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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.

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.

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.

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.

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.

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.

E-commerce companies employ deceptive techniques known as dark patterns (DPs) to manipulate customer decisions on online platforms. DPs are known to annoy and generate negative emotions in the consumers, which might be detrimental to a brand. Consequently, this study examines the influence of DPs on brand experience (BE) and consumer-based brand equity (CBBE) from the lens of stimulus-organism-response theory in an e-commerce organization.

Survey data was obtained from 419 respondents who frequently make transactions on e-commerce platforms. Subsequently, the authors used partial least squares-based structural equation modeling (PLS-SEM) via the R programming language to analyze the relationships.

It was found that DPs negatively impact the BE and CBBE of an e-commerce firm. Furthermore, BE positively impacts CBBE as well as mediates the relationship between DPs and CBBE. Additionally, no significant moderating influence of shopping frequency was discovered for the relation between DPs and BE.

The study investigates the connection between DPs, BE and CBBE in a general e-commerce context. Results may differ across specialized e-commerce sectors.

The study’s insights could motivate e-commerce managers to reevaluate their use of DPs. The study could help online platforms reorganize their business strategies and adopt a customer-centric approach, which might create a long-term positive relationship with customers. The results might spark discussions over redressing customer relations after previous exploitation via DPs.

To the best of the authors’ knowledge, this study is the first to empirically assess the impact of DPs on crucial brand parameters like BE and CBBE.

E-commerce companies often manipulate customer decisions through dark patterns to meet their interests. Therefore, this study aims to identify, model and rank the enablers behind dark patterns usage in e-commerce companies.

Dark pattern enablers were identified from existing literature and validated by industry experts. Total interpretive structural modeling (TISM) was used to model the enablers. In addition, “matriced impacts croisés multiplication appliquée á un classement” (MICMAC) analysis categorized and ranked the enablers into four groups.

Partial human command over cognitive biases, fighting market competition and partial human command over emotional triggers were ranked as the most influential enablers of dark patterns in e-commerce companies. At the same time, meeting long-term economic goals was identified as the most challenging enabler of dark patterns, which has the lowest dependency and impact over the other enablers.

TISM results are reliant on the opinion of industry experts. Therefore, alternative statistical approaches could be used for validation.

The insights of this study could be used by business managers to eliminate dark patterns from their platforms and meet the motivations of the enablers of dark patterns with alternate strategies. Furthermore, this research would aid legal agencies and online communities in developing methods to combat dark patterns.

Although a few studies have developed taxonomies and classified dark patterns, to the best of the authors’ knowledge, no study has identified the enablers behind the use of dark patterns by e-commerce organizations. The study further models the enablers and explains the mutual relationships.

E-commerce companies use dark patterns to manipulate customer decisions to survive in the crowded online market and make profit. Although some online customers are aware of the dark patterns, they cannot overcome such manipulations. Therefore, the purpose of this study is to identify and model the barriers to overcoming dark patterns using total interpretive structural modeling (TISM).

Barriers to overcoming dark patterns were identified from the extant literature and were validated by a panel of 18 domain experts. In the modeling phase, TISM technique was used to identify the relationships between the barriers and assign priority to the barriers. Finally, the barriers were plotted and classified into three categories.

User unawareness, trust in brands and normalization of aggressive marketing were found to be the highest priority barriers. Whereas, designer bias, user fatigue, short-term user benefits and design complexity were identified as the most challenging barriers because they have least dependence over the other barriers.

Because TISM results are based on the opinion of domain experts, other statistical techniques could be applied for validation.

This study would educate online customers, while assisting online user communities and regulatory bodies to devise strategies to overcome dark patterns. Additionally, business managers could use the study’s findings to encourage designers to embrace ethical design methods as a competitive advantage.

This study contributes to the research as it is first of its kind to examine the link between dark pattern barriers.

Due to the extreme competition in the e-commerce landscape, online companies resort to driving extra profits by manipulating user decisions via manipulative user interfaces and tricks, known as dark patterns. Although many online users are aware of such manipulative tactics, they struggle to combat dark patterns due to certain challenges. Therefore, this study identifies and ranks the most and least significant user challenges in resisting dark patterns in e-commerce.

Initially, the authors identified 11 user challenges against dark patterns in the existing literature. After collecting expert opinions about the challenges from 17 industry professionals, the authors used grey influence analysis (GINA) to identify the most significant challenges.

The study reveals that lack of user awareness, partial control over cognitive biases and user preference for short-term benefits are the most influential challenges. Conversely, the normalization of aggressive marketing, lack of collective user action and legal challenges were found to have minimal influence.

The study’s findings apply to the generic e-commerce industry and may differ in specialized sectors.

The study’s findings could encourage specialized consumer education regarding dark patterns to assist users in minimizing cognitive biases. Based on the insights, e-commerce companies might differentiate themselves by embracing ethical design methods and supporting consumer trust and transparency above manipulative techniques.

The study pioneered the use of GINA to model user challenges in combating dark patterns, providing fresh insights into how these challenges interact and influence user behavior. It is one of the first to show the cascading effects of user challenges in a manipulative e-commerce environment.