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Paul T. K. Lin’s Hsinhua Photo Collection Welcome rally at Be Dinh Hall in Hanoi for visiting Chinese party and government officials Photoprint Hsinhua News Agency (新華通訊社) 1971-3-10 Number: 386155/0105 中國黨政代表團訪問越南 越南首都河內市各界人民代表，三月六日晚在巴亭會堂舉行極其隆重的盛大集會，最熱烈地歡迎周恩來同志為團長、葉劍英和邱會作同志為副團長的中國黨政代表團。圖為黎筍同志在會上致開幕詞。 Chinese party and government delegation visits Viet Nam…Read more ›

Big-tech firms such as Alphabet (Google), Amazon, Apple, Meta (Facebook), Microsoft, NVIDIA, and Tesla are leading the development and commercialization of artificial intelligence (AI) by leveraging their strategic partnerships to access data, talent, and technical resources. These partnerships enable AI innovation and market expansion among big-tech firms, accelerating their dominance. This paper shares the results of a systematic literature review (SLR) of 74 papers to examine the motivations, operational practices, and challenges of big-tech AI partnerships. The findings highlight three key insights: first, AI partnerships are primarily formed to acquire strategic resources, reduce costs, and enhance reputation; second, big-tech firms rely on existing networks and complementary strengths, raising concerns about governance frameworks and power imbalances; and, third, smaller firms face tensions related to dependency, data control, and ethical considerations, requiring careful negotiation and governance mechanisms. Highlighting the understanding of these partnerships using the AI tech-stack frameworks of big-tech firms, and analysing polarities such as dominance versus dependency, this paper advances theoretical perspectives on strategic partnerships in AI ecosystems. It also highlights practical implications for partner managers navigating the changing power dynamics in AI strategic partnerships. The paper concludes with research gaps, including the need for research on decision-making tools for practitioners. We hope these insights support practitioners and academics to better understand the evolving role of big-tech strategic partnerships in shaping their unique AI ecosystems.

Stratcomm (a): Jess B

Robust assessment of artificial intelligence (AI) models in medical imaging is paramount for reliable clinical integration. This international collaborative review paper provides an overview of key evaluation metrics across diverse tasks, including classification, regression, survival analysis, detection, and segmentation, as well as specialized metrics for calibration, foundation models, large language models, and synthetic images. Challenges of comparing models statistically and translating metric scores to clinical practice are also discussed. For each section, the paper outlines fundamental metrics, identifies common pitfalls and misapplications, and offers recommendations for more robust evaluations. Key recommendations often involve utilizing multiple, complementary metrics tailored to the specific task and dataset properties, transparent reporting of methodology, and critically, considering the clinical utility and real-world implications of model performance. Ultimately, effective evaluation requires a comprehensive, context-aware approach that goes beyond statistical metrics to ensure model trust and clinical relevance. The authors hope this review will serve as a practical reference for researchers aiming to implement robust and clinically meaningful AI evaluations in medical imaging. • This review outlines the key metrics for evaluating medical imaging AI. • Common pitfalls and misapplications are critically examined, with corresponding recommendations provided for each. • Appropriate metric selection depends on the specific AI task. • Foundation and generative models require broader evaluation methods, beyond traditional evaluation metrics. • A multi-metric, context-aware evaluation is essential for reliability.

This concluding chapter reflects on the transformative journey through artificial intelligence (AI) adoption in small businesses and entrepreneurship, emphasizing the need for continuous learning, ethical implementation, and adaptability in an AI-driven world. It highlights key takeaways from previous chapters, reinforcing the practical, strategic, and ethical considerations necessary for successful AI integration. The chapter underscores AI’s role as a catalyst for business growth, not merely as a tool for automation but as an enabler of innovation, enhanced decision-making, and competitive advantage. Readers are encouraged to embrace AI as a strategic partner, leveraging its capabilities to augment human intelligence rather than replace it. Additionally, the discussion delves into future AI trends, urging businesses to remain agile, adaptable, and forward-thinking in their AI strategies. A significant focus is placed on ethical AI governance, addressing critical issues such as data privacy, algorithmic bias, transparency, and accountability. Entrepreneurs are provided with practical steps to ensure responsible AI deployment, fostering trust among customers, employees, and stakeholders. Finally, this chapter calls upon entrepreneurs to engage in lifelong learning, build AI-driven networks, and contribute to a responsible AI ecosystem. AI is not a one-time implementation but a continuous journey of exploration, adaptation, and ethical stewardship—one that will shape the future of business, innovation, and entrepreneurship.

In an era of rapid digital transformation, small and medium-sized enterprises (SMEs) are increasingly leveraging artificial intelligence (AI) and predictive analytics to enhance operational efficiency, optimize supply chain management, and improve overall business performance. This study explores the integration of AI-driven solutions in SMEs, focusing on their impact on demand forecasting, logistics optimization, and predictive maintenance. Using a mixed-methods approach, both quantitative and qualitative data were collected from 250 SMEs across various industries, supplemented by in-depth interviews with key decision-makers. The findings reveal that AI implementation leads to significant improvements in cost reduction (30% in logistics), operational efficiency (20% in inventory turnover), and predictive accuracy (15% improvement in maintenance planning). Despite these benefits, SMEs face challenges such as high initial investment costs, lack of AI expertise, and data management complexities. The study identifies best practices for overcoming these barriers, including phased AI adoption, strategic partnerships, and employee training programs. Additionally, the research highlights sector-specific AI applications, demonstrating its effectiveness in retail, e-commerce, and manufacturing industries. This study contributes to the growing discourse on AI adoption in SMEs by providing empirical evidence of its role in business optimization. It underscores the necessity of integrating AI into SME operations to remain competitive in a data-driven economy. The findings offer valuable insights for business leaders, policymakers, and technology providers, advocating for the development of AI frameworks that align with SME scalability and resource constraints. Future research should explore AI’s long-term impact on financial performance and sustainability, as well as its potential applications in emerging markets

Startups face both unprecedented opportunities and complex challenges in entering and sustaining themselves in global markets. Limited resources, market uncertainties, cultural diversity, and rapidly changing consumer demands often hinder their ability to make informed strategic decisions. Artificial Intelligence (AI) is emerging as a transformative enabler, offering data-driven insights, predictive analytics, and intelligent automation to overcome these barriers. This study explores the role of AI in empowering startups to expand and thrive in international markets. It examines how AI-driven tools—such as market intelligence platforms, customer behavior analytics, financial forecasting, and supply chain optimization—enhance decision-making, reduce risks, and foster innovation. Furthermore, the paper highlights AI’s capacity to personalize global marketing strategies, optimize resource allocation, and facilitate cross-border scalability. By integrating AI into their business models, startups can gain a competitive edge, accelerate internationalization, and adapt to dynamic global trends. The findings suggest that AI is not merely a support tool but a strategic partner, redefining how startups engage with global ecosystems and positioning them for long-term sustainability. Keywords: Artificial Intelligence (Ai), Startups, Global Markets, Market Expansion

The efficiency of various contemporary processes depends highly on the implementation of artificial intelligence (AI) in the numerous tasks. While there is a significant set of benefits that AI brings to the table and while the results of AI integration can be a boost in productivity and launch of innovations, there is a set of issues that has to be taken into account. Moreover, AI and cyber space are combining the plethora of opportunities for many countries and adjusting the situation globally. The research work of this paper aims to undertake an analysis to unveil how realism principles inform policy choices of Pakistan particularly the opportunities and threats that result from the contemporary Cold War like competition between the United States and China within the digital frontier. Thus, this research aims to increase understanding of Pakistan’s strategies and responses in relation to and on the basis of global cyber-geopolitics, including trends, strategic partnerships, and projections for the future. To this end, it seeks to map out the contingency at work in this fast-moving context and offer a sense of the systemic processes underpinning it, while shedding light on Pakistan’s place in this emerging global order

Artificial Intelligence (AI) has emerged as a transformative force across industries, with notable applications in sports coaching. Its capabilities, ranging from machine learning to real-time feedback systems, enable coaches to process complex data and enhance decision-making. This study investigates the integration of AI into sports coaching, emphasizing its potential to revolutionize training efficiency, optimize athlete performance, and reduce injury risks. However, AI implementation still faces challenges such as technical complexity, budgetary constraints, data privacy and ethical considerations, and psychological resistance. Research Objective: The purpose of this study is to explore the efficient use of AI in sport coaching, to propose strategies to address the challenges encountered in implementation, and to provide implementation guidelines for sport organizations. Research Methodology: This paper provides an in-depth analysis of the application of AI in data analysis and training adjustments, injury prediction, and personalized training regimens through literature review and case studies, and proposes effective implementation strategies in combination with actual cases. Findings: The study found that AI technology has significant advantages in enhancing training science and effectiveness, such as real-time data analysis tools and machine learning models that can improve training personalization and science, and AI-driven biomechanical analysis and injury prediction systems that can help to reduce the risk of injuries and improve safety for athletes. However, technical complexity, financial pressures, and data privacy concerns limit their use in small and medium-sized organizations. Research Conclusion: Successful implementation of AI technology in sports coaching requires a multi-layered strategy that includes ongoing training, strategic partnerships, and phased introduction. Future research should focus on combining with emerging technologies such as virtual reality and augmented reality, as well as ensuring the sustainability of the technology in long-term application, realizing deep integration with coaching expertise, and building a comprehensive and efficient training system.

Can technology become a strategic partner in human resource management? Traditionally, the HR function was perceived primarily as an administrative process relying on manual operations. However, the digital era, as in many other domains, has transformed the nature of HR management. The growing pace of global change has compelled organizations to adapt to technological innovation. The new era has fundamentally reshaped organizational approaches to work processes, creating an environment in which digital tools play a central role. Technological development has brought forth the need for process optimization. Consequently, HR management practices have gradually shifted from paper-based administrative operations toward a digital ecosystem. The introduction of Human Resource Information Systems (HRIS) has automated numerous HR functions, increasing efficiency and facilitating data-driven decision-making. Over time, the logical continuation of HRIS evolution became the integration of Artificial Intelligence (AI), which further enhanced and diversified HR processes. The synergy of these technologies has elevated HR management to a new analytical and data-driven dimension. With the growing economic and organizational influence of AI, its integration has become a transformative force across industries. The primary aim of this article is to explore the strategic significance of automation and artificial intelligence in human resource management. It examines the development of HRIS systems, their functional capabilities, and algorithmic integrations that ensure operational efficiency and data-based decision-making. Additionally, the paper presents international case studies illustrating the practical applications of HRIS and AI technologies in global organizations. Keywords: Artificial Intelligence (AI), Human Resource Information System (HRIS), automation

In China’s transitional economy, one of the major objectives of the government is to maintain social stability. We hypothesize that, through state ownership and appointment of executives, Chinese government officials can influence firms’ labor employment decisions by limiting layoffs when firms’ sales decline. Consistent with this hypothesis, we find that state-owned enterprises (SOEs) have stickier labor costs than non-SOEs, and the presence of politically connected managers makes labor costs even stickier in SOEs while having little effect in non-SOEs. Such effects are stronger in regions with weak market institutions and during time periods when government officials are to be promoted. We also show that the government reciprocates SOEs’ sticky labor policies with subsequent subsidies. This paper was accepted by Suraj Srinivasan, accounting.

The Effects of Political and Fiscal Incentives on Local Government Behavior: An Analysis of Fiscal Slack in China

This research challenges conventional approaches to artificial intelligence (AI) talent acquisition by investigating the substantial gap between traditional recruitment strategies and the actual distribution of AI-capable talent in the workforce. Analysis of professional data covering more than 3 million individuals reveals that the pool of professionals with substantive AI capabilities is approximately 20 times larger than those with explicit AI job titles. Using a multi-signal identification framework, we demonstrate how universities with strong technical foundations produce graduates who apply sophisticated AI skills across diverse roles without formal AI designations. The paper identifies key universities leading this hidden talent production, geographical distribution patterns with significant regional disparities, and considerable compensation differentials for equivalent skills. Evidence-based recruitment strategies for accessing this broader talent pool are presented, including skill-based talent identification, strategic university partnerships, and internal capability development approaches. Case studies from organizations that have successfully leveraged the hidden AI talent pipeline provide actionable frameworks for implementation. The findings suggest that organizations focusing exclusively on candidates with traditional AI credentials are missing substantial opportunities to build robust AI capabilities through this overlooked talent ecosystem. Limitations related to data collection, temporal constraints, and causal inference are acknowledged.

Tibetan self-immolation is an agonizing way to die that is at once an action and a series of actions. As a form of protest, it demands the attention of diasporic Tibetans, those in the PRC, and of activists, reporters, and news consumers around the world. It provokes boilerplate responses from Chinese news media and government officials. Some ambivalently describe it as futile because it has not altered People's Republic of China (PRC) policy in any way for the better. On the one hand, Elliot Sperling praises Chinese dissident Wang Lixiong for having "sought to articulate a way for Tibetan protests to move beyond self-immolation" ("On the Questions"). On the other, Sperling notes the ways in which these wretched acts have indeed been effective: "They have galvanized Tibetan sentiments and greatly strengthened the Tibetan sense of unity in the face of rule by China." Has it had any effect? To answer this question, we must distinguish between several different audiences. The struggle between Tibet and China is a battle fought on many fronts, and the Tibetan self-immolation movement is a fire that has spread from the initial location to daily newspaper columns, and, even more rapidly, to our digital screens.KeywordsPolitical TheatreSoft PowerCultural AutonomyStateless PersonDigital ScreenThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Chinese technology executives, government officials, and members of its research community are debating how far to push the country's strategy for promoting technology standards at home and abroad.