Enterprise AI Research Analysis
The effect of non-facial emojis-text congruency on user satisfaction in chatbot interactions
Mafalda Pescatore, Stefania Farace
Published online: 13 January 2026
Improving human-chatbot communication requires understanding how different design elements can make interactions clearer and more effective. This research examines emoji-text congruency as one mechanism that enhances user satisfaction in chatbot interactions. Across two experiments conducted in the travel (Study 1) and healthcare (Study 2) domains, we show that when emojis are semantically aligned with the accompanying text, users perceive the chatbot as more competent, which in turn increases satisfaction. To isolate this effect from anthropomorphic influences, the studies focus on non-facial emojis, which convey meaning without mimicking human expressions, thus avoiding potential discomfort associated with excessive human-likeness. However, the positive effect of congruency weakens when the chatbot adopts an overly informal language style. These findings highlight the importance of integrating visual and verbal cues coherently to improve user experience and inform the design of effective, professional virtual assistants.
Key Enterprise Impact Metrics
Quantifying the immediate benefits of optimized chatbot communication.
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Increase in User Satisfaction
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Boost in Chatbot Competence
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Service Domains Validated
Deep Analysis & Enterprise Applications
Select a topic to dive deeper, then explore the specific findings from the research, rebuilt as interactive, enterprise-focused modules.
Core Findings
Contextual Nuances
Strategic Implications
Semantic Alignment Drives Performance
Our research consistently demonstrates that a high congruency between non-facial emojis and accompanying text significantly boosts user satisfaction (H1). This positive effect is fundamentally driven by users' heightened perception of the chatbot's competence (H2), establishing a clear psychological mechanism for improved interactions.
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User Satisfaction from Congruent Emojis (Study 1)
In Study 1 (travel domain), user satisfaction was significantly higher in the high congruency condition (M=5.75) compared to text-only (M=4.58) and low congruency (M=3.34). Similarly, perceived competence followed the same trend: high congruency (M=5.76), text-only (M=4.70), and low congruency (M=3.22).
Enterprise Process Flow: The Mechanism
High NF Emojis-Text Congruency
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Enhanced Chatbot Competence
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Increased User Satisfaction
Industry Application: Competent Virtual Assistants
Leading enterprises like Bank of America's Erica and KLM Royal Dutch Airlines leverage virtual assistants to manage customer inquiries. Our findings underscore that effective communication, characterized by clarity and perceived competence, is paramount. By ensuring visual cues like non-facial emojis semantically align with text, these systems can significantly enhance user perception, mirroring the success of top-tier AI applications by making interactions more reliable and efficient.
Impact of Language Style Across Contexts
The positive effects of congruent NF emojis generalize across diverse service contexts, from hedonic (travel) to utilitarian (healthcare). However, the effectiveness of this congruence is sensitive to the chatbot's overall language style (H3). An overly informal or vernacular style can weaken the positive impact, particularly in high-stakes professional environments.
Informality Weakens Effect
Impact on Satisfaction in Professional Contexts (Study 2)
In Study 2 (healthcare domain), while high congruency still yielded higher satisfaction (M=4.54) compared to low congruency (M=4.04), the "informal high congruency" condition (M=3.89) did not outperform the text-only (M=4.15). This suggests that in contexts requiring formality, excessive informality can neutralize the benefits of coherent visual cues, emphasizing the need for a well-aligned communicative tone.
| Feature | Travel Context (Study 1) | Healthcare Context (Study 2) |
|---|---|---|
| Perceived Risk | Low (Hedonic) | High (Utilitarian) |
| Expectation for Interaction | Friendliness, Engaging Tone | Formality, Professionalism |
| Emoji Effectiveness (Congruent) | Strong positive impact on satisfaction and competence | Positive impact, but context-dependent |
| Informal Language Impact | Potentially acceptable, less impact | Weakens positive effects of congruency |
These findings highlight that a nuanced understanding of domain-specific user expectations regarding communication style is crucial for optimal chatbot design and deployment.
Optimizing AI-Driven Customer Service
Our findings provide actionable insights for enterprises developing and deploying AI chatbots. Non-facial emojis, when semantically aligned with text, serve as a powerful, non-anthropomorphic tool to enhance perceived chatbot competence and user satisfaction. This approach offers a cost-effective solution, particularly in overcoming issues like digital illiteracy and misinterpretation.
However, it is critical to ensure a holistic congruence: the visual cues (emojis) must align not only with the immediate text but also with the broader linguistic tone and the service context itself. In professional, high-stakes environments, an overly informal language style can negate the benefits of otherwise congruent emojis, leading to diminished user satisfaction.
Strategic Implementation Framework
Assess Service Context & User Expectations
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Design Congruent NF Emojis & Text
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Calibrate Chatbot Language Style
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Monitor & Iterate for Optimal CX
Future research should further explore the interaction between different emoji types (facial vs. non-facial), dynamic conversations, and the influence of generational cohorts and visual literacy on emoji interpretation. Comprehensive metrics, beyond self-reports, will further deepen our understanding.
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Your AI Implementation Roadmap
A phased approach to integrate advanced AI communication strategies into your enterprise, ensuring maximum impact and smooth transition.
Phase 1: Discovery & Strategy
Comprehensive assessment of current communication channels, identification of AI opportunities, and development of a tailored strategy for chatbot integration. Focus on user experience and business objectives.
Phase 2: Design & Development
Crafting chatbot personas, designing congruent emoji-text interactions, and developing initial prototypes. Includes rigorous A/B testing for language style and visual cues to optimize perceived competence and satisfaction.
Phase 3: Deployment & Optimization
Rollout of AI chatbots in selected service domains, continuous monitoring of user interactions, feedback collection, and iterative optimization. Fine-tuning for specific contexts (e.g., travel vs. healthcare).
Phase 4: Scaling & Advanced Integration
Expanding AI capabilities across more service areas, integrating with other enterprise systems, and exploring advanced features like multimodal communication (audio, video, images) for richer user experiences.
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