Big Data Analytics, Firm Size, and Performance

Published Online:https://doi.org/10.1287/stsc.2022.0007

References

  • Accenture (2014) Big success with big data survey. Accessed August 1, 2023, https://www.accenture.com/us-en/_acnmedia/accenture/conversion-assets/dotcom/documents/global/pdf/industries_14/accenture-big-data-pov.pdf.Google Scholar
  • Adner R, Puranam P, Zhu F (2019) What is different about digital strategy? From quantitative to qualitative change. Strategy Sci. 4:253–261.LinkGoogle Scholar
  • Autor DD, Dorn D, Katz LF, Patterson C, Van Reenen J (2020) The fall of the labor share and the rise of superstar firms. Quart. J. Econom. 135(2):645–709.CrossrefGoogle Scholar
  • Balakrishnan N, Lai CD (2009) Continuous Bivariate Distributions (Springer Science & Business Media, Boston).Google Scholar
  • Barach M, Kaul A, Leung M, Lu S (2019) Strategic redundancy in the use of big data: Evidence from a two-sided labor market. Strategy Sci. 4:298–322.LinkGoogle Scholar
  • Barton D, Court D (2012) Making advanced analytics work for you. Harvard Bus. Rev. 90(10):78–83.Google Scholar
  • Bessen J (2017) Information Technology and Industry Concentration. Boston University School of Law, Law & Economics Paper Series, Boston.Google Scholar
  • Bessen J (2020) Industry concentration and information technology. J. Law Econom. 63(3):531–555.Google Scholar
  • Bloom N, Eifert B, Mahajan A, McKenzie D, Roberts J (2012) Does management matter? Evidence from India. Quart. J. Econom. 128:1–51.CrossrefGoogle Scholar
  • Branstetter L, Lima F, Taylor LJ, Venancio A (2014) Do entry regulations deter entrepreneurship and job creation? Evidence from recent reforms in Portugal. Econom. J. (London) 124(577):805–832.CrossrefGoogle Scholar
  • Brynjolfsson E, MacAfee A (2014) The Second Machine Age: Work, Progress, and Prosperity in a Time of Brilliant Technologies (W.W. Norton & Company, New York).Google Scholar
  • Brynjolfsson E, McElheran K (2016) Data in action: Data-driven decision making in U.S. manufacturing. US Census Bureau Center for Economic Studies Paper No. CES-WP-16-06, Rotman School of Management Working Paper No. 2722502, National Bureau of Economic Research, Cambridge, MA.Google Scholar
  • Brynjolfsson E, Hitt L, Kim HH (2011) Strength in numbers: How does data-driven decision making affect firm performance? Preprint, submitted XX, https://dx.doi.org/10.2139/ssrn.1819486.Google Scholar
  • Brynjolfsson E, Rock D, Syverson C (2017) Artificial intelligence and modern productivity paradox: A clash of expectations and statistica. NBER Working Paper No. 24001, National Bureau of Economic Research, Cambridge, MA.Google Scholar
  • Chan M, Petrin A, Warzynski F (2023) The effect of R&D on quality, productivity, and welfare. NBER Working Paper No. 30950, National Bureau of Economic Research, Cambridge, MA.Google Scholar
  • Cohen W, Klepper S (1996) Firm size and the nature of innovation within industries: The case of process and product R&D. Rev. Econom. Statist. 78(2):232–243.CrossrefGoogle Scholar
  • Comerford RE, Sokol DD (2016) Antitrust and regulating big data. George Mason Law Rev. 23:1129.Google Scholar
  • Conti R (2014) Do non-competition agreements lead firms to pursue risky R&D projects? Strategic Management J. 35(8):1230–1248.CrossrefGoogle Scholar
  • Danaher PJ, Smith MS (2011) Modeling multivariate distributions using copulas: Applications in marketing. Marketing Sci. 30(1):4–21.LinkGoogle Scholar
  • Davenport T (2014) Big Data at Work: Dispelling the Myths, Uncovering the Opportunities (Harvard Business Review Press, Boston).CrossrefGoogle Scholar
  • De Loecker J, Eeckhout J 2017. The rise of market power and the macroeconomic implications. NBER Working Paper No. 23687, National Bureau of Economic Research, Cambridge, MA.Google Scholar
  • Farboodi M, Mihet R, Philippon T, Veldkamp L (2019) Big data and firm dynamics. Amer. Econom. Rev. Papers Proc. 109:38–42.CrossrefGoogle Scholar
  • George G, Haas M, Pentland A (2016) Big data and management. Acad. Management J. 57(2):321–326.CrossrefGoogle Scholar
  • Golovko E, Valentini G (2014) Selective learning‐by‐exporting: Firm size and product vs. process innovation. Global Strategy J. 4:161–180.CrossrefGoogle Scholar
  • Gui R, Meierer M, Algesheimer R (2020) REndo: A package to address endogneity without external instrumental variables. J. Statistical Software, Forthcoming.Google Scholar
  • Hall B (2011) Innovation and productivity. NBER Working Paper No. 17178, National Bureau of Economic Research, Cambridge, MA.Google Scholar
  • Iacus SM, King G, Porro G (2012) Causal inference without balance checking: Coarsened exact matching. Political Anal. 20(1):1–24.Google Scholar
  • Kakatkar C, Bilgram V, Füller J (2020) Innovation analytics: Leveraging artificial intelligence in the innovation process. Bus. Horizons 63(2):171–181.CrossrefGoogle Scholar
  • Klepper S (1996) Entry, exit, growth, and innovation over the product life cycle. Amer. Econom. Rev. 86(3):562–583.Google Scholar
  • Kumar V, Zhang X, Luo A (2014) Modeling customer opt-in and opt-out in a permission-based marketing context. J. Marketing Res. 51(4):403–419.CrossrefGoogle Scholar
  • Lambrecht A, Tucker C (2015) Can big data protect firm from competition? Preprint, submitted December 18, https://dx.doi.org/10.2139/ssrn.2705530.Google Scholar
  • Marra G, Radice R (2020) Copula link-based additive models for right-censored event time data. J. Amer. Statist. Assoc. 115:886–895.CrossrefGoogle Scholar
  • Muller O, Fay M, von Brocke J (2018) The effect of big data and analytics on firm performance: An econometric analysis considering industry characteristics. J. Management Inform. Systems 35(2):488–509.CrossrefGoogle Scholar
  • Nelsen RB (2006) An Introduction to Copulas. Springer Series in Statistics (Springer, New York).Google Scholar
  • Park S, Gupta S (2012) Handling endogenous regressors by joint estimation using copulas. Marketing Sci. 31(4):567–586.LinkGoogle Scholar
  • Petrin A, Train K (2010) A control function approach to endogeneity in consumer choice models. J. Marketing Res. 47(1):3–13.Google Scholar
  • Powell T, Dent-Micallef A (1997) Information technology as competitive advantage: The role of human, business, and technology resources. Strategic Management J. 18(5):375–405.CrossrefGoogle Scholar
  • Rosen S (1981) The economics of superstars. Sherwin Rosen. Amer. Econom. Rev. 71(5):845–858.Google Scholar
  • Rubinfeld D, Gal M (2017) Access barriers to big data, 59 Arizona Law Review 339, National Bureau of Economic Research, Cambridge, MA.Google Scholar
  • Saunders A, Tambe P (2015) Data assets and industry competition: Evidence from 10K fillings. Preprint, submitted September 18, https://dx.doi.org/10.2139/ssrn.2537089.Google Scholar
  • Tambe P (2014) Big data investment, skills, and firm value. Management Sci. 60(6):1452–1469.LinkGoogle Scholar
  • Teece DJ (2022) Big tech and strategic management: How management scholars can inform competition policy. Acad. Management Perspect. 37:1–15.Google Scholar
  • Van Alstyne M, Parker G, Choudary SP (2016) Pipelines, platforms, and the new rules of strategy. Harvard Bus. Rev. 1–19.Google Scholar
  • Wamba SF, Akter S, Edwards A, Chopin G, Gnanzou D (2015) How “big data” can make big impact: Findings from a systematic review and a longitudinal case study. Internat. J. Production Econom. 165:234–24.CrossrefGoogle Scholar
  • Zhang X, Kumar V, Cosguner K (2017) Dynamically managing a profitable email marketing program. J. Marketing Res. 54(6):851–866.Google Scholar
INFORMS site uses cookies to store information on your computer. Some are essential to make our site work; Others help us improve the user experience. By using this site, you consent to the placement of these cookies. Please read our Privacy Statement to learn more.