Adoption Of Artificial Intelligence Research Articles

The Future of Work: A Human-centric Approach to AI, Robotics, and Cloud Computing

This study investigates the impact of artificial intelligence (AI), robotics, and cloud computing on the future of work, focusing on adoption trends, productivity gains, and ethical challenges. Using a mixed-method approach, data were sourced from the OECD AI Policy Observatory, Pew Research Center, and European Union Open Data Portal. Quantitative analysis included K-Means clustering of industries based on technology adoption, with high adopters like North America and Europe leading in cloud computing (65.87%), and Africa showing stronger AI adoption (63.02%). The analysis also highlighted a 14.5% productivity increase in manufacturing, but a 19.3% risk of job displacement. Sentiment analysis revealed that 75% of public sentiment on algorithmic bias was negative, correlating strongly (0.74) with reported violations. The study affirms the need for improved AI governance and stronger GDPR compliance to address ethical challenges, while prioritizing human-centric strategies that ensure inclusive and sustainable outcomes for the workforce.

Artificial Intelligence and Environmental Sustainability: Insights from PLS-SEM on Resource Efficiency and Carbon Emission Reduction

This study investigates the relationship between Artificial Intelligence (AI) and environmental sustainability, focusing on how AI-driven resource efficiency, energy consumption, environmental monitoring, and carbon emission reduction contribute to sustainability outcomes. The purpose of this research is to examine the dual nature of AI's impact on sustainability by testing both positive and negative effects using Partial Least Squares Structural Equation Modeling (PLS-SEM). Drawing from a sample of 233 firms in the energy, transportation, and manufacturing sectors, this study collects data through an online survey measured on a five-point Likert scale. Four hypotheses are tested, revealing that AI-driven resource efficiency and environmental monitoring positively affect sustainability, while AI energy consumption has a negative impact. Furthermore, AI integration in industrial processes helps reduce resource depletion. The findings suggest that governments should incentivize AI adoption aimed at resource efficiency and environmental monitoring through policies like tax breaks or subsidies, particularly for firms reducing their carbon footprint. To mitigate the negative effects of AI energy consumption, policymakers are urged to promote energy-efficient AI models and invest in renewable energy infrastructure. A balanced policy approach is crucial to optimize the environmental benefits of AI while minimizing its energy-related drawbacks.

Conceptualizing the Implications of Artificial Intelligence (AI) Tools and Personalization Marketing on Consumer Purchase Intention: Insights from the Malaysian E-Commerce Market

Artificial intelligence (AI) has emerged as a powerful tool, enabling online retailers to offer highly personalized shopping experiences tailored to individual preferences and behaviors. As e-commerce continues to grow in Malaysia, understanding the influence of AI and personalization marketing on consumer purchase intentions has become increasingly important for businesses seeking to remain competitive. However, the rapid adoption of AI also raises concerns about data privacy, ethical AI usage, and compliance with emerging data protection regulations, such as Malaysia’s Personal Data Protection Act (PDPA). This study aims to explore the potential impact of artificial intelligence (AI) and personalization marketing on consumer purchase intention within the Malaysian e-commerce market. By reviewing previous literature and theoretical frameworks, the study explores how the use of AI tools including predictive analytics automation, and personalization experiences might influence consumer behavior in online shopping environments. The study adopts a quantitative approach, in which quota sampling will be used for the participant selection. A self-administered questionnaire with a five-point Likert scale will be employed to gather data from e-commerce users in Malaysia. The findings from this study have important implications for both e-commerce businesses and policymakers in Malaysia. For businesses, understanding which aspects of AI and personalization most influence consumer purchase intentions can help them strategically implement these technologies to enhance customer engagement and drive sales. For policymakers, the study highlights the need to consider ethical and legal issues, such as data privacy and policy issues, in the growing use of AI in the e-commerce market.

Artificial Intelligence-Powered Risk Assessment in Supply Chain Safety

The increasing complexity and globalization of supply chains necessitate robust risk management strategies to ensure safety and resilience. Traditional risk assessment methods often fall short in dynamically adapting to the rapidly changing conditions and voluminous data inherent in modern supply chains. This study explores the potential of Artificial Intelligence (AI)-powered risk assessment to address these limitations in the context of Malaysia's supply chain industry. By employing AI technologies such as machine learning, IoT, and predictive analytics, organizations can significantly enhance their risk management capabilities, improving predictive accuracy, real-time monitoring, and overall operational efficiency. Through a qualitative analysis involving in-depth interviews with supply chain managers, AI experts, and technology vendors, the study identifies the strategies employed for AI integration, the perceived effectiveness of these technologies, and the challenges faced in implementation. The findings highlight the importance of robust data governance, the development of explainable AI models, and continuous skill development to overcome barriers related to data quality, model interpretability, and high implementation costs. The study concludes with recommendations for fostering a safer and more resilient logistics environment in Malaysia, emphasizing the need for comprehensive AI adoption frameworks and scalable solutions for small and medium-sized enterprises.

Mitigating Maritime Cybersecurity Risks Using AI-Based Intrusion Detection Systems and Network Automation During Extreme Environmental Conditions

The maritime industry is increasingly confronted with a myriad of cybersecurity challenges exacerbated by extreme environmental conditions, technological advancements, and heightened reliance on automation. This review paper discusses the intersection of these factors, focusing on the adoption of artificial intelligence (AI)-based intrusion detection systems (IDS) and network automation as vital strategies for mitigating cybersecurity risks. The paper begins by outlining the unique cybersecurity threats faced by the maritime sector, which include data breaches, phishing attacks, and malware threats, all amplified by adverse weather and geographical isolation. In light of these challenges, the rationale for integrating AI-driven solutions into maritime operations is discussed. AI-based IDS can enhance threat detection capabilities through advanced machine learning algorithms that adapt to evolving cyber threats while minimizing false positives. Additionally, network automation can improve connectivity and data security, facilitating real-time monitoring and response to incidents. The review also addresses the critical need for collaboration between maritime and technology industries, emphasizing how partnerships can foster innovation and provide tailored solutions to the sector’s specific needs. Furthermore, the paper examines current implementations and case studies that illustrate successful applications of AI and automation in adverse maritime conditions. While recognizing the potential benefits, the review highlights the technical and operational challenges inherent in these implementations, including data integration, regulatory compliance, and cultural differences between sectors. Ultimately, this paper aims to provide a comprehensive overview of the state of maritime cybersecurity and the pivotal role of AI and automation in shaping a resilient, secure maritime future. The findings underscore the importance of ongoing research and development, collaborative efforts, and the necessity of adaptable strategies to safeguard maritime operations against the evolving landscape of cyber threats.

Research on the Influence of AI Technology and Optimization Strategy on the Chinese Manufacturing Enterprises about Digital Transformation

Abstract: The rapid evolution of the digital economy has fostered a favorable environment for the growth of the artificial intelligence (AI) industry, catalyzing a transformative shift across diverse sectors. AI technology is driving the digital transformation landscape, contributing to the emergence of a novel economic framework characterized by enhanced unity, fairness, and operational efficiency. Particularly in China's manufacturing domain, substantial governmental support has propelled the widespread adoption of AI, thereby facilitating significant strides towards industrial modernization. However, despite the promising trajectory, notable challenges persist. These include the scarcity of AI talent, concerns regarding data security such as leakage, and the ongoing refinement of compatibility with existing systems. Addressing these challenges necessitates a multifaceted approach. Efforts should prioritize bolstering personnel training initiatives in AI-related domains through collaborative engagements with industry, universities, and research institutes. This cooperative endeavor can nurture a skilled workforce adept at navigating the complexities of AI implementation. Moreover, stringent measures must be implemented to safeguard sensitive data, including robust encryption protocols, secure computing practices, and meticulous access controls to mitigate the risks of data breaches. Furthermore, promoting standardization processes and embracing data format conversion tools are critical steps towards enhancing interoperability across disparate systems. In conclusion, this discourse underscores the pivotal role of AI in driving the digital transformation journey of Chinese manufacturing enterprises. By addressing pertinent challenges and embracing strategic initiatives, these enterprises can fortify their resilience, navigate future complexities, and achieve sustainable development in the digital era.

Digitalizations Enhancement in HR Practices: The Impact of Incorporating AI in the Process of Recruitment and Selection

Many organizations desires to attract the most value-adding talent has pushed their increased adoption of artificial intelligence in the processes of recruitment and selection. As AI can be generated into tools to conduct an analysis of applicants and provide an overview, it reduces the recruiters time allocation on administrative tasks to focus on making more advanced decisions to execute the businesss desired strategic directions and developments. This paper mainly examines the impact of AI in the talent acquisition processes on the three aspects of practical, ethical, and strategic impacts. The practical approach can improve the efficiency in recruitment and selection to find the best-fit candidate through AI algorithms for certain roles. The ethical approach of AI can ensure a fair talent acquisition process that reduces bias and discrimination to support final decision-making. The strategic approach allows organizations to use AI in conducting recruitment and selection practices with considerations on the future growth of the business, prospecting the required talents to achieve such growth. The attraction of using AI in the hiring process can lead to a more digitalized and trendy employer branding, making organizations appeal and expand to innovative pools of candidates. However, there are downsides to all approaches, including the lack of human involvement, the risks of data security and breaches, and the consistency of employer branding in utilizing AI technology. In exploring the benefits and limitations of AIs impact on talent acquisition, there should be a more structured approach to unleashing this technology in the HR practice.

The Integration of Artificial Intelligence in Physicians' Daily Practice for Diagnosis, Prediction, And Disease Management in the Arab World

Artificial intelligence (AI) is developing rapidly. Integrating AI into healthcare practices in the Arab world will transform diagnosis, prediction, and disease management. Today, countries such as Saudi Arabia and the United Arab Emirates (UAE) are at the forefront of AI adoption in healthcare, supported by strategic frameworks such as Saudi Arabia's Vision 2030 and the UAE National Artificial Intelligence Strategy 2031. However, adopting AI in daily healthcare practice on a large scale faces many obstacles that need to be overcome. Keywords: Artificial intelligence, AI, healthcare practice, diagnosis, prediction, and disease management, Arab world.

The Impact of the Development of Artificial Intelligence on Unemployment Rates

Abstract: The proliferation of artificial intelligence (AI) is a transformative force in global labor markets, presenting dual possibilities: the displacement of traditional jobs and the creation of new employment opportunities. This paper explores the dynamic relationship between AI development and unemployment rates, using empirical data and regression analysis from 2012 to 2022. It examines the impact of AI across various sectors, highlighting how technological advancements and public engagement with AI via platforms like Google Trends influence employment patterns. In addition, it also reveals that AI development has a generally negative correlation with unemployment rates, suggesting that increased AI adoption may be linked to lower unemployment levels. The findings also underscore the importance of adaptive policies and education systems to harness AIs potential for job creation, which contributes to the discourse on AIs economic implications by providing a nuanced analysis of its influence on employment, essential for policymakers and stakeholders in navigating the challenges and opportunities presented by AI technologies.

Overcoming e-learning hurdles: strategies for enhancing stem education in Africa through digital innovation

ABSTRACT Closing the divide between educational practices and industry demands is recognised to enhance the societal relevance of education. Consequently, African countries improve their educational systems by incorporating technological tools such as artificial intelligence, e-textbooks, and web-based learning in primary schools. This research investigates the impact of digital leadership and the external and internal barriers teachers face when adopting e-learning methods within educational institutions. The study gathered responses from STEM-trained teachers, and the data was subjected to analysis using SPSS version 23. The findings reveal a negative relationship between digital leadership and teachers’ internal and external barriers, with no statistically significant impact on using artificial intelligence in schools. Moreover, digital leadership positively influences the use of e-textbooks and web-based learning. Conversely, teachers’ internal and external barriers exhibit adverse effects on integrating e-textbooks and web-based learning while positively impacting the adoption of artificial intelligence in educational settings. The study underscores the need for advancing STEM programs, noting their role in fostering the acceptance and utilisation of e-learning mechanisms.

A comprehensive survey of artificial intelligence adoption in European laboratory medicine: current utilization and prospects.

As the healthcare sector evolves, Artificial Intelligence's (AI's) potential to enhance laboratory medicine is increasingly recognized. However, the adoption rates and attitudes towards AI across European laboratories have not been comprehensively analyzed. This study aims to fill this gap by surveying European laboratory professionals to assess their current use of AI, the digital infrastructure available, and their attitudes towards future implementations. We conducted a methodical survey during October 2023, distributed via EFLM mailing lists. The survey explored six key areas: general characteristics, digital equipment, access to health data, data management, AI advancements, and personal perspectives. We analyzed responses to quantify AI integration and identify barriers to its adoption. From 426 initial responses, 195 were considered after excluding incomplete and non-European entries. The findings revealed limited AI engagement, with significant gaps in necessary digital infrastructure and training. Only 25.6 % of laboratories reported ongoing AI projects. Major barriers included inadequate digital tools, restricted access to comprehensive data, and a lack of AI-related skills among personnel. Notably, a substantial interest in AI training was expressed, indicating a demand for educational initiatives. Despite the recognized potential of AI to revolutionize laboratory medicine by enhancing diagnostic accuracy and efficiency, European laboratories face substantial challenges. This survey highlights a critical need for strategic investments in educational programs and infrastructure improvements to support AI integration in laboratory medicine across Europe. Future efforts should focus on enhancing data accessibility, upgrading technological tools, and expanding AI training and literacy among professionals. In response, our working group plans to develop and make available online training materials to meet this growing educational demand.

Exploring the Impact of Artificial Intelligence Generative Tools on Research in Higher Education Institutions: A Perspective from Portugal

Artificial Intelligence (AI) generative tools have emerged as transformative instruments in various domains, including research and academia. It is important to see what is positive and what is not. This study focuses on the integration of GAI in Portuguese higher education institutions and explores its multifaceted implications and potential. Our research was conducted through a comprehensive survey between April and June 2023, garnering 77 responses. To this purpose, the analysis will have several insights into the research process, namely, to assess the frequency of use and objectives of using generative AI for higher education research and to explore possible trends and future directions in the adoption and application of generative AI in this field.

The Impact of AI Tools on Employment Trends and Skill Demands

Artificial intelligence (AI) is revolutionizing our work, lifestyles, and modes of communication. The integration of AI-driven technologies brings about automation of tedious tasks, enhances decision-making capabilities, and boosts overall productivity. Nonetheless, the adoption of AI carries profound implications for both the labor market and skill prerequisites. On one hand, the advent of AI is poised to usher in new job opportunities, particularly in domains such as machine learning, data science, and AI engineering. Conversely, AI's implementation is expected to automate numerous routine and manual job functions, resulting in the displacement of workers in sectors like manufacturing, transportation, and customer service. This transition underscores the necessity for workers to cultivate fresh skill sets and competencies. Skills such as data analysis, critical thinking, and advanced problem-solving will ascend in importance, whereas conventional proficiencies like manual dexterity and routine problem-solving will diminish in relevance. Furthermore, the effects of AI on employment prospects and skill requisites will vary across different industries and geographical regions. While certain sectors may undergo substantial job displacement, others may witness a surge in demand for adept personnel. Thus, policymakers and industry leaders must adopt a proactive stance toward reskilling and upskilling initiatives to ensure workforce competitiveness. Ultimately, the integration of AI tools is poised to catalyze transformative shifts in the labor market and skill landscape. While it presents novel opportunities and enhances productivity, it simultaneously necessitates adaptive measures from workers to thrive in an evolving job market.

Integrating AI and Blockchain Technologies for Enhanced Risk Management and Operational Efficiency in Indonesian Airport Customs: Challenges and Opportunities

The rapid growth of global trade has increased the need to modernize customs operations, especially in Indonesia, where traditional methods struggle with fraud and inefficiency. This study explores the integration of disruptive technologies, specifically Artificial Intelligence (AI) and blockchain, to enhance risk management and operational efficiency in Indonesian customs. Using a qualitative methodology, including literature reviews, case studies, and interviews with customs officials, the research highlights AI's ability to automate risk assessments and fraud detection, while blockchain ensures secure, transparent transaction records. Despite the significant benefits, challenges such as personnel resistance, outdated infrastructure, and regulatory uncertainties hinder implementation. The study emphasizes the need for comprehensive training for customs officers and collaboration with Cross-Border Regulatory Agencies (CBRAs) to overcome these obstacles. Case studies from Singapore and the UAE provide valuable insights into best practices for integrating these technologies. With strategic investment and strong stakeholder commitment, Indonesia can modernize its customs systems, improve efficiency, and strengthen national security. The study concludes with recommendations for creating a comprehensive integration roadmap, developing skills initiatives, and strengthening regulatory frameworks to support the successful adoption of AI and blockchain in Indonesian customs.

AI in Vocational Training: A Qualitative Interview Study of Early-Stage Practitioners in the Real Estate Brokerage Industry in China

This study aims to unveil the individual and sectoral factors influencing the integration and utilization of artificial intelligence (AI) in vocational training among early-stage practitioners in China's real estate brokerage industry. The study involved in-depth interviews with 33 participants, each possessing less than two years of industry experience and demonstrating either experience with or a keen interest in AI applications. Rooted in the transformative potential of AI in vocational training, thematic analysis was employed to reveal the intricate interplay of individual factors—motivation, attitude, self-efficacy, and digital literacy—alongside sectoral elements such as policies, practices, and organizational culture. The findings illuminate the significant impact of these factors on the acceptance and utilization of AI, underscoring the complexity of participant attitudes, shaped by perceived usefulness and ease of use. Diverse levels of self-efficacy and digital literacy among participants regarding AI adoption were also evident. On the sectoral front, policies play a dual role, either providing support or imposing restrictions on AI adoption, while practices and organizational culture wield substantial influence over the opportunities and challenges associated with AI use. These empirical insights and best practices offer stakeholders valuable guidance to enhance employability and productivity in this ever-evolving industry. Importantly, this study advocates for a more inclusive approach in future research, addressing limitations in sample representativeness and recommending the integration of quantitative methodologies. This approach is crucial for fostering a comprehensive understanding of AI utilization in vocational training, ensuring robust insights for future advancements in the industry.

Digital Newsroom Transformation: A Systematic Review of the Impact of Artificial Intelligence on Journalistic Practices, News Narratives, and Ethical Challenges

Artificial Intelligence (AI) fundamentally changes journalism, yet a comprehensive understanding of its impact is limited. This study presents the first systematic review to thoroughly analyze the influence of AI on journalistic practices, news narratives, and emerging ethical challenges. A rigorous analysis of 127 studies selected from 2478 original articles reveals trends in AI adoption in newsrooms, changes in journalists’ roles, innovations in news presentation, and emerging ethical implications. The key findings show a significant increase in the use of AI for news writing automation (73% of news organizations), data analysis (68%), and content personalization (62%). While AI improves efficiency and accuracy, 42% of studies reported concerns about reduced levels of nuance and context in AI-generated news. We also identified the emergence of hybrid “journalist–programmer” roles (52% of studies) and the need for “AI literacy” among journalists (38% of studies). The most prominent ethical challenges include algorithm transparency (82% of studies), data privacy (76%), and accountability relative to AI content (71%). Regional analysis reveals significant gaps in AI adoption, with important implications for global information equity. This review highlights the ongoing transformation in journalism, identifies critical gaps in current research, and offers an agenda for future investigation. Our findings provide valuable insights for media practitioners, policymakers, and researchers seeking to understand and shape the future of journalism in the age of AI.

Impact of AI on Work Life Balance of Working Women in Hotel Industry

Artificial intelligence (AI) has brought about significant advancements as well as challenges for the hotel industry. AI enhances efficiency by automating routine tasks, allowing employees to focus on personalized services . Tools like chatbots ease workloads, benefiting women employees. However, AI also raises concerns about job security due to potential role replacement. According to a report by the Hotel Association of India, in 2020, the percentage of women employees in the Indian hospitality industry stood at around 35%. This study aims to explore the impact of AI on the work life balance of women employees in the hotel industry in Delhi, focusing on hotel employees and including positive and negative aspects of AI-driven advancements. To explore these impacts, a quantitative study method is used via Google Forms. The study uses both primary and secondary data. Primary data are collected through questionnaires and interviews of women employees in the hotel industry in Delhi, while secondary data are gathered from industry reports and academic literature. The findings aim to provide insight into how AI influences the work-life balance of women employees, throwing light on issues like job security and employee productivity of women in the hotel industry. This study can be helpful to the hotel industry in guiding the adoption of AI to improve women's work-life balance while addressing job security and productivity concerns. Key Words: Artificial Intelligence (AI), Hotel Industry, Work-Life Balance, Women, Job Security, Employee Productivity

The impact of AI on enterprise energy management: from the perspective of carbon emissions

Given the significant impact of artificial intelligence (AI) technology on corporate energy management and the lack of research in this area, this paper employs text mining techniques to objectively assess the relative level of AI adoption among Chinese listed companies. Using econometric modelling methods, we verify these hypotheses and investigate both the direct and indirect effects of AI on corporate carbon emission intensity. our research finds that the carbon emission intensity of Chinese enterprises significantly decreased in the early stage, then stabilized, and has notably decreased again in recent years. The average level of AI among listed Chinese enterprises shows an overall upward trend, but the growth rate has slowed down. The level of AI in private enterprises is significantly higher than that in other types of enterprises, while the level of AI in state-owned enterprises is relatively lower. The level of AI in enterprises has a significant negative impact on carbon emission intensity, presenting an “S”-shaped relationship, characterized by initial emission reduction, mid-term rebound, and subsequent emission reduction. AI technology reduces the level of carbon emissions in enterprises by enhancing their green development standards and promoting technological innovation. There are significant differences in the impact of AI levels on carbon emission intensity across different types and regions of enterprises. The empirical conclusions remain robust after addressing endogeneity issues or variable substitution. This study provides important insights for corporate energy transitions and sustainable development, as well as for the formulation of government energy policies.

How learning about harms impacts the optimal rate of artificial intelligence adoption

SUMMARY This paper examines recent proposals and research suggesting that artificial intelligence (AI) adoption should be delayed until its potential harms are fully understood. Conclusions on the social optimality of delayed AI adoption are shown to be sensitive to assumptions about the process by which regulators learn about the salience of particular harms. When such learning is by doing – based on the real-world adoption of AI – this generally favours acceleration of AI adoption to surface and react to potential harms more quickly. This case is strengthened when AI adoption is potentially reversible. This paper examines how different conclusions regarding the optimality of accelerated or delayed AI adoption influence and are influenced by other policies that may moderate AI harm.

Digital and AI transformation in the contemporary art industry in China

This study examines the digital and artificial intelligence (AI) transformation in the contemporary art industry in China. This industry in China is undergoing a distinct digital transition and is “ahead” of other countries, having fully integrated digital technologies and AI (digiAI) into policies, regulations, organizations, and professional practices. A systematic, large-scale national integration of digiAI has led to its widespread adoption by artists and arts professionals. However, little is known about how or when this rapid and extensive integration and subsequent adoption occurred or about its impacts on professional practices. This study draws on research conducted between 2023 and 2024, including 30 interviews with contemporary Chinese visual artists, 23 interviews with arts professionals, a survey of 110 professional contemporary visual artists, and a systematic review of government policy. Findings indicate that the government began integrating digital technology into the contemporary art industry in 2016, further promoted digital technology integration in 2021, and introduced regulations to support AI usage in 2023. The data reveal a significant spike in the adoption of digital technologies by professionals between 2019 and 2020, followed by a rise in AI adoption in 2023. DigiAI has been accepted and now used across different kinds of arts professions, various types of visual artists, and several age groups. Digital and AI tools are now being applied in both creative and non-creative aspects of arts practices.