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        <datestamp>2026-10-01T05:43:21Z</datestamp>
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          <dc:title>Data Sheet 1_Acceptance and self-reported continued use of AI virtual avatars: an extended UTAUT model with the moderating role of use frequency.pdf</dc:title>
          <dc:creator>Changsheng Liu (276799)</dc:creator>
          <dc:creator>Miaohui Wu (19759434)</dc:creator>
          <dc:creator>Ge Wu (1897054)</dc:creator>
          <dc:subject>Applied Psychology</dc:subject>
          <dc:subject>AI virtual avatar</dc:subject>
          <dc:subject>artificial intelligence</dc:subject>
          <dc:subject>behavioral intention</dc:subject>
          <dc:subject>continued-use tendency</dc:subject>
          <dc:subject>facilitating conditions</dc:subject>
          <dc:subject>PLS-SEM</dc:subject>
          <dc:subject>use frequency</dc:subject>
          <dc:subject>UTAUT</dc:subject>
          <dc:description>&lt;p&gt;AI-driven virtual avatars are increasingly encountered in livestreaming and digital-content settings. This cross-sectional study examined an extended Unified Theory of Acceptance and Use of Technology (UTAUT) model using survey data from 165 Chinese adults reporting prior exposure to AI virtual-avatar livestreams or videos. PLS-SEM showed that effort expectancy and social influence were positively associated with behavioral intention, while performance expectancy was not statistically significant (β = 0.107, p = 0.176). Behavioral intention and facilitating conditions were positively associated with self-reported continued-use tendency. Use frequency had no significant direct association with this outcome, but positively moderated its association with facilitating conditions and negatively moderated its association with behavioral intention. The model explained 68.7% of the variance in intention and 71.0% in continued-use tendency. PLSpredict yielded positive Q&lt;sup&gt;2&lt;/sup&gt;predict values and lower RMSE than the linear-model benchmark for all six endogenous indicators. The findings highlight frequency-related differences in continued-use associations and are interpreted as exploratory given the cross-sectional design and overlap among several measures.&lt;/p&gt;</dc:description>
          <dc:date>2026-10-01T05:43:21Z</dc:date>
          <dc:type>Dataset</dc:type>
          <dc:type>Dataset</dc:type>
          <dc:identifier>10.3389/fpsyg.2026.1957852.s001</dc:identifier>
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          <dc:rights>CC BY 4.0</dc:rights>
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