Enterprise AI Analysis
Generative AI in CBeC: Trust, Heuristic Cues & Information Overload
This study aims to examine, through the lens of cognitive heuristics theory, the relationship between generative artificial intelligence (GAI) technological affordance heuristic (TAH) cues, including website vividness, perceived anthropomorphism, personalization, and website design, and user trust and continuous use intention in cross-border e-commerce (CBeC). Additionally, this study investigates the mediating role of trust between GAI TAH cues and continuous use intention while information overload is treated as the moderating factor.
The results showed that website vividness, personalization, and website design have significant positive impacts on trust, which in turn significantly influences continuous use intention. Trust mediated the relationships between these GAI TAH cues and continuous use intention, while information overload moderated the relationships between the cues and trust. In contrast, perceived anthropomorphism had no significant effect on trust, was not moderated by information overload, and did not indirectly influence continuous use intention through trust. Overall, the findings elucidate how GAI TAH cues affect trust and continuous use intention through heuristic theory, further confirming the role of GAI in CBeC.
This study expanded the heuristic theory to creatively integrate with GAI TAH cues to reveal the impact on trust, The findings yielded also illustrate the role of information overload as the moderator of the relationships between GAI TAH cues and trust which in turn influence continuous use intention of CBeC.
Executive Impact
Unlocking Sustained Engagement in Cross-Border E-commerce
This analysis, based on a recent study, examines how Generative Artificial Intelligence (GAI) heuristic cues, user trust, and information overload collectively shape continuous use intention within Cross-Border e-Commerce (CBeC) platforms. It integrates cognitive heuristics theory with the MAIN model to explore the impact of GAI’s technical affordances on vendors' decisions.
The findings highlight the critical role of website vividness, personalization, and website design in building trust and driving continuous engagement. While perceived anthropomorphism showed no significant effect, the study crucially identifies information overload as a powerful moderator, strengthening the positive influence of these heuristic cues on trust. Trust, in turn, mediates the path to sustained platform use, offering actionable insights for enhancing AI adoption and retention in the CBeC ecosystem.
0
Valid Responses Analyzed
0
Daily GAI Usage Rate
0
1-2 Hrs Daily GAI Engagement
0
Trust Explained Variance
0
Continuous Use Intent Variance
Deep Analysis & Enterprise Applications
Select a topic to dive deeper, then explore the specific findings from the research, rebuilt as interactive, enterprise-focused modules.
Generative AI Heuristics and Trust Formation
Generative Artificial Intelligence (GAI) tools are increasingly vital in Cross-Border e-Commerce (CBeC), assisting vendors with everything from customer queries to product listings. Our analysis leverages the MAIN (Modality, Agency, Interactivity, Navigability) model and cognitive heuristics theory to understand how specific GAI technical affordance heuristic (TAH) cues—such as website vividness, personalization, and website design—influence vendor trust. These cues act as mental shortcuts, allowing users to rapidly assess the credibility and reliability of GAI, which is crucial for fostering adoption in complex CBeC environments.
Information Overload as a Critical Moderator
In the fast-paced and data-intensive world of CBeC, vendors often face significant information overload, making systematic evaluation of AI tools challenging. This study critically examines information overload as a moderating factor, demonstrating how it amplifies the reliance on heuristic cues. When information overload is high, vendors are more likely to depend on easily digestible cues provided by GAI's technology to form trust-based decisions. This insight is vital for designing GAI tools that perform effectively under pressure, turning a potential cognitive burden into an opportunity to build stronger user trust.
Driving Continuous Use in CBeC
Continuous use intention goes beyond initial adoption; it signifies sustained engagement and loyalty to CBeC platforms. For platform providers, retaining vendors through continued GAI usage is paramount. Our research establishes trust as a pivotal mediator, directly linking positive perceptions of GAI's heuristic cues to a vendor's intention to continuously use the platform. Understanding these pathways allows for the strategic development of GAI features that not only attract but also retain users, ultimately contributing to the long-term success and growth of CBeC operations.
| MAIN Model Cue | GAI TAH Cue | Description |
|---|---|---|
| Modality Cues | Website vividness | Evokes rich imagery, reducing ambiguity and cognitive effort, processed heuristically due to lifelike representation. |
| Agency Cues | Perceived anthropomorphism | Mimics human characteristics, activating social heuristics, attributing human traits to non-human entities. |
| Interactivity Cues | Personalization | Adapts content to user preferences, reflecting system responsiveness and two-way interaction. |
| Navigability Cues | Website design | Organizes layout and content to improve usability, reflecting heuristic judgments of reliability and ease of use. |
Research Model Flow
Heuristic Cues (GAI TAH)
→
Information Overload (Moderator)
→
Trust (Mediator)
→
Continuous Use Intention
| Hypothesis | Relationship | Decision |
|---|---|---|
| H1 | Website Vividness → Trust | Supported |
| H2 | Perceived Anthropomorphism → Trust | Not Supported |
| H3 | Personalization → Trust | Supported |
| H4 | Website Design → Trust | Supported |
| H5 | Trust → Continuous Use Intention | Supported |
| H6 | IO * Website Vividness → Trust | Supported |
| H7 | IO * Perceived Anthropomorphism → Trust | Not Supported |
| H8 | IO * Personalization → Trust | Supported |
| H9 | IO * Website Design → Trust | Supported |
| H10 | Website Vividness → Trust → Continuous Use Intention | Supported |
| H11 | Perceived Anthropomorphism → Trust → Continuous Use Intention | Not Supported |
| H12 | Personalization → Trust → Continuous Use Intention | Supported |
| H13 | Website Design → Trust → Continuous Use Intention | Supported |
No Significant Effect
Perceived Anthropomorphism on Trust
The study found that perceived anthropomorphism (H2), its moderation by information overload (H7), and its mediation effect (H11) were not supported. This suggests that excessive human-likeness in GAI tools might evoke discomfort or reliability concerns, potentially due to an 'uncanny valley effect', countering potential benefits.
Enhancing Trust in High Information Overload Scenarios
The research reveals a critical insight: when business users on CBeC platforms experience high information overload, their reliance on GAI's heuristic cues (like website vividness, personalization, and website design) significantly strengthens trust. For example, the effect of website vividness on trust becomes much stronger under high information overload (β = 0.724) compared to low (β = 0.047). This indicates that in complex, data-rich environments, well-designed GAI tools become indispensable, acting as cognitive shortcuts that build trust and drive continuous use. Enterprises should strategically deploy AI assistance in these contexts to maximize its impact.
Key Theoretical Contributions
- Developed a comprehensive framework integrating the MAIN model and heuristic theory to examine how GAI-embedded TAH cues influence continuous use intention in CBeC platforms.
- Emphasized the pivotal mediating role of trust in linking GAI's heuristic cues to users' continuous use intention, validating mechanisms in B2B environments.
- Revealed the moderating influence of information overload, strengthening the positive effect of vividness, personalization, and website design on trust in CBeC contexts.
Actionable Practical Implications
- Platform managers should optimize GAI-embedded heuristic cues such as vividness, personalization, and website design, while adopting balanced anthropomorphism to maximize acceptance.
- Leverage information overload by proactively identifying high cognitive-load situations and intelligently triggering GAI-based assistance to reduce decision fatigue and improve trust.
- Position trust as a core mechanism by enhancing algorithm transparency, ensuring data security, and providing clear rationales for AI-generated recommendations.
Research Limitations & Future Directions
- Study did not differentiate between paid or free GAI services, potentially influencing user adoption behaviors.
- Findings may have limited generalizability due to data collection from a single platform (Alibaba International Station) and lack of cross-country comparisons.
- Absence of certain control variables (firm size, digital experience, vendor type) could further enrich analysis.
- Cross-sectional design restricts establishing causal relationships; longitudinal or experimental designs recommended for future research.
ROI Calculator
Estimate Your Enterprise AI Impact
Quantify the potential efficiency gains and cost savings for your organization by leveraging Generative AI solutions, based on industry benchmarks.
Annual Cost Savings
$0
Annual Hours Reclaimed
0
Implementation Roadmap
Your Path to Enterprise AI Integration
A structured approach to integrating AI within your enterprise, ensuring maximum impact and sustainable growth.
Discovery & Strategy Alignment
Assess current processes, identify AI opportunities, define clear objectives, and align AI strategy with overall business goals. This phase involves stakeholder interviews and a comprehensive needs analysis.
Pilot Program & Proof of Concept
Develop and deploy a small-scale AI pilot, testing key functionalities and measuring initial impact. Gather feedback and refine the solution based on real-world performance data.
Full-Scale Integration & Training
Integrate AI solutions across relevant departments, ensuring seamless workflow transitions. Provide comprehensive training to all end-users to maximize adoption and proficiency.
Performance Monitoring & Optimization
Continuously monitor AI system performance, gather user feedback, and implement iterative improvements. Scale the solution as needed to adapt to evolving business requirements.
Ready to Transform Your Enterprise with AI?
Schedule a personalized consultation with our AI strategists to discuss your unique challenges and explore tailored solutions.
Ready to Get Started?
Book Your Free Consultation.
Let's Discuss Your AI Strategy!
Lets Discuss Your Needs
Select a Date
Sun
Mon
Tue
Wed
Thu
Fri
Sat