Exploring the impact of influencers’ characteristics on the cosmetics purchase intentions of TikTok users

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The rapid expansion of social media has transformed marketing strategies, with influencer marketing emerging as a powerful tool for consumer engagement. Particularly in the cosmetics business, TikTok has become well-known as a major platform where influencers influence buying habits. Given the increasing reliance on influencer recommendations, understanding the key characteristics that drive purchase intentions is crucial for businesses aiming to optimize their marketing strategies. This study investigates the impact of TikTok influencers’ characteristics, such as physical attractiveness, trustworthiness, expertise, and interactivity, on Vietnamese consumers’ cosmetics purchase intentions. Grounded in social learning theory, the research explores how users observe and emulate influencers’ behaviors in their decision-making process. A mixed-method approach was adopted, beginning with qualitative interviews with 20 TikTok users to identify key influencer attributes affecting purchase decisions. This was followed by a quantitative survey of 312 Vietnamese TikTok users with prior experience purchasing cosmetics. The study employed statistical analyses, including correlation and regression tests, to determine the significance of influencer characteristics on purchase intention. The study revealed notable gender differences in cosmetics purchasing motivations, with women showing a higher propensity to purchase due to their greater engagement with these products. The attractiveness of influencers is the strongest predictor of purchase intention, followed by trustworthiness, and interactivity, whereas expertise does not play a critical role. These insights provide valuable implications for cosmetic brands, emphasizing the importance of selecting visually appealing influencers while maintaining credibility and audience engagement.

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    • Figure 1. Research model
    • Table 1. Summary of constructs and measurement items
    • Table 2. Demographic profile of respondents (n = 12)
    • Table 3. Words mentioned by interviewees in qualitative interviews
    • Table 4. Reliability of variables
    • Table 5. Compare mean results by gender
    • Table 6. Analyzing correlations between variables
    • Table 7. Model summary (method used: stepwise)
    • Table 8. Coefficients in regression analysis
    • Table 9. Excluded variable
    • Conceptualization
      Ta Huy Hung, Thi Mai Le
    • Data curation
      Ta Huy Hung, Van Huy Trinh
    • Formal Analysis
      Ta Huy Hung, Van Huy Trinh
    • Funding acquisition
      Ta Huy Hung, Thi Mai Le
    • Methodology
      Ta Huy Hung, Thi Mai Le
    • Supervision
      Ta Huy Hung, Thi Mai Le
    • Validation
      Ta Huy Hung, Thi Mai Le
    • Visualization
      Ta Huy Hung, Van Huy Trinh
    • Writing – review & editing
      Ta Huy Hung, Thi Mai Le
    • Software
      Van Huy Trinh, Thi Mai Le
    • Writing – original draft
      Van Huy Trinh, Thi Mai Le
    • Investigation
      Thi Mai Le
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    Acknowledgment
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