Artificial intelligence in elderly healthcare: A scoping review.

Aim/Purpose: This paper aims to investigate and understand the intentions of management undergraduate students in Hangzhou, China, regarding the adoption of Artificial Intelligence (AI) technologies in their education. It addresses the need to explore the factors influencing AI adoption in the educational context and contribute to the ongoing discourse on technology integration in higher education. Background: The paper addresses the problem by conducting a comprehensive investigation into the perceptions of management undergraduate students in Hangzhou, China, regarding the adoption of AI in education. The study explores various factors, including Perceived Relative Advantage and Trialability, to shed light on the nuanced dynamics influencing AI technology adoption in the context of higher education. Methodology: The study employs a quantitative research approach, utilizing the Confirmatory Tetrad Analysis (CTA) and Partial Least Squares Structural Equation Modeling (PLS-SEM) methodologies. The research sample consists of management undergraduate students in Hangzhou, China, and the methods include data screening, principal component analysis, confirmatory tetrad analysis, and evaluation of the measurement and structural models. We used a random sampling method to distribute 420 online, self-administered questionnaires among management students aged 18 to 21 at universities in Hangzhou. Contribution: This paper explores how management students in Hangzhou, China, perceive the adoption of AI in education. It identifies factors that influence AI adoption intention. Furthermore, the study emphasizes the complex nature of technology adoption in the changing educational technology landscape. It offers a thorough comprehension of this process while challenging and expanding the existing literature by revealing the insignificant impacts of certain factors. This highlights the need for an approach to AI integration in education that is context-specific and culturally sensitive. Findings: The study highlights students’ positive attitudes toward integrating AI in educational settings. Perceived relative advantage and trialability were found to impact AI adoption intention significantly. AI adoption is influenced by social and cultural contexts rather than factors like compatibility, complexity, and observability. Peer influence, instructor guidance, and the university environment were identified as pivotal in shaping students’ attitudes toward AI technologies. Recommendations for Practitioners: To promote the use of AI among management students in Hangzhou, practitioners should highlight the benefits and the ease of testing these technologies. It is essential to create communication strategies tailored to the student’s needs, consider cultural differences, and utilize the influence of peers and instructors. Establishing a supportive environment within the university that encourages innovation through policies and regulations is vital. Additionally, it is recommended that students’ attitudes towards AI be monitored constantly, and strategies adjusted accordingly to keep up with the changing technological landscape. Recommendation for Researchers: Researchers should conduct cross-disciplinary and cross-cultural studies with qualitative and longitudinal research designs to understand factors affecting AI adoption in education. It is essential to investigate compatibility, complexity, observability, individual attitudes, prior experience, and the evolving role of peers and instructors. Impact on Society: The study’s insights into the positive attitudes of management students in Hangzhou, China, toward AI adoption in education have broader societal implications. It reflects a readiness for transformative educational experiences in a region known for technological advancements. However, the study also underscores the importance of cautious integration, considering associated risks like data privacy and biases to ensure equitable benefits and uphold educational values. Future Research: Future research should delve into AI adoption in various academic disciplines and regions, employing longitudinal designs and qualitative methods to understand cultural influences and the roles of peers and instructors. Investigating moderating factors influencing specific factors’ relationship with AI adoption intention is essential for a comprehensive understanding.

PurposeThe introduction of artificial intelligence (AI) technology has had a substantial influence on the retail industry. However, AI adoption entails considerable responsibilities and risks for senior managers. In this study, the authors developed an evaluation and selection mechanism for successful AI technology adoption in the retail industry. The multifaceted measurement and identification of critical factors (CFs) can enable retailers to adopt AI technology effectively and maintain a sustainable competitive advantage.Design/methodology/approachThe evaluation and adoption of organisational AI technology involve multifaceted decision-making for management. Therefore, the authors used the analytic network process to develop an AI evaluation framework for calculating the weight and importance of each consideration. An expert questionnaire survey was distributed to senior retail managers and 17 valid responses were obtained. Finally, the Vlse Kriterijumska Optimizacija Kompromisno Resenje (VIKOR) method was used to identify CFs for AI adoption.FindingsThe results revealed five CFs for AI adoption in the retail industry. The findings indicated that after AI adoption, top retail management is most concerned with factors pertaining to business performance and minor concerned about the internal system's functional efficiency. Retailers pay more attention to technology and organisation context, which are matters under the retailers' control, than to external uncontrollable environmental factors.Originality/valueThe authors developed an evaluation framework and identified CFs for AI technology adoption in the retail industry. In terms of practical application, the results of this study can help AI service providers understand the CFs of retailers when adopting AI. Moreover, retailers can use the proposed multifaceted evaluation framework to guide their adoption of AI technology.

Fruits and vegetables preservation based on AI technology: Research progress and application prospects

The aim of the paper is twofold. First to examine the role of the board of directors in facilitating the adoption of AI and ML in Saudi Arabian banking sector. Second, to explore the effectiveness of artificial intelligence and machine learning in protection of Saudi Arabian banking sector from cyberattacks. A qualitative research approach was applied using in-depth interviews with 17 board of directors from prominent Saudi Arabian banks. The present study highlights both the opportunities and challenges of integrating artificial intelligence and machine learning advanced technologies in this highly regulated industry. Findings reveal that advanced artificial intelligence and machine learning technologies offer substantial benefits, particularly in areas like threat detection, fraud prevention, and process automation, enabling banks to meet regulatory standards and mitigate cyber threats efficiently. However, the research also identifies significant barriers, including limited technological infrastructure, a lack of cohesive artificial intelligence strategies, and ethical concerns around data privacy and algorithmic bias. Interviewees emphasized the board of directors’ critical role in providing strategic direction, securing resources, and fostering partnerships with artificial intelligence technology providers. The study further highlights the importance of aligning artificial intelligence and machine learning initiatives with national development goals, such as Saudi Vision 2030, to ensure sustained growth and competitiveness. The findings from the present study offer valuable implications for policymakers in banking in navigating the complexities of artificial intelligence and machine learning adoption in financial services, particularly in emerging markets.

As the online retail market expands and brand competition intensifies, e-commerce platforms and retailers are facing increasing service and operational costs. The emergence of artificial intelligence (AI) technology has transformed the consumer shopping experience while simultaneously reducing retailers' operational costs. However, platforms must judiciously balance the operational efficiency improvements enabled by AI technology with the potential risks stemming from intensified competition and rising technology adoption costs. This study constructs a two-stage supply chain model involving a retailer and a platform, who are selling national brand products and substitutable private label brand, respectively. The platform strategically adopts and shares AI technology with retailer to optimise sales performance. We systematically analyze the viability of AI adoption and its impact on brand competition. The findings suggest that under moderate cost conditions, the platform's AI adoption may result in a lose–lose situation for both firms. When AI R&D costs are low, sharing AI technology, despite potentially weakening the platform's private-label competitiveness and reducing overall market demand, can still lead to a win-win situation for both parties. Additionally, extended analysis indicates that changes in consumer preferences have a limited effect on AI investment decisions, although different AI applications may introduce more complex supply chain dynamics.

The adoption of artificial intelligence (AI) in healthcare is growing rapidly. Remote patient monitoring (RPM) is one of the common healthcare applications that assist doctors to monitor patients with chronic or acute illness at remote locations, elderly people in‐home care, and even hospitalized patients. The reliability of manual patient monitoring systems depends on staff time management which is dependent on their workload. Conventional patient monitoring involves invasive approaches which require skin contact to monitor health status. This study aims to do a comprehensive review of RPM systems including adopted advanced technologies, AI impact on RPM, challenges and trends in AI‐enabled RPM. This review explores the benefits and challenges of patient‐centric RPM architectures enabled with Internet of Things wearable devices and sensors using the cloud, fog, edge, and blockchain technologies. The role of AI in RPM ranges from physical activity classification to chronic disease monitoring and vital signs monitoring in emergency settings. This review results show that AI‐enabled RPM architectures have transformed healthcare monitoring applications because of their ability to detect early deterioration in patients' health, personalize individual patient health parameter monitoring using federated learning, and learn human behavior patterns using techniques such as reinforcement learning. This review discusses the challenges and trends to adopt AI to RPM systems and implementation issues. The future directions of AI in RPM applications are analyzed based on the challenges and trends.This article is categorized under: Application Areas > Health Care Technologies > Artificial Intelligence Technologies > Internet of Things

Purpose This paper aims to explore the factors influencing auditors’ willingness to learn and use artificial intelligence (AI) technologies in auditing. This study extended the Unified Theory of Acceptance and Use of Technology by incorporating the willingness to learn as a key factor for AI adoption in auditing. Design/methodology/approach Data were collected through validated questionnaires form 150 auditors selected via simple random sampling from a population of 300 statutory auditors. Partial least squares structural equation modeling was used to analyze the relationships between variables. Findings This study finds that performance expectancy and effort expectancy significantly and positively influence auditors’ willingness to learn AI technologies. However, social influence does not significantly impact their willingness. A positive relationship is observed between willingness to learn and the actual use of AI in auditing process. Practical implications The findings suggest the need for government, professional associations and universities seeking to develop AI-focused training programs and incorporate AI into curricula, ensuring accounting students are equipped to meet future demands. Originality/value This study contributes new insights into the factors influencing auditors’ willingness to adopt AI technologies, focusing on Jordanian auditors and addressing the gap in literature related to AI adoption in auditing within developing economies.

Using novel comprehensive administrative micro data on business applications in the United States over the period 2004–2023, we examine the characteristics of business applications and resulting startups that aim to develop Artificial Intelligence (AI) technologies or use AI in providing goods and services. AI-related applications begin rising around 2012, accelerate after 2016, and surge in 2023, consistent with major advances in AI technologies around these years. This growth is highly uneven across states and sectors. AI applications are more likely to come from corporations, transition to startups at higher rates, and anticipate greater initial employment than other applications. Startups originating from these applications achieve higher employment and revenue, but have a lower survival rate, reflecting an ‘up-or-out' dynamic. Despite higher revenue, they exhibit a higher labor share and do not have higher labor productivity. The patterns suggest an accelerating AI diffusion through new business formation and a growing contribution from AI-related startups to business dynamism.

Artificial intelligence (AI) is transforming the mental health care environment. AI tools are increasingly accessed by clients and service users. Mental health professionals must be prepared not only to use AI but also to have conversations about it when delivering care. Despite the potential for AI to enable more efficient and reliable and higher-quality care delivery, there is a persistent gap among mental health professionals in the adoption of AI. A needs assessment was conducted among mental health professionals to (1) understand the learning needs of the workforce and their attitudes toward AI and (2) inform the development of AI education curricula and knowledge translation products. A qualitative descriptive approach was taken to explore the needs of mental health professionals regarding their adoption of AI through semistructured interviews. To reach maximum variation sampling, mental health professionals (eg, psychiatrists, mental health nurses, educators, scientists, and social workers) in various settings across Ontario (eg, urban and rural, public and private sector, and clinical and research) were recruited. A total of 20 individuals were recruited. Participants included practitioners (9/20, 45% social workers and 1/20, 5% mental health nurses), educator scientists (5/20, 25% with dual roles as professors/lecturers and researchers), and practitioner scientists (3/20, 15% with dual roles as researchers and psychiatrists and 2/20, 10% with dual roles as researchers and mental health nurses). Four major themes emerged: (1) fostering practice change and building self-efficacy to integrate AI into patient care; (2) promoting system-level change to accelerate the adoption of AI in mental health; (3) addressing the importance of organizational readiness as a catalyst for AI adoption; and (4) ensuring that mental health professionals have the education, knowledge, and skills to harness AI in optimizing patient care. AI technologies are starting to emerge in mental health care. Although many digital tools, web-based services, and mobile apps are designed using AI algorithms, mental health professionals have generally been slower in the adoption of AI. As indicated by this study's findings, the implications are 3-fold. At the individual level, digital professionals must see the value in digitally compassionate tools that retain a humanistic approach to care. For mental health professionals, resistance toward AI adoption must be acknowledged through educational initiatives to raise awareness about the relevance, practicality, and benefits of AI. At the organizational level, digital professionals and leaders must collaborate on governance and funding structures to promote employee buy-in. At the societal level, digital and mental health professionals should collaborate in the creation of formal AI training programs specific to mental health to address knowledge gaps. This study promotes the design of relevant and sustainable education programs to support the adoption of AI within the mental health care sphere.

Everything related to Artificial Intelligence (AI) technology has a crucial role to play in helping to improve efficiency, lowering expenses, and optimizing decision-making across numerous industries, including accounting, where Generation Z accountants, particularly in Batam, must continuously develop their skills and readiness to leverage AI despite challenges related to data privacy and security. This research seeks to evaluate the readiness of a Generation Z accountant to embrace artificial intelligence (AI) technology by applying the Technology Acceptance Model (TAM). In the context of the fourth industrial revolution, AI adoption is crucial for improving efficiency and effectiveness in accounting practices. The study employs a quantitative approach with purposive sampling, involving 220 Generation Z accountant participants from Batam City. The analysis, conducted using the PLS-PM method in R programming and data processing with Google Colab statistics, reveals that the Technology Readiness (TR) variable significantly influences the interest of Generation Z accountants in adopting AI technology. While the perceived ease of use (PEOU) variable showed no significant effect, perceived usefulness (PU) emerged as the dominant factor in technology adoption. The R-Squared value of 0.920 indicates that 92% of the variables influencing AI technology adoption are explained by TR and PU. This study highlights the importance of technology readiness in helping Generation Z accountants adapt to technological advancements and offers recommendations for enhancing education, training, and technology development within the accounting profession.

Text classification plays an essential role in the medical domain by organizing and categorizing vast amounts of textual data through machine learning (ML) and deep learning (DL). The adoption of Artificial Intelligence (AI) technologies in healthcare has raised concerns about the interpretability of AI models, often perceived as "black boxes." Explainable AI (XAI) techniques aim to mitigate this issue by elucidating AI model decision-making process. In this paper, we present a scoping review exploring the application of different XAI techniques in medical text classification, identifying two main types: model-specific and model-agnostic methods. Despite some positive feedback from developers, formal evaluations with medical end users of these techniques remain limited. The review highlights the necessity for further research in XAI to enhance trust and transparency in AI-driven decision-making processes in healthcare.

The amalgamation of artificial intelligence (AI) has led to significant changes in the healthcare industry, revolutionizing patient care, diagnostics, treatment, and research. This chapter discusses the recent advancements in AI technology that are influencing healthcare research and application. With the combination of large-scale healthcare databases and machine learning innovations, predictive analytics platforms have been created, enabling early disease detection, risk assessment, and personalized treatment plans. AI-powered diagnostic tools, which use natural language processing and computer vision, have shown remarkable accuracy in analyzing medical images and notes, thereby speeding up diagnosis processes. AI-powered robotics in surgery, guided by algorithms, improve precision, reduce invasiveness, and enable remote patient monitoring through telemedicine. AI also accelerates drug discovery, predicting drug effectiveness, and reducing time and cost for targeted therapies. To address ethical and privacy concerns, transparency and regulatory adaptations are being implemented. AI’s convergence with genomics and wearables promotes personalized treatments based on genetic and real-time data, enhancing interventions. The continuous advancements in AI technology have the potential to improve patient outcomes and diagnosis. While challenges persist, AI’s potential to revolutionize healthcare remains evident through expert collaboration, technological innovation, and patient-centric care.

In recent years, artificial intelligence (AI) technologies have greatly advanced and become a reality in many areas of our daily lives. In the health care field, numerous efforts are being made to implement the AI technology for practical medical treatments. With the rapid developments in machine learning algorithms and improvements in hardware performances, the AI technology is expected to play an important role in effectively analyzing and utilizing extensive amounts of health and medical data. However, the AI technology has various unique characteristics that are different from the existing health care technologies. Subsequently, there are a number of areas that need to be supplemented within the current health care system for the AI to be utilized more effectively and frequently in health care. In addition, the number of medical practitioners and public that accept AI in the health care is still low; moreover, there are various concerns regarding the safety and reliability of AI technology implementations. Therefore, this paper aims to introduce the current research and application status of AI technology in health care and discuss the issues that need to be resolved.

The use of artificial intelligence (AI) technologies has been indicated to influence various aspects of second or foreign language (L2) education across diverse contexts. Nevertheless, how the adoption of AI may influence the professional wellbeing of English as a foreign language (EFL) teachers in under-resourced settings has received limited empirical attention. To address this gap, the present study implemented an experimental design to investigate the impact of AI technologies on Turkish EFL teachers’ professional wellbeing during a semester-long intervention. A total of 76 teachers completed the professional wellbeing scale before and after participating in a course mediated by AI tools. The participants were assigned to control and experimental groups. The results of ANCOVA revealed a significant improvement in the experimental group teachers’ professional wellbeing from the pretest to post-test, indicating the effectiveness of the intervention course. The study highlights the need for professional development programs powered by AI technologies to enhance under-resourced EFL teachers’ AI literacy and wellbeing.

Artificial intelligence (AI) technology is gradually integrating into the entire process of green supply chain management (GSCM), providing a systematic solution for enterprises to improve productivity and performance. This paper focuses on Chinese manufacturing enterprises, aiming to explore the multi-factor synergistic mechanism influencing differences in GSCM levels from a temporal perspective under the drive of AI. Based on 2019–2023 panel data of enterprises, this paper innovatively integrates the random forest algorithm with dynamic qualitative comparative analysis (QCA) to reveal the configurational effects of technological, organizational, and environmental factors in enterprises’ GSCM practices. The findings demonstrate that no single factor is a necessary condition for enterprises to implement GSCM; configurational analysis identifies two driving models: “AI technology innovation-driven (Configuration 1 and Configuration 2)” and “strategic resource-driven (Configuration 3)”; Configuration 1 combines research and development (R&D) investment and green awareness among executives with the enabling role of government subsidies; Configuration 2 couples R&D Investment with strong funding capacity, again facilitated by the presence of government subsidies; Configuration 3 combines AI technology adoption and green awareness among executives, supported by the necessary funding capacity and government subsidies. Additionally, inter-group analysis reveals no significant temporal effect among configurations but shows phased evolutionary characteristics. This paper has thoroughly explored the complex paths for enhancing GSCM of manufactory enterprises under the influence of AI. It is recommended that the government refine and strengthen targeted subsidy policies to better support the adoption and integration of AI in advancing GSCM within the manufacturing sector. Concurrently, manufacturers must align technology, organizational structure, and external factors, specifically through core AI technology improvements, enhanced executive green awareness, and the mobilization of government and external funding. These advancements have led to high-level GSCM within enterprises, allowing them to achieve high-quality and sustainable development.