This article focuses on how cultural identity shapes people’s reaction towards AI. This article specifically argues that differences in individualism and collectivism is the reason for why AI is adopted and trusted at different rates in certain countries. The results have shown that in individualistic cultures, like the US, people see AI as something separated from themselves, and it has a chance to violate their rights of autonomy and privacy. But in collectivist cultures countries like China or India, people would be more willing to view AI as some kind of extension to themselves, and they tend to believe it can help maintain societal harmony and achieve common goals. The article also mentions that culture difference can influence people’s decision-making, data sharing, and privacy in the aspect of the use of AI. For example, individualist societies tend to care more about privacy and the ability for self-control, while collectivism has a higher acceptance of AI systems that emphasize consensus and social connections. The authors use cross-culture support for these view points. The research shows that different cultures have differences in AI trust and self information disclosure. 

This article specifically discusses the various factors that can affect the application of AI technology in hospitals. It mainly focuses on people’s trust in AI. They are worried that their personal privacy will be leaked, and about whether the medical staff has received training. All these will determine whether AI can truly be accepted by everyone. This is in line with the research topic of our group. It makes me more clear that AI can develop not only on whether the technology is good enough, but also on whether society accepts it and whether there are relevant policies to support it. This article uses many real cases from hospitals, making people feel particularly credible. However, its limitation might be that the conclusions within it may not be applicable to AI applications in other places, such as schools.

Filgueiras focuses on how authoritarian and democratic countries affect AI development by looking at 30 different countries and finding that authoritarian regimes generally have a better and stronger AI outcome. The author credits this to be because of the  centralized decision-making, fewer regulation constraints, and greater control over data on the contrary of democracy having checks and balances and slower policy making. Though authoritarian AI development is stronger, democratic countries are more ethical and have better characteristics for AI governance. Filgueria’s work is important because this goes beyond education and economics. These regimes are important when adding depth to our project since we can weigh out what societal factors really are the best, but at what cost. This article also gives us a good research group as it looks at 30 different countries that overlap with those in our data base which is important when making cross comparisons. 

Hassan, Kushniruk, and Boritsky reviewed the factors influencing AI applications in real-world healthcare settings. Their core conclusion highlights that beyond algorithms, critical factors include, social and organizational trust driven by explainability, transparency, and verifiable performance.Also clear governance and oversight, robust data integration capabilities, workflow adaptability and usability,  leadership support, and staff training. Fundamentally, whether AI transitions from pilot projects to practical implementation hinges on human and institutional factors. Lack of trust, regulation, and skills will lead to stagnation in adoption and hinder scalability. This conclusion resonates with findings from other healthcare and industry studies, providing valuable cross-validation. It is important to note that this study is limited to the healthcare sector and focuses on thematic analysis rather than causal verification. Therefore, caution should be exercised when extrapolating these conclusions to other industries.

This article examines how socioeconomic factors can shape national AI development, which directly relates to our research question. Lazǎr et al. argue that one main cultural dimension has an effect that is statistically significant. While economic metrics like GDP per capita, GDP growth, spending on research and development, and urbanization all had significant positive effects on AI development (which they use the quantitative variable called “AI intensity” to measure), of the cultural factors, only uncertainty avoidance was significant. Uncertainty avoidance describes how comfortable a society is with ambiguity and risk. This study offers direct evidence that can be used in our project, and signals a key starting point for which economic and cultural predictors to examine first. This also complements other literature in the field that emphasizes the consideration of societal factors in addition to technological ones in national AI development. One potential limitation which is also acknowledged by the authors is the limited sample size of only 60 countries, which makes it more difficult to find statistically meaningful relationships, especially when the differences are more nuanced. 

This article studied the different AI strategies and governance models that are designed and adopted by national governments. Radu examines strategies from 2016-2019 and concludes that hybrid AI governance models are the most popular. In other words, both public and private actors play a key role in AI policymaking. She also finds that states tend to prioritize ethics-oriented frameworks while instituting a purposeful vagueness about responsibilities in AI governance. While this is crucial for flexibility, she finds that accountability is weakened as well. This article contributes to the discourse on how to responsibly govern AI, especially with the lack of rigidity in the current governance. It also provides context for how to analyze different national strategies and their attempts to balance both innovation and social responsibility. While this also serves as a strong foundation and offers insights to methodologically analyzing something that might be considered subjective, its main limitation is its sample size, which mainly considers developed nations.

This article uses data from over 60 countries in the Global AI Index to analyze which ones are the key drivers for the development of AI. Their research mentioned that how much money is invested in education, the maturity of the technology industry, whether the government provides policy support, and whether the digital infrastructure is solid are all the main reasons affecting the development of AI. This answers the question of which conditions of our group can help AI move forward and which will hold it back. Its greatest advantage is that it is supported by real data, and the conclusions it draws make me feel reliable. It mainly showcases the general attitude towards AI at the national level through big data. There are still many invisible factors that can affect the development of AI.

This article explores the main factors that affect how organizations adopt AI systems. Khanfar et al. group these factors into five categories: individual, social, organizational, environmental, and technological. Using a systematic review of 91 studies, they identify the most common influences and challenges across industries. The article is important because it provides a structured understanding or what drives or limits AI use in practice. This shows that adoption depends as much on people and institutions as it does on technology itself. For our thesis, it gives us a useful framework to connect national and organizational readiness. It highlights that successful AI development requires coordination between technological capability, leadership, and cultural support. It also helps us show that awareness and perception play a huge role in influencing how AI is embraced across different contexts.

This article examines how different governments design and justify their national AI strategies through the lens of public values. Hjaltalin and Sigurdarson analyze 28 strategy documents and find that governments tend to emphasize values such as efficiency, transparency, innovation, and fairness, but to varying degrees depending on their political systems. The authors argue that these differences show how countries balance technological progress with ethical and social responsibility. This article is important because it highlights the connection between governance, political culture, and the direction of AI development. For our thesis, it adds a crucial dimension by showing that AI policy reflects national priorities and value systems, not just economic goals. It provides us with evidence for a potential argument that how a society governs AI is a reflection of what it values most, whether that is innovation, accountability, or public trust. 

Yu, Yang, Xu, and Pan’s 2024 study, published in Applied Sciences, focuses on the application of AI video generation tools in the video production industry. Through expert interviews and a questionnaire survey of Chinese video practitioners, it identifies non-technical factors, such as market demand, collaborative efficiency, AI acceptance and trust, institutional environment, corporate training, and process adjustments, that directly determine whether tools can be implemented beyond the demonstration phase. This research provides concrete answers from the entertainment and film industry to the question “Which social factors most effectively drive AI development?” Specifically, clear market and institutional incentives, organizational learning and collaboration mechanisms, and positive user attitudes collectively propel the large-scale adoption of AI, driving technological advancements in turn. While the study’s samples and scenarios are concentrated within China’s video industry, requiring caution in extrapolation, its findings offer significant reference value.