<p dir="ltr">In the dynamic landscape of digital platforms, which includes various e-commerce and online review platforms, reputation systems play a fascinating role in shaping consumer behavior and influencing market outcomes. Seller-level reputation badges and individual recognition interventions, such as status badges and exemplar reviews, serve as important tools for enhancing trust and guiding customer decisions. Despite the prevalent use of customer reviews and ratings, the additional value provided by these reputation mechanisms warrants comprehensive investigation. This dissertation delves into the multifaceted impacts of seller-level reputation badges and exemplar reviews on consumer behavior and platform performance through two distinct essays.</p><p dir="ltr">In the first essay, we utilize a distinctive panel dataset from Airbnb to estimate the comprehensive effects of the seller reputation badge on both individual sellers and the overall platform. This study diverges from existing research that predominantly examines the influence of product-level reputation badges on individual sellers. By employing a difference-in-differences methodology and an exogenously determined schedule of updating the Superhost badge status, we find a substantial positive impact of the Superhost badge system on the bookings for Airbnb sellers and the platform. Additionally, we employ counterfactual estimators to uncover evidence of temporal dynamics in the badge system's positive effects. Our findings from counterfactual estimators indicate that the influence of the Superhost badge intensifies considerably within the initial weeks following its implementation, after which it reaches a state of equilibrium. Moreover, our analysis suggests that the Superhost badge exerts a more significant positive effect on the bookings of properties managed by hosts with multiple listings than those with a single property. These observations underscore the significant value of instituting a seller reputation badge system, even for online platforms with customer review mechanisms.</p><p dir="ltr">In a related analysis, the second essay investigates the strategies employed by online review platforms, which often involve targeted interventions such as status badges and exemplar reviews, to enhance user contribution to the platform's review system. However, a minority of individuals receive these interventions, and limited research exists on how reviews written by such intervention recipients result in second-order effects on nonrecipients' contributions. In this study, we leverage a quasi-experimental framework on a leading Asian review platform to evaluate the impact of exemplar reviews on users' review-writing behavior. We utilize a combination of econometric methods and natural language processing techniques to show an increase in the quantity and quality of user contributions following exposure to exemplar reviews. We also observe that users adapt their writing styles in response to exemplar reviews. Importantly, the effect of an exemplar review is moderated by the user's review-writing experience. Specifically, our findings reveal that less experienced reviewers, as opposed to their more seasoned counterparts, tend to increase their review volume and length upon reading exemplar reviews. However, they need more progress regarding review readability and perceived helpfulness. Further investigation indicates that reviews by users exposed to exemplar content gradually exhibit increasing homogeneity over time in terms of length, sentiment, readability, and topic diversity, suggesting a self-herding behavior that has not been previously documented in the literature on user-generated content. These insights provide valuable practical implications for platform managers on effectively utilizing reward programs to enhance the sustainability of their review platforms.</p>