The role of AI face-swapping technology anxiety in negative eWOM contagion: SEM and fsQCA findings

Abstract

Negative electronic word of mouth (eWOM) is a critical barrier to the diffusion of new technologies. Understanding the mechanisms underlying negative eWOM contagion can assist new technology providers in mitigating the effects of such contagion. From a theoretical perspective, how negative eWOM contagion towards artificial intelligence (AI) face-swapping technology occurs warrants further investigation. Accordingly, this study adopted the stressor–strain–outcome framework as a basis to develop a research model for investigating the mechanisms underlying negative eWOM contagion towards AI face-swapping technology. A total of 500 valid survey responses were obtained from Chinese users of AI face-swapping technology; the responses were analysed through a combination of structural equation modelling (SEM) and fuzzy-set qualitative comparative analysis (fsQCA). The SEM results indicated that negative eWOM affected users’ perceptions of the intrusiveness of AI face-swapping technology, their perceptions of its authenticity, and their technology anxiety. Additionally, both perceived intrusiveness and perceived authenticity influenced technology anxiety. The effects of technology anxiety and perceived intrusiveness on negative eWOM behaviour, in the absence of perceived authenticity, were also confirmed. Furthermore, AI awareness moderated the effects of negative eWOM on perceived intrusiveness and technology anxiety but did not moderate the effects of negative eWOM on perceived authenticity. The fsQCA results revealed three conditional configurations associated with negative eWOM behaviour, which complemented the SEM results.

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