Why Do We Follow Virtual Influencer Recommendations? Three Theoretical Explanations from Brain Data Tested with Self-Reports

计算机科学 心理学 数据科学 认知心理学 人机交互 情报检索
作者
Anika Nissen,Colin Conrad,Isabella Seeber,Aaron J. Newman
出处
期刊:Journal of the Association for Information Systems [Association for Information Systems]
卷期号:26 (4): 1042-1080
标识
DOI:10.17705/1jais.00930
摘要

Virtual influencers have received significant recent research attention. Past work has investigated users’ perceptions of their human-likeness, uncanniness, trust, and ability to persuade. However, the findings are mixed, motivating new theoretical approaches and investigations into the antecedents of the typically utilized variables. We thus took an exploratory, inductive approach by conducting two neuroimaging experiments with complementary brain imaging techniques and then derived theoretical explanations based on the findings. We discovered three key antecedents that impact human and virtual influencer evaluations: (1) expectancy violation, (2) emotion, and (3) cognitive effort. To validate their explanatory power, we tested their effects on the intention to follow influencer recommendations using uncanniness, trust and distrust as serial mediators in a third behavioral study. Results confirm our interpretation of neural results and reveal three explanatory paths towards following intentions with human and virtual influencers: expectancy violation → uncanniness; emotion → trust/distrust; and cognitive effort → follow intentions. Given the lack of theorizing on expectancy violation, emotion, and cognitive effort in the existing research on virtual influencers, we offer a significant theoretical contribution to the field by showing how these features fundamentally predict further evaluations. Our results can guide design theories for the creation of virtual influencer accounts, help companies to better evaluate the predictors for successful virtual influencer marketing, and inform future information systems studies interested in taking an exploratory, inductive approach using neurophysiological data.
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