Expert Economists Jorge Padilla, Salvatore Piccolo and Helder Vasconcelos wrote an article for the Journal of Industrial and Business Economics. The article investigates the role of business models as drivers of the accuracy of consumer information collected by digital platforms. The results of the authors’ research shed new light on the link between alternative business models, consumer privacy and information collection in the digital sector, and may help explain why some platforms tend to protect consumer privacy more than others.
We examine the drivers of the accuracy of the consumer information collected by a digital platform. In an environment where consumers mind their privacy, we compare a pure-intermediation model, where the platform plays a matching function only, by connecting buyers and sellers, with a hybrid business model, where the platform also introduces its private label to compete with third-party sellers. We show that the platform’s incentive to collect information in the two models depends on the intensity of intra-platform competition and on its bargaining power vis-à-vis third-party sellers. When end-users perceive the platform’s private label and the third-party sellers’ products as relatively close substitutes (strong intra-platform competition) and the intermediary has a strong bargaining position in the negotiation with the sellers, it tends to acquire less accurate information under the hybrid model than in the pure-intermediation model, at the benefit of consumer privacy. Otherwise, more information is acquired under the hybrid model. These results shed new light on the link between alternative business models, consumer privacy and information collection in the digital sector, and may help explain why some platforms tend to protect consumer privacy more than others.
Online platforms are the architects of the digital revolution. Thanks to these platforms, nowadays, consumers and sellers enjoy multiple trading solutions. In addition to meeting physically in stores, they can also trade in a virtual, impersonal, and presumably anonymous world. The reduction of search costs, the increased delivery speed, and higher market transparency are the bright side of this revolution. However, these companies also manage a massive amount of consumer data. Platforms such as Amazon, Apple, and Google, to name a few, collect, package, and disclose users’ data to third parties that use this knowledge for commercial, marketing, and, in the worst case, fraudulent purposes. The information that platforms collect covers a broad spectrum of individual data, ranging from users’ individual characteristics, such as gender, age, and location, to their browsing patterns, prior transactions, social interaction, etc. Platforms can, therefore, forecast consumers’ tastes, habits, and social preferences, and monetize this information through personalized offers.1 Consumer data may also land in wrong ‘hands’ and be used for illegal purposes that damage consumers and their privacy (e.g., credit card and/or identity cloning). This is allegedly the major dark side of the digital revolution.
A flourishing academic literature has started to investigate the interaction between data management, marketing strategies, and competition in platform markets (see, e.g., Bergemann & Bonatti, 2019; Jullien, 2012; Peitz & Reisinger, 2015, for recent surveys). However, these models are silent on the link between platforms’ business models and the accuracy of the consumer data that they collect and eventually disclose to self-interested third-party sellers.
Notably, while some online businesses have mainly maintained a brokerage activity (e.g., eBay and Google) others operate under hybrid business models and have developed their own private labels to compete with third-party sellers operating through their marketplaces (e.g., Amazon and Apple). Do all these online intermediaries have the same incentives to acquire and disclose users’ personal information? If not, what are the determinants of different approaches to information and privacy management? Is the choice of business model—i.e., pure intermediation vs. hybrid platforms one of these key factors?
In this paper, we study the drivers of the accuracy of the information that digital intermediaries collect and disclose. Specifically, we compare the incentives to collect demand information by a an online intermediary (platform) operating under two alternative business models: a pure-intermediation model, where it plays a matching function only by connecting a third-party seller with buyers, and a hybrid model where, in addition to its traditional middlemen role, the platform also introduces its own private label in the marketplace and competes with the third-party seller distributing through its marketplace. We argue that there is no objective presumption that pure intermediation platforms collect more or less information than hybrid platforms. Platforms’ business model is not neutral to data collection. This observation should be considered carefully by privacy authorities, especially because in the EU the GDPR is based on the ’data minimization principle.’2
This article was originally published by the Journal of Industrial and Business Economics and in PDF format here. The views expressed are those of the authors only and do not necessarily represent the views of Compass Lexecon, its management, its subsidiaries, its affiliates, its employees, or clients.