The Impact of Big Data on Firm Performance: An Empirical Investigation

By Patrick Bajari, Victor Chernozhukov, Ali Hortaçsu, Junichi Suzuki. AEA Papers and Proceedings, 2019

169 citations recorded by the source corpus.

We examine the impact of “big data” on firm performance in the context of forecast accuracy using proprietary retail sales data obtained from Amazon. We measure the accuracy of forecasts in two relevant dimensions: the number of products (N), and the number of time periods for which a product is available for sale (T). Theory suggests diminishing returns to larger N and T, with relative forecast errors diminishing at rate 1/sqrt(N)+1/sqrt(T). Empirical results indicate gains in forecast improvement in the T dimension but essentially flat N effects.

Subfields: Economics and Econometrics, Management Science and Operations Research, Marketing

View Paper: https://www.semanticscholar.org/paper/38838fc06519d94bcecc63dbd2b5b57bde3a60d7

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Citations in the corpus

Economic Policy for Artificial Intelligence (2018, background): “Under this argument, AI is unlikely to generate antitrust concerns (Bajari et al. 2018).”
Economics, Fairness and Algorithmic Bias (2020, unknown): “Bajari et al. (2018) discussed returns to scale in data, and implications for market power and concentration. do not have clear interpretations.”
Artificial intelligence, firm growth, and product innovation (2024, background): “While some recent work provides evidence on the use of AI technologies in specific industries such as finance (D’Acunto et al., 2019) and retail (Bajari et al., 2019),4 our comprehensive data allow us to measure AI investments across a wide range of industries, offering a new measure of technology…”
AI and the Economy (2018, methodology): “For example, using data from Amazon, Bajari et al. (2018) provide evidence that data on a larger number of products does not improve forecast errors, but data over more time pe‑ riods for these products does (though at a diminishing rate).”