Abstract
Sora promises to redefine the way visual content is created. Despite its numerous forecasted benefits, the drivers of user willingness to use the text-to-video (T2V) model are unknown. This study extends the extended unified theory of acceptance and use of technology (UTAUT2) with perceived realism and novelty value. Using a purposive sampling method, we collected data from 940 respondents in the US and analyzed the sample using covariance-based structural equation modeling and fuzzy set qualitative comparative analysis (fsQCA). The findings reveal that all hypothesized relationships are supported, with perceived realism emerging as the most influential driver, followed by novelty value. Moreover, fsQCA identifies five configurations leading to high and low willingness to use, and the model demonstrates high predictive validity, contributing to theory advancement. Our study provides valuable insights for developers and marketers, offering guidance for strategic decisions to promote the widespread adoption of T2V models.
Abstract (translated)
Sora誓言重新定义视觉内容创作的方式。尽管预测带来了很多好处，但用户使用文本转视频（T2V）模型的意愿驱动因素仍然是未知的。本研究在采用有向抽样方法收集了940名美国受访者的数据后，利用相关结构方程模型和模糊集合定性比较分析（fsQCA）对样本进行分析。研究结果表明，所有预测关系均得到支持，感知现实主义成为最具有影响力的驱动因素，其次是新颖性价值。此外，fsQCA识别出导致高和低意愿使用的五个配置，模型具有高度预测能力，有助于理论发展。我们的研究为开发人员和市场人员提供了宝贵的洞见，为推广T2V模型的广泛采用提供了指导。
URL
https://arxiv.org/abs/2405.03986
