Transformative role of artificial intelligence in library management: A review

Academic Summary Firms face intensifying pressure to innovate amid escalating US–China trade tensions. This study investigates how they integrate artificial intelligence (AI) to respond to such disruptions. We reconceptualize induced innovation theory as adversity‐induced innovation theory and test hypotheses using data from listed Chinese manufacturing firms, WTO tariff records, and customs data (2015–2022). Results indicate that higher tariff exposure increases AI integration, particularly among firms with stronger customer engagement needs. AI integration improves supply chain efficiency, sales performance, and market value. However, performance benefits follow an inverted U‐shape: extreme tariff exposure diminishes AI's compensatory effects. The study extends induced innovation theory to geopolitical adversities and advances stakeholder engagement theory by identifying customer engagement as a boundary condition for technology adoption. We establish causal relationships between tariff‐induced AI integration and firm performance through instrumental variable analysis and robustness checks. Findings inform how firms can strategically use technological innovation and customer collaboration to strengthen resilience under external shocks. Managers should view trade tensions as catalysts for AI integration rather than merely as obstacles. Managerial Summary Firms facing trade tensions should view them as catalysts for strategic AI integration rather than merely as obstacles. Our research shows that firms experiencing higher tariff exposure that invest in AI achieve measurable improvements in supply chain efficiency, sales performance, and market valuations. However, AI's compensatory benefits diminish at extreme tariff levels, revealing an optimal investment threshold that managers should consider. The benefits of AI integration are particularly strong for firms with high customer engagement needs, such as those with concentrated customer bases, substantial international sales, or significant customer‐specific investments. To maximize returns, managers should prioritize AI technologies that strengthen customer relationships by enhancing communication, responsiveness, and service personalization during trade disruptions. Successful implementation requires navigating technical and organizational challenges, including talent acquisition, data quality concerns, and integration complexities. Despite these hurdles, our findings confirm AI's strategic value as a compensatory innovation tool that not only mitigates tariff impacts but also transforms supplier‐customer relationships. By aligning AI initiatives with customer engagement objectives, firms can turn geopolitical adversity into a competitive advantage. Companies that integrate AI strategically can maintain operational continuity, preserve market position, and emerge stronger from trade disruptions.

The convergence of blockchain, artificial intelligence (AI), and cloud computing is catalyzing a paradigm shift in developing secure, intelligent, and scalable digital infrastructures. This triad of technologies is increasingly utilized to improve performance, transparency, and trust in engineering-driven and socio-technical environments. This study systematically reviews the evolution, integration strategies, and applications of blockchain, AI, and cloud computing in digital ecosystems. The analysis is based on 108 peer-reviewed studies spanning the years 2012 to 2025. A comprehensive literature analysis was conducted to identify trends, synergies, and sector-specific implementations of these systems. The review explores how their integration supports real-world engineering and operational use cases. Blockchain contributes to decentralized architectures, secure data exchange, and identity verification. AI supports adaptive behavior, autonomous decision-making, and predictive analytics. Cloud computing offers the scalable infrastructure necessary for deployment. Key challenges addressed include interoperability, latency, security trade-offs, and resource allocation. Use cases in digital finance, supply chain management, and industrial automation demonstrate the effectiveness of this integration in building resilient, ethically aligned, and high-performance infrastructures. The findings offer valuable insights and technical considerations for engineers and architects seeking to design next-generation cyber-physical systems that are secure, intelligent, and socially responsive. Clinical Trial Number Not applicable. This article accentuates the extensive use of blockchain, cloud computing, and AI principles, emphasizing their crucial significance in the contemporary digital environment and paths in which blockchain technology may be employed to automate financial transactions, enhance the security of personal data and records, facilitate cross-border transactions, verify identities, execute smart contracts, utilize cryptocurrencies, and manage supply chains. This research systematically assesses the development and practical application of blockchain, cloud computing, and artificial intelligence technologies over the past decade. This study primarily focuses on monitoring the progress, practical applications, and benefits of blockchain technology in the context of financial transactions. This article probes the symbiotic relationship between artificial intelligence, cloud and blockchain technology, analyzing the evolution and fundamental concepts of their integration from 2012 to 2025. This paper also presents a detailed analysis of 108 research works acquitted over the past decade. The study provides a comprehensive examination of the current state and fundamental principles related to the integration of artificial intelligence, cloud, and blockchain technology. This paper offers a comprehensive analysis of the major obstacles confronted by blockchain technology, examining its wide-ranging applications over several industries and its socko role in transaction systems and the domain of cryptocurrencies.

Artificial Intelligence (AI) is increasingly transforming higher education across the globe by reshaping teaching, learning, research, and institutional management. In developing countries such as Tanzania, the integration of AI into Higher Education Institutions (HEIs) presents both significant opportunities and complex challenges. Despite the presence of infrastructural and resource limitations, AI has the potential to revolutionize the educational landscape by improving access to quality learning materials, supporting data-driven decision-making, and enhancing administrative efficiency. This study explores the multifaceted impacts of AI on Tanzanian HEIs, with a specific focus on its current applications, benefits, challenges, and policy implications. The research draws upon recent empirical studies and secondary data to analyze how AI technologies such as intelligent tutoring systems, predictive analytics, and automated assessment tools contribute to personalized learning experiences and improved academic outcomes. Moreover, the study identifies the major barriers to AI adoption, including inadequate technological infrastructure, limited institutional capacity, insufficient AI-related policies, and persistent ethical concerns surrounding data privacy and algorithmic bias. Findings reveal that although AI adoption in Tanzanian HEIs is on the rise, the implementation remains fragmented and uneven across institutions. To fully harness AI’s transformative potential, the study recommends the development of comprehensive national AI policies, investments in digital infrastructure, and targeted capacity-building programs for educators and administrators. Additionally, the research emphasizes the need for ethical frameworks that promote fairness, inclusivity, and transparency in AI utilization. Overall, this paper underscores the necessity of a strategic and policy-driven approach to ensure that AI contributes effectively and equitably to the advancement of higher education in Tanzania.

The headway of information and communication technology has led to significant changes in various industries, particularly in e-commerce. The integration of artificial intelligence (AI) and immersive technologies such as augmented reality (AR) and virtual reality (VR) has created new opportunities for e-commerce to improve customer experiences. This study analyzes the effects of implementing AI and immersive technology in e-commerce, focusing on customer engagement as a mediating factor. A sample of 99 participants was examined and analyzed using PLS-SEM by SmartPLS 4 software, and the findings indicate that AI and immersive technology have a positive, significant impact on online purchase intention. Customer engagement cannot mediate the relationship between artificial intelligence and online purchase intention. However, it does mediate the relationship between immersive technology and online purchase intention. Keywords: Artificial Intelligence; Immersive technology; Customer Engagement; Purchase Intention; e-commerce

The integration of artificial intelligence (AI) in education has the potential to revolutionize teaching and learning, particularly in the development of students’ critical thinking skills. This study explores science instructors' familiarity, perceptions, and experiences with using AI to enhance students' critical thinking skills, as well as the level of institutional support for AI integration in teaching. A quantitative survey was conducted among 20 science instructors from higher education institutions in Isabela, Philippines. The findings reveal that while instructors acknowledge AI's potential to improve educational outcomes, there is a significant gap in formal AI training and literacy among educators. Positive correlations were found between AI literacy, AI integration, and critical thinking development, suggesting that as AI literacy increases, AI integration and enhancement of critical thinking skills also increase. Regression analysis identified AI integration as a significant predictor of critical thinking development. Challenges remain in the effective implementation of AI, including concerns about overreliance on AI-generated responses and the need for clear assessment guidelines. Interestingly, years of teaching experience did not significantly influence participants’ AI literacy, perceptions, or integration. This study highlights the importance of developing comprehensive AI literacy programs for educators and integrating AI into curriculum structures to balance AI-enhanced learning with human-centered pedagogy. These findings emphasize the need for thoughtful implementation and ongoing research to effectively leverage AI in promoting critical thinking skills in science education.

This study explores the integration of Artificial Intelligence (AI) into the Reverse Logistics Process (RLP) to enhance decision-making, operational efficiency, and resource recovery. Through a systematic literature review, the applications of AI across critical RLP stages (planning, execution and control) are identified. Key findings highlight the transformative role of AI in optimizing network design, improving product collection and inspection, and supporting decision-making for the disposition of recovered items. AI technologies and functionalities such as machine learning, predictive analytics, and decision-support systems demonstrate significant potential for automating complex processes, reducing operational costs, and improving logistical precision. However, barriers to AI adoption in the RLP include high implementation costs, organisational resistance, lack of specialist personnel, and limited technological infrastructure. The review also identifies enablers such as advancements in the Internet of Things, blockchain, and big data analytics, which facilitate AI adoption and integration in RLP. The study concludes that AI is essential for establishing resilient and adaptive RL systems and offers substantial opportunities to address uncertainty while improving efficiency and decision-making. Future research should focus on overcoming adoption barriers, advancing predictive models, and integrating AI with other Industry 4.0 technologies to enhance RLP outcomes. This review fills a significant gap in the literature by offering a structured analytical framework that categorises AI applications based on decision types and RLP stages, providing an integrated perspective not previously addressed in the field.

This research reflects on the evolving Artificial intelligence (AI) technology in the creative advertising industry. With emphasis on the concerns in the advertising space, where AI is underway in threatening the novelty of originality in creative ideation harnessed by human creativity. AI technologies such as OpenAI GPT for audio production and StyleGAN for photo realistic generation is starting to replace human creation for creative work. The technology has convincing factors in benefit where the automation reduces time with speed and a more economical for production costs. The creativity in the advertising industry stands vulnerable with forthcoming AI technology’s impact and in contrast to the approach towards the support for inevitable innovation. Synthesised results of advertising materials, dominated by generative adversarial networks is garnering popularity, with the convincing content currently accepted by marketers. This thesis has three focus areas: keeping the balance with creativity with AI integration into creative workflows, debating the novelty of original creative mastery versus AI technology which is dependent on data to develop creative outputs and exploration of how AI collaborated with creative professionals enables efficacy of campaigns. As AI continues to be a threat to the novelty of originality, this will need further understanding on societal and economic implications. This research serves to evaluate further AI’s role in parameters for enhancing and transforming the creativity in the advertising paradigm. Ultimately, to assess the acceptance or resistance for the future.

Objective : To analyze foreign experience in the use of artificial intelligence (AI) in oncology, as well as examine current advances in AI and its impact on clinical practice. Material and methods. A systematic review of foreign literature was carried out, current AI programs and technologies in oncology were analyzed. The total number of identified records via the PubMed/MEDLINE search was 7680. After publication selection in accordance with the PRISMA guidelines, 32 studies that met all criteria were randomly included in the review and formed the basis for the analysis. Results . AI demonstrates high efficiency in cancer diagnostics, including early detection of tumors, analysis of medical images and pathological data. A literature review on the use of AI models in oncology revealed exponential growth from 2010 to 2022, confirming the active development of the field. Diagnostic and treatment reports generated by the AI technologies indicated a comparable level of accuracy to that of experienced oncologists; they also revealed the ability to improve clinical outcomes. Furthermore, the introduction of AI stimulates the development of personalized treatment, increased patient adherence to therapy and optimization of the work of medical organizations. AI’s impact was revealed on physicians (reduced diagnostic and treatment errors), patients (personalized support), and hospitals (smart management systems). Conclusion . AI is becoming an integral part of modern oncology, offering new opportunities to improve diagnostics, treatment, outcome prediction, and patient support. A study of the dynamics of publications and AI models indicates that the use of AI in clinical oncology is rapidly developing, opening up new prospects for the creation of smart hospitals, simplification of medical data exchange, development of personalized medicine, and improvement of the quality of patient care. The integration of AI with emerging technologies such as wearable devices and multimodal analysis promises to revolutionize oncology practice.

This research explores the integration and impact of artificial intelligence (AI) within Emirati media organizations, focusing on adoption determinants, obstacles, and interventions. Utilizing a mixed-methods approach, the study incorporates qualitative data from 21 in-depth interviews with media professionals and a thematic analysis of 30 scholarly articles published between 2019 and 2024. The research aims to provide a nuanced understanding of AI's transformative effects on the UAE media landscape. Key findings reveal that AI is significantly enhancing content creation, distribution, and audience engagement processes within Emirati media organizations. AI technologies such as machine learning, natural language processing, and predictive analytics are being leveraged to improve operational efficiency, streamline workflows, and deliver personalized content to audiences. These advancements are driving competitive advantages and elevating the overall quality of media output. Despite these benefits, several challenges hinder widespread AI adoption. Technological barriers, including limited access to advanced AI tools and infrastructure, pose significant obstacles. Additionally, there is a pronounced shortage of skilled personnel capable of implementing and managing AI systems, which exacerbates concerns over job displacement and workforce anxiety. Ethical considerations, such as ensuring unbiased AI algorithms and protecting user privacy, are also critical issues that require attention. The study underscores the importance of continuous training and upskilling for media professionals to adapt to the evolving technological landscape. It highlights the role of robust ethical guidelines and governance frameworks in promoting responsible AI use. Government support, through targeted policies, funding initiatives, and incentives, is identified as a crucial factor in facilitating AI integration and addressing adoption barriers. The research concludes by providing strategic recommendations for media organizations and policymakers. It advocates for the development of comprehensive AI adoption roadmaps that include capacity-building initiatives, ethical oversight, and collaborative efforts between industry stakeholders. By addressing these areas, Emirati media organizations can harness the full potential of AI to drive innovation, improve audience engagement, and maintain competitiveness in a rapidly digitizing global media environment. This study contributes to the growing body of knowledge on AI in media, offering valuable insights into the specific context of the UAE. It provides a foundation for future research on AI's role in media and serves as a guide for organizations seeking to navigate the complexities of AI adoption.

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.

Technical Education (TE) serves as a critical component for fostering economic development by equipping individuals with the necessary skills to thrive in various industries. Meanwhile the rapid advancement of technology, particularly in the realm of Artificial Intelligence (AI), has created a pressing need for educational systems worldwide to adapt and evolve. As the global economy increasingly relies on AI technologies, it is imperative for TE programmes to align their curricula with these advancements to equip students with relevant skills but there seems to be scarcity of studies that dwell on integration of AI in TE and there is the need to assess the role played by artificial intelligent in repositioning the programme. Therefore, this paper focused on the assessment of the role of Artificial Intelligence in reposition of Technical Education (TE) for economic development in Nigeria. To achieve the broad aim, this study identifies existing challenges, and highlights the transformative potential of integrating AI into TE. The population of the study was 300 NCE students of Kano Federal University of Education. A total enumeration sampling technique was applied because the population is of manageable size. A validated 15 items questionnaire, designed by the researcher on four point scale ranging from strongly agree (4) – Strongly Disagree (1) was used for data collection. Reliability of the instrument was ensured using split-half reliability technique which yielded a cumulative coefficient of 0.79. Data collected were analyses using mean and standard deviation to answer the research questions. The findings revealed that there is need to integrate AI into the current state of TE; the major challenges and barriers that hinder AI integration in TE is unavailability of uniform content to serve the AI application and that introduction of AI into the curriculum of TE will contribute to the advancement of Nigerian economic development. There is an urgent need for substantial investment in AI-related infrastructure within TE institutions to providing access to AI tools and technologies and TE programmes should be updated to include AI-related courses and concepts.

Artificial Intelligence (AI) is rapidly inducing revolutionary transformations in entire aspects of life. Integrating AI into the educational landscape is heralding a new era of pedagogical innovations and redefining learning experiences. As AI technologies continuously evolve, the integration of AI-driven systems in the instructional settings is an inevitable progression and a formidable opportunity. This study is a scholarly attempt to explore the multi-faceted implications of AI integration in the spectrum of education highlighting its potential to revamp the traditional models. Thorough analysis of extant literature reveals that AI-driven platforms facilitate personalized learning experience which can accommodate diverse needs and learning paces of individual students. In addition to this, AI technologies have the potential to revolutionize assessment techniques. As the conventional assessment models fail to capture the true reflections of learning progress, AI assisted models can track, measure, analyze the academic output and provide real-time feedback which is transparent and free form bias. Moreover, AI-powered models have streamlined the administrative tasks as well. By automating routine jobs, optimizing resource allocation, and improving decision making, AI is minimizing human effort and maximizing productivity. However, AI integration poses some challenges and drawbacks. The concerns like data privacy and security, inequality, accessibility, algorithm bias, dehumanization, over-reliance on AI technologies and other ethical considerations are also looming. This study also presents certain future directions and policy perspectives on how to overcome these challenges and build an educational landscape which has the potential to fully utilize the AI-driven systems for academic achievement and learning progress of the students. As AI is revolutionizing entire spheres of life including education, it is imperative to critically engage with both opportunities and challenges posed by AI ensuring the principles of fairness, inclusivity, and integrity.

Introduction: The integration of artificial intelligence (AI) into higher education is swiftly revolutionizing pedagogical methodologies, in conjunction with learning processes and research paradigms. The interdisciplinary potential of AI within academic settings was examined in this study, employing a case study conducted at the University of Tirana. Through the utilization of bibliometric analysis and survey-based research, this study comprehensively investigates the swiftly emerging trends in AI applications, students' significant familiarity with AI technologies, as well as the substantial challenges impeding broader adoption. The bibliometric analysis highlights significant exponential growth in AI research, particularly within pivotal domains such as finance and accounting, thereby emphasizing the swiftly increasing relevance of blockchain and automation. The survey indicates a robust enthusiasm among students for AI in educational settings. More than 90 percent of students actively incorporate AI tools in their project work. Nonetheless, resource limitations and ethical considerations, including privacy, data security, and algorithmic bias, pose considerable challenges to the widespread adoption of AI, despite the prevailing enthusiasm. Objective: The aim of this research is to examine the incorporation of Artificial Intelligence (AI) within the realm of higher education. This analysis concentrates on the applications, advantages, and challenges associated with AI, with the intent of advancing interdisciplinary research and educational methodologies at the University of Tirana. Theoretical Framework: This research expands upon the principles of AI adoption within the educational sector, alongside an examination of ethical considerations and multidisciplinary collaboration. Theories pertaining to technological integration and adaptive learning systems serve as the foundational framework for comprehending the implications of AI in the realm of education. Method: The methodology employs bibliometric analysis to examine AI-related research trends utilizing data from SCOPUS and conducts a survey to assess students' familiarity with and perceptions of AI. Data collection was facilitated through bibliometric instruments and an online survey incorporating Likert-scale and open-ended questions. Results and Discussion: The results underscore an increased focus on artificial intelligence (AI) and blockchain within scholarly research, wherein students exhibit considerable engagement and interest in AI applications. Nevertheless, limitations in resources and ethical issues, including privacy and bias, persist as primary challenges. The discourse underscores the imperative for investment in infrastructure and the incorporation of ethical education. Research Implications: This research highlights the imperative for higher education institutions to integrate artificial intelligence tools, cultivate adaptive curricula, and address ethical considerations in order to adequately prepare students for a future shaped by AI. The implications of these findings also pertain to educational policy and the formulation of interdisciplinary research strategies. Originality/Value: The study contributes by delivering a comprehensive bibliometric analysis and gives insights into student engagement with artificial intelligence. Its significance resides in presenting actionable recommendations to enhance the integration of artificial intelligence in higher education.

Artificial Intelligence (AI) is increasingly revolutionizing the food and beverage service industry by offering innovative solutions to enhance operational efficiency and personalize customer experiences. This abstract explores the transformative impact of AI technologies such as machine learning, predictive analytics, and robotics on various aspects of hospitality operations. AI-driven automation streamlines kitchen processes, optimizes inventory management, and reduces operational costs, thereby improving service consistency and profitability (Alsmadi et al., 2020; Li et al., 2021).AI facilitates personalized client interactions by analyzing vast datasets to understand individual preferences, dietary restrictions, and purchasing behaviors. This capability empowers establishments to offer tailored menu recommendations, personalized promotions, and interactive dining experiences that cater to diverse consumer needs (Chen et al., 2019; Huang et al., 2020). AI's integration into the food and beverage service industry significantly enhances customer satisfaction and loyalty while concurrently boosting revenue through targeted marketing and operational efficiencies. By leveraging AI technologies such as machine learning and predictive analytics, establishments can tailor offerings to meet individual preferences and anticipate consumer behavior, thereby fostering stronger customer relationships. Moreover, the future trajectory of AI in this sector foresees advancements in IoT integration, AR applications, and blockchain technology. These innovations are set to further transform operations by optimizing efficiency, enriching customer interactions, and ensuring robust supply chain transparency. IoT devices, for instance, will facilitate real-time data collection and analysis, thereby enhancing inventory management precision and optimizing resource allocation to meet demand fluctuations effectively. AR applications will offer immersive dining experiences, allowing customers to interact with digital menus or view culinary presentations. Blockchain technology will enhance transparency and traceability, ensuring food safety and compliance with regulatory standards. Together, these innovations will enable food and beverage establishments to innovate and differentiate themselves in a competitive market landscape, ultimately shaping the future of dining experiences worldwide to further optimize operations and enhance transparency in supply chain management (Jiang et al., 2023; Wang et al., 2023).

This study explores the transformative role of artificial intelligence (AI) in environmental health and public safety within the USA, focusing on pollution monitoring, emergency response, and sustainable practices for public. With the growing challenges posed by climate change, pollution, and emerging public health threats, the integration of Artificial Intelligence (AI) in environmental health and public safety strategies has become imperative. This comprehensive review explores the diverse array of AI applications implemented in the United States to address environmental issues and enhance public safety measures. The paper analyzes the multifaceted role of AI across various domains, including air and water quality monitoring, disease surveillance, disaster response, and infrastructure resilience. The advancements in AI technologies that have revolutionized data collection, analysis, and prediction in environmental health are examined. Machine learning algorithms, sensor networks, and satellite imagery are examined as tools for real-time monitoring and early detection of environmental hazards. Additionally, the paper investigates the integration of AI in public health surveillance systems, showcasing how predictive analytics and data-driven models contribute to the identification and containment of infectious diseases. Furthermore, the study sheds light on the incorporation of AI in disaster management, emphasizing the role of predictive modeling and risk assessment in optimizing emergency response strategies. The implementation of smart city technologies and intelligent infrastructure systems is discussed, highlighting how AI contributes to enhancing public safety and minimizing the impact of natural disasters. The review also critically evaluates the ethical, legal, and privacy considerations associated with the widespread adoption of AI in environmental health and public safety initiatives. It addresses concerns related to data security, algorithmic biases, and the need for transparent and accountable governance frameworks. Through an in-depth analysis of case studies, policies, and initiatives, this review provides insights into the successes and challenges of AI implementation in the USA. It concludes with recommendations for future research directions and policy considerations to ensure the responsible and effective integration of AI technologies in safeguarding environmental health and public safety. The findings presented in this review contribute to the broader discourse on leveraging AI for sustainable and resilient communities in the face of evolving environmental and public health challenges.