PBESProceedings of Business and Economic Studies2209-26412209-265XBio-Byword Scientific Publishing Pty. Ltd.10.26689/PBES.v9i9.15609ArticleInternal and External Drivers of Compulsive Buying in Digital Consumption Contexts and the Mediating Role of Contextual Factors: A Study of Consumer Behavior in Beijinghttps://artdesignp.com/journal/PBES/9/9/10.26689/PBES.v9i9.15609HouYuhan2026992026-09-30Digital consumption platforms reduce the friction of purchase decision-making through algorithmic recommendations, scarcity cues, payment convenience, and fragmented usage patterns, and also make some consumers more prone to uncontrolled purchasing under negative emotional states and specific contexts. This study takes 621 consumers with independent consumption capacity in Beijing as the research object and establishes a research and analysis framework of “internal susceptibility factors and the external consumption environment interact with contextual triggers, thereby contributing to compulsive buying behavior. It comprehensively uses reliability and factorability tests, the Kruskal-Wallis test, XGBoost regression and SHAP interpretation, Bootstrap mediation analysis, and context sensitivity grouping comparison. The results showed that the average total score of the sample compulsive buying was 49.995 (SD = 9.693), and 66.5% of the internal stratification of the study was in the middle group and 22.9% was in the high group; The nine internal factors showed significant differences between groups, and the shopping addiction model has the greatest effect (ε² = 0.104); Except for the non-significant difference in the atmosphere of live-streaming e-commerce, all other factors were significant, and the effect of fragmented leisure and consumption habits is the greatest (ε² = 0.096). The XGBoost model achieved an R² of 0.78 on the test set. Emotional dysregulation and impulse control, emotion regulation needs, advanced consumption and debt sensitivity, perfectionism and hoarding tendencies, and contextual triggers made substantial contributions to the model’s predictions. The three contextual triggers, browsing shopping apps when lonely or bored, frequently placing orders under stress, and making compensatory purchases after setbacks, formed strong indirect associations between most internal and external factors and compulsive buying. High-context-sensitivity people will be activated by the live atmosphere and algorithm recommendations, and low-context-sensitivity people rely more on the endogenous action of emotion regulation. Judging from the conclusion of the study, compulsive buying is not the product of a single personality factor or marketing factor, but the joint effect of internal susceptibility and external scenarios in concrete life scenarios.Compulsive buying, Digital consumption, Emotion regulation, Contextual triggers, SHAP

[1]  Faber RJ, O’Guinn TC, 1992, A Clinical Screener for Compulsive Buying. Journal of Consumer Research, 19(3): 459–469. [2]  Tang Q, Yang W, Liu X, et al., 2026, The Relationship Between Online Compulsive Buying and Internet Addiction: An Investigation on Their Phenomenological Overlap and Shared Mechanisms in Chinese University Students. Current Psychology, 45(8): 845. [3]  Solymos Á, Internet Financial Fraud and Security Awareness in Hungary. [4]  Zulauf K, Wagner R, 2022, Online Shopping Therapy: If You Want to be Happy, Shop Around. Journal of International Consumer Marketing, 34(3): 332–345. [5]  Verma T, Arora DS, 2025, Influence Without Intrusion: Decoding the Role of Digital Nudging in Shaping Online Decisions. Electronic Commerce Research, 1–40. [6]  Le MT, 2025, Psychological Drivers of E-Impulsive Buying: Investigating the Mediating Role of Loneliness Coping Strategies in Fintech Adoption Contexts. Electronic Government, an International Journal, 21(5): 523–545. [7]  Li M, Ma Q, 2025, The Effect of Social Media Fatigue on Compulsive Buying. Behaviour Information Technology, 44(7): 1429–1445. [8]  Dew J, Xiao JJ, 2011, The Financial Management Behavior Scale: Development and Validation. Journal of Financial Counseling and Planning, 22(1): 43–59. [9]  Fadilah N, Masarroh I, Wildan MA, 2025, Effectiveness of Recommendation Algorithms on Impulsive Buying in E-Commerce Platforms: A Systematic Literature Review. Priviet Social Sciences Journal, 5(8): 186–198. [10] Ridgway NM, Kukar-Kinney M, Monroe KB, 2011, The Measurement of Compulsive Buying and Its Application to Internet Buyers. In Compulsive Buying, 67–78, Routledge. [11] Li Y, Arham AF, Majid MZA, 2026, Consumer Perceived Value and Impulsive Buying in Social Commerce: The Moderating Role of Fear of Missing Out. Frontiers in Communication, 11: 1843640. [12] Aggarwal P, Vaidyanathan R, 2003, Use It or Lose It: Purchase Acceleration Effects of Time-Limited Promotions. Journal of Consumer Behaviour: An International Research Review, 2(4): 393–403. [13] Kumar A, Salo J, Bezawada R, 2024, The Effects of Buy Now, Pay Later (BNPL) on Customers’ Online Purchase Behavior. Journal of Retailing, 100(4): 602–617. [14] Woodruffe HR, 1997, Compensatory Consumption: Why Women Go Shopping When They’re Fed Up and Other Stories. Marketing Intelligence Planning, 15(7): 325–334. [15] Cao T, Prentice C, Wang Q, et al., 2025, Compensatory Consumption: A Review and Research Agenda Using the Theory-Context-Characteristics-Methodology Framework. International Journal of Consumer Studies, 49(5): e70112.