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Artificial intelligence (AI) is one of the emerging technologies of this time. AI is a widely used technology in library services that can transform the best services in the age of information technology. This paper aims to highlight the use of AI in library operations. Several research has been undertaken on this subject, but that only address a few applications. AI and libraries have a substantial nexus; nevertheless, the use and awareness of AI in library services are still creating question marks addressed in this paper. This study will help the policy stakeholder, librarians and scholars in the field to address these issues before the deployment of AI in library services.

This study is based on a qualitative method using content analysis techniques. An extensive review of literature on “artificial intelligence”, “smart libraries” was carried to ascertain the emerging technologies in the smart library domain. Literature was searched against various keywords like artificial intelligence, smart technologies, Internet of Things, electronic resource management, data mining and ambient intelligence. This study highlights the pros and cons of AI in library services and its possible solutions.

The findings of this study show that AI is a vibrant technology that can be used in library services; however, some obstacles like adequate funds, the attitude of librarians and technical skills are a few obstacles that hamper AI in library operations. The findings also reveal that using AI in library operations will accelerate libraries in the right direction. Furthermore, this study highlights various applications that can be deployed without spending costs.

This paper may be of interest to academic, librarians, policymakers, researchers and the government to have a perspective on initiatives in the country on application of technology in library services. This study can introduce the current status and potential of this technology to bring the technology revolution in library and information center services.

This study will motivate library professionals to take advantage of AI in library services and further accelerate library operations in the right direction.

This study covers the understanding of AI in library services that will help the librarian’s and information professionals leverage AI in library scenarios. Furthermore, the practical implication of AI in library services will bring positive change in implementing AI.

Due to the rapid development of artificial intelligence (AI) technology, increasing the use of AI in healthcare is critical, but few studies have explored the extent to which physicians cooperate with AI in their work to achieve productive and innovative performance, which is a key issue in operations management (OM). We conducted empirical research to answer this question.

We developed a conceptual model based on the ambidextrous perspective. To test our model, we collected data from 200 Chinese hospitals. One senior and one junior physician from each hospital participated in this research so that we could get a more comprehensive view. Based on the sample of 400 participants and the conceptual model, we examined whether different types of AI use have distinct impacts on physicians’ productivity and innovation by conducting hierarchical regression and post hoc tests. We also introduced team psychological safety climate (TPSC) and AI technology uncertainty (AITU) as moderators to investigate this topic in further detail.

We found that augmentation AI use is positively related to overall productivity and innovative job performance, while automation AI use is negatively related to these two outcomes. Furthermore, we focused on the impacts of the ambidextrous use of AI on these two outcomes. The results highlight the positive impacts of complementary use on both outcomes and the negative impact of balance on innovative job performance. TPSC enhances the positive impacts of complementary use on productivity, whereas AITU inhibits the negative impacts of automation and balanced use on innovative job performance.

In the age of AI, organizations face greater trade-offs between performance and technology management. This study contributes to the OM literature from the perspectives of operational performance and technology management in three ways. First, it distinguishes among different AI implementations and their diverse impacts on productivity and innovative performance. Second, it identifies the different conditions under which automation AI use and augmentation are superior. Third, it extends the ambidextrous perspective by becoming an early adopter of this approach to explore the implications of different types of AI use in light of contingency factors.

Nowadays, food problems are likely to arise because of the increasing global population and decreasing arable land. Therefore, it is necessary to increase the yield of agricultural products. Pesticides can be used to improve agricultural land products. This study aims to make the spraying of cherry trees more effective and efficient with the designed artificial intelligence (AI)-based agricultural unmanned aerial vehicle (UAV).

Two approaches have been adopted for the AI-based detection of cherry trees: In approach 1, YOLOv5, YOLOv7 and YOLOv8 models are trained with 70, 100 and 150 epochs. In Approach 2, a new method is proposed to improve the performance metrics obtained in Approach 1. Gaussian, wavelet transform (WT) and Histogram Equalization (HE) preprocessing techniques were applied to the generated data set in Approach 2. The best-performing models in Approach 1 and Approach 2 were used in the real-time test application with the developed agricultural UAV.

In Approach 1, the best F1 score was 98% in 100 epochs with the YOLOv5s model. In Approach 2, the best F1 score and mAP values were obtained as 98.6% and 98.9% in 150 epochs, with the YOLOv5m model with an improvement of 0.6% in the F1 score. In real-time tests, the AI-based spraying drone system detected and sprayed cherry trees with an accuracy of 66% in Approach 1 and 77% in Approach 2. It was revealed that the use of pesticides could be reduced by 53% and the energy consumption of the spraying system by 47%.

An original data set was created by designing an agricultural drone to detect and spray cherry trees using AI. YOLOv5, YOLOv7 and YOLOv8 models were used to detect and classify cherry trees. The results of the performance metrics of the models are compared. In Approach 2, a method including HE, Gaussian and WT is proposed, and the performance metrics are improved. The effect of the proposed method in a real-time experimental application is thoroughly analyzed.

The study investigates the consumer’s attitude to using artificial intelligence (AI) devices in hospitality service settings considering social influence, hedonic motivation, anthropomorphism, effort expectancy, performance expectancy and emotions.

This study employed a quantitative methodology to collect data from Bangladeshi consumers who utilized AI-enabled technologies in the hospitality sector. A total of 343 data were collected using a purposive sampling method. The SmartPLS 4.0 software was used to determine the constructs' internal consistency, reliability and validity. This study also applied the partial least squares structural equation modeling (PLS-SEM) to test the research model and hypotheses.

The finding shows that consumer attitude toward AI is influenced by social influence, hedonic motivation, anthropomorphism, performance and effort expectancy and emotions. Specifically, hedonic motivation, social influence and anthropomorphism affect performance and effort expectations, affecting consumer emotion. Moreover, emotions ultimately influenced the perceptions of hotel customers' willingness to use AI devices.

This study provides a practical understanding of issues when adopting more stringent AI-enabled devices in the hospitality sector. Managers, practitioners and decision-makers will get helpful information discussed in this article.

This study investigates the perceptions of guests' attitudes toward the use of AI devices in hospitality services. This study emphasizes the cultural context of the hospitality industry in Bangladesh, but its findings may be reflected in other areas and regions.

Artificial intelligence (AI) has sparked interest in various areas, including marketing. However, this exhilaration is being tempered by growing concerns about the moral and legal implications of using AI in marketing. Although previous research has revealed various ethical and legal issues, such as algorithmic discrimination and data privacy, there are no definitive answers. This paper aims to fill this gap by investigating AI’s ethical and legal concerns in marketing and suggesting feasible solutions.

The paper synthesises information from academic articles, industry reports, case studies and legal documents through a thematic literature review. A qualitative analysis approach categorises and interprets ethical and legal challenges and proposes potential solutions.

The findings of this paper raise concerns about ethical and legal challenges related to AI in the marketing area. Ethical concerns related to discrimination, bias, manipulation, job displacement, absence of social interaction, cybersecurity, unintended consequences, environmental impact, privacy and legal issues such as consumer security, responsibility, liability, brand protection, competition law, agreements, data protection, consumer protection and intellectual property rights are discussed in the paper, and their potential solutions are discussed.

Notwithstanding the interesting insights gathered from this investigation of the ethical and legal consequences of AI in marketing, it is important to recognise the limits of this research. Initially, the focus of this study is confined to a review of the most important ethical and legal issues pertaining to AI in marketing. Additional possible repercussions, such as those associated with intellectual property, contracts and licencing, should be investigated more deeply in future studies. Despite the fact that this study gives various answers and best practices for tackling the stated ethical and legal concerns, the viability and efficacy of these solutions may differ depending on the context and industry. Thus, more research and case studies are required to evaluate the applicability and efficacy of these solutions in other circumstances. This research is mostly based on a literature review and may not represent the experiences or opinions of all stakeholders engaged in AI-powered marketing. Further study might involve interviews or surveys with marketing professionals, customers and other key stakeholders to offer a full knowledge of the practical difficulties and solutions. Because of the rapid speed of technical progress, AI’s ethical and regulatory ramifications in marketing are continually increasing. Consequently, this work should be a springboard for more research and continuing conversations on this subject.

This study’s findings have several practical implications for marketing professionals. Emphasising openness and explainability: Marketing professionals should prioritise transparency in their use of AI, ensuring that customers are fully informed about data collection and utilisation for targeted advertising. By promoting openness and explainability, marketers can foster customer trust and avoid the negative consequences of a lack of transparency. Establishing ethical guidelines: Marketing professionals need to develop ethical rules for the creation and implementation of AI-powered marketing strategies. Adhering to ethical principles ensures compliance with legal norms and aligns with the organisation’s values and ideals. Investing in bias detection tools and privacy-enhancing technology: To mitigate risks associated with AI in marketing, marketers should allocate resources to develop and implement bias detection tools and privacy-enhancing technology. These tools can identify and address biases in AI algorithms, safeguard consumer privacy and extract valuable insights from consumer data.

This study’s social implications emphasise the need for a comprehensive approach to address the ethical and legal challenges of AI in marketing. This includes adopting a responsible innovation framework, promoting ethical leadership, using ethical decision-making frameworks and conducting multidisciplinary research. By incorporating these approaches, marketers can navigate the complexities of AI in marketing responsibly, foster an ethical organisational culture, make informed ethical decisions and develop effective solutions. Such practices promote public trust, ensure equitable distribution of benefits and risk, and mitigate potential negative social consequences associated with AI in marketing.

To the best of the authors’ knowledge, this paper is among the first to explore potential solutions comprehensively. This paper provides a nuanced understanding of the challenges by using a multidisciplinary framework and synthesising various sources. It contributes valuable insights for academia and industry.

The service industry is facing the huge impact of digital transformation, in which artificial intelligence (AI) plays one of the most important roles. This study aims to expand the understanding of the AI acceptance framework and confirm whether consumers’ digital skills have a moderating effect on the research model.

Hypotheses were tested using a data set of 1,641 individuals. Partial least squares structural equation modeling and multi-group analysis were used to estimate the model.

The results indicate that antecedent factors influence consumers’ willingness to use AI devices in services. The two groups of different digitally savvy respondents differ because the influence of anthropomorphism, social influence and hedonic motivation on respondents’ perceived efforts to use AI devices in service delivery depends on respondents’ digital skills.

The novel contribution of this study is reflected in a comprehensive model that explains the moderating effect of individual digital skills on willingness to use AI devices. The attitudes of experienced and digitally skilled consumers are valuable and highlight some important theoretical, practical implications and future lines of research.

In the current post-human society, artificial intelligence (AI) and algorithms are rapidly being deployed in newsrooms around the world to enhance processes of news idea conception, newsgathering, writing, packaging and dissemination. Although AI adaptation has been ongoing especially in Western Newsrooms over the last decade, this process is only budding in sub-Saharan newsroom contexts. This study explores perceptions, use, prospects and challenges in the adaptation of AI and algorithms in newsrooms. This qualitative survey draws insights from 33 respondents from newspapers, radio stations, online media and community media in Uganda, Tanzania, Rwanda and Ethiopia. The study found varied levels of AI adoption in several newsrooms with some newsrooms not yet using AI while others were fully experimenting with a variety of tools, functionalities – even producing their own AI tools and also in change employment patterns to accommodate the skills needed within this new field. In some of the ‘inactive AI newsrooms’ individual journalists took the onus on themselves to learn and use the disruptive technologies and while the general attitudes towards AI were positive among journalists, the attitudes among management was generally considered poor. The study concludes for the benefits to be maximally leveraged, several of the bottlenecks in application must be addressed. These include the integration of ‘humans-in the loop’, journalistic principles, decolonial and local contextual perspectives in AI development and use. Such perspectives and synergies would need to be drawn from media ecosystems – including journalism education, research, policy, industry and developers.

This article reviews the quantitative marketing literature on artificial intelligence (AI) through an economics lens. We apply the framework in Prediction Machines: The Simple Economics of Artificial Intelligence to systematically categorize 96 research papers on AI in marketing academia into five levels of impact, which are prediction, decision, tool, strategy, and society. For each paper, we further identify each individual component of a task, the research question, the AI model used, and the broad decision type. Overall, we find there are fewer marketing papers focusing on strategy and society, and accordingly, we discuss future research opportunities in those areas.

Introduction: Artificial intelligence (AI) assists recruiters in effectively and efficiently nominating applicants precisely and accurately. It helps in the screening of resumes without biasness. This chapter will identify different AI technology and various organisations using it fully or partially.

Purpose: This chapter aims to get insights about various AI tools that assist human recruiters, save time and cost, and provide modern experiences. It will help identify various applications that are currently in use and their features. It also helps in finding out the benefits and the challenges faced by the recruiters and the applicants while assimilating those applications in hiring.

Need for the Study: The study will be helpful to all those recruiting firms who are presently using AI or not using it to understand the benefits and challenges they might face.

Methodology: The chapter will be based on reviews and industry reports. This chapter will include a study related to human resource (HR) functions where AI is used. To give more insights into AI technology, this study mentions various applications like Mya, Brazen, etc., and their usefulness in recruitment. Also, special emphasis would be given to the recruitment functions as most companies use AI. Some companies like Deloitte and Oracle are using AI fully or partially will also be incorporated.

Findings: The study finds out that although many companies have started to use AI tools for recruitment, they have not explored all the algorithms that can be used to complete the whole recruitment and selection process. Companies like Loreal use AI for candidate applications and recruiter screening, but human recruiters stand strong for assessments and interviews. AI’s widespread use presents human resource management (HRM) practitioners with both opportunities and challenges.

Practical implications: The basic idea of the study is to scrutinise the related literature and find out the features, advantages and limitations/challenges of using AI which would be helpful for recruiters in better understanding of the technology-driven recruitment.