UTAUT predictors of online life insurance purchase intention in China: Optimism as a moderator

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Type of the article: Research Article

Abstract
Digital distribution is reshaping insurance markets, yet people remain cautious about purchasing complex, high-involvement life insurance products through online channels. Prior technology-adoption studies commonly apply UTAUT, but evidence on the relative importance of its core predictors in online life insurance and on whether optimism meaningfully conditions these effects in an emerging-market setting remains limited. This study addresses this gap by testing a UTAUT-based model of online life insurance purchase intention in Guangxi Province, China, and by assessing optimism as a potential moderator. Survey data from 707 responses were analyzed using partial least squares structural equation modeling (PLS-SEM). The measurement model demonstrated satisfactory model fit (SRMR = 0.026; NFI = 0.934). In the structural model, performance expectancy (β = 0.142, p = 0.001), effort expectancy (β = 0.205, p < 0.001), social influence (β = 0.030, p < 0.001), and facilitating conditions (β = 0.172, p < 0.001) each showed significant positive effects on behavioral intention, with social influence exerting the strongest impact. The model explained a substantial share of variance in intention (R² = 0.90). The results showed that performance expectancy, effort expectancy, social influence, and facilitating condition had significant positive effects on consumers’ behavioral intention to purchase online life insurance. However, none of the four proposed moderation hypotheses involving optimism were supported, indicating that optimism did not significantly moderate these relationships. Overall, the findings suggested that life insurance companies should focus on improving UTAUT predictors to strengthen customers’ purchase intention rather than enhancing customer optimism.

Acknowledgment
The study appreciates Professor Dr Mohamad Bin Bilal Ali for the grammatical advice.

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    • Figure 1. Research model and hypotheses
    • Figure 2. Structural model result
    • Table 1. Descriptive statistics of the full sample
    • Table 2. Reliability and validity results
    • Table 3. Model fit results
    • Table 4. Hypotheses testing
    • Conceptualization
      Shaohua Ben
    • Data curation
      Shaohua Ben
    • Formal Analysis
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    • Funding acquisition
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    • Investigation
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    • Methodology
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    • Project administration
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    • Resources
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    • Software
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    • Supervision
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    • Validation
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    • Visualization
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    • Writing – original draft
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    • Writing – review & editing
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