Research@CATS - Platform Business

Platform Business

Strategic and operational problems in management of platform businesses, applications of platform thinking, transformational impact of platforms.

Platforms, or technology-enabled business marketplaces, are transforming the way we think about business. For 200 years, businesses had been about creating large scale in various aspects and connecting them in a linear fashion: raw materials, parts and components, resources of production and transformation, distribution, etc., and using this large-scale structure to efficiently create and supply something of value to the customer.

Today’s platform technologies change this in a fundamental way: business and customer value is created by enabling others to create and provide value – and then performing necessary tasks (such as discovery, matching, fulfillment, financial payments, trust etc.) to make it is easy for this to happen.

While a typical traditional business might work with hundreds of thoroughly vetted business partners under bespoke contracts, platforms typically feature a dynamic ecosystem with thousands or millions of partners enabled with information technologies that support lightweight automated contracts. CATS faculty have examined several exciting research questions motivated by this new architecture. 

Ecosystem Management in Advertising-driven Platforms with External Contributors

This paper examines ecosystem management issues in multi-sided platforms that provide infrastructure to coordinate the activities and interactions of contributors, consumers, and advertisers, and which motivate external contributors through revenue-sharing of ad payments. It provides insights on how various platform design parameters interplay with content provision decisions of contributors and the revenue-sharing arrangement between contributors and the platform.

Bundling for Flexibility and Variety: An Economic Model for Multi-Producer Value Aggregation

A new business form that is increasingly prominent, especially in platform business models, is an economic structure in which value is co-created by multiple producers and aggregated into a common bundle by a producer-consortium or independent firm. Examples include in-home video entertainment, technology goods and services, multi-sourced data platforms, and patent pools. This paper develops an economic model to study demand, production choices, revenue-sharing, and relative market power in such markets. Beyond these specific questions, it provides an architecture to rigorously answer additional questions in platform competition, market power, and effects of industrial realignment.

The Business of Electric Vehicles: A Platform Perspective

While platform business tactics are very visible and dominant in industries and firms that have embraced platform thinking, many businesses and firms are engaged in a platform game without realizing it, often to negative consequences. This article focuses on the electric vehicle (EV) segment. 

Jonas Boehm, Hemant K. Bhargava, and Geoffrey Parker, “The Business of Electric Vehicles: A Platform Perspective”, Foundations and Trends in Technology, Information and Operations Management (forthcoming, last revised July 2020).


Platform Data Strategy

This paper provides an analysis and guidelines for developing data strategy for different types of platforms, and it identifies promising research opportunities into platform data strategy to better inform future academic research, strategic decision-making, and regulatory analysis.

Sales Force Compensation Design for Two-Sided Market Platforms

Mutli-sided platforms typically thrive on the positive feedback loop of indirect network effects. However, many platforms also have to engage in active selling, through the use of sales agents to mobilize network participants. This paper is the first to examine sales force compensation design under network effects and develops a series of insights regarding how network effects alter compensation design.

Using Machine Learning to Predict Price Dispersion

Theory suggests two sources of price dispersion amongst homogenous goods: market frictions or product heterogeneity. We collected posted-price listings for Kindle Fire tablets from eBay to determine if listing heterogeneity can explain the high degree of dispersion we observe. Using a basic set of controls and empirical techniques in line with the previous literature, we can explain only 13% of variation in posted prices, which is also in keeping with previous research.

However, we can explain 42% of the dispersion by applying machine learning to a richer set of variables, which we extract from raw downloaded HTML pages. We interpret this number as a bound on the role of market frictions in driving price dispersion. Variables describing the amount of information in the listings, the style of the listings, and the content of the listings’ text are effective price predictors independently of one another. Our analysis suggests that the content of the listings’ text plays a primal role in generating the predictions of the machine learning estimator. We repeat our analysis on a cross-section of products across a variety of categories on eBay, including household products, sporting goods, and other consumer electronics, and we find a comparable degree of price predictability across all of the products.

How Efficient are Decentralized Auction Platforms?

We provide a model of a decentralized, dynamic auction market platform (e.g., eBay) in which a continuum of buyers and sellers participate in simultaneous, single-unit auctions each period. Our model accounts for the endogenous entry of agents and the impact of intertemporal optimization on bids. We estimate the structural primitives of our model using Kindle sales on eBay. We find that just over-one third of Kindle auctions on eBay result in an inefficient allocation with deadweight loss amounting to 14% of total possible market surplus.

We also find that partial centralization–for example, running half as many 2-unit, uniform-price auctions each day–would eliminate a large fraction of the inefficiency, but yield slightly lower seller revenues. Our results also highlight the importance of understanding platform composition effects–selection of agents into the market–in assessing the implications of market redesign. We also prove that the equilibrium of our model with a continuum of buyers and sellers is an approximate equilibrium of the analogous model with a finite number of agents.

Adapt or Die: Platforms, Timing, and … Do Platforms beat Products?

It was a lot of fun attending Apigee’s “Adapt or Die” event on September 27 in the heart of San Francisco, and speaking on the panel “Who Ubers Who: How to Survive and Thrive in a Digital World of Mash-Ups, Matchmakers, and Marketplaces.” The best part was the 10-minute thriller movie “Adapt or Die” made by Mike Slade and starring Alexandra Grossi, whom I had a chance to meet, and featuring Apigee CEO Chet Kapoor as the muffin man! A hilarious and engaging way to get the point across about APIs, what Apigee is all about. Most of all it was joyful to observe the intensely positive “Can-do,” “Yes, or Here’s how” (rather than “No”) philosophy and exuberant attitude of Apigee employees.