Earticle

We propose a mobile coupon strategy designed to increase the effect of push notifications on redemption. The proposed strategy recommends that firms deliver mobile coupons with distant expiration dates and remind them through push notifications framed negatively once these expiration dates become imminent, rather than frequently sending coupons with near expiration dates. We test the effectiveness of the proposed strategy using data collected through a randomized field experiment. The findings indicate that push notifications enhance coupon redemption rates for coupons that are held longer by customers than those that are recently received. Additionally, we found that sending negatively framed push notification messages to remind customers of imminent coupon expiration dates further resulted in higher coupon redemption rates. The findings can be employed to offer useful guidance on how to effectively design mobile coupons for achieving higher redemption rates.

Recommender systems reduce the information overload by providing users’ “top-pick” recommendations. However, selecting the “best” out of good options can be more challenging than separating good and bad ones. Based on the of attraction effects frameworks, we examine the effectiveness of recommender systems including “worstpicks”. The addition of an unfavored item may alleviate consumers’ cognitive load and ease comparisons—all of which means the improved performance of recommendation structures. For empirical validation, we employed a randomized field experiment involving 475,339 unique users, 93,282 fashion products and 25,854,168 total instances of exposure to recommendations. The findings show that the attraction effect (AE) model outperformed the rational choice (RC) equivalent. AE model was more effective on PCs than over mobile phones. For male consumers, the AE model outcompeted the RC equivalent, but such difference was not detected among female shoppers. Based on these findings, we discuss the theoretical and practical implications.

Despite the widespread use of artificial intelligence (AI) in mobile healthcare apps, the need for more transparency in AI algorithms hinders their effectiveness by preventing users from understanding the reasons behind AI-based information provision. To address this challenge, various types of explainable AI (XAI) are adopted to offer transparent explanations of AI. Despite significant debates surrounding AI intervention, limited research has been devoted to whether and how various XAI types affect user behavior differently. In this study, we conducted a randomized field experiment to investigate the effectiveness of three XAI algorithms: 1) feature importance, 2) feature attribution, and 3) counterfactual explanation in promoting users' health behavior. Drawing on the self-regulated learning theory, we expect that XAI focusing on counterfactual explanation increases strategic planning and outcome expectancy, resulting in better self-regulation behavior. Our findings indicate that counterfactual explanation significantly improves users' action planning behavior, leading to a 16.5% increase in workout duration and a 3.49% increase in health records compared to the control group. Our results are salient for users with a high level of AI susceptibility due to age, goal weight loss, and AI outcome. Our finding sheds light on the potential of algorithmic explanations to improve the effectiveness of AI interventions in the healthcare industry, with practical implications for designing more transparent and user-friendly healthcare apps.