Knowledge that Transforms

In this study, we focus on the largely overlooked but important topic: social value created by teleconsultations. Many countries suffer from the geographic imbalance of their medical professionals: there are abundant resources in urban cities but too few in rural areas. Teleconsultations have emerged as a promising solution to reduce this disparity because they can remotely deliver healthcare without relocating medical professionals. Yet it is unclear whether teleconsultations actually mobilize healthcare to underserved areas. To answer this question, we collaborate with a large online healthcare platform and analyze its teleconsulting data together with offline healthcare and regional data. Our results indicate that teleconsultations tend to connect physicians in resourceful regions with patients in underserved areas—a desirable pattern that alleviates the geographic healthcare disparity. However, we also find that social, information, and geography frictions persist. For instance, teleconsultations are less likely to occur as regions become farther apart, and financial and information constraints limit rural patients’ access to teleconsultations. We uncover the underlying mechanisms that drive such frictions and provide recommendations to reduce the frictions that hinder teleconsultations.

Intelligent systems—incorporating computational tools, learning algorithms, and statistical models—can generate knowledge to empower employees in how they conduct their work and increase their job performance. How can organizations realize this potential? Our in-depth study of transformation of work with intelligent systems in a technology maintenance service company provides managerial insights to this question. Although knowledge from intelligent systems can empower employees, employees will need to adapt how they work with intelligent systems to improve their job performance. Interestingly, they can leverage the empowerment to adapt in two ways: maximize benefits, where they use the system to its full potential in conducting work, and minimize disturbances, where they reduce role conflict with the system in conducting work. Although inexperienced employees leverage the empowerment to use the system to its full potential, experienced employees leverage the empowerment to minimize role conflict with the system. How empowered employees realize job performance gains requires understanding how employees channel their empowerment: maximize benefits through use of the system or minimize disturbances through role conflict with the system. Differentiating how inexperienced and experienced employees channel empowerment to increase job performance will enable managers to effectively manage the transformation of work for these two groups.

Research shows that consuming ratings influences purchase decisions in e-commerce and also has modest effects on belief in news articles on social media. We find that the act of producing ratings reduces belief in news articles on social media and induces social media users to think more critically. We propose this intervention as a method to encourage users to realize that, unlike in the product rating setting, social media users who submit their ratings for news articles typically lack firsthand knowledge of the events reported in the news, making it difficult for most users to rate news articles accurately. We asked 68 social media users to assess the believability of 42 social media articles and measured their cognitive activity using electroencephalography. We found that asking users to rate articles using a self-referential question induced them to think more critically—as indicated by increased activation in the medial prefrontal cortex and dorsolateral prefrontal cortex—and made them less likely to believe the articles. The effect extended to subsequent articles; after being asked to rate an article, users were less likely to believe other articles that followed it whether they were asked to rate them or not.

New platforms such as peer-to-peer marketplaces frequently face a “coordination problem” in attracting users, as potential users may be reluctant to join given the uncertainty of other users joining, and this could cause the new platform to fail. There are several mechanisms in the literature to help address this coordination problem, such as subsidies for early users and promises of refunds or buybacks if the platform fails; these mechanisms, however, are likely to favor incumbent and larger firms with established reputation and financial resources. Platform-specific tradable cryptographic tokens allow a platform such as a marketplace to trade future revenue for present revenue, which then can be used to address the coordination problem. If the new platform is capital-constrained, as is often the case for new entrants and unproven technology applications, then tokens can offer an attractive alternative. As this benefit results from the tradability of the tokens, regulators and policy makers should facilitate or at least not unduly constrain the issuance of tradable utility tokens by new platforms. There is no corresponding benefit from user anonymity, and thus no corresponding drawback from identity regulations to establish accountability.

Companies face a trade-off between creating stronger privacy protection policies for consumers and employing more sophisticated data collection methods. Justice-driven privacy protection outlines a method to manage this trade-off. We built on the theoretical lens of justice theory to integrate justice provision with two key privacy protection features, negotiation and active-recommendation, and proposed an information technology (IT) solution to balance the trade-off between privacy protection and consumer data collection. In the context of mobile banking applications, we prototyped a theory-driven IT solution, referred to as negotiation, active-recommendation privacy policy application, which enables customer service agents to interact with and actively recommend personalized privacy policies to consumers. We benchmarked our solution through a field experiment relative to two conventional applications: an online privacy statement and a privacy policy with only a simple negotiation feature. The results showed that the proposed IT solution improved consumers’ perceived procedural justice, interactive justice, and distributive justice and increased their psychological comfort in using our application design and in turn reduced their privacy concerns, enhanced their privacy awareness, and increased their information disclosure intentions and actual disclosure behavior in practice. Our proposed design can provide consumers better privacy protection while ensuring that consumers voluntarily disclose personal information desirable for companies.

Social media has become a vital platform for voicing product-related experiences that may not only reveal product defects, but also impose pressure on firms to act more promptly than before. This study scrutinizes the rarely studied relationship between these voices and the speed of product recalls in the context of the pharmaceutical industry in which social media pharmacovigilance is becoming increasingly important for the detection of drug safety signals. Using Federal Drug Administration drug enforcement reports and social media data crawled from online forums and Twitter, we investigate whether social media can accelerate the product recall process in the context of drug recalls. Results based on discrete-time survival analyses suggest that more adverse drug reaction discussions on social media lead to a higher hazard rate of the drug being recalled and, thus, a shorter time to recall. To better understand the underlying mechanism, we propose the information effect, which captures how extracting information from social media helps detect more signals and mine signals faster to accelerate product recalls, and the publicity effect, which captures how firms and government agencies are pressured by public concerns to initiate speedy recalls. Estimation results from two mechanism tests support the existence of these conceptualized channels underlying the acceleration hypothesis of social media. This study offers new insights for firms and policymakers concerning the power of social media and its influence on product recalls.

A consensus is beginning to emerge that the next phase of artificial intelligence (AI) induction in business organizations will require humans to work with AI in a variety of work arrangements. This article explores the issues related to human capabilities to work with AI. A key to working in many work arrangements is the ability to delegate work to entities that can do them most efficiently. Modern AI can do a remarkable job of efficient delegation to humans because it knows what it knows well and what it does not. Humans, on the other hand, are poor judges of their metaknowledge and are not good at delegating knowledge work to AI—this might prove to be a big stumbling block to create work environments where humans and AI work together. Humans have often created machines to serve them. The sentiment is perhaps exemplified by Oscar Wilde’s statement that “civilization requires slaves…. Human slavery is wrong, insecure and demoralizing. On mechanical slavery, on the slavery of the machine, the future of the world depends.” However, the time has come when humans might switch roles with machines. Our study highlights capabilities that humans need to effectively work with AI and still be in control rather than just being directed.

Practice- and policy-oriented abstract for “Research Spotlights” Although enjoying rapid development, online lending also endures some unusual risk, that is, platform risk. We address a new problem at the macro platform level, platform risk evaluation, and explore types of information and methods that are effective in predicting platform risk. We identify four types of information, that is, platform characteristic, risk management, commercial competition, and online word of mouth, and examine their utilities, separately and jointly, in predicting platform risk. We also propose the use of survival analysis, especially the mixture survival model, in predicting whether and when a platform will default. We carry out a cross-stage analysis using data crawled from two leading web portals for online lending in China with the two stages separated by the recent dramatic policy intervention. The results reveal the differences among the four identified factors in terms of predictive utility, the heterogeneity between the two types of default platforms, and differences between the start-up and stable periods of platform development. Based on the results, we derive some insights and examine the cross-stage changes and commonalities. We provide both lessons learned from the past and practical implications for market managers and lenders in the current online lending market.

Information asymmetry between sellers and buyers is inherent in online markets where transactions often occur between strangers. Trust-building mechanisms such as seller feedback ratings have reduced these problems because a seller’s feedback ratings build buyers’ trust in the seller before they engage in a transaction. However, these ratings are retrospective, that is, they generate information about a transaction after it is completed, rather than during the transaction itself. Additionally, they are based on other users’ experiences, possibly in different contexts, not based on any direct interaction between the prospective buyer and the seller. To address this problem, we study public buyer–seller engagement via question and answer during online auctions and find that seller engagement (responding to buyers’ questions) can affect buyer behavior, including those who do not ask any questions. Our analysis shows that the impact of the seller’s engagement on buyer behavior varies with product type and seller reputation (feedback ratings). A key insight is that sellers with higher reputation reap greater benefits from this engagement than other sellers. We also find that the cost of an additional negative feedback rating outweighs the benefit of a positive one.

This paper employs a design science approach and proposes a new composite metric, eigen attention centrality (EAC), as a proxy for information flows associated with a node that considers both attention to a node and coattention with other nodes in a network. We apply the EAC metric in the context of a financial market where nodes are individual stocks and edges are based on coattention relationships among stocks. Composite information from different channels is used to measure attention and coattention. We evaluate the effectiveness of the EAC metric on predicting abnormal returns of stocks by (1) using multiple prediction methods and (2) comparing EAC with a set of alternative network metrics. Our analysis shows that EAC significantly outperforms alternative models in predicting the direction and magnitude of abnormal returns of stocks. Using the EAC metric, we derive a stock portfolio and develop a trading strategy that provides significant and positive excess returns. Lastly, we find that composite information has significantly better predictive performance than separate information sources, and such superior performance owes to information from social media instead of traditional media.