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All Journal Journal of Artificial Intelligence and Development Journal of Conflict and Social Class (JCSC)
Yitian Zhang
Accounting, UW-Madison, WI, USA
Humanities Computer Science & IT Environmental Science Mathematics Social Sciences Other

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Community Service-Based Accounting Education for Financial Awareness Development in the United States Yitian Zhang
Journal of Conflict and Social Class (JCSC) Vol. 1 No. 02 (2024): Journal of Conflict and Social Class (JCSC)
Publisher : CV Edujavare Publishing

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70610/jcsc.v1i02.1287

Abstract

The rapid development of global economic systems and digital financial technology has increased the importance of accounting literacy and financial awareness in modern society. Accounting education is considered an essential strategy for improving financial management skills, economic responsibility, and sustainable community development. This community service program aims to strengthen financial awareness through community service-based accounting education in the United States. The program employed a qualitative participatory approach involving students, workers, small business owners, and community members through workshops, mentoring activities, accounting simulations, and collaborative learning sessions. Participants were introduced to budgeting techniques, bookkeeping principles, financial reporting systems, digital accounting technologies, and ethical financial management practices to improve accounting competence and responsible financial behavior. The results demonstrated that participants experienced significant improvement in accounting literacy, budgeting competence, financial recording abilities, and confidence in managing financial information systematically. In addition, collaborative educational activities encouraged active participation, problem-solving skills, and awareness regarding financial transparency and accountability. Despite challenges related to technological access, educational diversity, and varying levels of accounting understanding, continuous mentoring and participatory educational approaches contributed positively to the implementation process. Overall, community service-based accounting education provided valuable opportunities for improving financial awareness, strengthening economic resilience, and supporting sustainable community development in the United States. The educational approaches implemented in this program can also serve as important references for developing accounting literacy and financial empowerment programs in other educational and social contexts worldwide.
A Personalized Causal Inference Framework for Media Effectiveness Using Hierarchical Bayesian Market Mix Models Xin Ni; Yitian Zhang; Yanli Pu; Ming Wei; Qi Lou
Journal of Artificial Intelligence and Development Vol. 3 No. 1 (2024): Journal of Artificial Intelligence and Development
Publisher : Edujavare Publishing

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70610/jaid.v3i1.546

Abstract

This study presents a novel framework for personalized causal inference in media effectiveness using Hierarchical Bayesian Market Mix Models (ABM). The proposed approach integrates individual-level data with aggregate market information to estimate personalized media effects while addressing the challenges of data sparsity and high dimensionality. By combining the identity layer and the optimization process in a Bayesian hierarchical model, the model captures heterogeneity across consumers and provides robust predictions of individual causality. Affect different media. The framework is used for e-commerce business data, which includes 500,000 customers across 50 markets in 24 months. The model shows better prediction performance than the integrated business model, with a 30.4% reduction in RMSE. Empirical results reveal significant heterogeneity in media effectiveness across channels and consumer segments. Email marketing emerges as the most effective channel on average, followed by TV advertising, digital display ads, and social media engagements. Sensitivity analyses and robustness checks, including alternative prior specifications and placebo tests, support the validity of the estimated causal effects. The findings provide valuable insights for media planning and marketing strategy, highlighting the importance of tailored budget allocation and campaign design approaches. This research contributes to the growing body of literature on personalized marketing analytics and offers a powerful tool for estimating individualized media effects in complex marketing environments.
Co-Authors Ming Wei Qi Lou Xin Ni Yanli Pu