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When does AI pay off? AI-adoption intensity, complementary investments, and R&D strategy

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  • Lee, Yong Suk
  • Kim, Taekyun
  • Choi, Sukwoong
  • Kim, Wonjoon

Abstract

This paper examines how high-tech venture performance varies with AI-adoption intensity. We find that firm revenue increases only after sufficient investment in AI, and the benefits of AI adoption are greater at firms that also invest in complementary technologies and pursue internal R&D strategy. Specifically, AI adoption at low levels does not suggest significant revenue growth, but, as the intensity of AI adoption increases revenue growth occurs. We find that such performance gains from adoption is larger among firms that invest in complementary technologies such as cloud computing and database systems. Moreover, the positive relationship between AI adoption intensity and revenue growth is stronger among firms that pursue a more exclusive R&D strategy specific to the venture.

Suggested Citation

  • Lee, Yong Suk & Kim, Taekyun & Choi, Sukwoong & Kim, Wonjoon, 2022. "When does AI pay off? AI-adoption intensity, complementary investments, and R&D strategy," Technovation, Elsevier, vol. 118(C).
    • Handle: RePEc:eee:techno:v:118:y:2022:i:c:s0166497222001377
    DOI: 10.1016/j.technovation.2022.102590
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    as
    1. Erik Brynjolfsson & Daniel Rock & Chad Syverson, 2021. "The Productivity J-Curve: How Intangibles Complement General Purpose Technologies," American Economic Journal: Macroeconomics, American Economic Association, vol. 13(1), pages 333-372, January.
    2. Rachel Griffith & Stephen Redding & John Van Reenen, 2004. "Mapping the Two Faces of R&D: Productivity Growth in a Panel of OECD Industries," The Review of Economics and Statistics, MIT Press, vol. 86(4), pages 883-895, November.
    3. Timothy F. Bresnahan & Shane Greenstein, 1999. "Technological Competition and the Structure of the Computer Industry," Journal of Industrial Economics, Wiley Blackwell, vol. 47(1), pages 1-40, March.
    4. Philippe Aghion & Céline Antonin & Simon Bunel, 2019. "Artificial Intelligence, Growth and Employment: The Role of Policy," Economie et Statistique / Economics and Statistics, Institut National de la Statistique et des Etudes Economiques (INSEE), issue 510-511-5, pages 149-164.
    5. Andreas Fuster & Matthew Plosser & Philipp Schnabl & James Vickery, 2019. "The Role of Technology in Mortgage Lending," The Review of Financial Studies, Society for Financial Studies, vol. 32(5), pages 1854-1899.
    6. John Duffy & Chris Papageorgiou & Fidel Perez-Sebastian, 2004. "Capital-Skill Complementarity? Evidence from a Panel of Countries," The Review of Economics and Statistics, MIT Press, vol. 86(1), pages 327-344, February.
    7. Jovanovic, Boyan & Nyarko, Yaw, 1996. "Learning by Doing and the Choice of Technology," Econometrica, Econometric Society, vol. 64(6), pages 1299-1310, November.
    8. Daron Acemoglu & Pascual Restrepo, 2018. "Artificial Intelligence, Automation, and Work," NBER Chapters, in: The Economics of Artificial Intelligence: An Agenda, pages 197-236, National Bureau of Economic Research, Inc.
    9. Oleg V. Petrenko & Federico Aime & Tessa Recendes & Jeffrey A. Chandler, 2019. "The case for humble expectations: CEO humility and market performance," Strategic Management Journal, Wiley Blackwell, vol. 40(12), pages 1938-1964, December.
    10. Martin Beraja & David Y Yang & Noam Yuchtman, 2023. "Data-intensive Innovation and the State: Evidence from AI Firms in China," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 90(4), pages 1701-1723.
    11. Daron Acemoglu & David Autor & Jonathon Hazell & Pascual Restrepo, 2020. "AI and Jobs: Evidence from Online Vacancies," NBER Working Papers 28257, National Bureau of Economic Research, Inc.
    12. Andrew Atkeson & Patrick J. Kehoe, 1993. "Industry evolution and transition: the role of information capital," Staff Report 162, Federal Reserve Bank of Minneapolis.
    13. J. Klinger & J. Mateos-Garcia & K. Stathoulopoulos, 2018. "Deep learning, deep change? Mapping the development of the Artificial Intelligence General Purpose Technology," Papers 1808.06355, arXiv.org.
    14. Alekseeva, Liudmila & Azar, José & Giné, Mireia & Samila, Sampsa & Taska, Bledi, 2021. "The demand for AI skills in the labor market," Labour Economics, Elsevier, vol. 71(C).
    15. Daron Acemoglu & Pascual Restrepo, 2018. "Artificial Intelligence, Automation and Work," Boston University - Department of Economics - Working Papers Series dp-298, Boston University - Department of Economics.
    16. Bo Cowgill, 2019. "Bias and Productivity in Humans and Machines," Upjohn Working Papers 19-309, W.E. Upjohn Institute for Employment Research.
    17. Avi Goldfarb & Bledi Taska & Florenta Teodoridis, 2020. "Artificial Intelligence in Health Care? Evidence from Online Job Postings," AEA Papers and Proceedings, American Economic Association, vol. 110, pages 400-404, May.
    18. Robert Bartlett & Adair Morse & Richard Stanton & Nancy Wallace, 2019. "Consumer-Lending Discrimination in the FinTech Era," NBER Working Papers 25943, National Bureau of Economic Research, Inc.
    19. Tobias Kretschmer, 2005. "Competing technologies in the database management systems market," Working Papers 05-17, NET Institute, revised Oct 2005.
    20. Matthew J. Lindquist & Joeri Sol & Mirjam Van Praag, 2015. "Why Do Entrepreneurial Parents Have Entrepreneurial Children?," Journal of Labor Economics, University of Chicago Press, vol. 33(2), pages 269-296.
    21. Jovanovic, Boyan & Rousseau, Peter L., 2005. "General Purpose Technologies," Handbook of Economic Growth, in: Philippe Aghion & Steven Durlauf (ed.), Handbook of Economic Growth, edition 1, volume 1, chapter 18, pages 1181-1224, Elsevier.
    22. Nicholas Bloom & John Van Reenen, 2010. "Why Do Management Practices Differ across Firms and Countries?," Journal of Economic Perspectives, American Economic Association, vol. 24(1), pages 203-224, Winter.
    23. Satinder Singh & Andy Okun & Andrew Jackson, 2017. "Learning to play Go from scratch," Nature, Nature, vol. 550(7676), pages 336-337, October.
    24. Claudia Goldin & Lawrence F. Katz, 1998. "The Origins of Technology-Skill Complementarity," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 113(3), pages 693-732.
    25. Cohen, Wesley M & Levinthal, Daniel A, 1989. "Innovation and Learning: The Two Faces of R&D," Economic Journal, Royal Economic Society, vol. 99(397), pages 569-596, September.
    26. Eckhardt, Jonathan T. & Shane, Scott A., 2011. "Industry changes in technology and complementary assets and the creation of high-growth firms," Journal of Business Venturing, Elsevier, vol. 26(4), pages 412-430, July.
    27. Karen Eggleston & Yong Suk Lee & Toshiaki Iizuka, 2021. "Robots and Labor in the Service Sector: Evidence from Nursing Homes," NBER Working Papers 28322, National Bureau of Economic Research, Inc.
    28. Raphaël Franck & Oded Galor, 2022. "Technology-Skill Complementarity in Early Phases of Industrialisation," The Economic Journal, Royal Economic Society, vol. 132(642), pages 618-643.
    29. Robert Seamans & Manav Raj, 2018. "AI, Labor, Productivity and the Need for Firm-Level Data," NBER Working Papers 24239, National Bureau of Economic Research, Inc.
    30. Ajay Agrawal & Joshua S. Gans & Avi Goldfarb, 2019. "Artificial Intelligence: The Ambiguous Labor Market Impact of Automating Prediction," Journal of Economic Perspectives, American Economic Association, vol. 33(2), pages 31-50, Spring.
    31. Frank T. Rothaermel, 2001. "Incumbent's advantage through exploiting complementary assets via interfirm cooperation," Strategic Management Journal, Wiley Blackwell, vol. 22(6‐7), pages 687-699, June.
    32. Victor M. Bennett & Daniel A. Levinthal, 2017. "Firm Lifecycles: Linking Employee Incentives and Firm Growth Dynamics," Strategic Management Journal, Wiley Blackwell, vol. 38(10), pages 2005-2018, October.
    33. Maryam Farboodi & Roxana Mihet & Thomas Philippon & Laura Veldkamp, 2019. "Big Data and Firm Dynamics," AEA Papers and Proceedings, American Economic Association, vol. 109, pages 38-42, May.
    34. Sinan Aral & Erik Brynjolfsson & Lynn Wu, 2012. "Three-Way Complementarities: Performance Pay, Human Resource Analytics, and Information Technology," Management Science, INFORMS, vol. 58(5), pages 913-931, May.
    35. Arora, Ashish & Forman, Chris & Yoon, Ji Woong, 2010. "Complementarity and information technology adoption: Local area networks and the Internet," Information Economics and Policy, Elsevier, vol. 22(3), pages 228-242, July.
    36. S. K. Majumdar & O. Carare & H. Chang, 2010. "Broadband adoption and firm productivity: evaluating the benefits of general purpose technology," Industrial and Corporate Change, Oxford University Press and the Associazione ICC, vol. 19(3), pages 641-674, June.
    37. Griliches, Zvi, 1969. "Capital-Skill Complementarity," The Review of Economics and Statistics, MIT Press, vol. 51(4), pages 465-468, November.
    38. Augereau, Angelique & Greenstein, Shane, 2001. "The need for speed in emerging communications markets: upgrades to advanced technology at Internet Service Providers," International Journal of Industrial Organization, Elsevier, vol. 19(7), pages 1085-1102, July.
    39. Andreas Hornstein & Per Krusell, 1996. "Can Technology Improvements Cause Productivity Slowdowns?," NBER Chapters, in: NBER Macroeconomics Annual 1996, Volume 11, pages 209-276, National Bureau of Economic Research, Inc.
    40. David, Paul A, 1990. "The Dynamo and the Computer: An Historical Perspective on the Modern Productivity Paradox," American Economic Review, American Economic Association, vol. 80(2), pages 355-361, May.
    41. Prasanna Tambe & Lorin M. Hitt & Erik Brynjolfsson, 2012. "The Extroverted Firm: How External Information Practices Affect Innovation and Productivity," Management Science, INFORMS, vol. 58(5), pages 843-859, May.
    42. Bresnahan, Timothy, 2010. "General Purpose Technologies," Handbook of the Economics of Innovation, in: Bronwyn H. Hall & Nathan Rosenberg (ed.), Handbook of the Economics of Innovation, edition 1, volume 2, chapter 0, pages 761-791, Elsevier.
    43. Bruno Cassiman & Reinhilde Veugelers, 2006. "In Search of Complementarity in Innovation Strategy: Internal R& D and External Knowledge Acquisition," Management Science, INFORMS, vol. 52(1), pages 68-82, January.
    44. Thomas C. Powell & Anne Dent‐Micallef, 1997. "Information technology as competitive advantage: the role of human, business, and technology resources," Strategic Management Journal, Wiley Blackwell, vol. 18(5), pages 375-405, May.
    45. repec:hal:spmain:info:hdl:2441/7n49nkmngd8448a5ts5gt5ade0 is not listed on IDEAS
    46. Lee, Yong Suk, 2018. "Government guaranteed small business loans and regional growth," Journal of Business Venturing, Elsevier, vol. 33(1), pages 70-83.
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