Short answer

When designing AI virtual anchors for e-commerce, focus on making their presence and function immediately apparent to the user. Simultaneously, cultivate trust and positive emotions through responsiveness and personalization, as these will mediate the path to purchase.

Field: Innovation & Markets
Source: Journal of theoretical and applied electronic commerce research (2025)
Method: Mixed-methods research combining Latent Dirichlet Allocation (LDA), Structural Equation Modeling (SEM), and Fuzzy-Set Qualitative Comparative Analysis (fsQCA).
Evidence: Strong effect

The direct visibility of AI virtual anchors in e-commerce is a stronger predictor of consumer purchase intention than their professionalism or personalization, with trust and emotional states acting as crucial intermediaries. This innovation & markets research insight is drawn from a 2025 study published in Journal of theoretical and applied electronic commerce research. Using Mixed-methods research combining latent dirichlet allocation (lda), structural equation modeling (sem), and fuzzy-set qualitative comparative analysis (fsqca)., researchers explored how this design variable affects real-world outcomes. The key design takeaway: When designing AI virtual anchors for e-commerce, focus on making their presence and function immediately apparent to the user. Simultaneously, cultivate trust and positive emotions through responsiveness and personalization, as these will mediate the path to purchase.

Study

Innovation & MarketsNew This WeekStrong effect

Public visibility of AI virtual anchors drives purchase intention, while trust and pleasure mediate the effect.

Journal of theoretical and applied electronic commerce research · 2025

Key Findings

01Public visibility of virtual anchors has a significant positive direct impact on purchase intention.
02Professionalism, responsiveness, and personalization primarily enhance consumer pleasure and trust, with limited direct influence on purchase decisions.
03Emotional states (arousal, pleasure, and trust) mediate the relationship between anchor characteristics and purchase intention.
04High purchase intention is associated with visibility, responsiveness, and trust, with responsiveness being a necessary condition and trust a pivotal hub.
05Low purchase intention is linked to deficiencies in visibility, responsiveness, and trust.

Application

Design takeaway

How to apply

When developing AI-powered customer service agents or product recommenders for online platforms, ensure their interface is intuitive and their purpose is immediately understood. Design their interactions to build trust and positive emotional connections.

Project actions

01When designing an AI interface, consider how visible and understandable its core function is to the user.
02Think about how to build trust with the user through the AI's interactions, even if it's not the primary driver of immediate action.

Method & Evidence

AimTo investigate how the attributes of AI-driven virtual anchors influence consumer purchase intentions and to understand the mediating roles of trust and emotional states.

MethodMixed-methods research combining Latent Dirichlet Allocation (LDA), Structural Equation Modeling (SEM), and Fuzzy-Set Qualitative Comparative Analysis (fsQCA).

ProcedureThe study integrated LDA to identify key themes, SEM to model relationships between virtual anchor attributes, emotional states, and purchase intention, and fsQCA to explore configurational pathways leading to high or low purchase intention.

ContextE-commerce live-streaming environments.

Variables

IV["Public visibility of AI virtual anchors","Professionalism of AI virtual anchors","Responsiveness of AI virtual anchors","Personalization of AI virtual anchors"]

DV["Consumer purchase intention","Consumer pleasure","Consumer arousal","Consumer trust"]

CV["E-commerce live-streaming context","AI-driven virtual anchors"]

Strengths & Limitations

Strengths

+Integration of multiple advanced research methodologies (LDA, SEM, fsQCA).
+Provides both theoretical extensions (SOR, PAD models) and practical guidance.

Limitations

The study's context is specific to live-streaming e-commerce, so results might differ for other online shopping formats.

Reliability & validity

The study employs robust statistical methods like SEM and fsQCA, which are designed to assess reliability and validity of the proposed models and relationships. The triangulation of methods also enhances construct validity.

Think critically

How might the 'public visibility' of an AI differ across various digital platforms, and how could this impact its effectiveness in driving purchase intention?

Design Principles

"Maximize AI visibility and cultivate trust through responsiveness to drive consumer purchase intention in digital commerce."

Understanding the interplay between AI characteristics and consumer psychology is vital for designing effective e-commerce strategies. This research highlights that while sophisticated AI attributes are important for building trust and positive emotions, the sheer presence and clarity of the AI's function (visibility) can have a more immediate impact on purchase decisions.

What This Means for Your Design

For online shopping, people are more likely to buy something if they can easily see and understand the AI helper. The AI being trustworthy and making people feel good also helps, but seeing it clearly is the most important thing for making a sale.

How to use in your project

1.Reference this study when discussing the impact of AI interface design on user behaviour and purchase decisions in your design project.

Add to My Project

Quick Cite

Paragraph starter

This research highlights the critical role of AI visibility in driving consumer purchase intention within e-commerce live-streaming. Findings suggest that while trust and emotional engagement are important mediators, the direct impact of an AI's clear presence and function significantly influences buying decisions, offering valuable insights for the design of effective AI-driven sales tools.

Source

Journal of theoretical and applied electronic commerce research

AI Digital Human Responsiveness and Consumer Purchase Intention: The Mediating Role of Trust

journal · 2025

View source

Questions About This Research

What does the research say about public visibility of ai virtual anchors drives purchase intention, while trust and pleasure mediate the effect?: When designing AI virtual anchors for e-commerce, focus on making their presence and function immediately apparent to the user. Simultaneously, cultivate trust and positive emotions through responsiveness and personalization, as these will mediate the path to purchase. Evidence: Journal of theoretical and applied electronic commerce research (2025).
Why does "Public visibility of AI virtual anchors drives purchase intention, while trust and pleasure mediate the effect." matter for design?: Understanding the interplay between AI characteristics and consumer psychology is vital for designing effective e-commerce strategies. This research highlights that while sophisticated AI attributes are important for building trust and positive emotions, the sheer presence and clarity of the AI's function (visibility) can have a more immediate impact on purchase decisions.
How can designers apply this research?: When designing AI virtual anchors for e-commerce, focus on making their presence and function immediately apparent to the user. Simultaneously, cultivate trust and positive emotions through responsiveness and personalization, as these will mediate the path to purchase.
What were the main findings?: Public visibility of virtual anchors has a significant positive direct impact on purchase intention.. Professionalism, responsiveness, and personalization primarily enhance consumer pleasure and trust, with limited direct influence on purchase decisions.. Emotional states (arousal, pleasure, and trust) mediate the relationship between anchor characteristics and purchase intention.. High purchase intention is associated with visibility, responsiveness, and trust, with responsiveness being a necessary condition and trust a pivotal hub.
What research method was used?: Mixed-methods research combining Latent Dirichlet Allocation (LDA), Structural Equation Modeling (SEM), and Fuzzy-Set Qualitative Comparative Analysis (fsQCA)..
How strong is the evidence?: Evidence strength is rated Strong effect, based on a 2025 journal from Journal of theoretical and applied electronic commerce research.
What should I do differently in my next project?: When developing AI-powered customer service agents or product recommenders for online platforms, ensure their interface is intuitive and their purpose is immediately understood. Design their interactions to build trust and positive emotional connections.
What are the limitations?: The study's findings are specific to e-commerce live-streaming contexts and may not generalize to all online retail environments or AI applications.