Engaging in Dialogues: The Impact of Comment Valence and Influencer-Viewer Interaction on the Effectiveness of YouTube Influencer Marketing

Abstract

YouTube is a popular social marketing platform. Marketers or advertisers can collaborate with a YouTube influencer to present marketing messages. However, negative user-generated comments may affect the effectiveness of message delivery. Thus, one pretest and two main studies were conducted to investigate the influence of negative comments on consumers and the strategy to combat the negativity. The first study examined the influence of comment valence on product attitude and perceived trustworthiness of influencer. The second study examined how the increased frequency of influencer–viewer interaction mitigated the damage inflicted by negative comments. The findings of the studies reveal that negative comments have a strong influence on consumers. However, if an influencer is actively replying to negative comments, the negative influence is likely to be mitigated. Theoretical and practical contributions of the studies were discussed.

Keywords:

• Influencer marketing
• negativity bias
• the MAIN model
• user-generated comments
• YouTuber

Notes

1 The results of the ANOVA tests in study 1 when familiarity constructs were not controlled for and when responses that contain misclassified comments were included.

Trustworthiness of influencer: F(2, 131) = 4.394, p < .05, η² = .055. The difference between the negative comment group (M = 5.498, SD = 1.22) and the no comment control group (M = 6.00, SD = .851) as well as the difference between negative comments group and positive comment group (M = 6.016, SD = .919) were significant (p < .05).

Product attitude: F(2, 131) = 21.480, p < .001, η² = .221. The difference between negative comment group (M = 4.875, SD = 1.77) and the no comment control group (M = 6.30, SD = .761) as well as the difference between negative comments group and positive comment group (M = 6.204, SD = 1.385) were significant (p < .001).

2 Additional one-way ANCOVA tests were conducted for study 1, in which gender and age were controlled for. Gender, F(1, 110) = .643, p = .424, and age, F(1, 110) = 1.856, p = .176, were not significant predictors of perceived influencer trustworthiness. Similarly, gender, F(1, 110) = .017, p = .898, and age, F(1, 110) = .286, p = .594, did not significantly influence product attitude. Hence, two factors did not affect the influence of comment valence on the two dependent variables.

3 Additional two-way ANCOVA tests were conducted for study 2 in which gender and age were controlled for. In the equation that examined the comment valence’s and interaction frequency’s influence on the dependent variables, gender, F(1, 192) = .186, p = .667, and age, F(1, 192) = 2.315, p = .130, were not significant predictors of perceived influencer trustworthiness. Similarly, gender, F(1, 192) = .013, p = .910, and age, F(1, 192) = .080, p = .777, did not significantly influence product attitude.