Inteligencia artificial en las operaciones aéreas

Application of artificial intelligence: benefits and limitations for human potential and labor-intensive economy – an empirical investigation into pandemic ridden Indian industry

Extant research has provided initial evidence on the application of artificial intelligence (AI) for corporate sustainable development. However, a research gap remains in understanding how AI specifically drives corporate green development, particularly in the realm of green investments. This research investigates the impact of AI adoption on corporate green investment (CGI) based on the sample of China's A‐share listed companies (2008–2022). By employing econometric techniques to examine panel data, an instrument variable with a two‐stage least squares approach, the Heckman selection model, and a series of robustness tests, this research finds that AI adoption positively correlates with CGI. The mechanism analysis reveals that AI adoption curbs managerial myopia and enhances technological resources to drive green investment levels. Moreover, the impact of AI adoption on CGI is pronounced when companies are headed by younger CEOs, those with higher education qualifications, and those with global exposure. This research enhances our overall comprehension of how digital innovation may be strategically utilized to promote sustainable development. It also provides significant insights for managers and policymakers who seek to facilitate environmentally friendly economic growth.

The impact of artificial intelligence on people’s lives is demonstrated today. Previous literature has shown that the use of a specific technology is directly linked to the individuals’ intention to use it. The aim of this paper is to study the factors that determine the adoption and use of artificial intelligence and big data in Spain, using a research model based on the Unified Theory of Acceptance and Use of Technology (UTAUT), proposed by Venkatesh et al. (2003). This work addresses the specific gap in the validation of the original theoretical model of UTAUT in two dimensions, with respect to the adoption of artificial intelligence by citizens and with respect to the factors that influence this adoption, evaluating the previous ones and proposing some new ones considering the current context. The methodology used is based on a national survey, and it analyzes the research model using the statistical technique of Partial Least Squares Structural Equation Modelling (PLS-SEM), which details the mediating and moderating relationships between constructs. The results show that Intention to Use has a direct positive influence on the Use of artificial Intelligence and big data, confirming previous literature. Performance Expectancy is the strongest predictor of Intention to Use, and indirectly of the adoption of artificial intelligence and big data applications. Effort Expectancy, in its application to the adoption of AI and big data by citizens, is an indirect determinant mediated by the Intention to Use, but its total effect (direct + indirect) is not significant.

The widespread use and adoption of Artificial Intelligence (AI) applications among university students has drastically transformed the educational landscape. Recognizing the importance of this transformation, this study aims to investigate the factors affecting the adoption and use of AI applications among Pakistani research scholars. This study used an extended version of the unified theory of acceptance and use of the technology model and innovative resistance theory. The data were collected from 235 research scholars through a questionnaire. Descriptive statistics and a multiple linear regression test were used to analyze the collected data. The study found that Pakistani research scholars used various AI applications for research purposes such as ChatGPT, Grammarly, ChatPDF, and SciSpace. This study found that personal innovativeness, performance expectancy, social influence, and trust significantly influence research scholars’ behavioral intention to use AI applications. In contrast, the impact of effort expectancy, facilitating conditions, and resistance to innovation on students’ behavioral intention to use AI tools was statistically insignificant. The findings offer actionable insights for educators, policymakers, and technology developers aiming to enhance AI adoption in higher education.

PurposeThis paper aims to explore factors impacting citizen intention toward artificial intelligence (AI) adoption, considering government regulation as a moderating variable. It focuses on the Palestinian Cellular Communications Sector in Gaza Strip, providing insights into the citizen-AI relationship dynamics. The research contributes to enhancing comprehension of AI technology from clients’ perspective.Design/methodology/approachTo test the hypotheses, a questionnaire was used in an empirical study to collect primary data. In total, 347 Palestinian citizens responded to the survey.FindingsThe findings of this paper reveal that perceived usefulness, perceived ease of use, perceived risks, social influence, user experience and privacy and security concerns significantly influence citizen intention toward AI adoption. Furthermore, government regulations as a moderating variable strengthen the impact of perceived usefulness, perceived ease of use, perceived risks, social influence, user experience and privacy and security concerns on citizen intention toward AI acceptance and adoption. Thus, further research should explore specific domains and cultural contexts to gain a more comprehensive understanding of the factors shaping acceptance and adoption.Research limitations/implicationsThe findings of the study should be understood in the context of their limitations. First, the study ignored cultural or domain-specific subtleties in favor of generic characteristics, which calls for more research in these particular circumstances. Second, relying on self-reported data might result in biases and limitations due to subjectivity in reporting, indicating the necessity for alternate data gathering methods and approaches in future research.Practical implicationsPolicymakers, developers and organizations working to promote the acceptability and implementation of AI applications should consider the practical implications of this study’s results. To secure the long-term use of AI technologies in a responsible and user-centric way, policymakers should give priority to public education and awareness, user-centered design and ethical AI development techniques. They should also stimulate partnerships and create monitoring systems.Originality/valueThis paper investigates the originality of factors that influence citizen intention toward AI acceptance and adoption. It uniquely examines the moderating role of government regulations in shaping this intention. By addressing this novel aspect, the paper contributes to advancing our understanding of the complex dynamics surrounding citizen intentions toward AI applications.

Objective: Major innovations are underway in the practice of clinical neuropsychology, as they are in the neurosciences and psychology more generally. Artificial intelligence (AI) is poised to offer numerous advantages over traditional neuropsychological practices, the most important of which is to improve clinical decision-making and thereby reduce diagnostic errors. However, the emergence, rapid availability, and adoption of AI, like other technological advances, has ethical implications. The purpose of this article is to present the ethical issues of primary importance in the adoption and application of AI in clinical neuropsychology and further advance the discussion of AI, ethics, and neuropsychology. Method: Benefits and risks of AI use in clinical neuropsychology are examined in the context of general bioethical principles. Results: Some of the primary anticipated risks that may lead to harmful outcomes for patients include: (1) threats to privacy and security, (2) bias in AI models, (3) lack of professional competence, (4) limitations to informed consent, (5) inequity in access to AI, (6) overreliance on AI, and (7) lack of accountability. Conclusions: Awareness and understanding of the ethical implications of technological advances, including AI, are essential for maintaining patient welfare at the center of clinical care and for preparing clinicians to anticipate ethical challenges and avoid dilemmas or address them effectively when they are encountered. Advanced preparation enables neuropsychologists to promote the ethical and responsible use of AI, for the benefit of both practitioners and patients.

The COVID-19 pandemic has expedited the adoption of artificial intelligence (AI) in the healthcare industry. The need for rapid diagnosis and treatment, as well as the demand for remote care and monitoring, has led to an increased focus on AI solutions that can improve healthcare delivery and patient outcomes. AI-powered technologies such as predictive analytics, natural language processing, and computer vision have been deployed to support screening and diagnosis, drug discovery, and vaccine development. Additionally, AI-powered chatbots and virtual assistants have been used to triage patients and provide remote care. While the adoption of AI in healthcare has brought tremendous benefits, there are still challenges to be addressed. This paper will explore the adoption, benefits, and challenges of AI in the healthcare industry, shedding light on the prowess of AI in revolutionizing healthcare while also underscoring the need for careful implementation and ethical considerations. This study will conclude with 5 case studies of top U.S. hospitals that have adopted AI for diverse purposes.

Objectives: This research aimed to empirically examine the transformative impacts of Artificial Intelligence (AI) adoption on financial reporting quality in Jordanian banking, with internal controls as a hypothesized mediation mechanism. Methodology: Quantitative survey data was collected from 130 bank personnel. Multi-item reflective measures assessed AI adoption, internal controls, and financial reporting quality—structural equation modelling analysis relationships between constructs. Findings: The research tested four hypotheses grounded in agency and contingency theories. Confirmatory factor analysis demonstrated sound measurement models. Structural equation modelling revealed that AI adoption significantly transformed financial reporting quality. The mediating effect of internal controls on the AI-quality relationship was supported. Specifically, the path from AI adoption to quality was significant, indicating a positive impact. Despite internal controls strongly predicting quality, its mediating effect significantly shaped the degree of transformation driven by AI adoption. The indirect effect of AI on quality through internal controls was also significant. Findings imply a growing diffusion of AI applications in core financial reporting systems. Practical implications: Increasing AI applications focus on holistically transforming systems, reflecting committing adoption. Jordanian banks selectively leverage controls to moderate AI-induced transformations. Originality/value: This study provides essential real-world insights into how AI is adopted and impacts the Jordanian banking sector, a key player in a fast-evolving developing economy. By examining the role of internal controls, it deepens our understanding of how AI works in practice and offers practical advice for integrating technology effectively and improving information quality. Its mixed methods, unique context, and focus on AI’s impact on organizations significantly enrich academic literature. Recommendations: Banks should invest in integrated AI architectures, strategically strengthen critical controls to steer transformations, and incrementally translate AI innovations into core processes.

The utilisation of artificial intelligence in the context of nursing education has become increasingly extensive. However, various studies show differing perspectives and attitudes among nursing students, and the findings have not been systematically synthesised. To systematically review the perceptions and attitudes of nursing students on the application of artificial intelligence in nursing education. Mixed-methods systematic review. A comprehensive literature search was conducted across 10 databases, including PubMed, Cochrane, Embase, Web of Science, CINAHL, Scopus, China Science and Technology Journal Database, SinoMed, China National Knowledge Internet, and WanFang database, the inclusive years of articles searched were from 1969 to 2025. Two researchers independently screened the literature and extracted the data. The mixed methods assessment tool was used to evaluate the risk of bias in the included literature. The relevant data were extracted and synthesised according to the Joanna Briggs Institute's convergence synthesis method, ensuring the comprehensive integration of qualitative and quantitative results. These results were then integrated into the Technology Acceptance Model. A total of 28 articles were included, including 13 qualitative studies, 13 quantitative studies, and 2 mixed-method studies. According to the Technology Acceptance Model, the perceptions and attitudes of nursing students on the nursing education's adoption of artificial intelligence were integrated into 10 categories of three comprehensive themes: (i) Nursing students' perceptions and attitudes of the ease of use of artificial intelligence in nursing education, including 3 categories; (ii) nursing students' perceptions and attitudes on the usefulness of artificial intelligence in nursing education, including 4 categories; (iii) nursing students' behavioural intention, including 3 categories. Overall, our study demonstrated that nursing students had an active willingness to utilise artificial intelligence. However, they acknowledged that certain issues persist regarding the ease and practicality of artificial intelligence in nursing education. No patients or members of the public were directly involved in this systematic review, as the study synthesised existing literature.

Recent advancements in artificial intelligence (AI) applications in medicine have been significant over the past 30 years. To monitor current research developments, it is crucial to examine the latest trends in AI adoption across various medical fields. This bibliometric analysis focuses on AI applications in cardiology. Unlike existing literature reviews, this study specifically examines journal articles published in the last decade, sourced from both Scopus and Web of Science databases, to illustrate the recent trends in AI within cardiology. The bibliometric analysis involves a statistical and quantitative evaluation of the literature on AI application in cardiovascular medicine over a defined period. A comprehensive global literature review is conducted to identify key research areas, authors, and their interrelationships through published works. The leading institutions and most influential authors in research on the role of AI in cardiology were located in the United States, the United Kingdom, and China. This study also provides researchers with an overview of the evolution of research in AI and cardiology. The main contribution of this study is to highlight the prominent authors, countries, journals, institutions, keywords, and trends in the development of AI in cardiology.

The adoption of Artificial Intelligence (AI) in the hotel industry in Nigeria has experienced dramatic growth over the years in different areas, including technology. The integration of technology has totally changed customer experience and operational efficiency. The application of AI in the hotel industry comes with benefits and challenges, with emphasis on both customer experience and operational efficiency. The roles of AI in the hotel industry includes enhanced customer service, operational efficiency, personal experience, revenue management, and security and safety. Applications of AI in the hotel industry includes chatbots and virtual assistant, smart rooms and personalized experience, and automated check-ins and check-outs. The importance of customer experience impacts the hotels reputation, profitability and competitive standing, and factors influencing it includes service quality, technology and convenience, cleanliness and comfort, personalization, and security and safety. Operational key indicators are cost management, staff productivity, guest satisfaction score, automation and technology utilization, supply chain and inventory management, and energy and resource efficiency. The challenges of operational efficiency in Nigerian hotels includes high operational cost, skills gap and work challenges, limited adoption of technology, inconsistent power supply, and supply chai disruption. The factors impacting adoption of AI are cost, infrastructure and technology readiness, regulatory and data privacy concerns, technical expertise of skills gaps, customer acceptance and trust. The benefits of AI integration include competitive advantage and innovation, data driven decision making, enhanced customer service and personalization, enhanced security and safety, improved revenue management, and increased operational efficiency. A qualitative methodology approach in form of systematic review has been used. Findings and results clearly shows there are only a handful of hotels using AI powered solutions, but there is high potential for improvement in the industry.

In this chapter, we discuss the impact of artificial intelligence (AI) and machine learning (ML) application on Finance with the principal focus on the banking sector, how AI affects customers, maintains the customer relationship, influence the business performance and finally how the AI will change future of banking sector. AI is one of the front digital transformation strategies which can spread in the area of finance today. The use of this AI can improve core banking operations and tailor services which in turn will deliver over $250 billion in value across the industry (According to the McKinsey Global Institute). The banking sector of India as a financial service have recognized the potential and prospects of AI and ML and therefore it is being applied in almost all activities of banking operations. Analysts and experts estimate that AI will save roughly $1 trillion of the banking industry by 2030 [3]. According to Narrative Science, 32% of the participating banks in their 2018 report are already incorporating predictive analytics, recommendation engines, voice recognition and response times in their processes. ML is just a subset of AI which helps banks in bringing several benefits through analyzing mounds of financial data. Nowadays the traditional banking business is undergoing an exciting period of disruption, and at the same time it is evident from the 21st century that the worldwide banking sector is becoming digitalized as the trend spreads across the globe. Due to enormous changes with customer preferences, all industries now, including the banking sector, are adopting innovative methods to match the pace with changing demand and AI, ML and robotics are few among them. AI and ML impacts are clearly manifested in many areas of banking sector like credit scoring, creditworthiness, personal assistance, market research, fraud detection and asset management etc. AI is rapidly reshaping the business landscape of the financial industry including the banking industry. Banks should implement the AI technology in order to become more relevant and competitive, to provide personalized experience to customers, to improve and enhance the profit margin and to survive in the market amongst cutthroat competition. With the numerous available relevant technologies, application of AI and have deeply penetrated the banking sector and it is not only modernizing but also transforming this sector into a new horizon. Through the use of AI and ML the banks are leveraging the benefits of reduction in human error, strengthening customer base, reducing fraud, enhancing security and defence through utilizing knowledge data base and also controlling risk through minimizing credit score of customers. Nowadays, operation of banks is becoming more methodical and AI and ML have metamorphose every aspect of banking sector and has made its process faster. There are so many digital platforms used under AI and ML which are helpful to the banks for doing their business and operation smoothly. Some of them are handy while handling customer service, detection of fraud, creation of facial bio metrics and others are generous for computing the credit score and handling the customer queries. Few of these techniques used by the banks especially under AI are KASISTO (KAI), PEPPER, HooYu, DATAVISOR. PLAID and ZESTFINANCE etc. which prove to be, crucial for the banking sector for their glossy performance. So, it can be concluded form the study that in the banking sector the application and adoption of AI and ML enhances efficiency, increases profitability, serve customers more efficiently, offers data insights and manages risk of banks. AI and ML is proved to be the poised agitator for the banking industry. It is estimated by the experts that AI will bring huge gain to the banking industries which could be realized over the coming decades.

In this article, we will highlight the impact and application of Artificial Intelligence on Agriculture, along with the challenges in the adoption of Artificial Intelligence. Artificial Intelligence has become one of the most important technologies in every sector, including education, banking, robotics, agriculture, etc. In the agriculture sector, it is playing a very crucial role and it is transforming the agriculture industry. This article highlighted the application of Artificial Intelligence along with the challenges in the adoption of Artificial Intelligence and popular Artificial Intelligence start-ups used in agriculture. Today’s agriculture system has reached a different level due to technology like Artificial Intelligence and Robotics etc. This article highlights Artificial Intelligence with its applications and merits.

This study examines the impact of artificial intelligence (AI) adoption on economic growth and unemployment across G7 countries and India. As AI emerges as a transformative technology, there is a need to understand its effects on labour markets and develop appropriate policy frameworks. The research analyses historical patterns of automation and technological change to provide context for the current AI revolution. The study employs a mixed-methods approach, combining quantitative statistical analyses with qualitative assessments. Variables examined include GDP growth rates, R&D investment levels, AI adoption rates, and productivity gains. Descriptive statistics, correlation analyses, regression models, and cluster analyses were conducted to identify relationships between key variables. Results reveal a significant technology adoption gap between high-tech leaders and developing economies. A growth paradox was observed in developing tech economies, where rapid AI adoption did not necessarily translate to proportional economic gains. The research found complex relationships between technological advancement, unemployment rates, and investment levels across countries. Cluster analysis identified three distinct groups: Advanced Economies, Technology Leaders, and Developing Tech Economies. ANOVA and chi-square tests confirmed statistically significant differences between these clusters. Multiple regression analysis on unemployment rates provided insights into the factors influencing job displacement. Key findings include: 1) AI adoption shows potential to significantly boost GDP growth and productivity, though effects vary by country. 2) The relationship between technological progress and unemployment is nuanced, defying simplistic narratives. 3) Investment levels strongly correlate with technological advancement, but other factors also play important roles. 4) Leaders in AI adoption exhibit certain common characteristics, offering potential lessons for other nations.The study concludes that while AI presents substantial opportunities for economic growth, its benefits are not uniformly distributed. Policymakers must develop strategies for inclusive growth, equitable access to technological advancements, and robust safety nets to address potential economic stratification. Future research directions are suggested to further explore the long-term implications of AI on global economies and labour markets. This research contributes to the ongoing dialogue on AI's societal impact and provides evidence-based insights to inform policy decisions in an era of rapid technological change. The findings underscore the need for nuanced, context-specific approaches to AI adoption and regulation across different economic contexts.

This study examines the impact of artificial intelligence (AI) adoption on economic growth and unemployment across G7 countries and India. As AI emerges as a transformative technology, there is a need to understand its effects on labour markets and develop appropriate policy frameworks. The research analyses historical patterns of automation and technological change to provide context for the current AI revolution. The study employs a mixed-methods approach, combining quantitative statistical analyses with qualitative assessments. Variables examined include GDP growth rates, R&D investment levels, AI adoption rates, and productivity gains. Descriptive statistics, correlation analyses, regression models, and cluster analyses were conducted to identify relationships between key variables. Results reveal a significant technology adoption gap between high-tech leaders and developing economies. A growth paradox was observed in developing tech economies, where rapid AI adoption did not necessarily translate to proportional economic gains. The research found complex relationships between technological advancement, unemployment rates, and investment levels across countries. Cluster analysis identified three distinct groups: Advanced Economies, Technology Leaders, and Developing Tech Economies. ANOVA and chi-square tests confirmed statistically significant differences between these clusters. Multiple regression analysis on unemployment rates provided insights into the factors influencing job displacement. Key findings include: 1) AI adoption shows potential to significantly boost GDP growth and productivity, though effects vary by country. 2) The relationship between technological progress and unemployment is nuanced, defying simplistic narratives. 3) Investment levels strongly correlate with technological advancement, but other factors also play important roles. 4) Leaders in AI adoption exhibit certain common characteristics, offering potential lessons for other nations. The study concludes that while AI presents substantial opportunities for economic growth, its benefits are not uniformly distributed. Policymakers must develop strategies for inclusive growth, equitable access to technological advancements, and robust safety nets to address potential economic stratification. Future research directions are suggested to further explore the long-term implications of AI on global economies and labour markets. This research contributes to the ongoing dialogue on AI's societal impact and provides evidence-based insights to inform policy decisions in an era of rapid technological change. The findings underscore the need for nuanced, context-specific approaches to AI adoption and regulation across different economic contexts.