Research on the mechanisms of human-AI collaboration on hybrid problem-solving in social entrepreneurship: Based for the attention-based view

Abstract: Social entrepreneurship faces hybrid problems arising from conflicts between social and economic logics, posing significant challenges to entrepreneurs' attentional allocation and necessitating effective pathways for hybrid problem-solving. Drawing on the attention-based view, this project analyzes the mechanisms through which human-AI collaboration influences hybrid problem-solving in social entrepreneurship, and proposes a theoretical framework of "human-AI collaboration-attentional engagement-hybrid problem-solving." Specifically, the study examines three modes of human-AI collaboration and their respective mechanisms: AI-dominated collaboration enhances entrepreneurs' sustained attention engagement, thus optimizing data-driven resource utilization efficiency; human-dominated collaboration strengthens alternating attention engagement, thereby facilitating dynamic balance among multiple stakeholder demands; and interactive human-AI collaboration fosters creative integration of social and economic values through dual attentional synergy. The study systematically tests these modes and mechanisms through an integrated methodological approach encompassing textual analysis, behavioral experiments, and case studies. Theoretically, this research deepens the understanding of micro-level cognitive mechanisms underlying hybrid problem-solving in social entrepreneurship, extending the application contexts and boundaries of both the attention-based view and human-AI collaboration theory. Practically, it provides social entrepreneurs with cognitively empowering pathways for human-AI collaboration, thereby facilitating effective application of AI within social entrepreneurial contexts.