Prioritizing dark patterns in the e-commerce industry – an empirical investigation using analytic hierarchy process

Abstract

Purpose E-commerce companies use different types of dark patterns to manipulate choices and earn higher revenues. This study aims to evaluate and prioritize dark patterns used by e-commerce companies to determine which dark patterns are the most profitable and risky. Design/methodology/approach The analytic hierarchy process (AHP) prioritizes the observed categories of dark patterns based on the literature. Several corporate and academic specialists were consulted to create a comparison matrix to assess the elements of the detected dark pattern types. Findings Economic indicators are the most significant aspect of every business. Consequently, many companies use manipulative methods such as dark patterns to boost their revenue. The study revealed that the revenue generated by the types of dark patterns varies greatly. It was found that exigency, social proof, forced action and sneaking generate the highest revenues, whereas obstruction and misdirection create only marginal revenues for an e-commerce company. Research limitations/implications The limitation of the AHP study is that the rating scale used in the analysis is conceptual. Consequentially, pairwise comparisons may induce bias in the results. Practical implications This paper suggests methodical and operational techniques to choose the priority of dark patterns to drive profits with minimum tradeoffs. The dark pattern ranking technique might be carried out by companies once a year to understand the implications of any new dark patterns used. Originality/value The advantages of understanding the trade-offs of implementing dark patterns are massive. E-commerce companies can optimize their spent time and resources by implementing the most beneficial dark patterns and avoiding the ones that drive marginal profits and annoy consumers.

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