Artificial Intelligence in International Business: A Data-Driven Bibliometric Study

This research aimed to explore the impact of artificial intelligence (AI) on personalizing customer experience in international business and its benefits for companies in the global economy. It also evaluated AI's implications on supply chain management, focusing on efficiency and sustainability. Using a quantitative research approach and primary surveys with stakeholders in international business, the study revealed that AI significantly enhances supply chain management effectiveness through forecasting, financial operation optimization, and insight generation. AI technologies enable businesses to analyze vast amounts of data, accurately predicting customer preferences and behavior. This predictive capability enhances customer experience by providing personalized recommendations and services tailored to individual needs. For instance, AI can analyze purchasing patterns and browsing histories to suggest products that customers are likely to be interested in, thereby increasing the likelihood of purchase, and fostering customer loyalty. With AI and big data, businesses can develop precise opportunities for customer relationships, achieving higher market share and customer loyalty. AI-driven personalization strategies allow companies to target specific customer segments with customized marketing campaigns, improving engagement and conversion rates. Additionally, AI helps businesses understand and anticipate customer needs in different regions, allowing them to adapt their offerings to local preferences and trends. This regional adaptability is crucial in international business, where customer preferences can vary significantly across different markets. In supply chain management, AI's impact is profound. AI systems can optimize inventory management, predict demand fluctuations, and streamline logistics operations. By analyzing real-time data, AI can identify potential disruptions and suggest proactive measures to mitigate risks, ensuring a smooth and resilient supply chain. This capability not only enhances efficiency but also contributes to sustainability by reducing waste and improving resource utilization. In summary, AI plays a pivotal role in transforming customer experience and supply chain management in international business. By leveraging AI's predictive and analytical capabilities, companies can offer personalized services, enhance customer loyalty, and achieve higher efficiency and sustainability in their supply chain operations, ultimately benefiting the global economy.

The emergence of artificial intelligence (AI) has transformed global business, aiding operational efficiency and innovation. It utilizes machine learning and big data analytics, driving predictive market trends and strategic decision‐making. However, despite the rising discussion and accessibility of AI tools, understanding its impact on international business remains limited. This article explores AI's potential in international business strategies, practices, and activities. To address this aim, we reviewed 37 articles in the existing literature to critically explore AI within the context of international business. More specifically, we explored how AI can be applied to innovation approaches in international business, international market selection, entry modes, foreign exchange, international human resource management, international supply chains, managing across cultures, and more topics. AI has necessitated changes in workplace configurations and the need for organizational and employee adjustments in response to this technology. As a result of the foregoing issues on AI integration within international business, our analysis provided an exploratory discussion around its use, challenges, managerial implications, and suggested areas requiring future studies.

This perspective article explores how artificial intelligence (AI) is reshaping the theoretical foundations and strategic dynamics of international business (IB). We distinguish between predictive AI, generative AI, and agentic AI, examining how each transforms the behavior of multinational enterprises (MNEs). Moving beyond viewing AI as merely transactional or infrastructural technology, we conceptualize it as a cognitive substrate that transforms how MNEs learn, innovate, develop resources and capabilities, and make decisions across borders. By augmenting and partially substituting managerial cognition, AI reconfigures firm-specific and location-specific advantages, while challenging core assumptions of established IB frameworks and altering the governance of cross-border activities. AI also transforms global value chains through enhanced coordination, resilience, and transparency, yet it simultaneously introduces new risks related to algorithmic bias, sustainability, and dependency on digital infrastructures. The paper discusses the implications of AI adoption for MNEs and argues that existing IB theories may need to be complemented by new perspectives to account for AI disruption. The paper concludes by proposing a research agenda that integrates theoretical, strategic, institutional, and methodological insights for understanding AI’s transformative impact on IB.

AI-assisted detection and IoT application for employee engagement, 79 AI-connected consultation services and remote monitoring services delivered through AI devices, 53-54 AI-powered virtual learning assistants, 29 algorithms, 112 artificial intelligence-aided human resource management, 108 artificial intelligence-based people analytics transforming human resource management practices, 230 assist, 56-58, 63-64 challenges of AI application in HRM, 111-114 challenges of AI-enabled people analytics, 241-242 computer applications, 3 conceptual model for sustainable use of AI in HRM, 114-117 confluence of AI and HRM, 109-111 creating diverse team culture using, 33-34 effect, 3

This article analyzes the ethical challenges associated with the use of artificial intelligence (AI) in international business and evaluates different regulatory approaches to AI worldwide. The rapid development of AI offers significant opportunities for transforming business processes but simultaneously raises serious ethical questions about transparency, algorithmic bias, data protection, and accountability for AI-driven decisions. The paper examines the European Union’s AI Act, the U.S. AI Bill of Rights, and China’s strategy for achieving global AI leadership. It also explores the ecological aspects of AI development, particularly high energy consumption and AI’s potential in addressing environmental issues. The impact of AI on the labor market is analyzed, including structural changes and the need for employee retraining. The concept of prompt engineering as a key skill for effective interaction with generative AI is discussed. The conclusions emphasize the importance of ethical norms in global AI regulation and the necessity for cooperation among businesses, governments, and public organizations to ensure responsible AI development and implementation, highlighting the need for a balance between innovation and ethical considerations for sustainable AI development in international business.

Objective: The existing literature on artificial intelligence is reviewed with the aim of identifying interesting and novel research avenues. This leads to a focus on experiences, customisation, diversity and strategy as the main issues requiring further investigation. Research Design & Methods: A systematic literature review was conducted on articles related to artificial intelligence and business using various databases including Scopus, Web of Science and Google Scholar. Findings: Artificial intelligence is a hot topic particularly for international business managers who are incorporating technological innovation for competitive reasons. This article focuses on the link between artificial intelligence and international business in terms of future research opportunities. Implications & Recommendations: The results of this study will help business managers and practitioners understand the main trending areas related to artificial intelligence in a business setting. It is recommended that academic researchers and policy makers consider the future research suggestions stated in the article in order to help them derive new research directions. Contribution & Value Added: Potential research topics and questions are stated that help international business researchers and practitioners focus on key important areas. Thereby helping to consolidate the existing research but also offering new possibilities regarding the use of artificial intelligence in international business.

The expansion of the e-commerce sector beyond international boundaries has opened up new opportunities in international business. The first and most important problem for international e-commerce businesses is choosing the right products to sell. Scientific product selection is important not only for the smooth running of international e-commerce businesses, but also for the growth and development of thriving private labels. With development for Internet and rapid rise of e-commerce platforms, online shopping has become mainstream. While consumers are deeply involved, a large number of user comments appear on the Internet, and these online shopping comments often contain a lot of valuable information. Cross-border e-commerce can get useful suggestions and feedback about products through sentiment analysis of these texts. This can also find product problems, improve where they are not in place, and improve operational efficiency. This work studies the selection strategy of cross-border e-commerce products assisted by artificial intelligence technology. This paper uses artificial intelligence technology to conduct sentiment analysis of product reviews, which can help cross-border e-commerce companies make effective product selection. This work proposes CNN-BiGRU-ATT for sentiment polarity analysis of product reviews. First, the multi-channel convolutional neural network is utilized to extract local feature of different granularities. Then connect BiGRU for context sequence learning to memorize long-distance dependency information. This method not only solves the problem that CNN cannot perform context sequence learning, but also solves issue of gradient disappearance or explosion. Finally, attention is introduced after the BiGRU hidden layer to filter the important features.

The collaboration between artificial intelligence (AI) and humans is reshaping international business (IB) management dynamics, aiming to achieve global sustainable development. Recent IB literature indicates that managing AI brings benefits such as better resource reconfiguration, reduced transaction costs, and global sustainable development. However, existing IB literature provides only meager knowledge about the characteristics of AI and how these characteristics can be employed for international expansion at the intersection of sustainable development. In response, our aim is to construct these characteristics by employing directed qualitative content analysis of empirical AI research. Based on our three constructed characteristics of AI, we contribute to current IB literature by providing a framework to balance economic and social goals and utilizing AI for global sustainable development. Further, we provide future IB research themes to guide IB and AI research toward achieving a sustainable production and consumption agenda.

Artificial intelligence is poised to transform international business through the adoption of new digital technology practices as part of the fourth industrial revolution. The how, when, and why of artificial intelligence implementation in international business is still somewhat unknown but will likely significantly influence performance outcomes, market interpretation, risk prediction, consumer interactions, and technology‐based metrics. With greater emphasis on artificial intelligence‐based tools, the future practice of international business will require digital literacy. This means business managers need to improve the way they analyze artificial intelligence in terms of appreciating its usefulness while maintaining privacy and mitigation strategies. In this editorial, we discuss the current developments regarding artificial intelligence in international business with the goal of identifying future research areas of importance.

This paper presents and analyses the relevance of artificial intelligence for energy optimization companies and business internationalization, the mega-trended combination between digitalization and globalization impacts directly business internationalization. The methodology of research relied on a literature review and data analysis in quantitative qualitative way by focusing on a case study as well using bibliographic data from Scopus and operating the steps using VOSviewer to go further with research. The research question is” How relevant and connected or useful artificial intelligence for business internationalization, companies and energy optimization?”. The results show that embracing artificial intelligence could speed so far business operations and processes. Moreover, since the world is moving towards industry 5 and the economy of the future, artificial intelligence has become an important instrument for all the economic sector including energy that is related to business opportunities and companies’ internationalization and operations, energy is a business as well since it is about supply and demand and it always needs optimization and AI has been useful for that taking the example of smart grids, smart cities and houses, AI for renewable energy (solar and wind), industry and electrical vehicles. In conclusion, business internationalization relies on the use of digitalization that connects business operations and relationships. AI is important for business internationalization, companies and energy optimization.

Artificial intelligence (AI) and machine learning (ML) are transforming international business (IB) research by enabling the analysis of large-scale, multimodal data and uncovering patterns that drive theoretical and empirical advances. Yet, the methodological breadth and technical complexity of AI and ML pose significant challenges for many IB scholars. This paper offers a structured roadmap for integrating AI- and ML-based techniques into IB research. We review key methods, including supervised, unsupervised, generative AI, and multimoal approaches, and illustrate how they can enrich core IB constructs such as foreignness, legitimacy, internationalization strategy, corporate governance, distance, and deglobalization. In doing so, we highlight both opportunities and methodological challenges associated with integrating ML into IB research. By linking methodological innovation with conceptual advancement, this paper positions AI and ML not merely as analytical toolkits but as transformative forces reshaping the future of IB research.

The study of risk management in international business presents numerous essential aspects that complicate a thorough understanding and effective handling of risks. In the globalized world, companies face diverse risks that can significantly impact their operations. Political, economic, social, and legal factors become crucial elements shaping the business environment. The article aims to analyze risk management in international business, examining specific features of this area. It identifies key risks encountered by companies in the global market, including economic, political, legal, cultural, social, technological, and supply chain risks. Effective risk management in international business requires developing strategies for identifying, analyzing, and handling these risks, which helps companies minimize potential losses and ensure operational stability. The article also considers current trends shaping contemporary international business and notes that the future expansion and globalization of international business are closely linked to artificial intelligence (AI) adoption. The study finds that the widespread use of AI will bring substantial changes in employment structures and create new demands on employee qualifications, emphasizing the need for adaptation to new technologies. The study concludes that companies in international business face a wide spectrum of risks-economic, political, legal, social, and technological. Each risk type requires a detailed approach, as they vary across regions. Operating in globalized markets, international businesses experience economic interdependencies that present additional challenges for risk assessment and forecasting. Studying risk management in international business involves examining numerous interrelated factors that are dynamic and often unpredictable, making this task complex but essential for effective business operations. Effective risk management in international business involves developing strategies to identify, analyze, and manage these risks, helping companies minimize potential losses and ensure the stability of their operations.

This study aimed to provide an assessment of marketing on international business and to organize and classify the literature on marketing in order to clarify the importance of this tool and encourage future research in the projection of international business marketing (IBM) and the power of the ubiquity of this tool in the hands of the users. It is a bibliometric study, and this work focused on international business and marketing articles published in major international marketing, expansion, strategies, and business journals from 2000 to 2020; this topic is scattered across various journals and second data in many disciplines. The investigation reviewed and collected data on the specific paradigm of research that discusses, investigates, and evaluates the topic of IBM. The results showed a new paradigm applied to the topic of IBM and classified it into four first-order research streams: academic research, international business, international development strategies, and customers. The contribution of this study is important in order to identify new challenges and opportunities for marketers, researchers and international companies and in terms of disentangling existing contradictions, applying a new theoretical framework to make better future decisions by large companies, and providing a higher quality to new research in the context of IBM.

Purpose Artificial intelligence (AI) is expected to disrupt international businesses processes. However, given the complexity and inherent risks associated with AI investments, it remains debatable whether such projects offer tangible benefits to firms. In the global discussion, this paper aims to add an European perspective for international businesses by analyzing the value effects of AI project announcements by European companies. Design/methodology/approach This study is based on an event study methodology to test for abnormal market returns around the announcement of AI projects by European companies. The market perception is further controlled for differences in AI projects like additional strategic partnerships, industry-fixed effects and success in implementation. Findings Contrary to expectations, this event study finds no significant abnormal market reactions to AI project announcements by European companies on general. Project-specific characteristics also do not appear to meaningfully influence investor perception. However, the authors observe clear industry-specific effects: the automotive sector exhibits a comparatively positive market response, while pharmaceutical and telecommunications firms also show above-average abnormal returns around the announcement date. These findings suggest that investors evaluate AI initiatives in Europe through a sector-specific lens, resulting in diverging market reactions. Overall, the results further challenge assumptions about the uniformly US-centric valuation of AI announcements. Practical implications The findings suggest that European managers should align AI announcements with clear, sector-specific innovation strategies to enhance credibility and investor confidence. However, to ensure broader societal benefit, AI strategies must be inclusive, avoiding the concentration of value in select sectors. These insights highlight the need for context-sensitive approaches to AI communication and implementation across Europe independent from the US role model. Social implications The findings highlight the heterogeneous interpretation of AI projects across sectors in Europe. The sector-specific nature of investor reactions suggests that firms must adopt tailored AI communication strategies that resonate with the expectations and realities of their respective industries. This underscores the broader societal importance of fostering public trust in AI, not just as a technological innovation, but as a tool for meaningful and inclusive social and economic progress. Originality/value This study contradicts the common understanding of market perception on AI projects from the USA and adds a European perspective, which indicates that the value of AI projects differentiates geographically.

Universities that provide research opportunities and programs for students enhance the educational experience significantly. This enables students to investigate the impact of innovative thought processes through both study and experimentation. Consequently, students can leverage this experience to grasp the practical implications of research. Innovation and international business are essential for achieving competitive advantages in the current volatile business environment. Contemporary organizations actively seek innovation on both domestic and global fronts to bolster their competitiveness worldwide. As a result, the incorporation of innovation into the strategies of international enterprises has become a critical topic in modern discourse. This systematic investigation aims to enhance the comprehension of the relationship between innovation and international business, while also pinpointing emerging trends in innovation that companies must acknowledge to effectively address the opportunities and challenges arising from advancements in this sector within the global business context. Although there is a substantial body of literature on innovation and international business, thorough analyses that simultaneously explore both domains are still limited. This paper intends to examine various innovations integrated into the curriculum for students pursuing the specialty of 292 “International Business.” The findings indicate that business model innovation has become a vital component of contemporary business education. Therefore, university curricula should not only convey information but also undergo proactive transformation to reflect the evolving dynamics of different industries. By incorporating data analytics and Artificial Intelligence (AI) into the program, along with integrating case studies focused on problem-solving innovation, academic institutions will effectively align their educational offerings with industry requirements. The purpose of the article is to present the example of research proposal for dissertation on topic “Challenges and Opportunities of Artificial Intelligence Application in Effective Business Management in the Economy” must be written by students majoring for specialty 292 “International business” as an innovative component of the digital university program. The research findings have revealed that on completion of exemplified in the current research the proposal might trigger the formation of new research academic competencies possession of which will be able to provide a competitive advantage in the future professional employment withing certain business area. Moreover, besides numerous academic beneficial advantages for university students, research proposal is aimed to shape various professional skills, such as ability to collect necessary data, filter it, analyze, think critically and outside the box as wells as time management skills due to stated research deadlines and the abilities to organize individual research work personally. Additionally, the further prospects of the research are forecasted as ones in the area of developing writing scientific dissertation skills on specific business theme withing studied module