Dark patterns that manipulate consumer behaviour are now a pervasive feature of digital markets. Depending on the choice architecture utilised, they can affect the perception, behaviour and purchasing patterns of online consumers. Using a novel empirical design, we find strong evidence that individuals across all groups are susceptible to dark patterns, and only weak evidence that user susceptibility is materially affected by commonly used general proxies for consumer vulnerability (such as income, educational attainment or age). Our conclusions provide empirical support for broad restrictions on the use of dark patterns, such as those contained in the EU’s Digital Services Act, that protect all consumer groups. Our study also finds that added friction, in the form of required payment action following successful deployment of dark patterns, reduces their effectiveness. This insight highlights the instances in which dark patterns would be most effective – when no further action is required by the user. Consumer vulnerability is therefore more pronounced when dealing with online providers who store users’ payment details and can rely on a ‘single click’ to complete the purchase.

Dark patterns have become a pervasive issue around the world. However, Indonesia has not taken any action to deal with the situation. This article aims to analyse the issue of dark patterns in digital platforms from Indonesia’s consumer protection law perspective. It contends that Indonesia’s consumer protection law is not adequate in overcoming the issue of dark patterns in digital platforms. Several laws regulate basic principles and provisions that indirectly prohibit dark patterns, but these regulations can only be applied to certain forms of dark patterns and are difficult to implement. The article suggests that Indonesia must specifically regulate the prohibition of the practices of dark patterns in its current law. The regulations do not have to define the term “dark patterns” directly; however, they should expressly regulate prohibitions against the practice of using design interfaces in a way that subverts or impairs consumers’ autonomy and/or exploits consumers’ behavioural biases.

Dark patterns are user interface design elements which harm users but benefit vendors. These harms have led to growing interest from several stakeholders, including policymakers. We develop a high-level analytical framework – the dark patterns auditing framework (DPAF) – to support policymaker efforts concerning dark patterns. There are growing links between dark patterns and the behavioural science concept of sludge. We examine both literatures, noting several worthwhile similarities and important conceptual differences. Using two ‘sludge audits,’ and the DPAF, we examine 14 large online services to provide a high-level review of the user experience of these services. Our approach allows policymakers to identify areas of the user ‘journey’ (dark paths) where sludge/dark patterns persist. For regulators with constrained resources, such an approach more be advantageous when planning more granular analyses. Our approach also reveals several important limitations, notably, within some of the tools for sludge auditing which we develop, such as the ‘equal clicks principle.’ We discuss these limitations and directions for future research.

Dark patterns – design interfaces or features that subtly manipulate people in making suboptimal decisions – are ubiquitous especially in e-commerce websites. Yet, there is little research on the effectiveness of dark patterns, and even lesser studies on testing interventions that can help mitigate their influence on consumers. To that end, we conducted two experiments. The first experiment tests the effectiveness of different dark patterns within a hypothetical single product online shopping context. Results show that, indeed, dark patterns increase the purchase impulsivity across all dark patterns, relative to the control. The second experiment tests the effectiveness of three behaviorally informed interventions on four different dark patterns also in a hypothetical online shopping scenario, but this time offering multiple products instead of a single product. Between-subject analysis shows that not all interventions are equally effective, with uneven impact across dark patterns. However, within-subject results indicate that all interventions significantly reduce purchase impulsivity pre- versus post-intervention, indicating that any intervention is better than none when it comes to combating dark patterns. We then end by discussing the policy implications of our results.