Artificial Intelligence Industry Research Articles

Artificial Intelligence (AI) in the Hospitality Industry: A Review Article

Purpose – Artificial intelligence (AI) adoption is critical in the age of digital technology. This review article aims to evaluate the literature on AI in the hospitality industry. Method – A narrative synthesis was used in this review article. Moreover, the literature was reviewed systematically to explore AI in the hospitality industry. The literature and information were obtained from various books and research articles on EBSCO, Google Scholar, Scopus, Web of Science, and Science Direct. The inclusion criteria were studies that clearly defined AI in all aspects of the hospitality industry, were published and written in English and were peer-reviewed. Content analysis was employed. Results – The use of AI is a strategic and critical factor in economic development. Furthermore, AI technologies are increasingly being used as digital assistants. They help businesses in the hospitality industry in a variety of ways, including improving customer service, expanding operational capability, and lowering costs. However, there are some risks associated with AI advancements, such as job loss in low-tech sectors, loss of control due to robot autonomy, and safety, security, and privacy concerns. Conclusion – AI technologies have both positive and negative effects on the workforce and job employment in the hospitality industry. Recommendations – The recommendation is to consider a quantitative study regarding AI adoption in the hospitality industry or other sectors. Also, a qualitative approach could give a clear view of insight results for further study. Research Implications – This review article contributed to the existing literature on AI adoption in the hospitality industry. Hence, it could be used to guide future research on AI adoption in the hospitality industry. It may also aid academics in broadening their research by incorporating more potential elements. Practical Implications – This review article could lead to a better understanding of AI adoption in the hospitality industry. Moreover, it may assist business owners, managers, and marketers in the hospitality industry or any sector to achieve and enhance high business performance by implementing appropriate strategies to meet the needs and expectations of both customers and employees through the use of AI.

Artificial Intelligence in the Pharmaceutical Sector of India: Future Prospects and Challenges

The pharmaceutical sector in India has emerged as a global leader, contributing significantly to the production of affordable generic drugs and vaccines. Despite its success, the industry faces challenges from early research and development stages to market deployment. This research paper explores the future prospects and challenges of integrating Artificial Intelligence (AI) into the Indian pharmaceutical sector. Methodologically, the research relies on secondary data from 2011 to 2019, collected from various published sources, primarily the Reserve Bank of India. The evaluation of performance was conducted using compound annual growth rate. AI has the potential to address longstanding issues, enhance operational efficiency, and reduce costs associated with drug discovery and development. Successful integration requires collaborative efforts from the public and private sectors, emphasizing resource allocation for education, infrastructure, regulatory reforms, and workforce development. The incorporation of AI presents a transformative opportunity for the Indian pharmaceutical sector, offering advancements in drug development, manufacturing, and quality control. Despite the promising prospects, the paper acknowledges challenges in implementing AI, including financial limitations, workforce competency, data security concerns, and regulatory complexities. The study emphasizes the need for strategic collaboration to ensure responsible and effective utilization of AI in the pharmaceutical industry.

IMPACT OF COVID-19 ON THE AIRLINE INDUSTRY

The purpose of the paper is to provide an overview of the issues related to artificial intelligence (AI) applications in the Indian healthcare sector and provide input to policymakers. A qualitative approach has been used in this study to identify government initiatives, opportunities, and challenges for applications of AI and suggest improvements in policy areas relevant to AI in healthcare. The study helps by providing comprehensive inputs for framing policy on AI in healthcare industry in India. The study also highlights that if the proper actions are taken to overcome the various challenges associated with applications of AI in healthcare sector in India by the government, then the healthcare sector will immensely benefit. This article has taken an attempt to provide inputs concerning to policy initiatives, challenges, and recommendations for improving the healthcare system of India using different applications of AI The purpose of the paper is to provide an overview of the issues related to artificial intelligence (AI) applications in the Indian healthcare sector and provide input to policymakers. A qualitative approach has been used in this study to identify government initiatives, opportunities, and challenges for applications of AI and suggest improvements in policy areas relevant to AI in healthcare. The India. The study also highlights that if the proper actions are taken to overcome the various challenges associated with applications of AI in healthcare sector in India by the government, then the healthcare sector will immensely benefit. This article has taken an attempt to provide inputs concerning to policy initiatives, challenges, and recommendations for improving the healthcare system of India using different applications of AI The purpose of the paper is to provide an overview of the issues related to artificial intelligence (AI) applications in the Indian healthcare sector and provide input to policymakers. A qualitative approach has been used in this study to identify government initiatives, opportunities, and challenges for applications of AI and suggest improvements in policy areas relevant to AI in healthcare. The study helps by providing comprehensive inputs for framing policy on AI in healthcare industry in India. The study also highlights that if the proper actions are taken to overcome the various challenges associated with applications of AI in healthcare sector in India by the government, then the healthcare sector will immensely benefit. This article has taken an attempt to provide inputs concerning to policy initiatives, challenges, and recommendations for improving the healthcare system of India using different applications of A The purpose of the paper is to provide an overview of the issues related to artificial intelligence (AI) applications in the Indian healthcare sector and provide input to policymakers. A qualitative approach has been used in this study to identify government initiatives, opportunities, and challenges for applications of AI and suggest improvements in policy areas relevant to AI in healthcare.

An Artificial Intelligence-based tool to predict “unhealthy” wine and olive oil

The application of Artificial Intelligence (AI) in the agri-food industry has witnessed significant advancements, particularly in healthy food, quality assessment, and geographical origin determination of agri-products such as wine and olive oils, whose international market is constantly growing. This paper explores the applicability of Machine Learning (ML) models for predicting “unhealthy” wines and olive oils. As for the healthy factor, two main characteristics of wines and olive oils were evaluated: their quality and their geographical origin. In the study, 3 ML algorithms were compared (Random Forest, Linear Discriminant Analysis, and K-Nearest Neighbors) to predict just by observing their chemical characteristics: i) the quality of red and white wine and ii) the geographical origins of wine and olive oils. Real datasets were used for these case studies. The Synthetic Minority Over-sampling Technique (SMOTE) was used to manage imbalanced data, and well-adopted AI metrics were collected to evaluate the accuracy of the predictions made by the 3 ML algorithms. 3 datasets were analyzed: dataset #01 contains ∼7000 data entries related to 2 types of wine (red and white Portuguese wines) and 11 wine's chemical characteristics; dataset #02 contains 178 data entries related to 3 different Italian vineyards and 13 wine's chemical characteristics; dataset #03 contains 572 data entries related to 9 different Italian geographical regions for olive oil and 7 fatty acid characteristics. A total of ∼7700 data points, 5 wines and 9 olive oil types, and 31 chemical characteristics were analyzed. 12 quality metrics were collected per each dataset analysis. The designed ML models achieved good accuracy (>0.65) in predicting the wine quality, a very high accuracy (>0.99) in predicting its vineyard, and a very good accuracy (>0.97) in predicting the origin of olive oil. Moreover, an AI-based tool has been developed that stakeholders can use to predict the quality and geographical origin of wine and olive oil to avoid possible fraudulent and unhealthy product labels. The obtained prediction results showcase AI and ML's potential to help the agri-food industry, providing valuable insights and innovations for producers, distributors, and consumers alike. Integrating AI and ML in the agri-food industry may enhance product quality, consumer trust, and market transparency.

An empirical study on the dark side of service employees’ AI awareness: Behavioral responses, emotional mechanisms, and mitigating factors

Drawing on the stressor-emotion model, our study unveils a multi-faceted moderated mediation model that delineates how artificial intelligence (AI) awareness influences frontline service employees’ counterproductive work behavior towards customers (CWBC) and work-family conflict (WFC), mediated by negative emotions (NE). We introduce promotion focus and empowering leadership as first-stage moderators, and family motivation as a second-stage moderator, to explore their buffering effects on the negative outcomes triggered by AI awareness. Employing an experience sampling methodology, we gathered data from 92 frontline service employees in hospitality over two working weeks. The findings indicate that heightened AI awareness correlates with increased emotional distress, which in turn exacerbates WFC and CWBC. Notably, our analysis reveals that employees with a pronounced promotion focus and those under empowering leadership regimes exhibit reduced negative emotional responses to AI-induced stressors. Moreover, individuals driven by strong family motivation not only demonstrate unique resilience in managing the interplay between work-induced stress and family well-being but also show a significant reduction in counterproductive work behaviors. Overall, this research provides a novel lens to understand the broader implications of AI in the service industry by employing a dynamic and multilevel approach.

The influence of AI on the economic growth of different regions in China

High-quality development plays a crucial role in China’s economic progress in the new era. It represents a new concept of advancement and mirrors the increasing aspirations of the populace for an improved standard of living. In this context, the role of artificial intelligence (AI) in promoting sustainable development cannot be overemphasized. This paper explores how AI technologies can drive the transition to a green, low-carbon and circular economy. We have established an index system to measure the development level of the artificial intelligence industry and the high-quality development of the economy, which is relevant to the current state of the artificial intelligence industry and the advancement of the economy. Panel data from 2008 to 2017 has been utilized for this purpose. Global principal component analysis method and entropy value method are employed in the evaluation. Through in-depth analysis of the application of artificial intelligence and environmental protection in various provinces and cities, we clarify that artificial intelligence promotes innovation, saves resources, and is conducive to the development of green economy in the new era.

Enhancing Food Integrity through Artificial Intelligence and Machine Learning: A Comprehensive Review

Herein, we examined the transformative potential of artificial intelligence (AI) and machine learning (ML) as new fronts in addressing some of the pertinent challenges posed by food integrity to human and animal health. In recent times, AI and ML, along with other Industry 4.0 technologies such as big data, blockchain, virtual reality, and the internet of things (IoT), have found profound applications within nearly all dimensions of the food industry with a key focus on enhancing food safety and quality and improving the resilience of the food supply chain. This paper provides an accessible scrutiny of these technologies (in particular, AI and ML) in relation to food integrity and gives a summary of their current advancements and applications within the field. Key areas of emphasis include the application of AI and ML in quality control and inspection, food fraud detection, process control, risk assessments, prediction, and management, and supply chain traceability, amongst other critical issues addressed. Based on the literature reviewed herein, the utilization of AI and ML in the food industry has unequivocally led to improved standards of food integrity and consequently enhanced public health and consumer trust, as well as boosting the resilience of the food supply chain. While these applications demonstrate significant promise, the paper also acknowledges some of the challenges associated with the domain-specific implementation of AI in the field of food integrity. The paper further examines the prospects and orientations, underscoring the significance of overcoming the obstacles in order to fully harness the capabilities of AI and ML in safeguarding the integrity of the food system.

The Over-Concentration of Innovation and Firm-Specific Knowledge in the Artificial Intelligence Industry

The Over-Concentration of Innovation and Firm-Specific Knowledge in the Artificial Intelligence Industry

Comprehensive Assessment of Artificial Intelligence Tools for Driver Monitoring and Analyzing Safety Critical Events in Vehicles.

Human factors are a primary cause of vehicle accidents. Driver monitoring systems, utilizing a range of sensors and techniques, offer an effective method to monitor and alert drivers to minimize driver error and reduce risky driving behaviors, thus helping to avoid Safety Critical Events (SCEs) and enhance overall driving safety. Artificial Intelligence (AI) tools, in particular, have been widely investigated to improve the efficiency and accuracy of driver monitoring or analysis of SCEs. To better understand the state-of-the-art practices and potential directions for AI tools in this domain, this work is an inaugural attempt to consolidate AI-related tools from academic and industry perspectives. We include an extensive review of AI models and sensors used in driver gaze analysis, driver state monitoring, and analyzing SCEs. Furthermore, researchers identified essential AI tools, both in academia and industry, utilized for camera-based driver monitoring and SCE analysis, in the market. Recommendations for future research directions are presented based on the identified tools and the discrepancies between academia and industry in previous studies. This effort provides a valuable resource for researchers and practitioners seeking a deeper understanding of leveraging AI tools to minimize driver errors, avoid SCEs, and increase driving safety.

(WITHDRAWAL) THE IMPACT OF AI ON EMPLOYMENT AND ORGANISATION IN THE INDUSTRIAL WORKING ENVIRONMENT OF THE FUTURE

(Withdrawal) The publisher made the decision to withdraw the article after discovering that it is a copy of another source and that the document missing the necessary citations.

Embracing artificial intelligence (AI) with job crafting: Exploring trickle-down effect and employees’ outcomes

The wide application of AI in the service industry has dramatically changed job tasks and required knowledge. It becomes more urgent and necessary to craft their job proactively to cope with the changes. However, we have limited knowledge about how and why some employees craft their jobs to adjust to AI and whether it works. Integrating the AI literature with the job crafting literature, we introduce the construct of AI crafting as a domain-specific kind of job crafting and investigate its outcomes and antecedents. Using a three-wave, multi-source field study, we adopted multilevel path modeling to analyze the data. Results revealed that leader AI crafting was positively related to employee AI crafting, which in turn positively related to employee AI engagement and AI helping. Furthermore, employee-attributed performance enhancement/impression management motives enhanced/attenuated the indirect effects. We discuss the theoretical and practical implications of the findings and propose future research directions.

Role of artificial intelligence in customer engagement: a systematic review and future research directions

Role of artificial intelligence in customer engagement: a systematic review and future research directions

A Comparative Study on Machine Learning Models for Fault Detection and Classification in Manufacturing Integration of IoT and Artificial Intelligence in Industry 4.0 management

The present study presents an examination of fault detection and classification in manufacturing that is possible due to IoT and Artificial Intelligence in the Industry 4.0 structure. The study is based on IoT and compares three Artificial Intelligence algorithms such as Naive Bayes , Extreme Gradient Boosting , and k-Nearest Neighbors . The results displayed that in ten trials of the experiment achieving the accuracy from 0.82 to 0.89 . The interpretability tool allowed for the understanding of the models’ predictions. The consistent accuracy in both SHAP and LIME results, i.e. 0.80 and 0.89, suggest that both models performed effectively in model explanation. Thus, end users need to combine advanced machine learning algorithms with interpretable explanations to enhance fault detection and classification in manufacturing. This will consequently allow for better production processes and increased innovation in Industry 4.0 manufacturing.

Artificial Intelligence and International Trade Laws: In Light of Trade Policy and Data Sharing

Due to rapid increased in the interest in how Artificial Intelligence (AI) will affect recently, there has been significant emphasis directed into the impact of this emerging technology on international trade rules, particularly in relation to the economy and trade. We examine the world’s most powerful countries, how they revaluating the trade by using AI and introducing new laws relate to AI. We explore the possibility that AI approaches might provide predictions and connections that are more accurate than those produced by traditional economic models, for accurate predictors. Additionally, we discuss that AI may be used to examine trade patterns and events like trade wars, countries give high importance on comprehending and predicting future trade trends by using AI. Having access to the data is crucial because only open-government data can be used as the fuel for AI that can anticipate and suggest policies. In order to fully use the promise of AI technologies, policymakers should take into account the connections between trade and AI technologies that are explored in this article. The major focus of this investigation is on China’s endeavours to develop and expand its AI industry, as well as the actions that the country has made to establish itself as a dominant player in the global trade market. We also examine to get advantages of globalization international trade policies continue to be in the spotlight. We also highlight that the use AI approaches to understand how trade patterns are impacted by extreme events like pandemics and trade wars.

Embracing the Era of Artificial Intelligence: Innovation and Preparation in Nursing Education

Rapid recent advances in information technology have opened the door for artificial intelligence (AI)-related technologies to be applied extensively across many industries. The Ministry of Education has emphasized the importance of cultivating advanced-level professionals in diverse fields, particularly in smart machinery, the Asia-Silicon Valley sector, green energy technology, biotechnology, national defense, new agricultural, and circular economy industries, to enhance innovation and promote industrial competitiveness (Kuo, 2019). While interdisciplinary talent in AI and digital innovation is being actively developed elsewhere, nursing education remains in the exploratory phase of AI and digital technology talent cultivation. Although AI is now a well-known term, the competencies required for its application in nursing remain unclear. Moreover, most nursing professionals are unfamiliar with how to best integrate AI into nursing expertise or practice settings. With the application of AI in the healthcare industry now unstoppable, it is vital to consider how to help nursing students adapt to healthcare's new technology landscape (Huang et al., 2021). AI facilitates the digital simulation of human thought patterns, logic, and behaviors with the goal of assisting human users solve problems, especially those that are time-consuming and require repetitive processing. The development of AI requires interdisciplinary collaboration among domain experts, data scientists, software engineers, robotics experts, and computer programmers. Such collaboration is essential to developing products able to meet the demands of the times and to help students become competent future nursing professionals (Murray, 2018). Nurses spend the most time interacting with patients and are thus best able to understand the perceptions and challenges of patients and their families. Collaborating with professionals from interdisciplinary fields is the best strategy for achieving optimal healthcare outcomes. However, nursing schools have yet to provide a clear response to the impact of AI on nursing education. Nursing educational institutions must enable nursing students to comprehend the concepts and principles of AI and equip them with AI literacy to allow them to unleash their potential, continuously innovate, and stay abreast with the times (Ng et al., 2021). In this issue, experts and scholars currently engaged in AI-related research in the nursing discipline share their research findings in the realms of machine learning, deep learning, emotional recognition, and natural language application. These articles offer insights into the implications of AI, suggest how nursing education may best respond to emerging AI trends, and provide the authors' perspectives on nursing education reform. The editor hopes readers will be inspired to explore new concepts, gain a deeper understanding of the application and significance of AI, and apply AI to address clinical and educational challenges to foster competent nursing professionals for tomorrow.

소위 ‘약한 인공지능(AI)’ 영역에서의 민사책임에 관한 연구

Now, the core of the discussion is how artificial intelligence (AI), as a rapidly developing advanced technology, can solve many of the most difficult problems facing humanity beyond solving severe social problems. Therefore, the true value of artificial intelligence (AI) as a cutting-edge technology is to solve huge problems facing humanity, such as social problems such as gun violence, food shortages, incurable diseases, and global warming caused by carbon emissions, and to contribute to human development. The concern, though, is that no one knows exactly how current artificial intelligence (AI) works. In other words, it is similar to developing a bomb that does not know when it will explode without a safety device.
 Already, artificial intelligence (AI) is causing damage by providing false information. So, if we don't know how to control better artificial intelligence (AI) when it appears, dangerous things can happen. Therefore, there will be a need for international norms on artificial intelligence (AI), which will be made possible by international treaties. The European Union has already enacted the AI Act as the first regulatory law on AI. This contains stronger regulations on the AI industry than in the United States or Asia. In particular, the Act stipulates the obligation to display AI-generated content. Thus, the EU's AI regulation law can be evaluated as legislation designed to ensure the safety of AI-generated products, such as cars and toys. In addition, in September 2022, the European Union (EU) issued a directive on AI accountability. It focuses on the legal principles of compensation for damages caused by artificial intelligence (AI). 
 Legislation on punitive damages for deepfake harms is required. As a civil liability, the victim's damages should be recognized not only for mental damages, but also for aggravated damages for monetary damages, if any. As such a legislative measure, so-called punitive damages should be introduced for malicious and deliberate use of artificial intelligence (AI) as a perpetrator. In the so-called risk liability of artificial intelligence (AI), the operator of artificial intelligence (AI), which has enormous profits through the operation of the risk source, will have to bear the risk liability as a no-fault liability. Korea's product liability law has embraced this principle of risk liability. In order for product liability law to apply to artificial intelligence (AI), there must be a defect in the artificial intelligence (AI) installed in the product.

Impact of AI in the Animation Industry

Abstract: This manuscript develops into the transformative role of artificial intelligence (AI) in the field of animation. Over the years, AI has revolutionized various industries including healthcare, banking, e-commerce, agriculture, manufacturing, and others., and animation is no exception. This paper provides an overview of the applications of AI in animation, including character creation, motion synthesis, scene generation, generating facial expressions and body language for characters, planning out scene layouts and camera movements, and generating backgrounds and environments and more. It discusses the challenges and ethical considerations associated with AI-driven animation and presents insights into the future directions of AI in the animation industry

인공지능(AI)과 한국 부동산의 부패방지에 관한 연구

This study attempted to present discussions and directions regarding artificial intelligence(AI) and the prevention of corruption in Korean real estate. This Paper dealt with moral hazard, corruption prevention, and trust using real estate, the 4th industrial revolution(4IR), AI, and intelligent information technology as keywords. Prior research is focusing on economic utility and technical benefits related to AI and real estate. In contrast, this study pointed out the risk of moral hazard and corruption arising from the combination of AI and real estate, and conducted a paper to prevent moral hazard and corruption. In this respect, there is a difference between this study and existing previous studies. This is where the originality of this study appears. The results of this study on anti-corruption of AI and Korean real estate are as follows. As a result of the study, the problems of AI and Korean real estate, and anti-corruption were presented in three major categories: 1. AI and the problem of changing times, 2. Korean real estate and corruption problem, and 3. AI and real estate problem. In relation to this, this study largely derived three measures as implications of the research. The contents include, first, responses to the issues of AI and changing times, second, responses to Korean real estate and corruption issues, and third, measures to respond to AI and real estate issues. This study hopes to restore market trust through Artificial Intelligence(AI) and the prevention of corruption in Korean real estate, and to build a future where the benefits and utility provided by real estate, AI, and intelligent information technology are desirable.

Artificial intelligence development and dissemination impact on the sports industry labor market.

The objective of this study is to explore the impact of artificial intelligence (AI) development on the sports industry labor market, the ways in which AI has influenced the demand for labor, created new job opportunities, and impacted existing job roles. It refers to the inductive approach in the spirit technological determinism theory. It is based on the literature review and written qualitative, semi-structured interviews (N = 14) with sports human resources, management, and technology professionals (purposive sampling). Analysis involved inductive coding and line-by-line analytics of the data. The labor market implications of AI in the sports industry are multifaceted. New job roles are likely to emerge, demanding a blend of AI expertise, data-analysis skills, and sports domain knowledge. Professionals in roles such as sports data analysts and marketing experts may find increasing opportunities. However, certain jobs undergo transformation as AI automates routine tasks. It requires individuals to upskill or transition to roles that require a deeper understanding of AI. This necessitates the creation of responsibilities focused on ethical AI governance and oversight. It is important to research the impact of AI dissemination on the sports industry labor market in a holistic manner because the effects of AI are complex and far-reaching. While there are potential benefits to the implementation of AI, there are also potential risks and challenges that need to be addressed, the implementation of AI in the sports industry could have broader social and ethical implications that need to be considered.

Development and Threats of Artificial Intelligence in Industry and Workforce

The abstract explores the transformative impact of Artificial Intelligence (AI) on global industries, highlighting its role in enhancing efficiency and innovation while also posing challenges such as job automation, skills gaps, data misuse, and ethical concerns. Drawing from the Global Risk Report 2024 and recent regulatory actions by the European Union and Indonesia, the abstract discusses the pressing need for AI governance. However, it lacks specificity in articulating the study's objectives and scope, and could benefit from providing concrete examples or statistics to support its claims. A clearer organizational structure and citation of relevant sources would enhance the coherence and credibility of the abstract. Furthermore, while emphasizing the importance of human control over AI, the abstract could offer a more nuanced conclusion that underscores the significance of the study's findings in achieving this goal.