Given the significant social benefits of the big data revolution, an important empirical legal question arises: are government-mandated disclosures designed in a way that allows society to harness the power of the big data that they include? Mandated disclosures normally include an overwhelming volume of data that can be difficult to read and understand for the average individual consumer. If, however, the voluminous data included in the disclosures is machine-readable, such that it can be automatically extracted and processed by computers, disclosures might actually assist consumers in making better-informed buying decisions. Although legal scholars have extensively studied the level of human readability of disclosures, they have yet to study their machine readability. This Article aims to fill this research gap. Using the important U.S. quick-service (fast food) restaurant franchise industry as a case study, this Article examines whether disclosure documents, provided by franchisors to prospective franchisees, have the features of machine-readable data. It specifically tests whether disclosures are provided in an adequate digital format, and include unique data identifiers, structured format, and standardized taxonomy, which can be easily read and processed by computers. The sample of this study includes the financial balance sheets disclosed by one hundred dominant quick-service restaurant chains, including Subway, McDonald’s, KFC, and Dunkin’. The disturbing empirical results of this study indicate that franchise disclosures are normally non-machine readable. Given these results, this Article presents concrete recommendations to policy-makers on how to assure that disclosures in all industries keep up with the big data revolution.
Uri Benoliel, Have Disclosures Kept Up with the Big Data Revolution? An Empirical Test, 63 B.C. L. Rev. 1913 (2022), https://lawdigitalcommons.bc.edu/bclr/vol63/iss6/2