
doi: 10.2139/ssrn.3376904
Social media influencers are category enthusiasts who often post product recommendations. Firms sometimes pay influencers to skew their product reviews in favor of the firm. We ask the following research questions. First, what is the optimal level of affiliation (if any) from the firm's perspective? Affiliation introduces positive bias to the influencer's review but also decreases the persuasiveness of the review. Secondly, since affiliated reviews are often biased in favor of the firm, what is the impact of affiliation on consumer welfare? We find that the affiliation decision depends on the cost of information acquisition, the consumer's prior and awareness, and the disclosure regime. When the consumer's prior belief is low, the firm needs to affiliate less closely or not at all in order to preserve the influencer's persuasiveness, the change in the consumer's belief following the influencer's review. In contrast, when the consumer's prior belief is high, the firm fully affiliates with the influencer to both maximize awareness and prevent a negative review. We also show that the firm's involvement can be Pareto-improving if the information acquisition cost is relatively high, and a partial disclosure rule may increase consumer welfare.
| selected citations These citations are derived from selected sources. This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | 4 | |
| popularity This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network. | Top 10% | |
| influence This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | Average | |
| impulse This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network. | Average |
