Notes on Dark Patterns in E-commerce

Dark Patterns in E-commerce

Introduction

• E-commerce companies utilize deceptive techniques known as dark patterns (DPs) to manipulate consumer decisions on online platforms.
• DPs annoy users and generate negative emotions, potentially harming brand equity.
• This study analyzes the impact of DPs on brand experience (BE) and consumer-based brand equity (CBBE) using the stimulus-organism-response (SOR) theory.

Research Design

• Sample: Survey data from 419 respondents who frequently use e-commerce platforms.
• Methodology: Analysis conducted using partial least squares-based structural equation modeling (PLS-SEM) through R programming.

Key Findings

• DPs negatively affect BE and CBBE.
• Positive correlation where BE mediates the relationship between DPs and CBBE.
• Shopping frequency does not moderate the relationship between DPs and BE.

Dark Patterns Defined

• Dark Patterns are user interface tricks that coerce users into unintended actions, benefiting e-commerce firms (Brignull, 2023).
• Examples of DPs include:
  • Sneaking: Adding unwanted products to carts.
  • Misdirection: Distracting users from unfavorable choices.
  • Exigency: Creating false urgency or scarcity to expedite purchases.
  • Restriction: Making it difficult to perform certain actions.
  • Social Proof: Falsely displaying popularity of products (Gray et al., 2018).

Effects of Dark Patterns

• Negative Emotions: DPs evoke feelings like distress, mistrust, and irritation among consumers (Gray et al., 2021).
• BE: Encompasses emotions and thoughts triggered by branding stimulus (Brakus et al., 2009).
• CBBE: Relates brand knowledge to customer response, defined by awareness and image (Keller, 1993).

Hypothesis Development

1. H1: DPs negatively affect BE.
2. H2: DPs negatively affect CBBE.
3. H3: BE positively affects CBBE.
4. H4: BE mediates the relationship between DPs and CBBE.
5. H5: Shopping frequency moderates the relationship between DPs and BE.

Methodological Approach

• Population: Regular e-commerce users in India.
• Data Collection: Online questionnaire, with efforts made to ensure clarity and reliability.
• Analysis: PLS-SEM, focusing on assessing validity and reliability of constructs.

Results and Discussion

• Evidence supports negative effects of DPs on both BE (β=-0.19, p < 0.01) and CBBE (β=-0.21, p < 0.01).
• BE has a strong positive impact on CBBE (β=0.45, p < 0.01).
• Mediation analysis reveals BE as a mediator (indirect effect β=-0.09, p < 0.01).
• Shopping frequency does not significantly influence the DPs to BE relationship (β=-0.03, p > 0.05).

Practical Implications

• E-commerce managers should critically evaluate the use of DPs, as they undermine consumer trust and long-term relationships.
• Emphasize customer-centric strategies to avoid short-term exploitation for revenue gain.
• Adoption of ethical design practices is crucial for brand sustainability and trust.

Future Research Directions

• Investigate the specific impacts of individual DPs on BE and CBBE.
• Explore the role of customer trust, emotional attachment, and the effects in specialized e-commerce sectors.
• Encourage collaborations across disciplines to build user detection tools for DPs.