China's rapid growth in mass entrepreneurship and innovation has significantly impacted its entrepreneurial environment, particularly for university students. While student entrepreneurial aspirations are high, actual execution and success rates remain low. This study addresses the critical need to understand opportunity identification among Chinese university student entrepreneurs. The research questions focus on identifying the elements of entrepreneurial opportunity identification competence beyond basic recognition, understanding the correlations between these factors, and creating a conceptual model of this ability. The study utilizes a grounded theory approach to build a framework for understanding this capability within the unique Chinese context, where social networks play a significant role in opportunity recognition.

Existing research on entrepreneurial opportunity identification is categorized into three schools of thought: the Austrian School (emphasizing information heterogeneity), the cognitive school (focusing on entrepreneurs' alertness and cognitive processing), and the process school (detailing multi-stage processes). While various models exist, there is a lack of clarity on the interrelationships between the identified aspects. Moreover, research specifically focusing on university student entrepreneurs, particularly in the Chinese context, is limited. The study highlights the limitations of directly applying Western theories to the Chinese context due to cultural differences, particularly the importance of social networks in China. Previous Chinese research has touched upon the topic, but lacked qualitative or empirical investigation of the constituent elements of this ability.

This study employed a grounded theory approach using in-depth interviews with 21 exceptional Chinese university student entrepreneurs selected through purposive sampling. Participants were chosen based on their entrepreneurial achievements, profitability, and the duration of their ventures (to minimize memory biases). Interviews were conducted in comfortable settings, lasting 30–60 minutes each. The data collection process involved pre-interviews to refine the interview outline and ensure alignment with the research objectives. The data analysis process followed a three-level coding procedure: open coding, axial coding, and selective coding. Open coding involved systematically coding and labeling the interview transcripts, creating initial concepts and categories. Axial coding revealed logical connections between categories, creating subcategories and refining the initial concepts. Selective coding focused on identifying a “core category” to construct a comprehensive theoretical model. Theoretical saturation was tested using the Pandit NR method, with the final model derived through iterative analysis until no new categories emerged. The coding process involved multiple stages, starting from detailed transcripts to identification of nodes, basic concepts, primary categories, and finally, the core category.

The study identified a structural model of entrepreneurial opportunity identification capability among Chinese university students, categorized into implicit and explicit capabilities. Implicit capabilities are less visible and include: * **Entrepreneurial Drive:** This is divided into endogenous (internal motivation for self-realization and personal growth) and exogenous (external factors such as economic incentives, pressure to find employment, and role models). The study finds strong correlations between entrepreneurial drive and entrepreneurial behavior, with both endogenous and exogenous factors significantly impacting opportunity recognition. * **Environmental Insight:** This comprises alertness (acute perception of market and societal changes), insight (observing and analyzing markets and trends), and policy awareness (understanding government policies and regulations). Each aspect contributes to a thorough understanding of the environment and the opportunities it presents. Explicit capabilities are readily observable and measurable, and include: * **Learning Abilities:** This involves entrepreneurial learning (acquiring knowledge about entrepreneurship) and learning from failures (reflecting on past experiences, drawing lessons, and reframing setbacks as learning opportunities). Learning from failure was found to be crucial, depending on the ability to maintain resilience and a positive outlook. * **Networking Abilities:** This comprises network building (establishing relationships with relevant individuals) and network management (maintaining and leveraging those relationships for information and resources). Effective networking enables access to vital information and resources for opportunity recognition. * **Integration Abilities:** This involves integrating knowledge (synthesizing diverse knowledge sources) and integrating resources (creatively combining existing resources). Effective integration of knowledge and resources is crucial for creating value and generating opportunities. The study presents a visual model (Figure 2 in the paper) summarizing these implicit and explicit capabilities and their interrelationships.

The findings demonstrate that entrepreneurial opportunity identification among university students is a multi-faceted process involving both innate characteristics and acquired skills. The model developed integrates cognitive and process perspectives, providing a more holistic understanding compared to previous unidimensional approaches. The study underscores the importance of both internal motivation and external knowledge acquisition. The context-specific nature of the findings emphasizes the need for culturally sensitive approaches to entrepreneurship education and training. The strong emphasis on social networks in the Chinese context highlights the critical role of relationship building and management in facilitating opportunity recognition.

This study contributes significantly to both theory and practice by developing a structural model of entrepreneurial opportunity identification capability among Chinese university students. The model incorporates both implicit and explicit competencies, adding to our understanding of this critical aspect of entrepreneurship. The findings provide valuable insights for universities to design more effective entrepreneurship education programs, fostering a more robust pipeline of entrepreneurial talent. Future research should include larger and more diverse samples to enhance the generalizability of the model, as well as developing a quantitative measurement scale to validate the model's robustness.

The study's reliance on a limited sample of high-performing student entrepreneurs might limit the generalizability of the findings to the broader population of university students. Future research should include a broader range of students to ensure a more representative sample. Additionally, due to time and resource constraints, the study did not conduct large-scale quantitative analysis to validate the model. Future studies could focus on developing a measurement scale for entrepreneurial opportunity recognition ability to test the model's validity more rigorously.