Early Warning Model and Its Application Research on the Alienation of Online Consumption of Higher Vocational Students

Abstract

The advent of digital technology has significantly influenced consumer behaviors, especially among college students. This paper delves into the phenomenon of alienation in online consumption among higher vocational students in China, a demographic particularly vulnerable to the allure of virtual retail therapy. Drawing on data collected from five vocational colleges in Jiangxi Province, the study employs a mixed-methods approach, combining quantitative surveys with qualitative interviews to paint a comprehensive picture of students' consumption habits. The emergence of blind consumption, over-consumption, and a propensity for luxury items is evident, driven by a complex interplay of social pressures, emotional needs, and easy access to credit. The paper introduces an Early Warning Model (EWM), designed to detect early signs of consumption alienation and trigger timely interventions. The model integrates various data points, including consumption patterns, social influences, emotional well-being, and financial health, leveraging machine learning algorithms for predictive analytics. Real-life case studies demonstrate the EWM’s efficacy, showcasing its capacity to facilitate positive behavioral change and promote financial literacy.[1] The model not only serves as a crucial tool for educators, counselors, and parents but also lays the groundwork for policy interventions and educational programs aimed at nurturing responsible digital consumers. This research contributes to the existing body of knowledge on online consumption behaviors, providing a targeted analysis of higher vocational students and introducing an innovative solution to mitigate the risks associated with consumption alienation.