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19 Big Data and the Computational Social Science of Entrepreneurship and Innovation

  • Ningzi Li , Shiyang Lai and James Evans
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De Gruyter Handbook of Sociology of Innovation and Entrepreneurship
This chapter is in the book De Gruyter Handbook of Sociology of Innovation and Entrepreneurship

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.

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.

Chapters in this book

  1. Frontmatter I
  2. Contents V
  3. List of Figures IX
  4. List of Tables X
  5. 1 Introduction 1
  6. Theoretical Lenses
  7. 2 Ecological Approaches to Entrepreneurship 21
  8. 3 Ecological Approaches to Innovation 41
  9. 4 Evolutionary Perspectives on Entrepreneurship 61
  10. 5 Evolutionary Perspectives on Innovation 81
  11. 6 Institutional Theories of Entrepreneurship 95
  12. 7 Institutional Theories of Innovation 111
  13. 8 Market Categories and Entrepreneurship Research 131
  14. 9 Categories and Cognition in Innovation 145
  15. 10 The Social Structure of Entrepreneurship 159
  16. 11 The Social Structure of Innovation 175
  17. Data and Methods
  18. 12 The Global Entrepreneurship Monitor and Cross-National Research 195
  19. 13 European Riches: Registry Data 215
  20. 14 Using Patent Data in Innovation and Entrepreneurship Research: A Comprehensive Assessment and Recommendations 235
  21. 15 Film, Music, Books, Etc.: Artifacts of Cultural Innovation and Entrepreneurship 253
  22. 16 Innovation and Entrepreneurship in Asia: The Role of the State and Business Groups 269
  23. 17 Entrepreneurship and Innovation in Africa 289
  24. 18 Approaches to Causal Identification in Studies of Entrepreneurship and Innovation 313
  25. 19 Big Data and the Computational Social Science of Entrepreneurship and Innovation 329
  26. 20 Field Experiments in Entrepreneurship and Innovation 353
  27. The Origins of Ideas and Entrepreneurs
  28. 21 The Careers Perspective and Startups as Employers 381
  29. 22 Teams in Entrepreneurship and Innovation 391
  30. 23 Academic Entrepreneurs and Inventors 405
  31. 24 Communities of Entrepreneurship and Innovation 425
  32. 25 The Legal Environment for Innovation and Entrepreneurship 437
  33. 26 The Cultural Environment for Innovation and Entrepreneurship 467
  34. 27 Grand Challenges and Social Entrepreneurship 481
  35. The Mobilization of People and Resources
  36. 28 Narratives of Cultural Entrepreneurship 493
  37. 29 Social Relationships, Resource Mobilization, and Organizational Scaling 505
  38. 30 Status Effects in Entrepreneurship and Innovation 529
  39. 31 Early-Stage Investors 547
  40. 32 The Creation of Routines and Roles in Startups 565
  41. 33 Social Movements, Entrepreneurship, and Innovation 581
  42. Inequalities in Entrepreneurship and Innovation
  43. 34 Gender Gap in Entrepreneurship and Innovation 603
  44. 35 Innovation and Entrepreneurship: A Double-Edged Sword for Racially Minoritized Communities 623
  45. 36 Immigrant Entrepreneurs and Inventors 639
  46. 37 Entrepreneurship, Innovation, and Income Inequality 663
  47. List of Contributors 677
  48. Index 687
https://doi.org/10.1515/9783111085722-019

Chapters in this book

  1. Frontmatter I
  2. Contents V
  3. List of Figures IX
  4. List of Tables X
  5. 1 Introduction 1
  6. Theoretical Lenses
  7. 2 Ecological Approaches to Entrepreneurship 21
  8. 3 Ecological Approaches to Innovation 41
  9. 4 Evolutionary Perspectives on Entrepreneurship 61
  10. 5 Evolutionary Perspectives on Innovation 81
  11. 6 Institutional Theories of Entrepreneurship 95
  12. 7 Institutional Theories of Innovation 111
  13. 8 Market Categories and Entrepreneurship Research 131
  14. 9 Categories and Cognition in Innovation 145
  15. 10 The Social Structure of Entrepreneurship 159
  16. 11 The Social Structure of Innovation 175
  17. Data and Methods
  18. 12 The Global Entrepreneurship Monitor and Cross-National Research 195
  19. 13 European Riches: Registry Data 215
  20. 14 Using Patent Data in Innovation and Entrepreneurship Research: A Comprehensive Assessment and Recommendations 235
  21. 15 Film, Music, Books, Etc.: Artifacts of Cultural Innovation and Entrepreneurship 253
  22. 16 Innovation and Entrepreneurship in Asia: The Role of the State and Business Groups 269
  23. 17 Entrepreneurship and Innovation in Africa 289
  24. 18 Approaches to Causal Identification in Studies of Entrepreneurship and Innovation 313
  25. 19 Big Data and the Computational Social Science of Entrepreneurship and Innovation 329
  26. 20 Field Experiments in Entrepreneurship and Innovation 353
  27. The Origins of Ideas and Entrepreneurs
  28. 21 The Careers Perspective and Startups as Employers 381
  29. 22 Teams in Entrepreneurship and Innovation 391
  30. 23 Academic Entrepreneurs and Inventors 405
  31. 24 Communities of Entrepreneurship and Innovation 425
  32. 25 The Legal Environment for Innovation and Entrepreneurship 437
  33. 26 The Cultural Environment for Innovation and Entrepreneurship 467
  34. 27 Grand Challenges and Social Entrepreneurship 481
  35. The Mobilization of People and Resources
  36. 28 Narratives of Cultural Entrepreneurship 493
  37. 29 Social Relationships, Resource Mobilization, and Organizational Scaling 505
  38. 30 Status Effects in Entrepreneurship and Innovation 529
  39. 31 Early-Stage Investors 547
  40. 32 The Creation of Routines and Roles in Startups 565
  41. 33 Social Movements, Entrepreneurship, and Innovation 581
  42. Inequalities in Entrepreneurship and Innovation
  43. 34 Gender Gap in Entrepreneurship and Innovation 603
  44. 35 Innovation and Entrepreneurship: A Double-Edged Sword for Racially Minoritized Communities 623
  45. 36 Immigrant Entrepreneurs and Inventors 639
  46. 37 Entrepreneurship, Innovation, and Income Inequality 663
  47. List of Contributors 677
  48. Index 687
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