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.

Data sharing between upstream and downstream entities is vital for the success of a supply chain. However, distrust, privacy concerns, data misuse, and the asymmetric valuation of shared data between entities often hinder data sharing. This problem calls for a secure, efficient, fair, and trustworthy data‐sharing mechanism. The key to such a successful system hinges on how to trace the data usage, determine the value of the seller’s data to the buyer and then compensate the seller accordingly. To this end, we design and implement a blockchain‐enabled data‐sharing marketplace for a stylized supply chain. We demonstrate how a blockchain can be used to overcome these impediments in supply‐chain data sharing and provide a detailed tutorial with a step‐by‐step implementation for how to set up such a data exchange prototype using Hashgraph.

The 50-year march of Moore’s Law has led to the creation of a relatively cheap and increasingly easy-to-use world-wide digital infrastructure of computers, mobile devices, broadband network connections, and advanced application platforms. This digital infrastructure has, in turn, accelerated the emergence of new technologies that enable transformations in how we live and work, how companies organize, and the structure of entire industries. As a result, it has become important for all business students to have a strong grounding in IT and digital innovation in order to manage, lead, and transform organizations that are increasingly dependent on digital innovation. Yet, at many schools, students do not get such grounding because the required information systems core class is stuck in the past. We present a vision for a redesigned IS core class that adopts digital innovation as a fundamental and powerful concept (FPC). A good FPC serves as both a foundational concept and an organizing principle for a course. We espouse a particularly broad conceptualization of digital innovation that allows for a variety of teaching styles and topical emphases for the IS core class. This conceptualization includes three types of innovation (i.e., process, product, and business model innovation), and four stages for the overall innovation process (i.e., discovery, development, diffusion, and impact). Based on this conceptualization, we examine the implications of adopting digital innovation as an FPC. We also briefly discuss broader implications relating to (1) the IS curriculum beyond the core class, (2) the research agenda for the IS field, and (3) the identity and legitimacy of IS in business schools.

Natural disasters can have devastating economic and financial consequences for those affected. This research note explores the potential of artificial intelligence (AI) in disaster relief through lending services. By collaborating with a credit-scoring company, we investigate how AI-empowered lenders can effectively reduce delinquency rates for borrowers in the aftermath of disasters. Our findings reveal that borrowers applying to lenders that utilize AI in their loan assessment process experience improved outcomes in terms of delinquency reduction, particularly for borrowers with lower credit scores. This research underscores the positive impact of AI in the lending context, benefiting both lenders and borrowers. Furthermore, we highlight that AI indirectly supports disaster relief efforts through financing, providing a compelling use case for AI fairness in lending. Our findings have significant implications for leveraging AI as a valuable tool in mitigating the financial impact of disasters and promoting fairness in lending practices.

In social trading, less experienced investors (followers) are allowed to copy the trades of experts (traders) in real time after paying a fee. Such a copy-trading mechanism often runs into a transparency-revenue tension. On the one hand, social trading platforms need to release traders’ trades as transparently as possible to allow followers to evaluate traders accurately. On the other hand, complete transparency may undercut the platform’s revenue because followers can free ride. That is, followers can manually copy the trades of a trader to avoid paying the following fees. This study addresses this critical tension by optimizing the level of transparency through delaying the release of trading information pertaining to the trades executed by traders. We capture the economic impact of the delay using the notions of profit-gap and delayed-profit. We propose a mechanism that elucidates the economic effects of the profit-gap and delayed-profit on followers and, consequently, the amount of money following a trader: protection effect and evaluation effect. Empirical investigations find support for these two effects. We then develop a stochastic control formulation that optimizes platform revenue, where the control is the optimal delay customized at the trader level and calculated as a function of the current amount of money following a trader and the number of views on the trader’s profile page. The optimized revenue can be incorporated into an algorithm to provide a systematic way to infuse the platform’s goals into the ranking of the traders. A counterfactual study is conducted to demonstrate the performance of the optimal delay policy (versus a constant-delay policy) using data from a leading social trading platform operating in the foreign exchange market. This paper was accepted by Chris Forman, information systems.

Resale royalties, first introduced in the 1920s to support artists through a share of future resales, have now adopted by nonfungible token (NFT) marketplaces for digital art trading. Although these royalties are often viewed as beneficial for creators, our research reveals unexpected consequences. Using data from a major NFT marketplace, we find that NFTs with higher royalty rates sell for significantly lower prices and take longer to sell. Surprisingly, creators do not recoup these initial losses through royalty payments within four years. We discover that higher up-front minting costs lead creators to set higher royalty rates. We reveal a delayed gratification effect where creators with higher royalties accept lower up-front prices in hopes of future royalty income. We also find an overconfidence effect where confident creators, measured by their past sales and follower count, are more likely to lower initial prices. Our research contributes to the ongoing debate about royalty enforcement in NFT marketplaces and offers empirical evidence to inform platforms and creators. Platform managers should carefully consider both reducing up-front minting costs and implementing royalty rate limits to improve market liquidity. Creators should be cautious about setting high royalty rates as they may not provide the expected financial benefits.

Technologies have spawned finance innovations since the early days of computer applications in businesses, most recently reaching the stage of disruptive innovations, such as mobile payments, cryptocurrencies, and digitization of business assets. This has led to the emerging field called financial technology or simply FinTech. In this editorial review, we first provide an overview on relevant technological, pedagogical, and managerial issues pertaining to FinTech teaching and research, with a focus on market trading, artificial intelligence, and blockchain in finance. And then we introduce the articles appearing in this special section. We hope that our discussions of potential research directions and topics in FinTech will stimulate future research in the fields of information systems and finance toward making their unique marks in the FinTech evolution and the associated business and societal innovations.

Are Online Reviews of Physicians Reliable Indicators of Clinical Outcomes? A Focus on Chronic Disease Management