Given the multifaceted nature of crime trends shaped by a range of social, economic, and demographic variables, grasping the fundamental drivers behind crime patterns is pivotal for crafting effective crime deterrence methodologies. This investigation adopted a systematic literature review technique to distill thirty key factors from a corpus of one hundred scholarly articles. Utilizing the Principal Component Analysis (PCA) for diminishing dimensionality facilitated a nuanced understanding of the determinants deemed essential in influencing crime trends. The findings highlight the necessity of tackling issues such as inequality, educational deficits, poverty, unemployment, insufficient parental guidance, and peer influence in the realm of crime prevention efforts. Such knowledge empowers policymakers and law enforcement bodies to optimize resource allocation and roll out interventions grounded in empirical evidence, thereby fostering a safer and more secure societal environment.

The rapid rise of live streaming commerce in China has transformed the retail environment, with electronic word-of-mouth (eWOM) emerging as a pivotal factor in shaping consumer behavior. As a digital evolution of traditional word-of-mouth, eWOM gains particular significance in live streaming contexts, where real-time interactions foster immediacy and engagement. This study investigates how eWOM influences consumer purchase intentions within Chinese live streaming platforms, employing the Information Adoption Model (IAM) as theoretical framework. Using a grounded theory approach, this research applies NVivo for data coding and analysis to explore the cognitive and emotional processes triggered by eWOM during live streaming. Findings indicate that argument quality, source credibility, and information quantity significantly enhance consumer trust and perceived usefulness of information, which, in turn, drives information adoption and purchase intention. Furthermore, the study reveals that social interaction between live streaming anchors and audiences amplifies the influence of consumers’ internal states on information adoption. This study enhances the Information Adoption Model (IAM) by introducing social interaction as a moderator between consumers’ internal states toward live streaming eWOM and their adoption of information, highlighting the value of social interaction in live streaming. It also incorporates information quantity, showing how eWOM quantity affects trust and perceived usefulness. Furthermore, the study contributes to exploring how factors like argument quality, source credibility, and information quantity shape consumer trust and perceived usefulness, offering insights into the cognitive and emotional processes of information adoption in live streaming.

The advent of Artificial Intelligence (AI) has transformed Learning Management Systems (LMSs), enabled personalized adaptation and facilitated distance education. This study employs a bibliometric analysis based on PRISMA-2020 to examine the integration of AI in LMSs from an educational perspective. Despite the rapid progress observed in this field, the literature reveals gaps in the effectiveness and acceptance of virtual assistants in educational contexts. Therefore, the objective of this study is to examine research trends on the use of AI in LMSs. The results indicate a quadratic polynomial growth of 99.42%, with the years 2021 and 2015 representing the most significant growth. Thematic references include authors such as Li J and Cavus N, the journal Lecture Notes in Computer Science, and countries such as China and India. The thematic evolution can be observed from topics such as regression analysis to LMS and e-learning. The terms e-learning, ontology, and ant colony optimization are highlighted in the thematic clusters. A temporal analysis reveals that suggestions such as a Cartesian plane and a league table offer a detailed view of the evolution of key terms. This analysis reveals that emerging and growing words such as Learning Style and Learning Management Systems are worthy of further investigation. The development of a future research agenda emerges as a key need to address gaps.

Women’s financial literacy and financial inclusion have gained prominence in recent years. Despite progress, knowledge and access to finance remain common barriers for women, especially in emerging economies. Globally, domestic and economic violence has been recognized as a relevant social concern from a gender perspective. In this context, financial literacy and financial inclusion are considered to play a key role in reducing violence against women by empowering them with the necessary knowledge to manage their financial resources and make informed decisions. This study aims to evaluate the determinants that influence Peruvian female university students’ financial literacy and financial inclusion. To this end, a theoretical behavioral model is proposed, and a survey is applied to 427 female university students. The results are analyzed using a Partial Least Squares Structural Equation Model (PLS-SEM). The results validate all the proposed hypotheses and highlight significant relationships between financial literacy and women’s financial inclusion. A relevant relationship between financial attitude and financial behavior is also observed, as well as the influence of financial behavior and financial self-efficacy on financial literacy. The results also reveal that women feel capable of making important financial decisions for themselves and consider that financial literacy could help reduce gender-based violence. Based on these findings, theoretical and practical implications are raised. It highlights the proposal of a theoretical model based on antecedents, statistically validated in a sample of women in Peru, which lays the foundation for understanding financial literacy and financial inclusion in the Latin American region.

With the rapid development of artificial intelligence (AI) technology, its application in the field of auditing has gained increasing attention. This paper explores the application of AI technology in audit risk assessment and control (ARAC), aiming to improve audit efficiency and effectiveness. First, the paper introduces the basic concepts of AI technology and its application background in the auditing field. Then, it provides a detailed analysis of the specific applications of AI technology in audit risk assessment and control, including data analysis, risk prediction, automated auditing, continuous monitoring, intelligent decision support, and compliance checks. Finally, the paper discusses the challenges and opportunities of AI technology in audit risk assessment and control, as well as future research directions.

Since 2013, the state has introduced a number of policies to strictly control the number and scale of public hospitals and to control the rapid expansion of public hospitals. After the introduction of this series of policies, the number of public hospitals in China did not continue to grow, but the number of beds in public hospitals continued to grow. This paper uses difference-in-difference (DID) method to analyze the number of public hospitals with the corresponding data of the development of private hospitals after the introduction of the policy, and the results proves that the introduction of relevant policies has an impact on the number of public hospitals, but has a limited impact on the expansion of the scale of public hospitals. At the end of the article, positive policy suggestions are given to the development of hospitals in China, such as controlling the expansion of public hospitals, strictly controlling the number of beds in public hospitals, and vigorously developing private hospitals. Promoting the development of private hospitals is an important economic supplement to China’s health care.