Addressing the ambiguity surrounding the formation mechanisms of user innovation behavior in AI painting tools, this study builds upon the Stimulus-Organism-Response framework by incorporating Self-Determination Theory to enhance the explanatory power of individual psychological motivations. It aims to uncover the multifaceted drivers of user innovation behavior within human-machine collaborative contexts. User data were extensively collected in the form of scales, and a hybrid research technique incorporating Partial Least Squares Structural Equation Modeling, Necessary Condition Analysis, and Fuzzy-set Qualitative Comparative Analysis was used to organize and analyze the data. The study found: Degree of human-AI interaction, creative self-efficacy, and creative role identity were identified as having a direct correlation with users’ innovative behaviors, and the remaining independent variables had an indirect correlation with the dependent variables; tool performance, creative role identity, and perceived playfulness were the necessary conditions for users to achieve innovative behaviors; and four types of innovative behavioral conditioning configurations were further identified, i.e., Technology-Efficiency Synergy (81.5% coverage), Individual Cognitively Dominant (84% coverage), Interactive Empowerment (77.9% coverage) and Social Compensatory (40.6% coverage). The study proves the existence of multi-factor and non-linear goal achievement paths in the process of human-machine collaboration for innovation tasks, which can provide empirical support for the developers of generative AI tools when they carry out differentiated design, and also provide a new methodological perspective and research paradigm reference for the analysis of AI tool user behavior.
Fan et al. (Sat,) studied this question.