The classic digital divide theory asserts that unequal access to and unequal experience with information technologies may lead to unequal user outcomes. This paper introduces a new perspective to extend this theory: outcome divides can persist despite equal access and equal experience if users differ in their analytical ability to analyze and interpret available data for decision-making. We term this new data-to-decision skill as analytical ability and integrate it into the classic digital divide framework. We develop a new approach to operationalize analytical ability by contrasting humans’ actual performance against that of a standard machine learning model that makes similar analytical decisions based on the same information available to humans, essentially emulating a quasi-random counterfactual setting. To minimize the confounding impact of other divides, we validate the role of analytical ability in information-transparent environments like the blockchain-based trading markets, where all historical trading data is equally available to all users on the blockchain. We leverage data from EnjinX, a blockchain-enabled non-fungible token (NFT) marketplace that records all historical NFT transactions. We measure user outcomes by their flip trading performance, a standard metric captured via the percentage of exploited flipping opportunities. Our empirical analysis reveals that disparities in analytical ability may become the new bottleneck for outcome equity: flip trading performance could decrease by 66.86% when traders are incapable of analyzing the available blockchain information effectively. Our study contributes to the literature by extending the digital divide theory with the notion of the analytical ability divide. Moreover, we are among the first to rigorously quantify analytical ability and empirically test its impact based on the extended digital divide framework. Our study also offers important practical implications for platforms and policymakers to bridge this new divide in order to foster outcome equity.
Zhang et al. (Wed,) studied this question.