Abstract: Social entrepreneurship is increasingly recognized for its dual mission of achieving social value and economic sustainability. However, this dual mission inherently generates "hybrid problems" stemming from the deep-seated conflicts between social and economic logics. These hybrid problems manifest in three primary contradictions: hybrid value propositions, hybrid resource utilization, and hybrid governance. Consequently, social entrepreneurs face significant cognitive tension and a severe scarcity of attentional resources. While human-AI collaboration has emerged as a revolutionary paradigm for complex problem-solving, its specific mechanisms within the highly contextualized and hybrid environment of social entrepreneurship remain underexplored. Drawing on the Attention-Based View (ABV) and cognitive psychology, this research constructs an integrated theoretical framework of "human-AI collaboration—attentional engagement—hybrid problem-solving" to systematically investigate how different modes of human-AI collaboration reshape entrepreneurs' attentional allocation and subsequently influence hybrid problem-solving outcomes.
We posit that human-AI collaboration fundamentally alters the distribution of cognitive load. By offloading specific cognitive tasks to artificial intelligence, entrepreneurs can release and reallocate their limited attentional resources toward higher-order strategic and ethical judgments. We conceptualize attentional engagement into two distinct dimensions: sustained attention engagement and alternating attention engagement. Based on the division of labor, we identify three distinct modes of human-AI collaboration—AI-dominated, human-dominated, and interactive—each employing different problem-solving strategies and generating unique attentional perspectives.
First, the AI-dominated collaboration mode employs a solution-focused strategy, wherein AI autonomously defines the problem and generates solutions, leaving the entrepreneur to filter and optimize the results. This mode triggers a bottom-up attentional perspective. By assuming the heavy cognitive load of information processing and solution generation, AI significantly enhances the entrepreneur's sustained attention engagement. This sustained focus allows the entrepreneur to concentrate on long-term strategic alignment and ethical evaluation, thereby optimizing data-driven resource utilization efficiency and facilitating the resolution of highly structured hybrid problems. The effectiveness of this mode is moderated by human-AI factors, such as the entrepreneur's domain expertise, AI explainability, and human-AI trust, as well as the problem's degree of hybridity and structuredness.
Second, the human-dominated collaboration mode utilizes a problem-focused strategy. Here, AI provides a preliminary structural framework for the complex social problem, and the entrepreneur subsequently searches for and formulates solutions based on this framework. This mode cultivates a top-down attentional perspective. The AI-generated structural framework effectively reduces the entrepreneur's short-term information processing load, thereby strengthening their alternating attention engagement. Enhanced alternating attention enables the entrepreneur to flexibly switch between competing economic and social goals, facilitating a dynamic balance among multiple stakeholder demands. The efficacy of this mode is contingent upon the entrepreneur's cognitive style, emotional state, and the degree of human-AI cognitive alignment, along with the specific characteristics of the hybrid problem.
Third, the interactive human-AI collaboration mode adopts a problem-solution pair strategy, characterized by continuous, dynamic interaction and mutual feedback between the human and AI throughout the problem-solving process. This mode fosters an interactive attentional perspective, promoting dual attentional synergy that activates both sustained and alternating attention engagement. Through continuous cognitive alignment and adaptive feedback, this interactive mode empowers social entrepreneurs to manage high uncertainty and complex value conflicts, ultimately leading to the creative integration of social and economic values. The success of this interactive mechanism is moderated by the depth of AI participation, the level of human-AI trust, and the frequency of interaction.
Theoretically, this research advances the Attention-Based View by extending it from the macro-organizational level to the micro-cognitive level of individual entrepreneurs, integrating it with cognitive psychology concepts of vigilance and task-switching. Furthermore, it deepens the understanding of hybrid problem-solving by illuminating the specific cognitive mechanisms through which AI interventions alleviate attentional scarcity in multi-objective conflict scenarios. Practically, the findings offer social entrepreneurs cognitively empowering pathways for effective human-AI collaboration, providing actionable guidelines for the strategic adoption of AI technologies to navigate the complex landscape of social entrepreneurship.

Key words: social entrepreneurship, human-AI collaboration, problem-solving, attention-based view