Journal of Artificial Intelligence and Digital Economy

With the vigorous development of the digital economy, the traditional tax methods are difficult to adapt to the virtual digital trading activities, resulting in the problem of mismatch, and thus causing the related issues of the erosion of the fiscal tax base. By identifying the basic characteristics of the virtual, scale and interconnection of digital economic activities and combining the tax elements, this paper finds that there are problems such as unclear taxpayer, unclear tax object, and difficulty in defining the tax rate, which have caused the erosion of the tax base in many aspects, and the transnational activities have intensified the contradiction of the erosion of the tax base. As a result, the sources of government revenue are damaged, the gap between regional fiscal potential energy is further divided, and the space finance is vacant. Furthermore, it proposes to identify independent taxpayers, add resident offices, and actively integrate into the international tax system based on the independence of taxpayers and transaction businesses.

This paper stands at a key position in merging two prevailing trends within the healthcare public administration context: digitalization and integrated care (IC). These initiatives are introduced as solutions to solve challenges connected with the administration of chronic and multi-morbid conditions, which constitute a significant portion of healthcare expenditures in developing nations, including Namibia. In pursuit of these aims, the objectives are to navigate the digital frontier; we shall identify the obstacles hindering the development of the Namibian e-health strategy's digital health platform ecosystem (DHPE) and proffer recommendations for addressing these impediments. Ultimately, we aspire to establish an innovative DHPE-STS (Socio-Technical Solutions) that will proficiently direct the future of the Namibian e-health strategy. The prevalent fragmentation in service delivery, connected with rapid technological advancements, contributed to the inefficiencies in service delivery. To alleviate fragmentation, IC models have been implemented in developed nations and stand to significantly benefit from the advent of evolving electronic health platform solutions ecosystems (EHPs). Still, these interventions are relatively complicated and suffer from a lack of comprehensive analysis. Accordingly, this study examines these emerging solutions through an integrative literature review and a qualitative analysis, identifying 27 comprehensive platform solutions that facilitate coordination within chronic care ecosystems and develop innovative DHP oriented towards socio-technical considerations for the Namibian eHealth strategy. The findings provide an in-depth overview of the prevalent barriers and gaps associated with the 27 platform solutions examined, alongside a consolidative synthesis that conceptualizes socio-technical solution architectures, thereby integrating the components of people, processes, and technology within a multi-level IC framework. This clarifies the difficult orchestration required for managing cross-provider solutions in chronic care and enhances the understanding of researchers and decision-makers regarding the complexities and challenges inherent in healthcare transformation. Furthermore, development barriers and gaps warranting further research are also scrutinized.

With the booming development of Artificial Intelligence (AI) and digital economy, their deep integration is becoming a key engine for future economic growth. The purpose of this paper is to systematically analyse the current development status of AI and the digital economy, look forward to the integration trend of the two, and predict the future far-reaching impact at the economic and social levels. Firstly, this paper outlines the core application areas of AI technology and the basic development pattern of the digital economy at this stage, and analyses the roles of both in driving industrial upgrading and promoting efficiency improvement. Secondly, this paper focuses on the huge potential brought by the integration of AI and the digital economy, which is specifically reflected in the intelligent upgrading of the manufacturing industry, the optimisation of risk control and personalised services in the financial industry, the efficient scheduling of resources in the service industry, as well as the refinement of management and decision-making support in the construction of smart cities. Finally, this paper looks forward to the future integration of AI and the digital economy, and discusses its broad space in promoting high-quality economic development and improving social governance. At the same time, this paper also points out the data privacy, technical barriers, employment structure adjustment, ethical and legal challenges that may be faced in this integration process, and puts forward corresponding countermeasure suggestions to provide reference for promoting the deep synergy and healthy development of AI and digital economy.

With the rapid innovation of the global manufacturing industry, smart manufacturing has gradually become an important driving force to improve production efficiency and optimise product quality. Smart manufacturing not only integrates advanced automation, data analysis and optimisation technologies, but also promotes the innovation and upgrading of the manufacturing model. In the smart manufacturing system, artificial intelligence (AI) technology plays a crucial role, and its wide application is injecting new vitality into the traditional manufacturing industry, making the production process smarter, decision-making more forward-looking, and quality control more accurate. Through data-driven analysis and prediction, AI technology is able to identify and optimise bottlenecks in the production process, helping to realise a highly automated production model that reduces resource consumption and improves overall competitiveness. This paper focuses on specific application cases of AI technology in smart manufacturing, in-depth assessment of its effectiveness in improving production efficiency and product quality, and discusses the far-reaching changes brought by AI technology to the manufacturing industry and the challenges it faces, so as to provide a reference for the further development of smart manufacturing in the future.

With the rapid development of Artificial Intelligence (AI) technology, the Financial Technology (FinTech) industry is undergoing unprecedented changes.The innovative applications of AI in the financial sector cover all aspects from intelligent risk control to intelligent investment, automated financial services and customer experience optimisation, which have greatly improved the efficiency, accuracy and security of financial services. By analysing the current situation and development trend of AI in fintech and exploring its profound impact on the financial industry, this paper reveals the key role of AI in promoting personalised, automated and intelligent financial services. Specifically, the AI-driven intelligent risk control system effectively improves the accuracy of risk identification and management, enabling financial institutions to assess customer credit risk faster and more accurately; the intelligent investment consulting system provides users with customised investment advice and optimised asset allocation solutions through data mining and machine learning, which significantly improves the return on investment; and in terms of the automation of financial services, AI technology facilitates the automation of financial services such as the JPMorgan Chase's contract review system COIN and other applications, which not only improves operational efficiency, but also effectively reduces labour costs and error rates; in terms of customer experience optimization, Citibank optimizes its customer communication strategy through AI-driven sentiment analysis, achieving a double increase in customer satisfaction and loyalty. By summarising these real-life cases and application data, this paper further analyses the effectiveness and challenges of AI application in the financial industry, especially in terms of data privacy and security risks, the balance between technology and regulation, and the shortage of technical talents. In the future, with the maturity of AI technology and its in-depth application in financial compliance and privacy protection, AI is expected to become a new engine for the development of fintech and provide a broader prospect for the intelligent transformation of the global financial industry.

Due to the acceleration of global urbanisation, the construction of smart cities has become a key means of addressing the challenges of population density, resource scarcity and environmental stress. As the core technology of smart cities, the application of Artificial Intelligence (AI) in urban management, public safety, transport and energy management has dramatically improved the efficiency of city operations and service levels. For example, Singapore's smart traffic management system has significantly alleviated traffic congestion by using AI to analyse real-time traffic data and optimise signal control; China's ‘Skynet Project’ uses AI-driven face recognition and surveillance systems to effectively improve public safety; and San Francisco's smart grid system combines AI algorithms to dynamically deploy power resources, significantly reducing carbon emissions. San Francisco's smart grid system combines AI algorithms to dynamically deploy power resources, significantly reducing carbon emissions and improving the efficiency of energy management. Although AI applications show great potential in the construction of smart cities, the challenges of data privacy protection, system integration, security, and the formulation of policies and regulations still exist. The solutions proposed here are to enhance data security by promoting technical means such as data encryption and access control; to establish unified technical standards and protocols to ensure compatibility and interoperability between different systems; to adopt smart sensors and IoT devices to ensure accurate and real-time data collection as well as the government's gradual refinement of relevant laws and regulations. By analysing the above cases, this paper delves into the key challenges in the construction of smart cities and proposes feasible solutions to provide reference for the healthy and sustainable development of smart cities.

The emergence of the digital economy has reshaped the market competitive environment for enterprises, and has given a strong impetus to the transformation and upgrading of enterprises in the direction of intelligence. With the deep integration of information technology and business, enterprises are reshaping their value chains and innovation modes to achieve the transformation from ‘traditional’ to ‘intelligent’. Taking the definition of digital economy and the background of enterprise intelligence as the starting point, this paper analyses in depth the motivation, path choice and main challenges of enterprise intelligent transformation. With the success of Haier Group in increasing productivity from 15 per cent in 2018 to 35 per cent in 2022 within five years as well as the decline in the bad debt rate of Ping An's financial business in China, from 3.5 per cent in 2018 to 2.0 per cent in 2022. Strategic recommendations are made based on the practice, aiming to provide theoretical guidance and practical reference for the intelligent transformation of enterprises in the context of the digital economy era.