Technologies, such as Chat Generative Pre-Trained Transformer (ChatGPT, Smart PLS version 4), are prime examples of Generative Artificial Intelligence (AI), which is a constantly evolving area. SMEs, particularly startups, can obtain a competitive edge, innovate their business models, gain business value, and undergo a digital transformation by implementing these technologies. Continuous but gradual experimentation with these technologies is the foundation for their adoption. The experience that comes from trying new technologies can help entrepreneurs adopt new technologies more strategically and experiment more with them. The urgent need for an in-depth investigation is highlighted by the paucity of previous research on ChatGPT uptake in the startup context, particularly from an entrepreneurial perspective. The objective of this research study is to empirically validate the Generative AI technology adoption model to establish the direction and strength of the correlations among the adoption factors from the perspectives of the entrepreneurs. The data are collected from 482 entrepreneurs who exhibit great diversity in their genders, the countries in which their startups are located, the industries their startups serve, their age, their educational levels, their work experience as entrepreneurs, and the length of time the startups have been on the market. Collected data are analyzed using the Partial Least Squares Structural Equation Modeling (PLS-SEM) technique, which results in a statistical examination of the relationships between the adoption model’s factors. The results indicate that social influence, domain experience, technology familiarity, system quality, training and support, interaction convenience, and anthropomorphism are the factors that impact the pre-perception and perception phase of adoption. These factors motivate entrepreneurs to experiment more with the technology, thereby building perceptions of its usefulness, perceived ease of use, and perceived enjoyment, three factors that in turn affect emotions toward the technology and, finally, switching intentions. Control variables like age, gender, and educational attainment have no appreciable effect on switching intentions to alternatives of the Generative AI technology. Rather, the experience factor of running businesses shows itself to be a crucial one. The results have practical implications for entrepreneurs and other innovation ecosystem actors, including, for instance, technology providers, libraries, and policymakers. This research study enriches the Generative AI technology acceptance theory and extends the existing literature by introducing new adoption variables and stages specific to entrepreneurship.

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从企业家的角度对生成式人工智能技术采用模型的实证评估

Chat Generative Pre-Trained Transformer（ChatGPT、Smart PLS 版本 4）等技术是生成人工智能 (AI) 的主要示例，这是一个不断发展的领域。中小企业，特别是初创企业，可以通过实施这些技术获得竞争优势、创新商业模式、获得商业价值并进行数字化转型。对这些技术进行持续但渐进的实验是其采用的基础。尝试新技术所获得的经验可以帮助企业家更有策略地采用新技术并进行更多试验。之前对初创企业中 ChatGPT 使用情况的研究很少，特别是从创业的角度来看，这突显了进行深入调查的迫切需要。本研究的目的是通过实证验证生成式人工智能技术采用模型，从企业家的角度确定采用因素之间相关性的方向和强度。数据收集自 482 名企业家，他们在性别、初创企业所在国家、初创企业服务的行业、年龄、教育水平、企业家工作经验以及初创企业的时间长短方面表现出极大的多样性。已上市。使用偏最小二乘结构方程模型 (PLS-SEM) 技术对收集的数据进行分析，从而对采用模型因素之间的关系进行统计检查。结果表明，社会影响力、领域经验、技术熟悉程度、系统质量、培训和支持、交互便利性和拟人化是影响采用前感知和感知阶段的因素。这些因素促使企业家更多地尝试该技术，从而建立对其有用性、易用性和享受感的认知，这三个因素反过来会影响对技术的情绪，并最终改变意图。年龄、性别和教育程度等控制变量对于将意图转变为生成人工智能技术的替代方案没有明显影响。相反，经营企业的经验因素显得至关重要。研究结果对企业家和其他创新生态系统参与者（包括技术提供商、图书馆和政策制定者）​​具有实际意义。这项研究通过引入新的采用变量和创业特定阶段，丰富了生成式人工智能技术接受理论，并扩展了现有文献。