Gaming disorder, also referred to as game addiction, has garnered increasing clinical attention and was officially recognized by the World Health Organization (WHO) in 2018. Previously categorized alongside behavioral addictions such as gambling, gaming disorder shares key characteristics, including compulsive engagement and persistent behavior despite adverse consequences. Psychological risk factors include high impulsivity, emotional dysregulation, and stress, often exacerbating mental health issues like depression and anxiety. Despite its global recognition, research on gaming disorder in Korean adults remains limited, leaving a gap in understanding how individuals exhibit traits associated with the disorder. This study aims to characterize the psychological traits of high-risk individuals for game disorder in Korea and compare them with low-risk individuals. Findings revealed that high-risk individuals are more prone to addictive behaviors such as internet addiction, binge eating, pathological gambling, and nicotine dependence, though not alcohol addiction. They were also characterized by higher impulsivity, lower self-control, and poorer emotion regulation, particularly a reduced use of cognitive reappraisal strategy. Furthermore, high-risk individuals reported elevated levels of stress, depression, and anxiety. These findings highlight potential risk factors for gaming disorder in adults and provide a foundation for developing targeted screening tools and early intervention strategies for at-risk individuals.

In this paper, we propose a new neural network architecture for item recommendation with structural information. Our model, structural neural recommender (SNR) is based on neural networks and operates on a hierarchy paradigm, aiming to explore the effectiveness of incorporating different structural information for recommendation. Many recent state-of-the-art neural network based recommendation models exploit the nonlinear transformations for modeling the complex user-item interaction patterns and user historical behaviors, ignoring the item-item structural relationship. This structural information, however, is uncomplicated to derive and useful for inferring item characteristics. To utilize this information, SNR simultaneously learns representation from user-item interactions and item-item relationships. Empirical studies on eight real-world datasets demonstrate the effectiveness of incorporating such structural information, by outperforming classic and recent baselines. We also conduct detail ablation studies and hyper-parameter analysis to provide further understanding towards the behaviors of our model. Following the model development, we conduct a field experiment to demonstrate that the effectiveness of algorithmic recommender systems can further increase by using different types of message framing when communicating recommendations to consumers. Our results suggest that recommendations framed with a relevance appeal (e.g. “Top 5 brands for you”) are more effective in general, yet recommendations that are framed with a popularity appeal (“Top 5 most popular brands”) are more effective for customers who were acquired via social media (versus non-social media) advertising or for those who have stronger (versus weaker) social orientation.