Game design
businesses may also use various, and harmful, OCA practices. Specifically, they worry that these practices, designed either deliberately or unintentionally, might negatively affect consumer choice, leading consumers to spend more, receive poor value services, or search less for alternatives. Harmful OCA practices may persist even in competitive markets due to low OCA awareness (and their effectiveness in influencing consumers even when recognised), potential profitability of OCA practices and/or certain features of a market. Setting the scene: behavioural biases Until relatively recently, regulators studied consumer outcomes under the assumption that consumers made rational decisions. However, the emergence of behavioural economics in recent years has shifted that thinking. In short, consumers are not always rational. Preferences are not always consistent, with us often relying on heuristics to short-cut decision making. Fortunately, many of these behavioural biases are systematic – meaning they can be anticipated and understood. This leads to some concerns, namely that businesses may be able to exploit these biases for commercial gain, and that digital markets have the potential to exacerbate them. However, regulators are also increasingly able to use evidence of these biases to intervene in markets more effectively.
of OCA practices in the sector. Our view is that the sector should therefore be braced for further investigation in this area going forward. Three categories of practice The CMA outlines a possible taxonomy of 21 OCA practices that could be used by businesses, as well as consumer and competition authorities, to help recognise, categorise and explain the impact of practices 3 . These are broadly categorised into three types (although these are often interlinked and can be grouped in different ways): a) Choice structure is how choices are presented to consumers. b) Choice information is the information provided to consumers when presenting choices. c) Choice pressure is how consumers’ choices may be indirectly influenced. The tables overleaf provide the full list of the practices considered in each category, together with ratings of the strength of existing academic evidence underlying them. These evidence 4 strength ratings are an assessment of the extent and quality of available academic research relating to each OCA practice. The majority of these practices can be, and often are, used beneficially, or are harmful only in certain circumstances. However, as suggested by the academic literature, some practices are almost always harmful (marked with “%” in the tables overleaf. Choice structure overview The CMA finds that there is strong evidence that choice structure practices alter consumer behaviour. Depending on how they are deployed, practices can have both positive and negative impacts on consumer choice. For example, well-designed ranking and defaults can assist in making decisions more efficiently, but these practices can also exploit consumers and lead to them choosing worse options. However, half of the choice structure practices set out by the CMA are found to almost always harm the consumer, with potential outcomes including more costly and/or inferior decisions, including purchasing unwanted products. Some
Emerging thinking: online choice architecture
Online Choice Architecture (OCA) is the design of the online environment where users interact with businesses. This design affects our decision making and actions when we browse, compare, play and shop online. In April 2022, the UK Competition and Markets Authority (CMA) published a summary paper and accompanying evidence base in relation to OCA. It is this paper that we summarise here and apply to the sector. It is worth noting that the gambling sector is featured prominently throughout the CMA’s paper, both in the context of previous investigations it has conducted and the volume of academic literature available that demonstrates the use
3 The presented taxonomy is not intended to be used as a binding CMA tool for future cases; the intentions is that the proposed taxonomy will evolve to reflect developments in technology, business practices and research. 4 The CMA uses the evidence measure taken from Ruggeri, Linden, Wang, Papa, Aff, Riesch & Green’s (2020) table of evidence standards. These standards are intended to support communicating the strength of empirical evidence to policymakers ranging from 1-star (the lowest rating, indicating that a concept has been discussed but lacks empirical validation) to5-star(the highest rating, meaning that result insights have been implemented and applied at scale).
IMGL Magazine • July 2022 • 27
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