Abstract
A defining puzzle of advanced economies over the past two decades is the coexistence of cheap, widely available productivity-enhancing technology with slow, highly uneven adoption — a divergence between frontier firms and a long tail of laggards that weighs on aggregate productivity (Andrews, Criscuolo and Gal, 2016; Syverson, 2011). The laggards are disproportionately small: U.S. Census data show that advanced technology use is rare and skewed toward large, old firms (Zolas et al., 2020), and OECD evidence shows the small-firm adoption gap widening precisely where technologies become analytical rather than transactional (OECD, 2021). Yet nearly everything known about this gap in developed countries is observational. The diffusion literature documents who adopts; it cannot say which barriers bind, or what intervention would change adoption, because adopters differ from non-adopters in unobserved ways that also drive performance. The experimental literature that could answer such questions has been built almost entirely in developing countries, while the single prominent randomized study of analytics adoption in a rich country — eBay’s Seller Hub rollout (Bar-Gill, Brynjolfsson and Hak, 2024) — concerns online platform sellers for whom the tool arrives frictionlessly inside software they already use. No randomized evaluation exists for the modal laggard: the brick-and-mortar, owner-operated small firm. This proposal describes such a trial. Exploiting an established field platform of 200–250 independent retail locations across Tennessee, Arkansas, and Mississippi and a sampling frame of 300+ operators, the study (i) fields a diagnostic survey with enumerator-verified behavioral measures of data fluency; (ii) randomly assigns operators to a control group or to one of three arms that hold analytical content fixed while varying delivery format, workflow integration, and interpersonal support — the three mechanisms that theory and our own pilot evidence identify as candidate binding constraints; and (iii) measures effects on engagement, managerial behavior, and operational performance over twelve months using high-frequency point-of-sale administrative data. The design converts the developed-country adoption question from a correlational to a causal one, at the firm margin where the adoption gap is largest and least understood.