Knowledge that Transforms

Facial recognition payment technology (FR) has the potential to disrupt the offline retailing industry by automating the payment process. However, some firms that adopted FR payment technology have experienced only moderate success, and many customers have expressed frustration using FR payment technology. By utilizing data sets from three retail chains, we find that customers are less likely to use FR payment technology during self-checkouts when more customers are in line behind them, waiting and watching (the social presence effect), and when more preceding customers use the other payment technology (the herding effect). These findings imply that (1) the design of FR technology can be improved to alleviate the social presence effect (such as adding a privacy screen filter or beautify the appearance of the consumer’s image), and (2) monetary incentives may be used to attract more users by leveraging the herding effect.

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.

This study uses observational data from two online retail sites to examine the effect of questions and answers on sales of experience goods. Particularly, we leverage the naturally occurring experiment where the Q&A capabilities are available on only one platform. Interestingly, we discover that answers, especially the depth of answers, positively affect sales. Additionally, the fraction of questions with at least one answer positively and significantly affects product sales. Our additional textual content analyses unveil that fit- and quality-oriented questions have different effects on sales, as do positive and negative answers. Insights from this work can help platform managers develop Q&A ecosystems and content management policies.

Managerial responses to negative reviews could be easily understood as a brand-safeguarding strategy by firms because negative reviews can damage a company’s reputation. However, it is unclear if managers should respond to positive reviews and if so, if such action helps or hurts the firm. We develop a theoretical framework to explicate the mechanisms underlying the effects of managerial responses to positive reviews on user reviewing behaviors in online platforms. We classify positive reviews into four types: one-sided affective reviews, two-sided affective reviews, one-sided instrumental reviews, and two-sided instrumental reviews. We classify managerial responses as tailored and template responses. Using natural language processing and deep learning algorithms, we extract information presented in the texts in the reviews and responses. We theorize and test which kinds of managerial responses to positive reviews are helpful and which of them are harmful. Overall, we find that a tailored response is more appropriate when responding to two-sided instrumental positive reviews and one-sided affective positive reviews, whereas template responses work for one-sided instrumental positive reviews and two-sided affective positive reviews. Not responding would be an effective strategy for mixed positive reviews.

To combat false information, social media sites have heavily relied on content moderation, mostly performed by human workers. However, human content moderation entails multiple problems, including huge labor costs, ineffectiveness, and ethical issues. To overcome these concerns, social media companies are aggressively investing in the development of artificial intelligence-powered false information detection systems. Extant efforts, however, have failed to understand the nature of human argumentation, delegating the process of making inferences of the truth to the black box of neural networks. They fail to attend to important aspects of how humans make judgments on the veracity of an argument, creating important challenges. To this end, based on Toulmin’s model of argumentation, we propose a computational framework that helps machine learning for false information identification understand the connection between a claim (whose veracity needs to be verified) and evidence (which contains information to support or refute the claim). The two experiments for testing model performance and explainability reveal that our framework shows stronger performance and better explainability, outperforming cutting-edge machine learning methods and presenting positive effects on human task performance, trust in algorithms, and confidence in decision making. Our results shed new light on the growing field of automated false information identification.

Predictive algorithmic scores can significantly impact the lives of assessed individuals by shaping decisions of organizations and institutions that affect them, for example, influencing the hiring prospects of job applicants or the release of defendants on bail. To better protect people and provide them the opportunity to appeal their algorithmic assessments, data privacy advocates and regulators increasingly push for disclosing the scores and their use in decision-making processes to scored individuals. Although inherently important, the response of scored individuals to such algorithmic transparency is understudied. Inspired by psychological and economic theories of information processing, we aim to fill this gap. We conducted a comprehensive empirical study to explore how and why disclosing the use of algorithmic scoring processes to (involuntarily) scored individuals affects their behaviors. Our results provide strong evidence that the disclosure of fundamentally erroneous algorithmic scores evokes self-fulfilling prophecies that endogenously steer the behavior of scored individuals toward their assessment, enabling algorithms to help produce the world they predict. Our results emphasize that isolated transparency measures can have considerable side effects with noticeable implications for the development of automation bias, the occurrence of feedback loops, and the design of transparency regulations.

Social trading is an emerging market in the sharing economy, allowing investors (followers) to duplicate the trades of other investors (leaders) in real time. We analyze the formation and dissolution of links in a large social trading network. Such networks are characterized by the rapid dissolution of links, increasing the importance of studying network dissolution. We investigate how social communication, along with financial performance and demographics, affects dynamic network evolution. We show that different types of social communication, such as posts and comments, have different implications for link formation and dissolution. Moreover, we find financial performance to be highly important for link formation and dissolution, whereas demographic characteristics are only relevant for link formation. In social trading, the extreme flexibility of followers in dissolving links and thereby, terminating their relationship instantaneously brings about large income uncertainty for leaders. Thus, a thorough understanding of network evolution and its determinants is crucial for leaders. Our results can provide guidance on when and how to communicate with followers. As vocal leaders on social media may exert a significant influence on financial markets—as demonstrated by recent the GameStop frenzy—a better understanding of the evolution of investment networks is also important for regulators.

Recognizing the importance of doctor engagement in online health communities (OHCs), managers and platform owners seek to foster doctor-patient interactions and encourage doctors’ knowledge sharing by introducing informal payments. This study investigates how informal payments in the form of monetary gifts affect doctor engagement, using the launch of a gifting feature by a leading OHC as a natural experiment that exogenously provides doctors with extra monetary incentives. We find that informal payments can have a crowding-out effect on doctors’ intrinsic motivation to engage in medical consultations. We also find that monetary and nonmonetary gifts play distinct roles in motivating doctor responses, with nonmonetary gifts having a more significant carryover effect on follow-up interactions and better promoting the doctor-patient relationship. Our findings additionally suggest that social status moderates the impact of digital gifting on doctor engagement. These findings provide useful implications for online health communities that have implemented or are planning to implement digital gifting to stimulate user engagement.

This paper investigates the ramifications of information feed integration on user engagements and contributions in online content-sharing platforms by exploiting a natural experiment occurred in a leading knowledge-sharing platform that integrated informal social posts with professional knowledge content in one feed. Our results show that the juxtaposition of incongruous types of content increased mindset switching and cognitive strain, thus hurting user engagements. We also reveal a novel crowding-out effect, viz., the integration heightened concerns that posting informal social posts would dilute the contributor’s professional image, thus inhibiting user contributions. Our findings hold important practical implications for all platforms that host (or are considering hosting) diverse types of user-generated content (UGC). Additional content curation tools can potentially enhance user engagement and retention, but their effectiveness hinges on a foundational and crucial element—the presentation format of heterogeneous content types. Essentially, the value of curating informal social posts in a knowledge-sharing platform would diminish when those content intrudes upon and conflict with the professional domain. This insight underscores that any UGC platforms, when adopting a diversity-oriented strategy, should pay close attention to heterogeneity between different content types for the purpose of optimizing user experiences and promoting user contributions.

Current studies show gamification, the integrating of game design elements into target systems, enhances user engagement and instrumental task outcomes. Despite its potential for improving behavioral outcomes, gamification can also lead to maladaptive behaviors, behaviors directed at misappropriating gamified systems. We conceptualized gamified system maladaptive behaviors (GSMB), which involve technology and gamified task maladaptations. We developed a model that depicts three drivers of GSMB from design elements, how they fulfill or frustrate psychological innate needs, which in turn drive GSMB, and how GSMB affect task performance. We tested how the three drivers of design elements affect GSMB in Study 1 by empirically examining users of a gamified system, Pocket Points. The results support our conceptualization of GSMB, and design issues as its antecedents. To further unpack this relationship, we then employed a within-subject experiment and a follow-up survey in Study 2. By manipulating the design issues, we found that GSMB adversely affect task performance, because these users may focus too intently on winning the game, at the expense of task performance. By assessing the fulfillment of psychological needs, our findings suggest that design in gamified systems may not uniformly fulfill the satisfaction of psychological needs and consequently triggers GSMB.