Article Per Year (5 Year)
p-Index From 2020 - 2025
1.037
P-Index
This Author published in this journals
All Journal Forest and Society IJIIS: International Journal of Informatics and Information Systems Journal of Applied Data Sciences Journal of Digital Market and Digital Currency
Shuang Li
Wuhan University
Agriculture, Biological Sciences & Forestry Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Environmental Science Social Sciences

Published : 5 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 5 Documents
Search

 1 
How government-public collaboration affects individual mitigation responses to flooding: A case study in Yellow River Delta area, China Xie, Lei; Wang, Yijie; Li, Shuang
Forest and Society Vol. 7 No. 2 (2023): NOVEMBER
Publisher : Forestry Faculty, Universitas Hasanuddin

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24259/fs.v7i2.22601

Abstract

In the top-down Chinese political system, flood management has traditionally been led by the government, with the general public playing a supporting role. Within this context, individual-level disaster prevention behaviors are strongly interacted with the government-public collaboration during the government-led flood management processes. This study aims to provide a comprehensive understanding of how government-public collaboration affects individuals’ flood mitigation responses in China. An online survey data with 550 respondents from the Yellow River Delta area was examined with regard to the individuals’ willingness to take positive mitigation actions, and ordinal logistic regression models were constructed to explore the influence of the government-public collaboration factors, which are digested into three aspects: public involvement, public awareness and political trust, that motivate individuals to take flood mitigation measures. The results demonstrate that public involvement and political trust are positively correlated with the likelihood of individuals’ adopting positive mitigation actions, while public awareness and self-reported preparedness were also positively correlated, although to a less significant degree. This study contributes to the current literature by increasing the understanding of how government-public collaboration determines individual mitigation actions in the Chinese collectivist cultural environment. The results of this study reveal that involving the public effectively and earnestly through various forms of community engagement are likely to promote individual-level disaster prevention behaviors, from this point of view, can help policymakers to guide local residents towards taking responsible flood risk management and preventative actions.
Quantitative Analysis of Educational Techniques for Psychological Development in Vocational Students in China Li, Shuang; Sangsawang, Thosporn; Thepnuan, Narumom; Pigultong, Matee; Punyayodhin, Sulaganya; Darboth, Kanokwan
Journal of Applied Data Sciences Vol 5, No 1: JANUARY 2024
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v5i1.173

Abstract

The research of objective were to: 1) examines environment, educational system, teacher-student relationship, self-awareness, and other aspects affecting Chinese vocational school students' psychological quality., and 2) development Psychological Quality for Vocational School Students in China Model for address unique psychological challenges and foster personal development in vocational education. Populations and sampling group were stents tests 7,000 Zigong, Rong County, and Dujiangyan vocational and technical students. The questionnaires used a percentage- based scoring standard, with a score below 50 indicating “strongly disagree,” 51 to 70 indicating “neutral,” 71 to 90 indicating “moderately agree,” and 91 to 100 indicating “strongly agree.” Data processing affects Zigong, Rong County, and Dujiangyan Chinese vocational school students' mental health. Statistical percentage of students picking each option. Guttman half coefficient was .802 after Split-Half Method testing of the data, indicating good split-half reliability and internal consistency. The questionnaire reveals how survey questions, sample size, and data processing affect Chinese vocational school students' mental health. The questions asked Zigong, Rong County, and Dujiangyan vocational and technical school students about mental health. 4,768 people completed 6,458 surveys. After deleting 97 low-reliability questionnaires with similar answers to seven consecutive items, 4,671 were valid. The Countermeasure Developing Model in China enhances the psychological quality of vocational school students by implementing multi-level therapy, methodical mental health education, and a supportive learning environment.
Policy Optimization Recommendation Algorithm Based on Mapping Network for Behavior Enhancement Shan, Linlin; Jiang, Guisong; Li, Shuang; Zhao, Shuai; Luo, Kunjie; Zhang, Long; Li, Yi
Journal of Applied Data Sciences Vol 3, No 3: SEPTEMBER 2022
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v3i3.67

Abstract

The algorithm of policy optimization with learning behavior enhancement based on mapping network technology was proposed, aiming to address the issues of lack and sparsity of learning behavior data and weak generalization ability of the model in AI education. Based on the basic recommendation algorithm and the framework of rein- forcement learning, and model introduces the correlation mapping network to realize the transformation of strong and weak correlation, so as to optimize the input agent policy to improve the performance model of course recommendation. Experiment on MOOC da- tasets show that the proposed algorithm model has a stable improvement compared with the baseline models, and can effectively improve the accuracy of course recommendation.
Data-Driven SEO Strategy Optimization to Enhance MSME Sales Performance on Indonesian E-Commerce Platforms Sangsawang, Thosporn; Li, Shuang
International Journal of Informatics and Information Systems Vol 8, No 3: September 2025
Publisher : International Journal of Informatics and Information Systems

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/ijiis.v8i3.262

Abstract

The rapid growth of digital commerce in Indonesia has created both opportunities and challenges for Micro, Small, and Medium Enterprises (MSMEs) seeking to increase their online visibility and sales. This study presents a data-driven approach to Search Engine Optimization (SEO) strategy optimization aimed at enhancing MSME sales performance on leading Indonesian e-commerce platforms, including Tokopedia and Shopee. Using a quantitative design, the research integrates Microsoft Excel for preliminary data exploration and Google Colab (Python) for advanced analysis and predictive modeling. The dataset, comprising over 1,000 transaction entries, includes key SEO-related indicators such as keyword rank, website traffic, backlinks, social media engagement score, advertising spend, and monthly sales. Ensemble regression models—Random Forest and Gradient Boosting—were employed to evaluate the predictive relationship between SEO factors and sales outcomes, validated through RMSE and R² metrics. The findings indicate that advertising expenditure (r = +0.83), backlinks (+0.29), and social media engagement (+0.25) are the most influential predictors of sales performance, while website traffic shows a weaker positive correlation (+0.13). These results highlight the critical role of integrated SEO and digital advertising strategies in improving MSME competitiveness. The study demonstrates that accessible analytical tools can empower MSMEs to make data-driven marketing decisions. Future research should expand model generalization across industries and explore additional digital variables to improve predictive accuracy.
Predicting Customer Conversion in Digital Marketing: Analyzing the Impact of Engagement Metrics Using Logistic Regression, Decision Trees, and Random Forests Li, Shuang; Pigultong, Matee
Journal of Digital Market and Digital Currency Vol. 2 No. 3 (2025): Regular Issue September 2025
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jdmdc.v2i3.38

Abstract

This research explores the impact of engagement metrics on predicting customer conversion rates within digital marketing, employing three advanced predictive modeling techniques: Logistic Regression, Decision Trees, and Random Forests. Using a comprehensive dataset of 8,000 customer interactions, the study evaluates critical engagement metrics such as PagesPerVisit, TimeOnSite, and EmailClicks to determine their influence on conversion outcomes. The results indicate that PagesPerVisit and TimeOnSite are the most significant predictors of customer conversion, with the Random Forest model outperforming others, achieving an accuracy of 87.1% and an ROC-AUC score of 0.6979. The Logistic Regression model demonstrated the highest recall for the conversion class at 99.8%, but its performance in predicting non-conversions was less robust, highlighting the challenges of imbalanced datasets. Decision Trees, while offering valuable interpretability, showed a lower accuracy of 79.6% and struggled with precision in identifying non-conversions. These findings suggest that enhancing on-site customer engagement and refining email marketing strategies are pivotal for improving conversion rates. The study contributes to the field of digital marketing analytics by providing empirical evidence on the relative importance of various engagement metrics and offering practical insights for optimizing digital marketing strategies. Additionally, it highlights the benefits of using ensemble methods like Random Forests to achieve more balanced and accurate predictions in customer conversion scenarios.
Co-Authors Darboth, Kanokwan Jiang, Guisong Luo, Kunjie Pigultong, Matee Punyayodhin, Sulaganya Sangsawang, Thosporn Shan, Linlin Shuai Zhao, Shuai Thepnuan, Narumom Wang, Yijie Xie, Lei Yi Li Zhang, Long