Kartik Hosanagar is the John C. Hower Professor of Technology and Digital Business and a Professor of Marketing at The Wharton School of the University of Pennsylvania. Kartik’s research work focuses on the digital economy, in particular the impact of analytics and algorithms on consumers and society, Internet media, Internet marketing and e-commerce.
Kartik serves as a department editor at the journal Management Science and has previously served as a Senior Editor at the journals Information Systems Research and MIS Quarterly. He is a ten-time recipient of MBA or Undergraduate teaching excellence awards at the Wharton School and has been recognized as one of the world’s top 40 business professors under 40. Kartik’s research has received several best paper awards. Kartik cofounded and developed the core IP for Yodle Inc, a venture-backed firm that was acquired by Web.com. Yodle was listed by Inc. Magazine among America’s fastest growing private companies. He has served on the advisory boards of Milo (acq. by eBay) and is involved with many other startups as either an investor or board member. His past consulting and executive education clients include Google, American Express, Citi and others. Kartik was a co-host of the SiriusXM show The Digital Hour.
Kartik graduated at the top of his class with a Bachelors degree in Electronics Engineering 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.
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Amandeep Singh, Kartik Hosanagar, Aviv Nevo (Working), Network Externalities and Cross-Platform App Development in Mobile Platforms.
Jing Peng, Ashish Agarwal, Kartik Hosanagar, Raghuram Iyengar (2018), Network Overlap and Content Sharing on Social Media Platforms, Journal of Marketing Research, 55, pp. 571-585.
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.
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.
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.
OIDD8990007 ( Syllabus )
OIDD8990009 ( Syllabus )
OIDD8990012 ( Syllabus )
OIDD8990013 ( Syllabus )
A student contemplating an independent study project must first find a faculty member who agrees to supervise and approve the student's written proposal as an independent study (MKTG 899). If a student wishes the proposed work to be used to meet the ASP requirement, he/she should then submit the approved proposal to the MBA adviser who will determine if it is an appropriate substitute. Such substitutions will only be approved prior to the beginning of the semester.
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), the business drivers of technology-related decisions in firms, and to stimulate thought on new applications for commerce (including disruptive technologies). The class provides a comprehensive overview of various emerging technology enablers and culminates in discussion of potential business impact of these technologies in the near future. No prior technical background is assumed and hence every effort is made to build most of the lectures from the basics. However, the Fall semester class will assume basic understanding of statistics and will focus more on big data analytics. Some assignments in the fall will involve data analytics using Python or R.
This course number is currently used for several course types including independent studies, experimental courses and Management & Technology Freshman Seminar. Instructor permission required to enroll in any independent study. Wharton Undergraduate students must also receive approval from the Undergraduate Division to register for independent studies. Section 002 is the Management and Technology Freshman Seminar; instruction permission is not required for this section and is only open to M&T students. For Fall 2020, Section 004 is a new course titled AI, Business, and Society. The course provides a overview of AI and its role in business transformation. The purpose of this course is to improve understanding of AI, discuss the many ways in which AI is being used in the industry, and provide a strategic framework for how to bring AI to the center of digital transformation efforts. In terms of AI overview, we will go over a brief technical overview for students who are not actively immersed in AI (topic covered include Big Data, data warehousing, data-mining, different forms of machine learning, etc). In terms of business applications, we will consider applications of AI in media, Finance, retail, and other industries. Finally, we will consider how AI can be used as a source of competitive advantage. We will conclude with a discussion of ethical challenges and a governance framework for AI. No prior technical background is assumed but some interest in (and exposure to) technology is helpful. Every effort is made to build most of the lectures from the basics.
This course is about understanding emerging technology enablers with a goal of stimulating thinking on new applications for commerce. 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, investment banking and venture capital in the tech sector. OIDD 6620 will be taught in the regular 1 CU format by Prof Lynn Wu. When taught by Prof Hosanagar, OIDD 6620 will be delivered in a 0.5 CU format. The shorter course will focus primarily on Mobile, Data/AI, and Web3.
The course provides an overview of AI and its role in business transformation. The purpose of this course is to improve understanding of AI, discuss the many ways in which AI is being used in the industry, and provide a strategic framework for how to bring AI to the center of digital transformation efforts. In terms of AI overview, we will go over a brief technical overview for students who are not actively immersed in AI (topics covered include Big Data, data warehousing, datamining, machine learning, etc). In terms of business applications, we will consider applications of AI in Media, Finance, Healthcare, Retail, and other industries. Finally, we will consider how AI can be used as a source of competitive advantage. We will conclude with a discussion of ethical challenges and a governance framework for AI. No prior technical background is assumed but some interest in (and exposure to) technology is helpful. Every effort is made to build most of the lectures from the basics.
Global Modular Course (GMC) - MBA
As part of the series "AI in Focus," Wharton's Eric Bradlow, Kartik Hosanagar, and Stefano Puntoni examine how AI will affect business and society as adoption continues to grow.…Read MoreKnowledge at Wharton - 11/9/2023
You may not notice it, but AI permeates almost every facet of your life. From ride shares to chatbots to your carefully tailored Facebook newsfeed, machine intelligence has shaped how we interact with the world and with each other. In this rapidly changing landscape, Wharton Prof. Kartik Hosanagar plans to…Wharton Stories - 03/11/2020