Consumer search behaviour, and the reactions of firms to it, is a very important topic that drives the efficiency of markets. This holds true particularly for online platforms, as they have considerably facilitated consumer search in recent years.
A platform might have an incentive to steer consumers to search results in its preferred way, as it collects commissions over sales. However, those incentives are not necessarily aligned with the ones of consumers, which may call for consumer protection and regulatory interventions, such as the EC Digital Markets Act in 2020, and various US bills in 2021.
In this project, we aim at developing a platform model that explicitly takes into account the platform’s gatekeeper role of providing access to information about products to consumers and the consumers’ search behaviour. We plan to analyse different instruments to steer consumers with respect to their competitive consequences. Among these instruments are different ranks in the listing, default settings for searches, and salience of paid-for-advertisements. We explicitly consider how consumers’ choice sets are determined by the search rule of a platform, that consumers have a limited amount of time to search, or face behavioural biases by forming incorrect reference points. All of these can potentially be exploited by platforms.
This project plans to consider in detail how consumers’ beliefs about a platform’s ranking mechanism affect consumers’ search for their best match and the equilibrium behaviour of platforms. We want to develop a search model that allows for flexible consumer behaviour and includes either knowledge or ignorance of commission fees. Our goal is to provide a framework that allows us to derive potential welfare consequences of distorted consumer beliefs about ranking criteria, and to assess different policy tools with respect to their effectiveness in reducing distortions of welfare in the market of ranking websites. This will also help us to understand better whether competition can mitigate or even exacerbate potential inefficiencies, and how multi-homing consumers may adjust their beliefs. This is an important step to gain knowledge on how transparent information about ranking criteria changes consumer search.
We also plan an analysis of the filtering choices of platforms on comparison websites. Prominence of filtering attributes induces consumers to put more emphasis on these attributes when comparing products, which may facilitate search. However, consumers need to spend time and effort to discover potentially important attributes of these products that are not listed in the filters. The filtering choices are, again, driven by consumer beliefs on how platforms choose their respective filters and rankings, and dependent on factors consumers focus on when comparing listings. We would like to build a general consumer search framework to analyse how filtering attributes affect consumers’ search and the platform’s optimal choice of these attributes.