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1. Introduction
In November 2018, the British Digital Minister lashed out at airlines for allegedly using algorithms to intentionally split up passengers who book together under the same surname, unless they were willing to pay for seats that are next to each other. During a parliamentary hearing these algorithms were described “as ‘a very cynical, exploitative means … to hoodwink the general public’” (Coffey, 2018). With the observed widespread use of algorithms to set online prices (CMA, 2018), the role of algorithms has increasingly become the focus of regulatory authorities worldwide; this holds true within the hospitality and tourism sector. For example, the Australian consumer watchdog (ACCC) accused the online hotel comparison website Trivago of misleading consumers to believe it was impartial and objective while in two-thirds of the searches its algorithms prioritized higher-priced hotel offers from advertisers who were willing to pay higher fees (Khadem, 2020). The ACCC also found that in its comparative price advertising Trivago's algorithm compared apples to oranges (e.g. a luxury room to a standard room), thereby giving consumers a false impression of savings. Trivago's deception however was not an isolated incident. Recently, the pricing algorithms of online travel agents were brought under scrutiny by Singaporean and British consumer watchdogs. Hotel comparison websites such as Booking.com and Expedia were investigated for deceptive practices, for example drip pricing, hi-low pricing, and pressure selling (CCCS, 2019; CMA, 2019), the latter continuing even after regulatory warnings were issued (Coffey, 2019).
Thus far, most of the work on the potential of pricing algorithms to bring about consumer harm is directed at online price discrimination. As online pricing is becoming increasingly reliant on automated decision-making and profiling, the communis opinio is that the large accumulation and analysis of consumer data bears the risk of amplifying existing informational inequalities between consumers and companies (Miller, 2014). If companies will gain an informational advantage over consumers, as a result of firms' increased abilities to collect and infer more (dynamic) individual data points, often without consumers' knowledge (OECD, 2018), the main concern is that companies will make use of this newfound informational advantage to algorithmically target and exploit specific (vulnerable) consumer groups (Barocas and Selbst, 2016). Yet, there is still little empirical evidence on online price discrimination (in hospitality...