Kartik Hosanagar

• Amandeep Singh, Kartik Hosanagar, Aviv Nevo (Working), Network Externalities and Cross-Platform App Development in Mobile Platforms.
• Amit Gandhi, Kartik Hosanagar, Amandeep Singh (Under Review), Machine Learning Instrument Variables for Causal Inference.
• 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.

• Chen Jin, Kartik Hosanagar, Senthil Veeraraghavan, Impact of Bilateral Rating Systems on Online Platforms. 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.
• Ashish Agarwal, Kartik Hosanagar, Michael B. Smith (Under Revision), Sponsored Search: Do Organic Results Help or Hurt.
• 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.
• All Research from Kartik Hosanagar »

Current Courses

• OIDD3140 - Enabling Technologies

  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.

  OIDD3140002 ( Syllabus )

• OIDD6620 - Enabling Technologies

  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.

  OIDD6620002 ( Syllabus )

  OIDD6620004 ( Syllabus )

  OIDD6620006 ( Syllabus )

Past Courses

• MKTG8990 - Independent Study

  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.

• OIDD3140 - Enabling Technologies

  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.

• OIDD3990 - Supervised Study

  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.

• OIDD6620 - Enabling Technologies

  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.

• OIDD6670 - A.I., Business & Society

  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.

• OIDD8970 - Global Modular Course

  Global Modular Course (GMC) - MBA

• Best Information Systems paper published in Management Science, 2018
• Finalist, Best Information Systems paper published in Management Science (for papers published 2012-2015), for "Do I Follow My Friends or the Crowd? Information Cascades in Online Movie Ratings", 2016
• “Goes above and beyond the call of duty” award recipient, 2010
• One of ten faculty nominated by the MBA student body for the Helen Kardon Moss Anvil Award, 2010
• MBA Excellence in Teaching Award, 2007
• Wharton Class of 2009 “Goes above and beyond the call of duty” award recipient, 2007

• Who Profits the Most From Generative AI?, MIT Sloan Management Review - 03/12/2024
• Generative AI Won’t Revolutionize Search — Yet, Harvard Business Review - 02/23/2023
• How AI and Humans Can Best Collaborate at Work, Wall Street Journal - 04/07/2022
• Atomic-backed Jumpcut uses data to advance diversity in film, TechCrunch - 06/10/2021
• Reining in algorithms with Wharton Professor Kartik Hosanagar, CNBC Squawk Alley - 01/01/2019
• To Pique Interest, Start-Ups Try a Digital Velvet Rope, The New York Times - 07/17/2011
• EBay bids for durability in changing digital world, USA Today - 03/31/2008
• Clicks are great but calls are better, Forbes - 04/23/2007
• An arduous path to green cards, Philadelphia Inquirer - 04/08/2006
• Can India conquer world with IT only?, Economics Times - 11/22/2005

Knowledge @ Wharton

• Overwhelmed by Too Many Choices When Shopping? Wharton Retail Expert Weighs In, Knowledge @ Wharton - 12/19/2024
• Michael Lee, Oatly North America’s EVP & Executive Creative Director, Knowledge @ Wharton - 12/19/2024
• Why Anchor Institutions Are Vital to Local Economies, Knowledge @ Wharton - 12/17/2024
• Is the American Dream of Homeownership Still Within Reach?, Knowledge @ Wharton - 12/17/2024
• Even Under Trump, AI Governance Is Here to Stay, Knowledge @ Wharton - 12/17/2024
• 2024 Ripple Rewind (Part 2), Knowledge @ Wharton - 12/17/2024
• What Are the Best Countries of 2024?, Knowledge @ Wharton - 12/16/2024
• Your Pride Could Affect Your Workplace Reputation, Knowledge @ Wharton - 12/12/2024
• Samantha Baker, VP of Commerce at theSkimm, Knowledge @ Wharton - 12/12/2024
• NFL Stats & Analysis with Ben Solak, Knowledge @ Wharton - 12/11/2024