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Despite extensive research on AI-enabled services, most studies have focused on behavioral and adoption intentions, often overlooking factors influencing continued usage. The post-adoption behavior of AI-enabled services remains underexplored. This study addresses this gap by developing and validating a comprehensive model that integrates parasocial relationship theory, trust-commitment theory, and key constructs from the Unified Theory of Acceptance and Use of Technology 2 (UTAUT2) and the Service Robot Acceptance Model (sRAM). Data from 356 users in Oman, a developing country, provide insights into AI-enabled service acceptance in daily life. The findings reveal that performance expectancy, hedonic motivation, and social influence positively impact engagement and continuance intentions, with perceived accuracy and innovativeness also playing significant roles. Additionally, the study establishes the moderating effect of perceived privacy risk on several hypotheses, thereby advancing the understanding of AI service continuance.
Salih et al. (Tue,) studied this question.
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