The impact of personalized advertising on impulse shopping behavior on Tiktok

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Type of the article: Research Article

Abstract
In today’s fast-evolving e-commerce ecosystem, personalized advertising utilizing user data such as preferences, behaviors, and demographics to deliver customized ad content has emerged as an essential tool for businesses aiming to connect with customers more effectively. The purpose of this study is to explore the influence of personalized advertising on consumers’ impulse buying behavior on TikTok in Vietnam. It investigates both direct and indirect effects through mediators – emotions, advertising value, perceived novelty and perceived relevance – and explores the moderating roles of self-control and privacy and security concerns. A quantitative approach was adopted using an online survey of 330 Vietnamese TikTok users aged 18-40 who had previously purchased via TikTok Shop. Measurement scales were adapted from prior studies and assessed on a five-point Likert scale. Data analysis employed SPSS 25 and AMOS 24, incorporating Cronbach’s alpha reliability tests, exploratory factor analysis (EFA), confirmatory factor analysis (CFA), structural equation modeling (SEM), and moderation analysis using Hayes’ macro. Personalized advertising has a significant direct effect on impulse buying behavior and indirect effects via emotions, advertising value, and perceived relevance. Emotions emerged as the strongest mediator, followed by advertising value and perceived relevance. While personalized advertising positively influences perceived novelty, novelty does not significantly affect impulse buying. The moderating role of self-control is negative, reducing the impact of emotions on impulse buying, whereas privacy and security concerns have no meaningful moderation effect. These insights enhance our understanding of the factors driving impulse buying behavior. This helps to suggest strategies for managers to optimize personalized advertising, ultimately improving marketing efficiency and encouraging customer purchases on the TikTok platform.

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    • Figure 1. Proposed research model
    • Figure 2. CFA analysis results
    • Figure 3. Results of SEM linear structural model testing
    • Figure 4. Simple 2×2 slope data plot – using plotting techniques
    • Table 1. Descriptive statistics of the sample
    • Table 2. Cronbach’s Alpha reliability test and KMO coefficient
    • Table 3. EFA rotation matrix
    • Table 4. Test of discriminant validity between research concepts
    • Table 5. Results of causal relationship testing between factors
    • Table 6. Mediating variable test results
    • Table 7. Results of testing moderator variables
    • Table A1. Measurement scales
    • Data curation
      Nguyen Ngoc Quang
    • Formal Analysis
      Nguyen Ngoc Quang
    • Investigation
      Nguyen Ngoc Quang
    • Resources
      Nguyen Ngoc Quang
    • Software
      Nguyen Ngoc Quang
    • Validation
      Nguyen Ngoc Quang
    • Writing – original draft
      Nguyen Ngoc Quang
    • Conceptualization
      Thuy Dao Cam
    • Methodology
      Thuy Dao Cam, Ngo Thuy Dung
    • Project administration
      Thuy Dao Cam
    • Supervision
      Thuy Dao Cam, Ngo Thuy Dung
    • Visualization
      Thuy Dao Cam
    • Writing – review & editing
      Thuy Dao Cam, Ngo Thuy Dung
    • Funding acquisition
      Ngo Thuy Dung