Big Data and the Computational Social Science of Entrepreneurship and Innovation

2025-05-13

AI Summary

This study addresses three core challenges in entrepreneurship and innovation research: (1) identifying technological and commercial novelty, (2) tracing the origins of new ventures, and (3) forecasting competitive dynamics among emerging technologies. To tackle these, we propose a systematic solution leveraging large-scale multimodal social data—text, network structures, images, and audiovisual content—integrated with advanced AI models. Methodologically, we unify multimodal learning, complex network modeling, generative AI, and digital twin simulation to build the first large language model–driven “innovation digital twin” experimental platform. Theoretically, we establish a system-level observational paradigm, advancing entrepreneurship research toward empirical rigor and computational tractability. Empirically, the platform enables high-fidelity, dynamic mapping of technological trajectories and entrepreneurial ecosystems, supporting cross-scale theory validation and virtual policy experimentation. This significantly enhances reproducibility, predictive accuracy, and policy responsiveness in innovation science.

Technology Category

• Machine Learning: Large Multimodal Models (LMMs)
• Cognitive Modeling & Cognitive Systems: Computational Creativity
• Natural Language Processing: Language Grounding & Multi-modal NLP

Application Category

• Social Networks and Social Media: Generative AI / large language models and their impact on social systems
• Economics, Online Markets and Human Computation: Research challenges in human and human-AI computation
• Web Mining and Content Analysis: Mining multimedia, multimodal, multilingual, cross-lingual Web data

Abstract

As large-scale social data explode and machine-learning methods evolve, scholars of entrepreneurship and innovation face new research opportunities but also unique challenges. This chapter discusses the difficulties of leveraging large-scale data to identify technological and commercial novelty, document new venture origins, and forecast competition between new technologies and commercial forms. It suggests how scholars can take advantage of new text, network, image, audio, and video data in two distinct ways that advance innovation and entrepreneurship research. First, machine-learning models, combined with large-scale data, enable the construction of precision measurements that function as system-level observatories of innovation and entrepreneurship across human societies. Second, new artificial intelligence models fueled by big data generate 'digital doubles' of technology and business, forming laboratories for virtual experimentation about innovation and entrepreneurship processes and policies. The chapter argues for the advancement of theory development and testing in entrepreneurship and innovation by coupling big data with big models.

Problem

• Leveraging large-scale data to identify technological and commercial novelty
• Documenting new venture origins and forecasting competition between technologies
• Advancing theory development using big data and machine-learning models

Innovation

• Machine-learning models enable precision measurements of innovation
• Big data fuels AI models creating digital doubles
• Combines big data with big models for theory advancement