName":"Tareq","middleName":"L.","lastName":"Mukattash","suffix":""},{"id":527044236,"identity":"da613d7d-0920-4019-909f-e622bdcb74b0","order_by":7,"name":"Mohammad Alqudah","email":"","orcid":"","institution":"The Hashemite University","correspondingAuthor":false,"prefix":"","firstName":"Mohammad","middleName":"","lastName":"Alqudah","suffix":""}],"badges":[],"createdAt":"2025-08-10 09:53:26","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7338088/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7338088/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":93210084,"identity":"0f82c56b-c29d-4587-a031-d100a5d9eb7a","added_by":"auto","created_at":"2025-10-10 08:41:00","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":64859,"visible":true,"origin":"","legend":"","description":"","filename":"AImanuscript.docx","url":"https://assets-eu.researchsquare.com/files/rs-7338088/v1/b713da72213735a7fde1198b.docx"},{"id":93210086,"identity":"2cfc4086-17ff-4df8-b2a5-518d027853a6","added_by":"auto","created_at":"2025-10-10 08:41:00","extension":"json","order_by":1,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":9259,"visible":true,"origin":"","legend":"","description":"","filename":"6e04d19a0062408093ef40d34b8945c9.json","url":"https://assets-eu.researchsquare.com/files/rs-7338088/v1/03e1b254bc8507aa4f7bb4b2.json"},{"id":93210085,"identity":"c17dd77d-df09-48ad-b5ea-6ca1046185aa","added_by":"auto","created_at":"2025-10-10 08:41:00","extension":"docx","order_by":2,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":26779,"visible":true,"origin":"","legend":"","description":"","filename":"AIqestionniare.docx","url":"https://assets-eu.researchsquare.com/files/rs-7338088/v1/cd73e007930f88d549674955.docx"},{"id":93210089,"identity":"e4d0ca20-2c26-423a-92d0-4e2bc72c03bc","added_by":"auto","created_at":"2025-10-10 08:41:00","extension":"xml","order_by":3,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":134360,"visible":true,"origin":"","legend":"","description":"","filename":"6e04d19a0062408093ef40d34b8945c91enriched.xml","url":"https://assets-eu.researchsquare.com/files/rs-7338088/v1/8f3977ee7f0dee16292305c3.xml"},{"id":93210087,"identity":"ff6737d0-f0db-4cbf-b354-b2eed0d77bae","added_by":"auto","created_at":"2025-10-10 08:41:00","extension":"xml","order_by":4,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":128803,"visible":true,"origin":"","legend":"","description":"","filename":"6e04d19a0062408093ef40d34b8945c91structuring.xml","url":"https://assets-eu.researchsquare.com/files/rs-7338088/v1/fab28c8a25e29a2403adfd73.xml"},{"id":93210090,"identity":"a31029a6-632b-4004-bdb6-654dc7bfcb54","added_by":"auto","created_at":"2025-10-10 08:41:00","extension":"html","order_by":5,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":150069,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-7338088/v1/0e61d626a264fde2ad2313e7.html"},{"id":93211241,"identity":"82958b76-cbd6-4123-8609-05644c703c33","added_by":"auto","created_at":"2025-10-10 08:57:00","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1056117,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7338088/v1/22bb8cd3-0ab8-4577-a658-0cc99d74cb03.pdf"},{"id":93210353,"identity":"8d885a7f-c424-4b32-98f2-6e5704802abb","added_by":"auto","created_at":"2025-10-10 08:49:00","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":26779,"visible":true,"origin":"","legend":"","description":"","filename":"AIqestionniare.docx","url":"https://assets-eu.researchsquare.com/files/rs-7338088/v1/a4b59e10f1880f8f1c469556.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"The Role of Technology Acceptance, AI Anxiety, and Demographic Factors in Jordanian Healthcare Decision Makers' Attitudes Toward Artificial Intelligence","fulltext":[{"header":"Introduction","content":"\u003cp\u003eArtificial intelligence (AI) adoption in healthcare is a promising new path that has revolutionized the practice of medicine and improved outcomes for patients. Given this opportunity, AI is a topic of debate in global healthcare systems. (Koo et al., 2024). AI technologies may assist in better and early diagnosis, decision support systems, and treatment plans, which may serve as a framework to enhance access, quality and healthcare outcomes (Kaya et al., 2024; Yu et al., 2018; Topol, 2019). However, healthcare decision-makers\u0026rsquo; (HDM) attitudes about these innovations are important if AI is to be effectively implemented (Jiang et al., 2017; Ye et al., 2019).\u003c/p\u003e\u003cp\u003eCertain psychological factors make the acceptance of AI in healthcare feature pronounced barriers, such as AI-related anxiety. AI anxiety is defined as the discomfort that people feel when they use or even contemplate AI systems (Kaya et al., 2024). Some of these are job insecurity, perceived loss of personal control over some part of their lives, matters that are ethical, or AI-related bias. Ye et al., (2019) conducted a cross-sectional study in China based in which they found that psychosocial factors such as anxiety, influence the implementation of AI in the health sector. HDMs who are more anxious about the integration of AI software may be rated higher in negative AI attitudes and hence, be resistant to or slower at implementing it. Cruz et al. (2023) have also described the effects of the AI anxiety trend among medical students; they found out that students who are less trusting of AI are those who do not seem to be eager to benefit from the use of technology, and this is normally so since such people have higher levels of anxiety. This means that AI anxiety can influence the implementation of novel health technologies.\u003c/p\u003e\u003cp\u003eThe Technology Acceptance Model (TAM), widely used to predict individuals' acceptance of new technologies, highlights two key determinants: perceived ease of use and perceived usefulness (Şahin \u0026amp; Yıldırım, 2024). In the context of healthcare, these determinants are critical because decision-makers are more motivated to implement and utilize AI if they view it in a positive light and within the context of easily incorporated into working practices (Babiker et al., 2024). While knowledge regarding the application of AI to healthcare exists, concerns regarding its use as well as its complexity continue to be factors that influence the adoption of such technology. Therefore, exploring the attitudes related to perceived usefulness and ease of use formed by HDMs, will help to evaluate how AI could be implemented in healthcare systems.\u003c/p\u003e\u003cp\u003eSeveral other factors including age, gender, level of education, and the level of technological literacy also contribute to the specific attitude towards the use of AI in health care. Early career professionals had more positive attitudes toward AI than their older colleagues (Morales-Garc\u0026iacute;a et al., 2024). In contrast, older employees may be less ready to accept modern technology because of an informed or uniformed fear of change (Bergdahl et al., 2023).\u003c/p\u003e\u003cp\u003eFurthermore, the ease with which AI systems are utilized\u0026ndash;how well they can perform a task, plays a role in attitude formation. In a study conducted by Arishi et al. (2023) on nurses in Saudi Arabia, attitudes toward AI capabilities were closely linked to comfort levels in using AI systems. Those who believed AI systems were capable and reliable reported more positive attitudes and greater comfort in adopting the technology.\u003c/p\u003e\u003cp\u003eJordan's healthcare sector has evolved significantly; the government has made efforts to improve healthcare delivery through both public and private initiatives with significant investments in facilities. (AlMomani et al., 2019). As Jordan embraces technological advancements to address these challenges, the adoption of AI in healthcare emerges as a promising step.\u003c/p\u003e\u003cp\u003eThe adoption of advanced healthcare technologies in Jordan, however, faces challenges, including varying levels of technological literacy among healthcare professionals and the need for comprehensive training programs. (Hamad et al., 2024)\u003c/p\u003e\u003cp\u003eThis work aimed to explore the effect that technology acceptance, AI anxiety, and demographic characteristics on the attitudes of Jordanian healthcare decision-makers towards AI. Since Jordan is seeking ways to upgrade its healthcare system by integrating smart technologies, exploring relevant factors is of critical importance for effective adoption and implementation. The results of this research will therefore be useful in giving healthcare policymakers in Jordan a guideline on how to address the needs and concerns of health decision-makers. This research also fills a gap in the literature regarding AI adoption in the region. It also contributes to the global conversation on implications and concerns regarding integrating AI into healthcare.\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003e\u003cstrong\u003e2.1 Study Design\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study employed a descriptive cross-sectional methodology to evaluate the association between AI attitudes and AI anxiety as well as AI technology acceptance among HDMs.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.2 study population\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe study targeted healthcare decision-makers (HDMs) in Jordan, defined as individuals in leadership roles within governmental, private, NGO, or academic healthcare sectors. Participants held leadership roles at different administrative levels, including senior managers (e.g., department directors), middle managers (e.g., unit supervisors), executive committee members, and board representatives involved in healthcare policy and governance\u003cem\u003e\u0026nbsp;(Huber, 2017)\u003c/em\u003e. Inclusion criteria were current employment in Jordan. Individuals not involved in healthcare leadership or working outside Jordan were excluded.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.3Sampling\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe estimated population of healthcare decision-makers (HDMs) in Jordan was set at 250, based on the total national health workforce of approximately 70,000 professionals (High Health Council, 2018) and international benchmarks indicating that HDMs typically represent 1–3% of the healthcare workforce (World Health Organization, 2016; OECD, 2016). Using the Raosoft sample size calculator (Raosoft, 2004) with a 95% confidence level, 5% margin of error, and 50% response distribution, the required sample size was calculated to be 152. A total of 152 HDMs participated, resulting in a 100% response rate relative to the calculated sample size.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.4 Questionnaire\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe main instrument for data acquisition consisted of a structured questionnaire which assessed the variables across three domains including General AI Attitudes Scale (GAAIS) (Schepman and Rodway, 2020; Kaya et al., 2022), AI Anxiety Scale (AIAS) (Wang and Wang, 2022), and Technology Acceptance Model (TAM) (Gagnon et al., 2012). A The TAM was modified to ensure item wording reflected AI use within healthcare decision-making contexts\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDemographics\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe survey collected demographic data which included age, administrative roles, and occupational sectors (Private Sector, NGOs, Universities, Government). It is noteworthy that despite literature indicating that gender does factor in attitudes regarding AI adoption and acceptance, female representation comprises approximately 27% of management and leadership positions across all levels in Jordan, and fewer than 10% occupy higher-level administrative roles. (Human Resources for Health 2030, 2018). Given this significant underrepresentation, we opted out of including gender as a variable because of the concern of it not yielding meaningful insights due to the limited sample size.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eGAAIS\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe questionnaire was modified from previous studies (Schepman and Rodway, 2020; Kaya et al., 2022) to evaluate HDMs attitudes towards the utilization of AI technology. It comprises 20 items rated on a five-point Likert scale ranging from “strongly disagree” (1) to “strongly agree” (5).\" Respondents evaluate 12 positive and 8 negative statements, with higher scores reflecting more favorable attitudes.\u0026nbsp; Reverse scoring was applied to the negative items to ensure consistency in interpretation.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAIAS\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe anxiety measure was derived from the Artificial Intelligence Anxiety Scale by Wang and Wang, 2022. Anxieties about AI learning, job replacement, sociotechnical blindness, and AI configuration are the four dimensions. The scale consists of 21 items rated on a 7-point Likert scale, ranging from 1 (“never”) to 7 (“always”). Higher scores indicate greater anxiety regarding the implementation and impact of AI technologies.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTAM model\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIn the context of the Technology Acceptance Model, we quantified the Perceived usefulness Perceived ease of use of AI. Participants evaluated remarks concerning the simplicity and perceived advantages of AI technology in their daily professional tasks using a five-point Likert scale ranging from “strongly disagree” (1) to “strongly agree” (5) (Gagnon, Orruño, Asua, Abdeljelil, \u0026amp; Emparanza, 2012).\u003c/p\u003e\n\u003cp\u003ePrevious research on AI in healthcare scales has confirmed their validity, with GAAIS having a Cronbach's alpha of 0.85, the AI Anxiety Scale at 0.88, and the TAM at 0.87, ensuring reliability of measurement.\u003c/p\u003e\n\u003cp\u003e2.5 \u003cstrong\u003eData collection\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eData was collected during a period of three months, from May 2024 to July 2024. A\u003cem\u003e\u0026nbsp;Google Form\u003c/em\u003e online survey was generated, and HDMs received the survey link via email. The email = included a consent form to participate in the study. On occasion, participants were given reminders to finalize the survey. The utilization of online surveys via Google Forms facilitates efficient dissemination, effortless participant interaction, and safe data preservation. (Radhaswati et al., 2022). A pilot run was performed involving (n=15) experts to assess the readability and understanding levels of the questionnaire items. Pilot participants were urged to provide feedback on any items they found ambiguous or challenging, facilitating enhancements to the questionnaire's overall clarity through necessary adjustments. Pilot participants and their responses were eliminated from the final sample to mitigate any potential bias or influence on the results.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e2.6 \u003cstrong\u003eData Analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eData were analyzed using IBM SPSS Statistics (Version 27). Descriptive statistics summarized participant characteristics and key variables. Hierarchical multiple linear regression was used to examine predictors of AI attitudes and anxiety, entering demographic and professional variables first, followed by AI-related factors. Logistic regression assessed determinants of high versus low AI acceptance based on median TAM scores. All models were tested for statistical assumptions, with significance set at p \u0026lt; 0.05.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003eA total of 152 health decision-makers in Jordanian healthcare setting participated. The Private sector constitutes the largest share, making up over half of the participants (n=79, 52.0%), while NGOs represent the smallest proportion at (n=19, 12.5%). The participants are classified into senior management (n=59, 38.8%), middle management (n=52, 34.2%) and executive committee members (n=26, 17.1%), and board members (n=15, 9.9%). The mean (SD) age of participants was 44.23 (10.36) years. Table 1 shows the socio-demographic characteristics of the study participants, including their distribution across work sectors (private, governmental, NGO, and academic), administrative levels (senior and middle management, executive committee members, and board members), and age \u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTable (1) Participants\u0026rsquo; socio-demographic characteristics\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"694\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 124px;\"\u003e\n \u003cp\u003eVariables\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 243px;\"\u003e\n \u003cp\u003eCategories\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003eFrequency\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 117px;\"\u003e\n \u003cp\u003ePercent\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 114px;\"\u003e\n \u003cp\u003eMean (SD)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 124px;\"\u003e\n \u003cp\u003eWork sectors\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 243px;\"\u003e\n \u003cp\u003ePrivate Sector\u003c/p\u003e\n \u003cp\u003eNGOs\u003c/p\u003e\n \u003cp\u003eUniversities\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eGovernmental\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e79\u003c/p\u003e\n \u003cp\u003e19\u003c/p\u003e\n \u003cp\u003e20\u003c/p\u003e\n \u003cp\u003e34\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 117px;\"\u003e\n \u003cp\u003e52.0\u003c/p\u003e\n \u003cp\u003e12.5\u003c/p\u003e\n \u003cp\u003e13.2\u003c/p\u003e\n \u003cp\u003e22.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 114px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 124px;\"\u003e\n \u003cp\u003eAdministrative levels\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 243px;\"\u003e\n \u003cp\u003eSenior Management\u003c/p\u003e\n \u003cp\u003eMiddle Management\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eExcusive committee members\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eBoard members\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e59\u003c/p\u003e\n \u003cp\u003e52\u003c/p\u003e\n \u003cp\u003e26\u003c/p\u003e\n \u003cp\u003e15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 117px;\"\u003e\n \u003cp\u003e38.8\u003c/p\u003e\n \u003cp\u003e34.2\u003c/p\u003e\n \u003cp\u003e17.1\u003c/p\u003e\n \u003cp\u003e9.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 114px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 124px;\"\u003e\n \u003cp\u003eAge /years\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 243px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 117px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 114px;\"\u003e\n \u003cp\u003e44.23 (10.36)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eNGOs: Non-Governmental Organization\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eLevels of general attitudes, anxiety and technology acceptance model toward artificial intelligence\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e. Our results demonstrated that the participants had a mean score of positive GAAIS 3.94, SD=0.59 denoting agreeableness toward AI. On other hand negative domain had a mean score of 2.60, SD=0.71 indicating to below neutral level.\u003c/p\u003e\n\u003cp\u003eThe highest scored domain was sociotechnical blindness (M=4.42, SD=1.77) followed by Job replacement anxiety (M=4.31, SD=1.75) while the lowest one was AI learning anxiety (M=3.38, SD=1.58). These results suggest the participants had a moderate to high level of anxiety toward AI\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eConsidering technology acceptance, the perceived usefulness had a mean score of 3.55 (1.02), while the perceived ease of use had a mean score of 3.51 (0.93) pointing their perception above neutral level regarding accepting of technology. Table 2 shows the Levels of general attitudes, anxiety and technology acceptance model toward artificial intelligence\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTable (2) Levels of general attitudes, anxiety and technology acceptance model toward artificial intelligence\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"636\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 354px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eStudy instruments\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 180px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMean\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSD\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 354px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eGeneral attitudes towards AI\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003ePositive general attitudes towards AI\u003c/p\u003e\n \u003cp\u003eNegative general attitudes towards AI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 180px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e3.94\u003c/p\u003e\n \u003cp\u003e2.60\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e0.59\u003c/p\u003e\n \u003cp\u003e0.71\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 354px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eArtificial intelligence anxiety\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003eAI learning anxiety\u003c/p\u003e\n \u003cp\u003eJob replacement anxiety\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eSociotechnical blindness\u003c/p\u003e\n \u003cp\u003eAI configuration anxiety \u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 180px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e3.38\u003c/p\u003e\n \u003cp\u003e4.31\u003c/p\u003e\n \u003cp\u003e4.42\u003c/p\u003e\n \u003cp\u003e3.96\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e1.58\u003c/p\u003e\n \u003cp\u003e1.75\u003c/p\u003e\n \u003cp\u003e1.77\u003c/p\u003e\n \u003cp\u003e1.86\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 354px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eTechnology acceptance model\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003ePerceived usefulness\u003c/p\u003e\n \u003cp\u003ePerceived ease of use\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 180px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e3.55\u003c/p\u003e\n \u003cp\u003e3.51\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e1.02\u003c/p\u003e\n \u003cp\u003e0.93\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003ePredicting the positive GAAIS\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eHierarchical multiple linear regression test was carried out to predict positive GAAIS as a function of participants\u0026rsquo; demographics, AI anxiety subscales and technology acceptance model subscales.\u003c/p\u003e\n\u003cp\u003eIn the first model the participants\u0026rsquo; \u0026nbsp;work sectors, administrative level namely ( middle management and excusive committee members and age explained 20.0% of the variance in positive attitudes toward AI \u0026nbsp;(\u003cem\u003eF=6.33, P\u0026lt;0.001\u003c/em\u003e).It was found that those working in universities (\u003cem\u003eB\u003c/em\u003e=0.403, \u003cem\u003eP\u003c/em\u003e=0.002) , NGOs (\u003cem\u003eB\u003c/em\u003e=0.325, \u003cem\u003eP\u003c/em\u003e=0.016) and governmental work sector (\u003cem\u003eB\u003c/em\u003e=0.219, \u003cem\u003eP\u003c/em\u003e=0.034) reported significantly higher positive attitudes score toward AI compared to those work in a private sector. On other hand the participants\u0026rsquo; age was significantly inversely correlated with attitudes toward AI (\u003cem\u003eB\u003c/em\u003e= -0.100, \u003cem\u003eP\u003c/em\u003e=0.025).\u003c/p\u003e\n\u003cp\u003eThen, the artificial intelligence anxiety subscales were added to the model (Model2) and it was demonstrated that the additional factors explained a further 21.8% variance in positive attitudes score toward AI (\u0026Delta;R \u003csub\u003echange\u003c/sub\u003e = 21.8%, F \u003csub\u003echange\u003c/sub\u003e 12.25, \u003cem\u003eP\u003c/em\u003e\u0026lt;0.001). Artificial intelligence anxiety subscales were inversely correlated with positive attitudes score toward AI, the highest predictor was AI anxiety learning (\u003cem\u003eB\u003c/em\u003e=0.090, \u003cem\u003eP\u003c/em\u003e=0.012), followed by sociotechnical blindness (\u003cem\u003eB\u003c/em\u003e=0.085, \u003cem\u003eP\u003c/em\u003e=0.032), then job replacement (\u003cem\u003eB\u003c/em\u003e=0.064, \u003cem\u003eP\u003c/em\u003e=0.039) and the lowest one was AI configuration (\u003cem\u003eB\u003c/em\u003e=0.071, \u003cem\u003eP\u003c/em\u003e=0.046).\u003c/p\u003e\n\u003cp\u003eWhen it comes to model (3) individual factors and artificial intelligence anxiety were controlled, the technology acceptance subscales were added to the model, an additional 3.7% of variance was explained (\u0026Delta;R \u003csub\u003echange\u003c/sub\u003e = 3.7%, F \u003csub\u003echange\u003c/sub\u003e 8.84, \u003cem\u003eP\u003c/em\u003e\u0026lt;0.001). Perceived usefulness demonstrates the strongest positive predictor for positive attitudes score toward AI (\u003cem\u003eB\u003c/em\u003e=0.09, \u003cem\u003eP\u003c/em\u003e=0.014) followed by perceived ease of use (\u003cem\u003eB\u003c/em\u003e=0.094, \u003cem\u003eP\u003c/em\u003e=0.034). Table (3)\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u0026nbsp;Predicting the negative GAAIS\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAnother separate Hierarchical multiple linear regression was utilized to predict negative GAAIS. Findings showed that in the first model, the participants\u0026rsquo; \u0026nbsp;work sectors, administrative level namely ( middle management and excusive committee members and age explained 16.0% of the variance in negative attitudes toward AI \u0026nbsp; (\u003cem\u003eF\u003c/em\u003e=4.98, \u003cem\u003eP\u003c/em\u003e\u0026lt; 0.001).It was found that those working in universities (\u003cem\u003eB\u003c/em\u003e=0.376, \u003cem\u003eP\u003c/em\u003e=0.016) , NGOs (\u003cem\u003eB\u003c/em\u003e=0.359, \u003cem\u003eP\u003c/em\u003e=0.026) and governmental work sector (\u003cem\u003eB\u003c/em\u003e=0.246, \u003cem\u003eP\u003c/em\u003e=0.040) reported significantly higher forgiving about the negative attitudes score toward AI compared to those work in a private sector. On other hand the participants\u0026rsquo; age was significantly inversely correlated with forgiving about the negative attitudes toward AI (\u003cem\u003eB\u003c/em\u003e= -0.11, \u003cem\u003eP\u003c/em\u003e= 0.045). Meaning older people had lower forgiving scores about the negative attitudes toward AI\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eIn the model (2) where the artificial intelligence anxiety subscales were added, it was noted that the additional factors explained a further 16.0% of variance in negative attitudes score toward AI (\u0026Delta;R \u003csub\u003echange\u003c/sub\u003e = 16.0%, F \u003csub\u003echange\u003c/sub\u003e 4.98, \u003cem\u003eP\u003c/em\u003e\u0026lt;0.001). Artificial intelligence anxiety subscales were inversely correlated with negative attitudes score toward AI. Among the subscales, the strongest predictor was AI anxiety learning (\u003cem\u003eB\u003c/em\u003e=-0.127, \u003cem\u003eP\u003c/em\u003e=0.007), followed by sociotechnical blindness (\u003cem\u003eB\u003c/em\u003e= -0.120, \u003cem\u003eP\u003c/em\u003e=0.011), then job replacement (\u003cem\u003eB\u003c/em\u003e= -0.104, \u003cem\u003eP\u003c/em\u003e=0.012) and the lowest one was AI configuration (\u003cem\u003eB\u003c/em\u003e= -0.108, \u003cem\u003eP\u003c/em\u003e=0.015). The negative relationship in this context reveals as the artificial intelligence anxiety subscales score increase, participants are less likely to hold forgiving or lenient about the negative attitudes toward AI drawbacks\u003c/p\u003e\n\u003cp\u003eWhen it comes to model (3) individual factors and artificial intelligence anxiety were controlled, the technology acceptance subscales were added to the model, an additional 6.5% of variance was explained (\u0026Delta;R \u003csub\u003echange\u003c/sub\u003e = 6.5%, F \u003csub\u003echange\u003c/sub\u003e 9.25, \u003cem\u003eP\u003c/em\u003e\u0026lt;0.001). Perceived usefulness demonstrates the strongest positive predictor for negative attitudes score toward AI (\u003cem\u003eB\u003c/em\u003e=0.120, \u003cem\u003eP\u003c/em\u003e=0.031) followed by perceived ease of use (\u003cem\u003eB\u003c/em\u003e=0.130, \u003cem\u003eP\u003c/em\u003e=0.036). Suggesting higher technology perception associated with higher forgiving score about the negative attitudes toward AI Table (4)\u003c/p\u003e\n\u003cp\u003eTable (3) Hierarchical multiple linear regression analysis results to predict positive attitudes toward artificial intelligence.\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" align=\"left\" width=\"977\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 186px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eVariables\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"top\" style=\"width: 313px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003eModel 1\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"top\" style=\"width: 244px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003eModel II\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"top\" style=\"width: 234px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003eModel\u0026nbsp;\u003c/em\u003e\u003c/strong\u003eIII\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 163px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003eB\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 75px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003eSE\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 74px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003ep\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 74px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003eB\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 97px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003eSE\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 74px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003ep\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003eB\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 74px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003eSE\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 74px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003eP\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 186px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eDemographic\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 163px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 75px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 74px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 74px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 97px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 74px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 74px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 74px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 186px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eWork sectors\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003eNGOs\u003c/p\u003e\n \u003cp\u003eUniversities\u003c/p\u003e\n \u003cp\u003eGovernmental\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 163px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e0.32\u003c/p\u003e\n \u003cp\u003e0.40\u003c/p\u003e\n \u003cp\u003e0.22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 75px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e0.13\u003c/p\u003e\n \u003cp\u003e0.12\u003c/p\u003e\n \u003cp\u003e0.10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 74px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e0.016\u003c/p\u003e\n \u003cp\u003e0.002\u003c/p\u003e\n \u003cp\u003e0.034\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 74px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e0.25\u003c/p\u003e\n \u003cp\u003e0.34\u003c/p\u003e\n \u003cp\u003e0.255\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 97px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e0.10\u003c/p\u003e\n \u003cp\u003e0.12\u003c/p\u003e\n \u003cp\u003e0.09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 74px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e0.016\u003c/p\u003e\n \u003cp\u003e0.007\u003c/p\u003e\n \u003cp\u003e0.005\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e0.15\u003c/p\u003e\n \u003cp\u003e0.17\u003c/p\u003e\n \u003cp\u003e0.17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 74px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e0.11\u003c/p\u003e\n \u003cp\u003e0.08\u003c/p\u003e\n \u003cp\u003e0.09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 74px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e0.18\u003c/p\u003e\n \u003cp\u003e0.01\u003c/p\u003e\n \u003cp\u003e0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 186px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAdministrative levels\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003eMiddle Management\u003c/p\u003e\n \u003cp\u003eExcusive committee members\u003c/p\u003e\n \u003cp\u003eBoard members\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 163px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e0.22\u003c/p\u003e\n \u003cp\u003e0.31\u003c/p\u003e\n \u003cp\u003e0.16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 75px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e0.10\u003c/p\u003e\n \u003cp\u003e0.12\u003c/p\u003e\n \u003cp\u003e0.15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 74px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e0.035\u003c/p\u003e\n \u003cp\u003e0.014\u003c/p\u003e\n \u003cp\u003e0.294\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 74px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e0.26\u003c/p\u003e\n \u003cp\u003e0.26\u003c/p\u003e\n \u003cp\u003e0.27\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 97px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e0.10\u003c/p\u003e\n \u003cp\u003e0.12\u003c/p\u003e\n \u003cp\u003e1.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 74px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e0.011\u003c/p\u003e\n \u003cp\u003e0.033\u003c/p\u003e\n \u003cp\u003e0.137\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e0.18\u003c/p\u003e\n \u003cp\u003e0.18\u003c/p\u003e\n \u003cp\u003e0.22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 74px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e0.09\u003c/p\u003e\n \u003cp\u003e0.09\u003c/p\u003e\n \u003cp\u003e0.13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 74px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e0.04\u003c/p\u003e\n \u003cp\u003e0.04\u003c/p\u003e\n \u003cp\u003e0.08\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 186px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAge\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 163px;\"\u003e\n \u003cp\u003e-0.10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 75px;\"\u003e\n \u003cp\u003e0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 74px;\"\u003e\n \u003cp\u003e0.025\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 74px;\"\u003e\n \u003cp\u003e-0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 97px;\"\u003e\n \u003cp\u003e0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 74px;\"\u003e\n \u003cp\u003e0.11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e-0.006\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 74px;\"\u003e\n \u003cp\u003e0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 74px;\"\u003e\n \u003cp\u003e0.11\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 186px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAI anxiety\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003eAI learning\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eJob replacement\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eSociotechnical blindness\u003c/p\u003e\n \u003cp\u003eAI configuration\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 163px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 75px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 74px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 74px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e-0.09\u003c/p\u003e\n \u003cp\u003e-0.06\u003c/p\u003e\n \u003cp\u003e-0.08\u003c/p\u003e\n \u003cp\u003e-0.07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 97px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e0.03\u003c/p\u003e\n \u003cp\u003e0.03\u003c/p\u003e\n \u003cp\u003e0.04\u003c/p\u003e\n \u003cp\u003e0.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 74px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e0.01\u003c/p\u003e\n \u003cp\u003e0.04\u003c/p\u003e\n \u003cp\u003e0.03\u003c/p\u003e\n \u003cp\u003e0.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e-0.1\u003c/p\u003e\n \u003cp\u003e-0.09\u003c/p\u003e\n \u003cp\u003e-0.07\u003c/p\u003e\n \u003cp\u003e-0.07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 74px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e0.03\u003c/p\u003e\n \u003cp\u003e0.03\u003c/p\u003e\n \u003cp\u003e0.03\u003c/p\u003e\n \u003cp\u003e0.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 74px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e0.00\u003c/p\u003e\n \u003cp\u003e0.02\u003c/p\u003e\n \u003cp\u003e0.01\u003c/p\u003e\n \u003cp\u003e0.03\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 186px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eTechnology acceptance model\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003ePerceived usefulness\u003c/p\u003e\n \u003cp\u003ePerceived ease of use\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 163px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 75px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 74px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 74px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 97px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 74px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e0.09\u003c/p\u003e\n \u003cp\u003e0.09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 74px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e0.03\u003c/p\u003e\n \u003cp\u003e0.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 74px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e0.01\u003c/p\u003e\n \u003cp\u003e0.03\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 186px;\"\u003e\n \u003cp\u003eR\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 163px;\"\u003e\n \u003cp\u003e0.238\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 75px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 74px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 74px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 97px;\"\u003e\n \u003cp\u003e0.39\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 74px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e0.50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 74px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 74px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 186px;\"\u003e\n \u003cp\u003eAdjusted R\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 163px;\"\u003e\n \u003cp\u003e0.200\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 75px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 74px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 74px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 97px;\"\u003e\n \u003cp\u003e0.418\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 74px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e0.455\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 74px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 74px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 186px;\"\u003e\n \u003cp\u003eAdjusted R\u003csup\u003e2\u0026nbsp;\u003c/sup\u003echange\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 163px;\"\u003e\n \u003cp\u003e---\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 75px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 74px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 74px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 97px;\"\u003e\n \u003cp\u003e21.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 74px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e3.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 74px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 74px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 186px;\"\u003e\n \u003cp\u003eF for Change in R\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 163px;\"\u003e\n \u003cp\u003e6.33**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 75px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 74px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 74px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 97px;\"\u003e\n \u003cp\u003e12.25**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 74px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e8.84**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 74px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 74px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e*Private sector and senior management are reference group\u003c/p\u003e\n\u003cp\u003eTable (4) Hierarchical multiple linear regression analysis results to predict negative attitudes toward artificial intelligence.\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"1006\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 203px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eVariables\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"5\" valign=\"top\" style=\"width: 273px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003eModel 1\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"5\" valign=\"top\" style=\"width: 268px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003eModel II\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"5\" valign=\"top\" style=\"width: 261px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003eModel\u0026nbsp;\u003c/em\u003e\u003c/strong\u003e \u003cstrong\u003e\u003cem\u003eIII\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 55px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003eB*\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 62px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003eSE\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 52px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003e\u0026Beta;*\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 52px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003eT\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 52px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003ep\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 55px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003eB*\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 52px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003eSE\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 58px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003e\u0026beta;*\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 52px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003et\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 52px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003ep\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 53px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003eB*\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003eSE\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 52px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003e\u0026beta;*\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 51px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003et\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 51px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003ep\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 203px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eDemographic\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 55px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 62px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 52px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 52px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 52px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 55px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 52px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 58px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 52px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 52px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 53px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 52px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 51px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 51px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 203px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eWork sectors\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003eNGOs\u003c/p\u003e\n \u003cp\u003eUniversities\u003c/p\u003e\n \u003cp\u003eGovernmental\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 55px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e0.359\u003c/p\u003e\n \u003cp\u003e0.376\u003c/p\u003e\n \u003cp\u003e0.246\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 62px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e0.160\u003c/p\u003e\n \u003cp\u003e0.154\u003c/p\u003e\n \u003cp\u003e0.122\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 52px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e0.178\u003c/p\u003e\n \u003cp\u003e0.197\u003c/p\u003e\n \u003cp\u003e0.156\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 52px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e2.249\u003c/p\u003e\n \u003cp\u003e2.445\u003c/p\u003e\n \u003cp\u003e2.012\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 52px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e0.026\u003c/p\u003e\n \u003cp\u003e0.016\u003c/p\u003e\n \u003cp\u003e0.040\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 55px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e0.246\u003c/p\u003e\n \u003cp\u003e0.295\u003c/p\u003e\n \u003cp\u003e0.265\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 52px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e0.122\u003c/p\u003e\n \u003cp\u003e0.143\u003c/p\u003e\n \u003cp\u003e0.122\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 58px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e0.156\u003c/p\u003e\n \u003cp\u003e0.155\u003c/p\u003e\n \u003cp\u003e0.167\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 52px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e2.012\u003c/p\u003e\n \u003cp\u003e2.006\u003c/p\u003e\n \u003cp\u003e2.352\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 52px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e0.046\u003c/p\u003e\n \u003cp\u003e0.041\u003c/p\u003e\n \u003cp\u003e0.020\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 53px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e0.059\u003c/p\u003e\n \u003cp\u003e0.176\u003c/p\u003e\n \u003cp\u003e0.374\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e0.104\u003c/p\u003e\n \u003cp\u003e0.178\u003c/p\u003e\n \u003cp\u003e0.146\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 52px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e0.041\u003c/p\u003e\n \u003cp\u003e0.077\u003c/p\u003e\n \u003cp\u003e0.200\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 51px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e0.561\u003c/p\u003e\n \u003cp\u003e0.988\u003c/p\u003e\n \u003cp\u003e2.558\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 51px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e0.571\u003c/p\u003e\n \u003cp\u003e0.325\u003c/p\u003e\n \u003cp\u003e0.012\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 203px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAdministrative levels\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003eMiddle Management\u003c/p\u003e\n \u003cp\u003eExcusive committee members\u003c/p\u003e\n \u003cp\u003eBoard members\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 55px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e0.282\u003c/p\u003e\n \u003cp\u003e0.444\u003c/p\u003e\n \u003cp\u003e0.066\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 62px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e0.122\u003c/p\u003e\n \u003cp\u003e0.165\u003c/p\u003e\n \u003cp\u003e0.132\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 52px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e0.179\u003c/p\u003e\n \u003cp\u003e0.220\u003c/p\u003e\n \u003cp\u003e0.040\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 52px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e2.310\u003c/p\u003e\n \u003cp\u003e2.691\u003c/p\u003e\n \u003cp\u003e0.501\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 52px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e0.022\u003c/p\u003e\n \u003cp\u003e0.008\u003c/p\u003e\n \u003cp\u003e0.617\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 55px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e0.278\u003c/p\u003e\n \u003cp\u003e0.264\u003c/p\u003e\n \u003cp\u003e0.047\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 52px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e0.139\u003c/p\u003e\n \u003cp\u003e0.110\u003c/p\u003e\n \u003cp\u003e0.123\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 58px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e0.109\u003c/p\u003e\n \u003cp\u003e0.167\u003c/p\u003e\n \u003cp\u003e0.029\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 52px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e1.995\u003c/p\u003e\n \u003cp\u003e2.402\u003c/p\u003e\n \u003cp\u003e0.380\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 52px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e0.048\u003c/p\u003e\n \u003cp\u003e0.018\u003c/p\u003e\n \u003cp\u003e0.704\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 53px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e0.098\u003c/p\u003e\n \u003cp\u003e0.367\u003c/p\u003e\n \u003cp\u003e0.240\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e0.116\u003c/p\u003e\n \u003cp\u003e0.159\u003c/p\u003e\n \u003cp\u003e0.123\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 52px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e0.069\u003c/p\u003e\n \u003cp\u003e0.182\u003c/p\u003e\n \u003cp\u003e0.152\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 51px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e0.842\u003c/p\u003e\n \u003cp\u003e2.312\u003c/p\u003e\n \u003cp\u003e1.940\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 51px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e0.401\u003c/p\u003e\n \u003cp\u003e0.022\u003c/p\u003e\n \u003cp\u003e0.054\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 203px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAge\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 55px;\"\u003e\n \u003cp\u003e-0.11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 62px;\"\u003e\n \u003cp\u003e0.005\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 52px;\"\u003e\n \u003cp\u003e-0.164\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 52px;\"\u003e\n \u003cp\u003e2.202\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 52px;\"\u003e\n \u003cp\u003e0.045\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 55px;\"\u003e\n \u003cp\u003e-0.007\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 52px;\"\u003e\n \u003cp\u003e0.005\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 58px;\"\u003e\n \u003cp\u003e-0.109\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 52px;\"\u003e\n \u003cp\u003e1.444\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 52px;\"\u003e\n \u003cp\u003e0.152\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 53px;\"\u003e\n \u003cp\u003e-0.006\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003e0.005\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 52px;\"\u003e\n \u003cp\u003e-0.09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 51px;\"\u003e\n \u003cp\u003e1.367\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 51px;\"\u003e\n \u003cp\u003e0.174\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 203px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAI anxiety\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003eAI learning\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eJob replacement\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eSociotechnical blindness\u003c/p\u003e\n \u003cp\u003eAI configuration\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 55px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 62px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 52px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 52px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 52px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 55px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e-0.127\u003c/p\u003e\n \u003cp\u003e-0.104\u003c/p\u003e\n \u003cp\u003e-0.120\u003c/p\u003e\n \u003cp\u003e-0.108\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 52px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e0.046\u003c/p\u003e\n \u003cp\u003e0.041\u003c/p\u003e\n \u003cp\u003e0.046\u003c/p\u003e\n \u003cp\u003e0.044\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 58px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e-0.270\u003c/p\u003e\n \u003cp\u003e-0.262\u003c/p\u003e\n \u003cp\u003e-0.256\u003c/p\u003e\n \u003cp\u003e-0.233\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 52px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e2.754\u003c/p\u003e\n \u003cp\u003e2.524\u003c/p\u003e\n \u003cp\u003e2.588\u003c/p\u003e\n \u003cp\u003e2.475\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 52px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e0.007\u003c/p\u003e\n \u003cp\u003e0.012\u003c/p\u003e\n \u003cp\u003e0.011\u003c/p\u003e\n \u003cp\u003e0.015\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 53px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e-0.105\u003c/p\u003e\n \u003cp\u003e-0.113\u003c/p\u003e\n \u003cp\u003e-0.102\u003c/p\u003e\n \u003cp\u003e-0.023\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e0.043\u003c/p\u003e\n \u003cp\u003e0.041\u003c/p\u003e\n \u003cp\u003e0.402\u003c/p\u003e\n \u003cp\u003e0.042\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 52px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e-0.226\u003c/p\u003e\n \u003cp\u003e-0.285\u003c/p\u003e\n \u003cp\u003e-0.218\u003c/p\u003e\n \u003cp\u003e-0.055\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 51px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e2.445\u003c/p\u003e\n \u003cp\u003e2.759\u003c/p\u003e\n \u003cp\u003e2.409\u003c/p\u003e\n \u003cp\u003e0.532\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 51px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e0.016\u003c/p\u003e\n \u003cp\u003e0.007\u003c/p\u003e\n \u003cp\u003e0.017\u003c/p\u003e\n \u003cp\u003e0.596\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 203px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eTechnology acceptance model\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003ePerceived usefulness\u003c/p\u003e\n \u003cp\u003ePerceived ease of use\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 55px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 62px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 52px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 52px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 52px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 55px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 52px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 58px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 52px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 52px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 53px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e0.120\u003c/p\u003e\n \u003cp\u003e0.130\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e0.055\u003c/p\u003e\n \u003cp\u003e0.062\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 52px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e0.185\u003c/p\u003e\n \u003cp\u003e0.173\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 51px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e2.183\u003c/p\u003e\n \u003cp\u003e2.114\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 51px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e0.031\u003c/p\u003e\n \u003cp\u003e0.036\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 203px;\"\u003e\n \u003cp\u003eR\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 55px;\"\u003e\n \u003cp\u003e0.200\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 62px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 52px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 52px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 52px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 55px;\"\u003e\n \u003cp\u003e0.38\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 52px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 58px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 52px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 52px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 53px;\"\u003e\n \u003cp\u003e0.448\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 52px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 51px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 51px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 203px;\"\u003e\n \u003cp\u003eAdjusted R\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 55px;\"\u003e\n \u003cp\u003e0.160\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 62px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 52px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 52px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 52px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 55px;\"\u003e\n \u003cp\u003e0.329\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 52px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 58px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 52px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 52px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 53px;\"\u003e\n \u003cp\u003e0.394\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 52px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 51px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 51px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 203px;\"\u003e\n \u003cp\u003eAdjusted R\u003csup\u003e2\u003c/sup\u003e change\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 55px;\"\u003e\n \u003cp\u003e---\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 62px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 52px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 52px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 52px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 55px;\"\u003e\n \u003cp\u003e0.169\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 52px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 58px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 52px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 52px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 53px;\"\u003e\n \u003cp\u003e0.065\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 52px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 51px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 51px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 203px;\"\u003e\n \u003cp\u003eF for Change in R\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 55px;\"\u003e\n \u003cp\u003e4.98**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 62px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 52px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 52px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 52px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 55px;\"\u003e\n \u003cp\u003e9.75**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 52px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 58px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 52px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 52px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 53px;\"\u003e\n \u003cp\u003e9.25**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 52px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 51px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 51px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003ePrivate sector and senior management are reference groups. * Positive sign indicates to have more forgiving about the negative attitudes due to reversing score\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eThe findings of this study suggest that HDMs in Jordan generally hold favorable attitudes toward AI, as indicated by a mean positive attitude score of 3.94 (SD\u0026thinsp;=\u0026thinsp;0.59). In contrast, negative attitudes were comparatively lower, with a mean score of 2.60 (SD\u0026thinsp;=\u0026thinsp;0.59). These results reflect an overall inclination to acknowledge AI's potential to enhance efficiency, accuracy, and decision-making in healthcare, an observation consistent with prior research (Busch et al., 2024).\u003c/p\u003e\u003cp\u003eSignificantly more positive attitudes were reported among HDMs affiliated with academic institutions, governmental bodies, NGOs, and corporates, compared to those in the private sector. This disparity may be attributed to greater exposure to AI-related policies, research initiatives, and institutional investments in these sectors (Al-Dmour et al., 2025). Familiarity with and engagement in AI technologies have been identified as critical factors in shaping positive perceptions (Issa et al., 2024; Al-Qerem et al., 2023), particularly in well-resourced environments where decision-makers are more likely to interact with AI systems.\u003c/p\u003e\u003cp\u003eThe broader regional context supports these findings, as cultural, governmental, and institutional frameworks across the Middle East and North Africa region influence AI adoption trajectories. Although countries like Saudi Arabia have demonstrated substantial progress in AI healthcare applications\u0026mdash;driven by robust governmental investment and policy support (Saeed et al., 2023)\u0026mdash;Jordan\u0026rsquo;s advancement may require more strategic outreach and education to foster wider acceptance among its healthcare workforces.\u003c/p\u003e\u003cp\u003eWhile growing interest in AI is evident, concerns surrounding its ethical use, data privacy, and practical implementation remain substantial. The lack of region-specific ethical frameworks for AI implementation in healthcare contributes to unease among stakeholders, particularly around patient data confidentiality, transparency, and accountability (Hasan et al., 2024; Amann et al., 2023).\u003c/p\u003e\u003cp\u003eThis study also found that younger HDMs expressed more positive attitudes toward AI, consistent with literature suggesting that younger professionals are more comfortable with emerging technologies (Smith et al., 2022; Hoffman et al., 2024, Li et al., 2024). These findings highlight the importance of tailored AI literacy and professional development programs aimed at older cohorts, helping bridge generational gaps in technology adoption.\u003c/p\u003e\u003cp\u003eAnxiety surrounding AI emerged as a key predictor of negative attitudes. Specifically, AI learning anxiety, more so than concerns about automation or sociotechnical complexity, was most strongly associated with lower acceptance. This aligns with research showing that job security concerns and perceived difficulty in learning AI tools hinder adoption (Wen et al., 2024; Terzi, 2020). However, structured training programs have been shown to mitigate these anxieties and enhance openness to AI integration (Patel et al., 2020; Green et al., 2019).\u003c/p\u003e\u003cp\u003eThe user-centered design of AI tools remains critical. Evidence suggests that intuitive, user-friendly interfaces reduce implementation barriers and foster positive engagement (Kimiafar et al., 2023; Lambert et al., 2023). Moreover, open discussion and transparency about AI's role can alleviate uncertainty and build trust among HDMs (Cho et al., 2024).\u003c/p\u003e\u003cp\u003eThe Technology Acceptance Model (TAM) offers valuable insights into these dynamics. Perceived usefulness (PU) was strongly associated with positive attitudes toward AI, while perceived ease of use (PEOU) showed more mixed influence. These findings reinforce the necessity for AI systems that are both effective and easy to navigate (Ibrahim et al., 2025).\u003c/p\u003e\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\u003ch2\u003eLimitations\u003c/h2\u003e\u003cp\u003eSeveral limitations need to be acknowledged as cross-sectional design limits our causal inferences, and several longitudinal studies will be required which track the development of attitudes over time as AI increasingly embeds within healthcare settings. Furthermore, the relatively small sample size may restrict the generalizability of findings. HDMs represent a niche and often inaccessible group, making large-scale recruitment challenging. Additionally, the study's cross-sectional design poses the risk of potential self-selection bias which may have skewed participation toward individuals with preexisting interest in AI.\u003c/p\u003e\u003cp\u003eOur sample contained many key decision-makers, but future research also needs to move to include perspectives from frontline health professionals and from patients themselves to fully understand how AI is likely to be accepted in healthcare.\u003c/p\u003e\u003cp\u003eSecond, ethical and legal issues related to AI in Jordan's healthcare sector need more detailed attention. Due to the challenges in accessing the cohort in a swift manner, during this study, policymakers may have developed regulatory frameworks that address issues related to transparency, accountability, and equal access to healthcare solutions driven by AI.\u003c/p\u003e\u003c/div\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThis is the first study in Jordan to explore the perspectives of healthcare decision-makers\u0026mdash;rather than students, nurses, or clinicians\u0026mdash;on AI adoption. This offers a unique contribution to literature, considering the pivotal role HDMs play in guiding health technology policy and institutional decision-making. The present study provides critical insight into the enablers and barriers shaping AI adoption among an influential cohort in Jordanian healthcare. Our results demonstrate the necessity for user-friendly AI tools, structured training programs to guide AI-literacy, and a growing need for region-specific ethical standards. Future research should aim to incorporate comparative, multi-country analyses, longitudinal designs, and larger samples to further inform effective, context-specific AI integration strategies.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cdiv class=\"DefinitionList\"\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003e\u003cb\u003eAI\u003c/b\u003e\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eArtificial Intelligence\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003e\u003cb\u003eHDM\u003c/b\u003e\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eHealthcare Decision-Maker\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003e\u003cb\u003eTAM\u003c/b\u003e\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eTechnology Acceptance Model\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003e\u003cb\u003eTAM\u003c/b\u003e\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eTechnology Acceptance Model\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003e\u003cb\u003eGAIAS\u003c/b\u003e\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eGeneral AI Attitudes Scale\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003e\u003cb\u003eAIAS\u003c/b\u003e\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eArtificial Intelligence Anxiety Scale\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003e\u003cb\u003eNGO\u003c/b\u003e\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eNon-Governmental Organization\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003e\u003cb\u003eSPSS\u003c/b\u003e\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eStatistical Package for Social Sciences\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003e\u003cb\u003eSD\u003c/b\u003e\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eStandard Deviation\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthical Considerations\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eEthical approval (2023/11/15/16436) was obtained from the Jordan Ministry of Health’s Institutional Review Board (IRB), and the study was conducted in accordance with the Declaration of Helsinki (World Medical Association, 2013). All participants provided written informed consent. They were fully informed about the voluntary nature of their participation, the anonymity and confidentiality of their responses, and their right to withdraw at any time without any consequences.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no competing interests\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work was supported by a competitive grant (8671) from the University of Sharjah to Sara Al-Ajlouny and Hindya Al-Maqableh. This research work was also supported by Abu Dhabi National Oil Company (ADNOC), Emirates NBD, Sharjah Electricity Water \u0026amp; Gas Authority (SEWA), Technology Innovation Institute (TII), and GSK as the sponsors of the 4\u003csup\u003eth\u003c/sup\u003e Forum for Women in Research (QUWA): Sustaining Women’s Empowerment in Research \u0026amp; Innovation at the University of Sharjah.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eS.A. and H.A. contributed equally to this work. S.A. and H.A. conceptualized and designed the study. S.A., H.A., A.A. and M.A. participated in data acquisition. Y.K., T.S., Y.A., and T.L.M. critically reviewed and revised the manuscript. All authors approved the final version of the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe sincerely express our gratitude to Mr. Anees Hjazeen, Biostatistician at the Royal Medical\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eServices, for his invaluable support in conducting the data analysis. His expertise and dedication ensured the accuracy and rigor of our results, contributing significantly to the quality of this study.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n \u003cli\u003eKoo T. , Zakaria A. , Ng J. , \u0026amp; Leong X.. Systematic review of the application of artificial intelligence in healthcare and nursing care. Malaysian Journal of Medical Sciences 2024;31(5):135-142. https://doi.org/10.21315/mjms2024.31.5.9\u003c/li\u003e\n \u003cli\u003eKaya, F. et al. (2022) \u0026lsquo;The Roles of Personality Traits, AI Anxiety, and Demographic Factors in Attitudes toward Artificial Intelligence\u0026rsquo;, International Journal of Human\u0026ndash;Computer Interaction, 40(2), pp. 497\u0026ndash;514. doi: 10.1080/10447318.2022.2151730.\u003c/li\u003e\n \u003cli\u003eYu KH, Beam AL, Kohane IS. Artificial intelligence in healthcare. Nat Biomed Eng. 2018 Oct;2(10):719-731. doi: 10.1038/s41551-018-0305-z. Epub 2018 Oct 10. PMID: 31015651.\u003c/li\u003e\n \u003cli\u003eTopol, E.J. High-performance medicine: the convergence of human and artificial intelligence. Nat Med 25, 44\u0026ndash;56 (2019). https://doi.org/10.1038/s41591-018-0300-7\u003c/li\u003e\n \u003cli\u003eJiang F, Jiang Y, Zhi H, Dong Y, Li H, Ma S, Wang Y, Dong Q, Shen H, Wang Y. Artificial intelligence in healthcare: past, present and future. Stroke Vasc Neurol. 2017 Jun 21;2(4):230-243. doi: 10.1136/svn-2017-000101. PMID: 29507784; PMCID: PMC5829945.\u003c/li\u003e\n \u003cli\u003eYe T, Xue J, He M, Gu J, Lin H, Xu B, Cheng Y. Psychosocial Factors Affecting Artificial Intelligence Adoption in Health Care in China: Cross-Sectional Study, J Med Internet Res 2019;21(10):e14316. doi: 10.2196/14316PMID: 31625950PMCID: 6913088\u003c/li\u003e\n \u003cli\u003eCruz JP, Sembekova A, Omirzakova D, Bolla SR, Balay-odao EM. General Attitudes Towards and Readiness for Medical Artificial Intelligence among Medical and Health Sciences Students in Kazakhstan JMIR Preprints. 01/06/2023:49536 DOI: 10.2196/preprints.49536\u003c/li\u003e\n \u003cli\u003eŞahin, M. G., \u0026amp; Yıldırım, Y. (2024). The general attitudes towards artificial intelligence (GAAIS): A meta-analytic reliability generalization study. International Journal of Assessment Tools in Education, 11(2), 303-319. https://doi.org/10.21449/ijate.1369023\u003c/li\u003e\n \u003cli\u003eBabiker, A., Alshakhsi, S., Al-Thani, D., Montag, C., \u0026amp; Ali, R. (2024). Attitude Towards AI: Potential Influence of Conspiracy Belief, XAI Experience and Locus of Control. International Journal of Human\u0026ndash;Computer Interaction, 41(13), 7939\u0026ndash;7951. https://doi.org/10.1080/10447318.2024.2401249\u003c/li\u003e\n \u003cli\u003eMorales-Garc\u0026iacute;a WC, Sairitupa-Sanchez LZ, Morales-Garc\u0026iacute;a SB, Morales-Garc\u0026iacute;a M. Adaptation and psychometric properties of a brief version of the general self-efficacy scale for use with artificial intelligence (GSE-6AI) among university students. InFrontiers in Education 2024 Mar 8 (Vol. 9, p. 1293437). Frontiers Media SA.\u003c/li\u003e\n \u003cli\u003eBergdahl J, Latikka R, Celuch M, Savolainen I, Mantere ES, Savela N, Oksanen A. Self-determination and attitudes toward artificial intelligence: Cross-national and longitudinal perspectives. Telematics and Informatics. 2023 Aug 1; 82:102013.\u003c/li\u003e\n \u003cli\u003eArishi, D. Y. O., Elseesy, N., \u0026amp; Al-Abdullah, N. (2023). Nurses\u0026rsquo; general attitudes, comfortableness, and perceived capabilities toward using artificial intelligence systems among nurses at Makkah city. Bioscience Research, 20(3), 682\u0026ndash;695.\u003c/li\u003e\n \u003cli\u003eAlmomani, H., Obaidat, M., Khazaleh, A., Muneizel, O., Afyouni, N. M., \u0026amp; Fayyad, S. M. (2019). Review of medical waste management in jordanian health care organisations. British Journal of Healthcare Management, 25(8), 1-8. https://doi.org/10.12968/bjhc.2019.0041\u003c/li\u003e\n \u003cli\u003eHamad, M., Qtaishat, F. A., Mhairat, E., AL-Qunbar, A., Jaradat, M., Mousa, A., \u0026hellip; \u0026amp; Alkhaldi, S. M. (2024). Artificial intelligence readiness among jordanian medical students: using medical artificial intelligence readiness scale for medical students (mairs-ms). Journal of Medical Education and Curricular Development, 11. https://doi.org/10.1177/23821205241281648\u003c/li\u003e\n \u003cli\u003eRaosoft, Inc. (2004). Sample size calculator. http://www.raosoft.com/samplesize.html\u003c/li\u003e\n \u003cli\u003eHRH2030. (2018). Women in health management: A case study in Jordan. Chemonics International Inc. https://haqqi.s3.eu-north-1.amazonaws.com/2018-10/HRH2030%20Jordan%20Women%20in%20Health%20Mana.pdf\u003c/li\u003e\n \u003cli\u003eSchepman A, Rodway P. Initial validation of the general attitudes towards Artificial Intelligence Scale. Comput Hum Behav Rep. 2020 Jan-Jul; 1:100014. doi: 10.1016/j.chbr.2020.100014. Epub 2020 May 18. PMID: 34235291; PMCID: PMC7231759.\u003c/li\u003e\n \u003cli\u003eWang, Y.-Y., \u0026amp; Wang, Y.-S. (2022). Development and validation of an artificial intelligence anxiety scale: An initial application in predicting motivated learning behavior. Interactive Learning Environments, 30(4), 619\u0026ndash;634. https://doi.org/10.1080/10494820.2019.1674887\u003c/li\u003e\n \u003cli\u003eGagnon MP, Orru\u0026ntilde;o E, Asua J, Abdeljelil AB, Emparanza J. Using a modified technology acceptance model to evaluate healthcare professionals\u0026apos; adoption of a new telemonitoring system. Telemed J E Health. 2012 Jan-Feb;18(1):54-9. doi: 10.1089/tmj.2011.0066. Epub 2011 Nov 14. PMID: 22082108; PMCID: PMC3270047.\u003c/li\u003e\n \u003cli\u003eRadhaswati, I. D. A. A. and Santosa, M. H. (2022). Teachers\u0026rsquo; perceptions: the use of google form as a media to assess primary school students. EDUTEC : Journal of Education and Technology, 5(4), 910-924. https://doi.org/10.29062/edu.v5i4.308\u003c/li\u003e\n \u003cli\u003eIBM Corp. (2021). IBM SPSS Statistics for Windows (Version 25.0). IBM Corp\u003c/li\u003e\n \u003cli\u003eBusch, F., Hoffmann, L., Xu, L., Zhang, L., Hu, B., Garc\u0026iacute;a-Ju\u0026agrave;rez, I., ... \u0026amp; COMFORT consortium. (2024). Multinational attitudes towards AI in healthcare and diagnostics among hospital patients. \u003cem\u003emedRxiv\u003c/em\u003e, 2024-09.\u003c/li\u003e\n \u003cli\u003eAl-Dmour R, Al-Dmour H, Basheer Amin E, Al-Dmour A. Impact of AI and big data analytics on healthcare outcomes: An empirical study in Jordanian healthcare institutions. DIGITAL HEALTH. 2025;11. doi:10.1177/20552076241311051\u003c/li\u003e\n \u003cli\u003eIssa, W. B., Shorbagi, A., Alshorman, A., Rababa, M., Majed, K. A., Radwan, H., \u0026hellip; \u0026amp; Fakhry, R. (2024). Shaping the future: perspectives on the integration of artificial intelligence in health profession education: a multi-country survey.. https://doi.org/10.21203/rs.3.rs-4396289/v1\u003c/li\u003e\n \u003cli\u003eAl‐Qerem, W., Eberhardt, J., Jarab, A. S., Bawab, A. Q. A., Hammad, A. M., Alasmari, F., \u0026hellip; \u0026amp; Al-Beool, S. (2023). Exploring knowledge, attitudes, and practices towards artificial intelligence among health professions\u0026rsquo; students in jordan. BMC Medical Informatics and Decision Making, 23(1). https://doi.org/10.1186/s12911-023-02403-0\u003c/li\u003e\n \u003cli\u003eSaeed A, Bin Saeed A, AlAhmri FA. Saudi Arabia Health Systems: Challenging and Future Transformations With Artificial Intelligence. Cureus. 2023 Apr 19;15(4):e37826. doi: 10.7759/cureus.37826. PMID: 37214025; PMCID: PMC10197987.\u003c/li\u003e\n \u003cli\u003eHasan HE, Jaber D, Khabour OF, Alzoubi KH. Ethical considerations and concerns in the implementation of AI in pharmacy practice: a cross-sectional study. BMC Med Ethics. 2024 May 16;25(1):55. doi: 10.1186/s12910-024-01062-8. PMID: 38750441; PMCID: PMC11096093.\u003c/li\u003e\n \u003cli\u003eAmann J, Vayena E, Ormond KE, Frey D, Madai VI, Blasimme A. Expectations and attitudes towards medical artificial intelligence: A qualitative study in the field of stroke. PLoS One. 2023 Jan 11;18(1):e0279088. doi: 10.1371/journal.pone.0279088. PMID: 36630325; PMCID: PMC9833517.\u003c/li\u003e\n \u003cli\u003eSmith, J. and Johnson, P. (2022) The Impact of AI on Medical Data Analysis: A Case Study of IBM Watson Health. Journal of Health Informatics, 45, 145-156.\u003c/li\u003e\n \u003cli\u003eLee, H.S. and Lee, J. (2021) Applying Artificial Intelligence in Physical Education and Future Perspectives. Sustainability, 13, Article 351. https://doi.org/10.3390/su13010351\u003c/li\u003e\n \u003cli\u003eHoffman J, Hattingh L, Shinners L, Angus R, Richards B, Hughes I, Wenke R, Allied Health Professionals\u0026rsquo; Perceptions of Artificial Intelligence in the Clinical Setting: Cross-Sectional Survey, JMIR Form Res 2024;8:e57204. URL: https://formative.jmir.org/2024/1/e5720 DOI: 10.2196/57204\u003c/li\u003e\n \u003cli\u003eLi M, Xiong X and Xu B (2024) Attitudes and perceptions of Chinese oncologists towards artificial intelligence in healthcare: a cross-sectional survey. \u003cem\u003eFront. Digit. Health\u003c/em\u003e\u003cstrong\u003e6\u003c/strong\u003e:1371302. doi: 10.3389/fdgth.2024.1371302\u003c/li\u003e\n \u003cli\u003eWen, F., Li, Y., Zhou, Y., An, X., \u0026amp; Zou, Q. (2024). A study on the relationship between AI anxiety and AI behavioral intention of secondary school students learning English as a foreign language. \u003cem\u003eJournal of Educational Technology Development and Exchange (JETDE)\u003c/em\u003e, \u003cem\u003e17\u003c/em\u003e(1), 130-154.\u003c/li\u003e\n \u003cli\u003eTerzi, R. (2020). An Adaptation of Artificial Intelligence Anxiety Scale into Turkish: Reliability and Validity Study. \u003cem\u003eInternational Online Journal of Education and Teaching\u003c/em\u003e, \u003cem\u003e7\u003c/em\u003e(4), 1501-1515.\u003c/li\u003e\n \u003cli\u003ePatel V, Khan MN, Shrivastava A, Sadiq K, Ali SA, Moore SR, Brown DE, Syed S. Artificial Intelligence Applied to Gastrointestinal Diagnostics: A Review. J Pediatr Gastroenterol Nutr. 2020 Jan;70(1):4-11. doi: 10.1097/MPG.0000000000002507. PMID: 31567886; PMCID: PMC6934912.\u003c/li\u003e\n \u003cli\u003eGreen, C. S., Bavelier, D., Kramer, A. F., Vinogradov, S., Ansorge, U., Ball, K. K., \u0026amp; Witt, C. M. (2019). Improving methodological standards in behavioral interventions for cognitive enhancement. Journal of Cognitive Enhancement, 3 (1), 2\u0026ndash;29. (Publisher: Springer Science and Business Media LLC)\u003c/li\u003e\n \u003cli\u003eKimiafar, K., Sarbaz, M., Tabatabaei, S. M., Ghaddaripouri, K., Mousavi, A. S., Mehneh, M. R., \u0026amp; Baigi, S. F. M. (2023). Artificial intelligence literacy among healthcare professionals and students: a systematic review. \u003cem\u003eFrontiers in Health Informatics\u003c/em\u003e, \u003cem\u003e12\u003c/em\u003e, 168.\u003c/li\u003e\n \u003cli\u003eLambert, S.I., Madi, M., Sopka, S. \u003cem\u003eet al.\u003c/em\u003e An integrative review on the acceptance of artificial intelligence among healthcare professionals in hospitals. \u003cem\u003enpj Digit. Med.\u003c/em\u003e\u003cstrong\u003e6\u003c/strong\u003e, 111 (2023). https://doi.org/10.1038/s41746-023-00852-5\u003c/li\u003e\n \u003cli\u003eCho KA, Seo YH. Dual mediating effects of anxiety to use and acceptance attitude of artificial intelligence technology on the relationship between nursing students\u0026apos; perception of and intention to use them: a descriptive study. BMC Nurs. 2024 Mar 28;23(1):212. doi: 10.1186/s12912-024-01887-z. PMID: 38539198; PMCID: PMC10976840.\u003c/li\u003e\n \u003cli\u003eIbrahim F, M\u0026uuml;nscher JC, Daseking M, Telle NT. The technology acceptance model and adopter type analysis in the context of artificial intelligence. Front Artif Intell. 2025 Jan 16; 7:1496518. doi: 10.3389/frai.2024.1496518. PMID: 39886023; PMCID: PMC11780378.\u003c/li\u003e\n \u003cli\u003eHigh Health Council. (2018). \u003cem\u003eNational Human Resources for Health Strategy for Jordan 2018\u0026ndash;2022\u003c/em\u003e. Amman, Jordan.\u003c/li\u003e\n \u003cli\u003eOrganisation for Economic Co-operation and Development (OECD). (2016). \u003cem\u003eHealth workforce policies in OECD countries: Right jobs, right skills, right places\u003c/em\u003e. OECD Publishing. https://doi.org/10.1787/9789264239517-en\u003c/li\u003e\n \u003cli\u003eHuber, D. (2017). \u003cem\u003eLeadership and Nursing Care Management\u003c/em\u003e (6th ed.). Elsevier Health Sciences.\u003c/li\u003e\n \u003cli\u003eWorld Medical Association. (2013). World Medical Association Declaration of Helsinki: Ethical Principles for Medical Research Involving Human Subjects. \u003cem\u003eJAMA\u003c/em\u003e, 310(20), 2191\u0026ndash;2194. https://doi.org/10.1001/jama.2013.281053\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"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":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Artificial Intelligence, Technology Acceptance, AI Anxiety, Healthcare Decision-Makers, Jordan, Attitudes toward AI","lastPublishedDoi":"10.21203/rs.3.rs-7338088/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7338088/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eArtificial intelligence (AI) is increasingly acknowledged as a transformational influence in healthcare, including early diagnostic responses, decision support, and therapeutic enhancement. Successful implementation of AI technologies is dependent on the attitudes of healthcare decision-makers (HDMs) who have a central role in influencing institutional and national adoption plans. This research aimed to explore the impact of technology acceptance, AI anxiety, and demographic factors on Jordanian HDMs' attitudes towards AI in healthcare institutions A cross-sectional approach was used, focusing on 152 healthcare decision-makers from governmental, NGO, and academic institutions in Jordan. Data was collected using a structured online questionnaire utilizing three validated instruments: the General AI Attitudes Scale, the Artificial Intelligence Anxiety Scale, and a modified Technology Acceptance Model. Hierarchical multiple linear regression was used to identify determinants of favorable and negative views about AI. The results indicated the most positive feelings toward AI among participants, shown by a mean positive attitude score of 3.94 (SD = 0.59) and a somewhat lower mean negative attitude score of 2.60 (SD = 0.71). Junior HDMs and those employed in academic institutions, NGOs, and governmental organizations had greater positive views compared to their private sector counterparts. Heightened anxiety over AI, especially linked to its learning capacities and sociotechnical ramifications, was strongly associated with negative perceptions. Furthermore, perceived value and ease of use were identified as significant factors influencing positive attitudes towards AI. These results emphasize the need for specialized training and intuitive AI solutions to enhance acceptance and enable the efficient use of AI in healthcare systems.\u003c/p\u003e","manuscriptTitle":"The Role of Technology Acceptance, AI Anxiety, and Demographic Factors in Jordanian Healthcare Decision Makers' Attitudes Toward Artificial Intelligence","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-10-10 08:40:55","doi":"10.21203/rs.3.rs-7338088/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2026-04-23T07:27:45+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-04-18T03:04:21+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-04-14T07:31:14+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"137607025160489494494868992776714092058","date":"2026-04-12T23:42:43+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"28198774676520901216614390652017863888","date":"2026-04-09T16:51:59+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-12-14T13:15:19+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-12-12T18:42:47+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"182581272796871759391868709822150257789","date":"2025-11-15T00:37:49+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"202811418268704632303733351759714555760","date":"2025-11-14T11:51:10+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"35798083056731050694765914829092429553","date":"2025-11-14T10:34:59+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-09-29T10:22:59+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-09-22T11:23:01+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2025-08-18T09:25:48+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-08-13T11:13:48+00:00","index":"","fulltext":""},{"type":"submitted","content":"Scientific Reports","date":"2025-08-13T11:10:28+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"b14f49aa-974e-4756-b23b-fde8ac230976","owner":[],"postedDate":"October 10th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[{"id":56019732,"name":"Health sciences/Health care"},{"id":56019733,"name":"Humanities/Health humanities"},{"id":56019734,"name":"Humanities/Medical humanities"}],"tags":[],"updatedAt":"2026-05-08T07:40:34+00:00","versionOfRecord":[],"versionCreatedAt":"2025-10-10 08:40:55","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7338088","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7338088","identity":"rs-7338088","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}