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Predictors of Consumer Trust among TikTok Affiliates: A Multiple Regression Analysis

Mikko Keanu Alterado Deziah French Bebanco Beyonce Louise Dalagonan Precious Arianne Grengia Leshly Paracullos Denniela Gold Ramos Alexia Summer Razonable Christine Dionne Sabio Vienna Yiesha Tan Almira A. Batulanon Joanne Diesca Earlynne H. Villegas

Oct 2026 · International Journal of Multidisciplinary Applied Business and Education Research · 0 citations

Abstract

e of the fastest growing social commerce platforms is TikTok, which allows affiliate marketers to reach consumers through short-form video, live-streaming and interactive content. There is increasing commercial use of TikTok, but there is little empirical evidence of factors that drive consumer trust in TikTok affiliate marketing in local Philippine settings. The objective of this study was to determine the influence of content quality, online customer reviews, and live-streaming engagement on consumer trust in TikTok affiliates in Digos City, Davao del Sur. The research design used was quantitative descriptive-regression design. Data were collected from 100 active TikTok users using a validated survey questionnaire. Data were analysed using descriptive statistics, Pearson correlation and multiple regression analysis. Results showed that respondents rated content quality (M = 4.20, SD = 0.70), online customer reviews (M = 4.13, SD = 0.48) and live-streaming engagement (M = 4.04, SD = 0.66) as high and consumer trust was rated very high (M = 4.24, SD = 0.52). All independent variables had a significant and positive relationship with consumer trust (p<.001). The multiple regression analysis showed that the model accounted for 57.8% of the variance in consumer trust (R^2 = .578). Online customer evaluations emerged as the most significant predictor (β =.432, p <.001) followed by live-streaming interaction (β =.292, p =.002) and content quality was not a significant predictor (β = .147, p =.122).

DOI Semantic Scholar

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