Artificial Intelligence Industry Research Articles

Artificial intelligence ethics in services: are we paying attention to that?!

ABSTRACT Scant information exists in the literature on the ethical considerations when adopting and implementing Artificial Intelligence (AI) in services. To address this research gap, this study conducts a bibliometric analysis of 286 research articles, identified from the Scopus and Web of Science databases, on AI ethics in services. The findings revealed that most of the research on AI ethics in services is from the United Kingdom, the U.S., and China. The co-occurrence analysis further revealed that AI ethics is a complex dimension, where several ethic types are found, namely digital, business, machine, and artificial. Additionally, AI effects should be considered from micro, meso, and macro perspectives to ensure that AI systems are ethically correct. The thematic findings highlighted that for AI systems to be safe and effective in industry, they should be responsible and human-centered. Developing such systems requires defining and implementing several features, including transparency, responsibility, explainability, and accountability. Finally, the obtained findings highlighted the need for increasing awareness of AI ethics in services as well as developing regulatory frameworks and laws in this context. The findings of this study can help various stakeholders (policymakers, developers, business owners, etc.) promote the safe and effective adoption of AI in industry.

Greening Automation: Policy Recommendations for Sustainable Development in AI-Driven Industries

This study delves into the dynamic relationship between artificial intelligence (AI) and environmental performance, with a specific focus on greenhouse gas (GHG) emissions across European countries from 2012 to 2022. Utilizing data on industrial robots, AI companies, and AI investments, we examine how AI adoption influences GHG emissions. Preliminary analyses, including ordinary least squares (OLS) regression and diagnostic assessments, were conducted to ensure data adequacy and model readiness. Subsequently, the Elastic Net (ENET) regression model was employed to mitigate overfitting issues and enhance model robustness. Our findings reveal intriguing trends, such as a downward trajectory in GHG emissions correlating with increased AI investment levels and industrial robot deployment. Graphical representations further elucidate the evolution of coefficients and cross-validation errors, providing valuable insights into the relationship between AI and environmental sustainability. These findings offer policymakers actionable insights for leveraging AI technologies to foster sustainable development strategies.

Life After COVID-19: Artificial Intelligence in Hotels; In-depth Literature Review

Purpose: This study aims to systematically review the impact of artificial intelligence (AI) on the hotel industry post-COVID-19, focusing on articles published between 2019 and 2024. Grounded in the Technology Acceptance Model (TAM), the research examines the relevance and application of AI within the industry, particularly in response to the challenges posed by the pandemic. Study design/methodology/approach: The study adopts an interpretive paradigm and employs thematic analysis to synthesise findings from various academic journals. Keywords such as ‘hotels,’ ‘tourism industry,’ ‘COVID-19,’ and ‘artificial intelligence’ are used to identify relevant literature. The methodology involves a detailed literature review and analysis to explore how AI technologies enhance operational efficiencies, service quality, and cost reductions in the hotel industry. Findings: The findings reveal that AI has become critical in adapting hotel operations to new market realities post-COVID-19. AI technologies offer innovative solutions that align with changing consumer expectations and industry standards, contributing to improved customer experiences and operational efficiency. Integrating AI into hotel management and customer service platforms is essential for the recovery and future growth of the hospitality sector. Originality/value: This study contributes to the existing literature by providing insights into the impact of AI on the hotel industry post-COVID-19. It highlights the importance of AI technologies in improving hotel customer experiences, operational efficiency, and profitability. The application of the TAM framework underscores the significance of perceived usefulness and ease of use in driving the acceptance and sustained use of AI in the hotel industry. The study recommends ongoing investment in AI technologies and adaptation to technological advancements to meet evolving customer needs and industry demands.

To Gain Sustainable Competitive Advantages (SCA) Using Artificial Intelligence (AI) Over Competitors

The motivation behind this examination is to explore the relationship between AI, DC, and SCA in the telecom business in Pakistan. The paper embraced a quantitative exploration plan and utilized a survey method to collect data from 235 telecom sector employees and managers of three distinct hierarchical levels. The paper applied SEM to examine the hypotheses and analyse the data. The paper found that artificial intelligence affected digital capacity (DC), DC meaningfully affected SCA, and DC intervened in the impact of AI on SCA. The study adds to the works on the link between AI, DC, and SCA in the telecom business. It gives experimental proof to help the hypotheses that artificial intelligence influences DC, DC influences SCA, and DC explains the impact of AI on SCA. The paper gives helpful experiences to telecom sector employees and policymakers. It suggests that telecom companies should make investments in AI technologies and applications to improve their DC, which can help them gain a competitive advantage. Also, it recommends that policymakers work with and support the telecom business to execute DC and AI because these advancements can help financial development, effectiveness, and innovation. The research aims to identify the association between AI, DC, and SCA in the telecom industry in Pakistan. It offers an original viewpoint on how artificial intelligence can improve DC and how DC can prompt SCA in the telecom business.

Future of Pharmaceutical Industry: Role of Artificial Intelligence, Automation and Robotics

Background The future of smart factories and pharmaceutical industries has evolved significantly since the 19th century. Computers have been used in the pharmaceutical field since the 1980s with the emergence of Artificial Intelligence (AI). In addition, automation and robotics are used in the pharmaceutical industry to improve the efficiency of pharmaceutical development and production. Purpose The present review article covers the future roles of AI, automation and robotics in pharmaceutical industries. Methodology The current review article employed a comprehensive search strategy across relevant databases, including PubMed, Scopus and Web of Science, utilizing keywords such as AI, Automation, Robotics and Pharmaceutical Industries. The articles considered a focus on recent advancements and emerging trends in the intersection of AI, automation and robotics within pharmaceutical sectors. Results AI, incorporating predictive machine learning and reasoning techniques, aids in the preclinical identification of molecules and forecasting potential lead compounds before conducting clinical trials. automation offers significant benefits in monitoring and predictive maintenance of production lines, power distribution and control machines. Robotic process automation improves efficiency by connecting computer terminals to handle various manufacturing process elements. Conclusion Artificial intelligence, automation and Robotics have sparked innovations in the healthcare business, benefiting the global ecosystem and healthcare delivery. Incorporating these advanced tools in pharmaceutical industries from raw material selection to final product development could improve the quality and safety of the pharmaceutical products and reduce the time and cost.

The Synergy of Ambidextrous Leadership, Agility, and Entrepreneurial Orientation to Achieve Sustainable AI Product Innovation

This study aims to explore potential mechanisms of ambidextrous leadership (AL) in product innovativeness from the perspective of organizational agility (OA) and entrepreneurial orientation (EO) in firms operating in the artificial intelligence (AI) industry. A quantitative research method was used with 405 questionnaires, and the respondents were randomly selected from reputable databases. Structural equation modeling was employed to evaluate the model fit and conduct hypothesis testing. The findings suggest that ambidextrous leadership demonstrates a significant positive influence on product innovativeness and OA; also, through the mediating role of OA, it is possible to analyze both the direct and indirect relationships among the factors. Additionally, the moderating effect of EO on the intercorrelations among these factors was explored. This study enhances existing knowledge on leadership dynamics in the context of new product development, highlights the importance of adaptability in leadership, and sheds light on the interplay between OA, EO, and new product innovation. This study highlights the role of product innovativeness in sustainable AI product development. Enhanced product innovativeness not only sustains AI product development but also promotes environmental sustainability. This is achieved through the minimization of energy use, reduction in material requirements, and prevention of pollution. Firms are using these insights to develop sustainable and eco-friendly products, as well as create new market opportunities while reducing environmental impact. This research underscores the interconnectedness of factors in this study and sustainability, providing a new perspective on sustainable AI product development.

Assessing the Copyright Infringement Risk of Generative AI Created Works

The rapid evolution of the Internet and big data technology has ushered in significant advancements in data production and utilization. However, this progress has brought to the forefront the intricate issue of copyright protection. This is particularly evident with the widespread adoption of generative artificial intelligence (AI), where concerns regarding data source protection, data processing standards, and the boundaries of data application have garnered considerable attention from both academia and industry. Big data mining techniques coupled with generative AI algorithms offer robust support for data collection and processing, but they also introduce risks of data infringement and pose challenges regarding algorithmic compliance and ownership rights over generated works. Addressing these issues is imperative. This paper recommends enhancing legal compliance standards throughout each stage of the generative AI process, recalibrating the scope of acceptable copyright use, and establishing a regulatory framework for generative AI works. These measures are crucial for fostering the sustainable and orderly advancement of the generative AI industry.

Tech Hubs and the AI Industry in Africa: What Drives?

Tech hubs—usually associated with San Francisco, London, Seoul, and Bangalore—are physical spaces that house activities that result in innovation, scientific advancement, and the creation of start-ups. Yet over 650 tech hubs now exist on the African continent, and many have a focus on artificial intelligence. The rapid emergence of tech hubs in Africa presents a puzzle, as traditional notions about where tech hubs emerge is challenged by this reality. This article explores how technical expertise and foreign direct investment contribute to the rising number of tech hubs in a country. Regression analysis supplemented with case study analyses of Ghana, Tanzania, and South Africa show that technical expertise and foreign direct investment are correlated with the number of tech hubs in an African country. This regression analysis focuses on the artificial intelligence industry, which has been highlighted as particularly important by national governments and hubs within the region. African countries are not typically thought of as global leaders in technology, yet the proliferation of tech hubs presents a narrative of a region fostering an exciting, innovative ecosystem.

Integrating artificial intelligence in industry 4.0: insights, challenges, and future prospects–a literature review

Integrating artificial intelligence in industry 4.0: insights, challenges, and future prospects–a literature review

AUTOMATION IN ANALYTICAL CHEMISTRY: THE ROLE OF AI IN CHROMATOGRAPHY

Artificial Intelligence (AI) has facilitated significant breakthroughs in drug discovery, the design of materials, and organic synthesis. The advancements in the latter group are especially remarkable due to the abilities of the latest computational methods (molecular design algorithms) that enable the exploration of extensive chemical spaces and enhance research in fields such as predicting molecule properties, designing molecules, retrosynthesis, predicting reaction conditions, and predicting reaction outcomes. A literary review was conducted following PRISMA guidelines. This study aimed to review existing data on the application of AI in separation chromatography. The evolution and utilization of AI in the pharmaceutical industry and its future aspects were articulated in this study. The utilization of AI can completely transform the field of chromatography analysis by facilitating expedited, more precise, and more effective data processing. By automating chromatography analysis, AI can enhance efficiency and minimize the potential for human mistakes. This advancement enables scientists to dedicate their efforts towards addressing intricate and demanding analytical issues. With the evolution of technology and the increasing adoption, we can anticipate more progress in chromatography analysis and analytical chemistry.

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.