Entrepreneurship research has long benefitted from embracing multiple perspectives, yet significant methodological and technical barriers have hindered the full potential of Big Data. The recent convergence of Large Language Models (LLMs) with diverse, large-scale datasets presents an unprecedented opportunity to lower these barriers, enabling scholars with varying skills and backgrounds to access, integrate, and analyze large-scale information. This convergence not only democratizes the research process—by reducing coding requirements and facilitating user-friendly data extraction—but also opens new avenues for theory refinement and extension. By harmonizing previously siloed datasets, researchers can uncover emergent patterns that challenge traditional assumptions and bridge disciplinary gaps. For instance, analyzing labor-market statistics, venture funding records, and social media sentiment through LLM-enhanced methods can reveal how complex, dynamic systems shape entrepreneurial success. This novel, integrative approach enriches the study of topics ranging from startup hiring practices to the diffusion of AI-based roles, offering deeper insights into both established and new theories. This PDW illustrates the synergy between LLMs and Big Data and attempts to broaden who participates in entrepreneurship scholarship, fostering collaborative endeavors and facilitating evidence-based knowledge creation that more accurately reflects the evolving realities of modern entrepreneurship.
