Similarities and differences between internet addiction and nicotine dependence —— motivation network

Motivation, encompassing positive reinforcement, negative reinforcement, and compulsion, significantly influences addictive behavior. Yet, there is little research available on these motivational dimensions and their connection to addictive behavior. This study addressed these gaps by investigating motivation in internet addiction (IA) and nicotine dependence (ND). Cross-sectional data from 472 nicotine users (NUs) and 1004 internet users (IUS) were examined using the Chinese version of the Habit, Reward, and Fear Scale. The Glasso network method was used to estimate the networks, and four interrelated centrality measures were calculated. IUs exhibited significantly lower total addiction motivation scores, as well as lower reward and fear dimensions, compared to NUs. In the Severity-Motivation Network, reward and fear motivations had the highest centrality. The habitual motivation of NUs demonstrated the highest closeness and betweenness centrality, showing a strong positive correlation with the degree of ND. The fear motivation of IUs exhibited a strong positive correlation with the degree of IA. In the symptom-motivation network, reward and habit nodes displayed higher centrality in NUs, whereas reward and fear nodes were most central for IUs. The findings indicate that reward, fear, and habit motivations are key factors in explaining behaviors associated with nicotine use and internet use. The unique positions of habitual motivation in ND and fear motivation in IA provide new perspectives for understanding addictive behaviors. These should be considered key motivational targets for clinicians to address. • The paper uses network analysis to explore addictive motivations, offering insights into the core mechanisms of addiction. • The paper emphasizes the distinct roles of habitual motivation in nicotine dependence and fear motivation in internet addiction, suggesting targeted treatments. • The study uses a relatively large sample size, which increases the robustness and generalizability of the results.