Haiyan Kong

As digital transformation continues to penetrate various sectors of society, the issue of the digital divide has become increasingly prominent. Against the backdrop of accelerating population aging, the barriers that older adults face in accessing and utilizing digital information have exerted a profound impact on their quality of life. This study employs tree-based ensemble learning algorithms to predict and identify the key factors of the digital divide that influence life satisfaction among older adults. It also evaluates the predictive performance of these models, thereby providing interpretive insights into the impact of the digital divide on subjective well-being. Using original data from the ‘2023 Report on Digital Information Divide Survey’ conducted by the National Information Society Agency of South Korea, this study constructs an analytical framework that integrates both predictive capability and interpretability. First, the XGBoost model is employed to conduct feature importance analysis, identifying 15 key variables that are highly influential in predicting life satisfaction. These variables are further examined using the SHAP method to provide interpretive insights into their contributions. Subsequently, multiple tree-based ensemble learning algorithms—including Random Forest, XGBoost, LightGBM, and CatBoost—are applied to compare their predictive performance. The results indicate that variables related to technological self-efficacy, digital information literacy, social capital, experience and perception of AI services, and household monthly income are significant predictors of life satisfaction among older adults. Among the models tested, CatBoost demonstrates superior overall predictive accuracy, suggesting its effectiveness in forecasting life satisfaction in this demographic. This study expands the application of machine learning in areas such as aging research and the digital divide and proves the effectiveness of ensemble learning algorithms in predicting digital divide factors that affect the life satisfaction of older adults. This approach provides a novel and powerful methodological for addressing complex social problems. Moreover, the study uncovers the structural configuration of key digital information factors associated with life satisfaction, offering data-driven insights into the mechanisms through which the digital divide influences well-being. These results have practical implications for enhancing digital inclusion, improving adaptability among older adults, and fostering a stronger sense of participation and happiness in digital society.

In the digital era, businesses are actively integrating advanced technology and innovative marketing strategies to achieve sustained growth. Notably, virtual endorsers play a key role in driving enterprises towards digital transformation in the field of digital marketing. Understanding consumer attitudes towards the use of virtual endorsers in digital marketing is especially important for enterprises employing digital tools to realize a sustainable business model. To this end, this research adopts the match-up hypothesis and source credibility model as its theoretical framework, delving into the impact of virtual endorser–product fit and credibility features (attractiveness, expertise, and trustworthiness) on product attitudes, as well as the interactive effects of these features with product types. We collected feedback data from 376 participants through an online questionnaire and validated our hypotheses using the PLS-SEM model. The results demonstrate that virtual endorser–product fit positively affects credibility, with higher credibility further enhancing consumers’ attitudes toward products. Additionally, the source credibility model partially mediates the relationship between the match-up hypothesis and product attitudes, with attractiveness exerting the most significant impact. Finally, we observed variations in consumer attitudes toward products endorsed by virtual endorsers based on product types. The findings of this study provide a solid theoretical basis for a deeper understanding of consumer attitudes towards the application of virtual endorsers in marketing and offer practical suggestions for businesses to leverage digital tools for sustainable development.

Objective: Despite previous research identifying factors such as age, education level, income, and interest in technology that influence digital literacy among the elderly, this study attempts to use machine learning algorithms, especially ensemble learning algorithms, to predict and identify the key factors that affect the digital information literacy of the elderly, so as to propose effective strategies to improve the elderly's ability to utilize digital information and better integrate into the digital society. Methods: This study used primary data on older adults from the Digital Divide Survey 2022 conducted by the Korea National Information Society Agency. A predictive model was built, and 15 variables that were highly important in predicting digital information literacy were identified. Prediction accuracy was assessed using an ensemble of algorithms including Random Forest, LGBM, XGBoost, AdaBoost, and CatBoost. Results: The study found that in addition to demographic factors and personal technology use ability factors, relationship support factors and social digital environment factors are also important predictors of digital information literacy for the elderly. Among different predictive models, the CatBoost model, based on boosting ensemble, exhibited the highest predictive accuracy at 86.2%, followed by Random Forest (85.5%), LGBM (85.2%), XGBoost (84.5%), and AdaBoost (83.8%). The predictive accuracies of these models were higher than those of traditional machine learning models, indicating the effectiveness of ensemble learning algorithms in predicting digital information literacy among the elderly. Conclusions: The academic significance of this study lies in the application of artificial intelligence technologies to the social sciences, specifically demonstrating the effectiveness of ensemble learning algorithms in predicting factors influencing the digital literacy levels of the elderly. This approach provides a novel and powerful tool for addressing complex social issues. The practical significance lies in the proposed strategies for improving the digital literacy of the elderly based on the research results, including education and training, social relationship support, social participation, technical support, and policy formulation, aiming to help the elderly better adapt to the digital environment, narrow the digital divide, and enhance the elderly's sense of participation and happiness in the digital society.

The advent of the Fourth Industrial Revolution and the spread of infectious diseases have increased people’s reliance on information and communication technology (ICT). However, older adults are the most marginalized group affected by ICT, and their activities and daily life have become more difficult than ever during the pandemic. Based on the theory of the TAM, this study analyzed the effect of ICT use on life satisfaction among older adults during the pandemic, and examined the mediating role of social capital. The partial least squares structural equation modeling (PLS-SEM) was applied to test the relationships among ICT use, social capital, and life satisfaction, and the Sobel test was used to test the mediating effect and statistical significance of social capital. The results of the analysis confirmed the positive effects of technology’s perceived usefulness and self-efficacy on attitudes and ICT use among older adults. ICT use had a positive effect on life satisfaction. In addition, it was found that social capital had a mediating effect on the relationship between ICT use and life satisfaction. During the pandemic, ICT use not only had a direct effect on life satisfaction but also had an indirect effect through social capital among older adults. The results of this study have implications for research on ICT use and life satisfaction among older adults, and have implications for formulating public policies for active aging. The results of this study can facilitate a comprehensive understanding of the antecedents and consequential factors of the use of digital information among older adults, which not only complements previous studies but also provides a theoretical basis for systematic research on digital literacy and well-being in older adults.