Kartik Hosanagar is a Professor at The Wharton School of the University of Pennsylvania. Kartik’s research work focuses on the digital economy, in particular Internet media, Internet marketing and e-commerce.
Kartik has been recognized as one of the world’s top 40 business professors under 40. He has received several teaching awards including the MBA and Undergraduate Excellence in Teaching awards at the Wharton School. His research has received several awards including the best paper award at the Consortium on Technology Policy and Management. Kartik is a cofounder of Yodle Inc, a venture-backed firm that has been listed among the top 50 fastest growing private firms in the US. He has served on the advisory board of Milo Inc (acq by eBay) and is involved with other startups as either an investor or board member. His past consulting and executive education clients include Google, Nokia, American Express, Citi and others.
Kartik graduated at the top of his class with a Bachelors degree in Electronics and a Masters in Information Systems from Birla Institute of Technology and Sciences (BITS, Pilani), India, and he has an MPhil in Management Science and a PhD in Management Science and Information Systems from Carnegie Mellon University.
Abstract: Platforms in the sharing economy such as Uber and Lyft adopt a bilateral rating system (BRS) that allows service providers to rate customers and to make accepting/rejecting decisions based on the customers' ratings while in the traditional online platforms (e.g., eBay), only customers have the privilege to rate the other party, i.e., a unilateral rating system (URS) is implemented. This novel feature of the rating system in the sharing economy changes the service provider's effort structure in a fundamental way, which in turn affects the pricing strategy of the platform and the welfare of service providers as well as customers. With a stylized model, we compare URS and BRS in the context of ride-sharing service to study their impact on the decisions as well as revenue/welfare of all stakeholders. Our results show that being empowered to turn down customers at the service providers' discretion (in BRS) may not always improve the economic situation of service providers. Specically, the platform could squeeze the service providers' profit margin (per order) forcing them to serve only highly rated customers and hence reducing both their transaction volume and the profit margin. This leads to a decline in the service quality compared with URS. Meanwhile, the platform could also suffer a loss from BRS when the customers' valuation to the service is high (e.g., in a city with a less developed public transportation system), as the service providers' "cherry-picking" behavior in selecting customers is particularly costly to the platform in that case. This makes the platform have to give some of its revenue back to the service providers to mitigate their over-selection behavior, which can potentially reduce the transaction volume substantially. In practice, the platform could change the decision time of drivers (to reject customers' request) based on the estimation of the customers' valuation (in each city) to the ride-sharing service and hence, switching between bilateral and unilateral rating systems effectively.
Jing Peng, Ashish Agarwal, Kartik Hosanagar, Raghuram Iyengar (Under Review), Network Overlap and Content Sharing on Social Media Platforms.
Abstract: Social media platforms allow users to connect and share content. The extent of information diffusion may depend on the characteristics of users’ connections, such as the overlap among users’ connections. We investigate the impact of network embeddedness (i.e., number of common followees, common followers, and common mutual followers between two users) on the information diffusion in directed networks. To accommodate the empirical observation that a user may receive the same information from several others, we propose a new hazard model that allows an event to have multiple causes. By analyzing the diffusion of sponsored ads on Digg and brand-authored tweets on Twitter, we find that the effect of embeddedness in directed networks varies across different types of “neighbors”. The number of common neighbors are not always conducive to information diffusion. Moreover, the effects of common followers and common mutual followers are negatively moderated by the novelty of information, which shows a boundary condition for previous finding on embeddedness in undirected networks. For marketing managers, these findings provide insights on how to target customers in a directed network at the micro level.
Panos Markopoulos and Kartik Hosanagar (Working), A Model of Product Design and Information Disclosure Investments.
Vibhanshu Abhishek, Kartik Hosanagar, Peter Fader (Working), The Long Road to Online Conversion: A Model of Multi-Channel Attribution.
Young Jin Lee, Yong Tan, Kartik Hosanagar (Under Revision), Do I Follow My Friends or the Crowds? Examining Informational Cascades in Online Movie Reviews.
Soumya Sen, Roch A. Guerin, Kartik Hosanagar (Under Revision), Shared or Dedicated Infrastructure? On the Impact of Reprovisioning.
Nitin Bakshi, Kartik Hosanagar, Christophe Van den Bulte (Working), New Product Diffusion with Two Interacting Segments or Products.
Vibhanshu Abhishek, Kartik Hosanagar, Peter Fader (2015), Aggregation Bias in Sponsored Search Data: The Curse and The Cure, Marketing Science, 34, pp. 59-77.
Abstract: There has been significant recent interest in studying consumer behavior in sponsored search advertising (SSA). Researchers have typically used daily data from search engines containing measures such as average bid, average ad position, total impressions, clicks and cost for each keyword in the advertiser's campaign. A variety of random utility models have been estimated using such data and the results have helped researchers explore the factors that drive consumer click and conversion propensities. However, virtually every analysis of this kind has ignored the intra-day variation in ad position. We show that estimating random utility models on aggregated (daily) data without accounting for this variation will lead to systematically biased estimates -- specifically, the impact of ad position on click-through rate (CTR) is attenuated and the predicted CTR is higher than the actual CTR. We demonstrate the existence of the bias analytically and show the effect of the bias on the equilibrium of the SSA auction. Using a large dataset from a major search engine, we measure the magnitude of bias and quantify the losses suffered by the search engine and an advertiser using aggregate data. The search engine revenue loss can be as high as 11% due to aggregation bias. We also present a few data summarization techniques that can be used by search engines to reduce or eliminate the bias.
Kartik Hosanagar, Yong Tan, Peng Han (Working), Dynamic Referrals in Peer-to-Peer Media Distribution.
Conducting business in a networked economy invariably involves interplay with technology. The purpose of this course is to improve understanding of technology (what it can or cannot enable) and the business drivers of technology-related decisions in firms. We will be discussing some of the new and most disruptive technologies right now to stimulate thought on new applications for commerce and new ventures, as well as their implications to the tech industry as a whole. Topics include social media, online advertising, big data, and cloud computing. The course will take a layered approach (from network infrastructure) to data infrastructure to applications infrastructure, or direct enablers of commerce) to first, understanding and then, thinking about technology enablers. Network infrastructure layers include fundamentals of wired and wireless infrastructure technologies such as protocols for networking, broadband technologies - for last (DSL, Cable etc) and other miles (advances in optical networking) and digital cellular communications. Data infrastructure layers include usage tracking technologies, search technologies and data mining. Direct application layers include personalization technologies (CRM), design technologies for content and exchanges, software renting enablers, application service provision, agents and security mechanisms. Finally some emberging technology enablers (such as bluetooth, biometrics and virtual reality) are identified and discussed.
This course is about understanding emerging technology enablers with a goal of stimulating thinking on new applications for commerce. No prerequisite or technical background is assumed. The class is self-contained (mainly lecture-based) and will culminate in a class-driven identification of novel businesses that exploit these enablers. No prerequisite or technical background is assumed. Students with little prior technical background can use the course to become more technologically informed. Those with moderate to advanced technical background may find the course a useful survey of emerging technologies. The course is recommended for students interested in careers in consulting, investement banking and venture capital in the tech sector.
As e-commerce competition gathers pace in India, players increasingly are looking to get the wallet share of online grocery buyers.Knowledge @ Wharton - 2018/04/26