Abstract
Dark patterns utilize interface elements to trick users into performing unwanted actions. Online shopping websites often employ these manipulative mechanisms so as to increase their potential customer base, to boost their sales, or to optimize their advertising efforts. Although dark patterns are often successful, they clearly inhibit positive user experiences. Particularly, with respect to customers’ perceived annoyance and trust put into a given brand, they may have negative effects. To investigate respective connections between the use of dark patterns, users’ perceived level of annoyance and their expressed brand trust, we conducted an experiment-based survey. We implemented two versions of a fictitious online shop; i.e. one which used five different types of dark patterns and a similar one without such manipulative user interface elements. A total of \(n=204\) participants were then forwarded to one of the two shops (approx. 2/3 to the shop which used the dark patterns) and asked to buy a specific product. Subsequently, we measured participants’ perceived annoyance level, their expressed brand trust and their affinity for technology. Results show a higher level of perceived annoyance with those who used the dark pattern version of the online shop. Also, we found a significant connection between perceived annoyance and participants’ expressed brand trust. A connection between participants’ affinity for technology and their ability to recognize and consequently counter dark patterns, however, is not supported by our data.
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Notes
- 1.
Online: https://darkpatterns.org [accessed: November 2nd 2020].
- 2.
Online: https://www.pewresearch.org/fact-tank/2019/01/17/where-millennials-end-and-generation-z-begins/ [accessed: February 12th 2021].
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Voigt, C., Schlögl, S., Groth, A. (2021). Dark Patterns in Online Shopping: of Sneaky Tricks, Perceived Annoyance and Respective Brand Trust. In: Nah, F.FH., Siau, K. (eds) HCI in Business, Government and Organizations. HCII 2021. Lecture Notes in Computer Science(), vol 12783. Springer, Cham. https://doi.org/10.1007/978-3-030-77750-0_10
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