Have Disclosures Kept up with the Big Data Revolution? An Empirical Test
30 Pages Posted: 1 Dec 2021 Last revised: 27 Jan 2022
Date Written: November 24, 2021
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 hardly read and understood by an individual consumer. However, if the voluminous data included in the disclosures is machine-readable, i.e., it can be automatically extracted and processed by computers, disclosures can ultimately assist consumers in making better-informed buying decisions.
While the level of readability of disclosures by humans has been extensively studied by legal scholars, their machine readability has not. This Article aims to fill this research gap. Focusing as a case study on the important U.S. quick service restaurant franchise industry, 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 100 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, the Article presents concrete recommendations to policy makers on how to assure that disclosures in all industries keep up with the big data revolution.
Keywords: Mandated Disclosure, Big Data, Machine Reading
JEL Classification: K12
Suggested Citation: Suggested Citation