Speech Recognition in Travel & Hospitality: Use Cases, Benefits, and Solutions

At a Glance

• Speech recognition helps travel and hospitality companies automate customer interactions, improve service speed, and reduce friction across booking, support, and on-site experiences.
• In travel, ASR is used in call centers, booking platforms, hotels, airports, and mobile apps to enable voice search, transcription, guest services, and passenger assistance.
• The technology delivers business value across teams by reducing operational costs, improving customer experience, supporting multilingual communication, and enabling scalable service delivery.
• Travel companies can choose between cloud, on-premise, hybrid, and edge deployment models depending on their requirements for scalability, latency, data control, and GDPR compliance.
• Successful implementation depends on selecting the right use case, testing through a pilot project, and choosing a solution that balances accuracy, integration, multilingual support, and security.

The travel and hospitality industry is entering a new phase of digital interaction, one where voice is becoming a primary interface between businesses and customers. As travelers move across devices, locations, and languages, they increasingly expect interactions that are instant, intuitive, and effortless.

However, many travel companies still rely on traditional systems that struggle to keep up with this demand. Overloaded call centers, fragmented booking experiences, and language barriers continue to impact both operational efficiency and customer satisfaction.

At the same time, businesses are under constant pressure to optimize costs while improving service quality. Scaling support teams is no longer sustainable, and incremental UX improvements are not enough.

Speech recognition technology offers a different approach. By enabling systems to understand and process spoken language in real time, it helps travel companies streamline operations, reduce friction across the customer journey, and deliver more responsive, scalable services.

In this article, we will explore how speech recognition is used across the travel and hospitality industry, examine key business use cases, review available solution types and deployment options, and outline how companies can choose the right technology for their needs.

What is Speech Recognition in Travel

Speech recognition in travel and hospitality is a technology that converts spoken customer requests into text and actionable data, enabling voice-based booking, automated customer support, and multilingual communication across global travel platforms.

In practical terms, it allows systems to “listen” to traveler speech and transform it into structured input that can be analyzed, processed, or used to trigger actions in real time.

Speech Recognition vs. Voice Assistants vs. NLP

Speech recognition (ASR) is the foundational layer of voice-driven applications. It captures audio input and converts it into text, which can then be interpreted by other technologies.

In most travel systems:

• ASR (Automatic Speech Recognition) converts speech into text;
• NLP (Natural Language Processing) interprets user intent;
• Voice interfaces or assistants execute actions and respond to users.

Together, these components enable seamless voice interactions across customer touchpoints.

Where ASR is Used in Travel & Hospitality

Speech recognition is widely used across the travel industry to improve both customer experience and operational efficiency.

Common applications include:

• Call centers for real-time transcription, analytics, and automation of customer interactions;
• Booking platforms for voice search and faster, more intuitive reservations;
• Hotels for in-room voice controls and guest service requests;
• Airports and transport hubs for voice-enabled kiosks, navigation, and passenger assistance.

In practice, speech recognition acts as the entry point for voice interaction, enabling travel companies to build scalable, multilingual, and user-friendly services.

Speech Recognition vs. Chatbots vs. Voice Assistants in Travel

In travel and hospitality, different conversational technologies are often used together, but they serve distinct roles. Understanding the differences between speech recognition, chatbots, and voice assistants helps businesses choose the right solution for specific use cases.

Speech Recognition (ASR)

Speech recognition is the foundational technology that converts spoken language into text. In travel, it is used to transcribe customer calls in real time, enable voice search in booking systems, and process spoken input in kiosks and mobile applications. However, speech recognition on its own does not interpret meaning or generate responses; it simply transforms voice into structured data that other systems can process.

Chatbots

Chatbots are text-based systems designed to simulate conversation with users through messaging interfaces. In the travel industry, they are commonly used to answer frequently asked questions, assist with bookings, manage itineraries, and provide customer support across websites and mobile apps. Unlike speech recognition, chatbots operate primarily with text input and require additional technologies if voice interaction is needed.

Voice Assistants

Voice assistants combine speech recognition with natural language processing and response generation, enabling full voice-based interaction. In travel and hospitality, they are used for voice-driven booking, in-room hotel assistants, and interactive systems in airports or transportation hubs. These systems can understand user intent, execute actions, and deliver responses in a conversational format, making them the most advanced form of user interaction among the three.

Key Differences at a Glance

• Speech Recognition → converts speech to text;
• Chatbots → interact via text;
• Voice Assistants → enable full voice interaction (ASR + NLP + actions).

How These Technologies Work Together

In real-world travel systems, these technologies are often combined: speech recognition captures user input, natural language processing interprets intent, backend logic or chatbot systems generate responses, and voice assistants deliver them in a conversational format. This layered architecture enables scalable and intelligent interaction systems across multiple customer touchpoints.

Why This Matters for Travel Companies

Choosing the right combination of technologies depends on the specific use case and business goals. Speech recognition is essential for processing voice input in call centers and voice interfaces, chatbots are effective for text-based customer support and automation, and voice assistants are best suited for fully conversational experiences such as voice booking or in-room hotel services. Understanding these differences allows companies to implement solutions that balance performance, cost, and user experience.

How Speech Recognition is Used in Hotels, Airlines, and Travel Apps (Real Use Cases)

Speech recognition is applied differently across travel segments, depending on customer touchpoints, operational needs, and user behavior. Below are the key ways ASR technology is used in hotels, airlines, and travel applications.

Speech Recognition in Hotels

In the hospitality sector, speech recognition is primarily used to enhance guest experience and automate in-room and service interactions.

Key applications include:

• Voice-Controlled Rooms. Guests can manage lighting, temperature, curtains, and entertainment systems using voice commands, improving comfort and accessibility.
• Voice Concierge Services. Hotels use speech recognition to handle common guest requests such as room service, housekeeping, wake-up calls, and local recommendations.
• Multilingual Guest Support. International guests can interact with hotel services in their native language, reducing communication barriers and improving satisfaction.
• Internal Staff Communication. Staff can use voice-enabled systems for faster coordination, task management, and service delivery.

This makes speech recognition particularly valuable for hotel chains aiming to deliver personalized and contactless guest experiences.

Speech Recognition in Airlines

Airlines use speech recognition to optimize customer support, reduce operational load, and improve passenger communication.

Key use cases:

• Call Center Automation. Voice bots and ASR systems handle booking inquiries, flight changes, and status updates, reducing wait times and support costs.
• Real-time Call Transcription and Analytics. Airlines analyze customer conversations to detect issues, monitor service quality, and improve decision-making.
• Voice-based customer self-service. Passengers can interact with automated systems to check flight details, baggage policies, or boarding information.
• Multilingual Communication at Scale. Speech recognition combined with translation allows airlines to support global passengers without expanding support teams.

For airlines, speech recognition directly impacts cost efficiency and scalability of customer service operations.

Speech Recognition in Travel Apps

In digital travel platforms, speech recognition improves usability, especially in mobile and on-the-go scenarios.

Key applications:

• Voice Search for Booking. Users can search for flights, hotels, or destinations using natural speech instead of typing, making the booking process faster and more intuitive.
• Voice-driven Navigation Inside Apps. Travelers can access itineraries, booking details, and recommendations through voice commands.
• AI-powered Travel Assistants. Speech recognition enables conversational interfaces that help users plan trips, modify bookings, and receive personalized suggestions.
• Accessibility Improvements. Voice interfaces make travel apps more inclusive for users with disabilities or limited typing ability.

This is especially important for mobile-first users who expect fast and frictionless interactions.

Key Use Cases of Speech Recognition in Travel & Hospitality

Below are the key use cases of speech recognition in travel and hospitality, highlighting how the technology is applied across different customer touchpoints and operational processes.

Call Center Automation

Speech recognition is transforming traditional call centers into more efficient and scalable support systems. By enabling real-time call transcription, businesses can instantly capture and process customer requests without relying solely on human agents. This data can then be used for conversation analytics, helping companies identify trends, detect customer sentiment, and uncover recurring issues. As a result, travel companies can partially or fully automate routine inquiries, significantly reducing operational costs while maintaining consistent service quality.

Voice Interfaces in Apps and Booking Platforms

Voice-enabled interfaces are simplifying how users interact with travel applications and booking platforms, particularly in mobile-first scenarios. Instead of navigating complex interfaces or typing queries, users can rely on voice search to quickly find flights, hotels, or destinations. This reduces friction in the booking process and makes interactions faster and more intuitive. In turn, businesses benefit from improved user experience and higher conversion rates, especially for on-the-go travelers.

Voice Services in Hotels

Voice-enabled systems allow guests to control room settings such as lighting, temperature, or entertainment without physical interaction. Additionally, voice concierge services can handle common requests, including room service orders, housekeeping, or local recommendations. According to the study, voice assistants can support customer comfort and retention, which is especially relevant for service-driven travel environments (ScienceDirect, 2025).

Airports and Transportation

Airports and transportation hubs are adopting speech recognition to improve passenger flow and reduce congestion. Voice-enabled self-service kiosks allow travelers to check in, access travel information, or complete transactions more quickly and intuitively. At the same time, voice navigation systems help passengers find gates, services, or directions within large and often complex environments. These applications enhance the overall travel experience while optimizing operational processes.

Multilingual Communication

Given the global nature of travel, multilingual communication remains a critical challenge. Speech recognition, when combined with translation technologies, enables real-time interaction between businesses and customers who speak different languages. This allows travel companies to provide more inclusive and accessible services, improving communication with international guests. As a result, businesses can enhance customer satisfaction and expand their reach across diverse markets.

In summary, speech recognition in travel is primarily used for automation, voice interaction, and multilingual communication across customer touchpoints.

Real Examples of Speech Recognition in Travel

Speech recognition is already widely adopted across the travel industry, with airlines, hotels, and transportation hubs integrating voice technologies into customer-facing and operational systems. Below are representative examples of how it is used in real-world scenarios.

Airlines Using Voice Bots in Call Centers

Major airlines are implementing voice bots powered by speech recognition to handle high volumes of customer requests in call centers.

These systems are used to:

• Automate flight status inquiries, booking changes, and cancellations;
• Reduce average handling time for support calls;
• Provide 24/7 customer service without increasing staff.

In practice, airlines use ASR combined with NLP to understand passenger requests and either resolve them automatically or route them to the appropriate agent.

As a result, airlines can reduce support costs while maintaining consistent service quality during peak travel seasons.

Hotels Using Voice Assistants for Guest Services

Global hotel chains are adopting in-room voice assistants to improve guest experience and enable contactless services.

Typical implementations include:

• Voice-controlled room settings (lighting, temperature, TV);
• Voice concierge for room service, housekeeping, and hotel information;
• Multilingual interaction for international guests.

These systems allow hotels to deliver a more personalized and convenient stay while reducing the workload on front desk staff.

This is particularly valuable in premium hospitality segments where customer experience directly impacts retention and brand perception.

Airports Using Voice-Enabled Kiosks and Navigation

Airports and transportation hubs are deploying speech recognition in self-service kiosks and navigation systems to improve passenger flow.

Common use cases:

• Voice-based check-in and ticketing at kiosks;
• Interactive voice navigation to help passengers find gates or services;
• Automated information systems for flight updates and airport services.

These solutions help reduce congestion, minimize queues, and improve the overall passenger experience in complex environments.

Voice interfaces are especially effective in large international airports where users may face language barriers and time pressure.

Travel Apps Using Voice Search and AI Assistants

Digital travel platforms increasingly integrate speech recognition to simplify user interactions, especially on mobile devices.

Examples include:

• Voice search for flights, hotels, and destinations;
• Conversational booking assistants;
• Voice-based itinerary access and updates.

This allows users to interact with travel services more naturally, without navigating complex interfaces or typing long queries.

As a result, travel apps see improved engagement and higher conversion rates in mobile-first scenarios.

Business Value of Speech Recognition for Different Roles

For C-level Executives

For executives, speech recognition is primarily a lever for cost optimization and scalable growth. By automating large volumes of customer interactions, especially in call centers and support operations, companies can significantly reduce operational expenses. At the same time, speech-driven systems enable businesses to scale their services without proportionally increasing headcount. This creates a clear return on investment, where improved efficiency and enhanced customer experience directly contribute to long-term profitability and competitive advantage.

For Product and CX Teams

For product managers and customer experience leaders, speech recognition opens up new ways to design more intuitive and accessible user journeys. Voice interfaces simplify interactions, particularly in mobile and on-the-go scenarios, reducing friction across booking and service flows. This leads to a more seamless user experience and can positively impact key metrics such as engagement and conversion rates. Additionally, voice-driven features create opportunities for differentiation in increasingly competitive digital travel platforms.

For Operations Teams

From an operational perspective, speech recognition helps streamline internal processes and reduce the burden on frontline staff. Routine requests can be automated, allowing teams to focus on more complex or high-value tasks. This not only improves efficiency but also enhances service consistency, especially during peak demand periods. As a result, operations teams can maintain high service standards while managing resources more effectively.

For Technology Teams

For engineering and IT teams, modern speech recognition solutions offer flexible integration options and fast time-to-market. Many providers deliver APIs and SDKs that allow seamless integration into existing systems such as CRM, PMS, or booking platforms. At the same time, different deployment models – cloud, on-premise, or hybrid, provide architectural flexibility, enabling teams to align the solution with security, performance, and infrastructure requirements.

Deployment Options for Speech Recognition: Choosing the Right Architecture

For travel and hospitality companies, selecting a speech recognition deployment model is a strategic decision that directly impacts system performance, scalability, data privacy, and total cost of ownership (TCO). Each approach represents a different balance between centralized and local processing, infrastructure control, and operational complexity. The optimal choice depends on specific use cases, such as real-time customer interactions, multilingual support, or offline environments, as well as regulatory and technical requirements.

Cloud-Based Speech Recognition

Cloud-based solutions rely on external infrastructure and are typically delivered through API-driven services. They enable fast deployment, elastic scaling, and seamless integration into applications such as booking platforms, mobile apps, and customer support systems. These solutions often leverage large-scale pretrained models, providing high recognition accuracy and continuous improvements. However, performance can depend on network conditions, and processing data in external environments may raise considerations around data privacy and compliance, particularly for global travel businesses.

On-Premise (Self-Hosted) Speech Recognition

On-premise deployment allows organizations to run speech recognition systems within their own infrastructure, providing full control over data processing, storage, and security policies. This approach is particularly relevant for airlines, hotel chains, and travel enterprises operating under strict regulatory frameworks or handling sensitive customer data. It also enables deeper customization, such as adapting models to specific accents, terminology, or operational scenarios. The trade-off is higher operational complexity, including infrastructure management, system maintenance, and MLOps requirements.

Hybrid Speech Recognition Solutions

Hybrid architectures combine cloud and on-premise deployment, allowing businesses to distribute workloads based on sensitivity, latency, or cost considerations. For example, real-time interactions or sensitive customer data can be processed locally, while less critical workloads, such as batch transcription or analytics, are handled in the cloud. This approach provides flexibility and aligns well with complex enterprise environments, enabling companies to balance scalability with compliance and control. However, it requires careful orchestration between systems and may increase architectural complexity.

Edge / On-Device Speech Recognition

On-device or edge-based solutions process speech directly on local devices such as kiosks, mobile phones, or in-room hotel systems. This approach reduces dependency on network connectivity and supports low-latency interactions, which is critical in environments like airports, aircraft, or remote locations. It is particularly useful for offline scenarios and real-time voice interfaces. However, edge deployments require model optimization due to limited computing resources, which may impact recognition accuracy compared to cloud-based systems.

How to Choose the Right Speech Recognition Solution

Choosing a speech recognition solution in travel and hospitality directly affects customer experience, service speed, and operational efficiency. The right approach depends on specific use cases such as call center automation, voice booking, in-room assistants, or airport navigation systems.

• Language and Accent Support. Travel businesses operate in multilingual environments. A reliable solution should support a wide range of languages and accurately recognize different accents. This is especially important for international travelers interacting with booking platforms, hotel staff, or support services.
• Accuracy in Real Travel Environments. Speech recognition must perform well in noisy and dynamic environments. Airports, train stations, hotel lobbies, and outdoor locations introduce background noise and overlapping speech. High accuracy in these conditions is critical for maintaining a smooth customer experience.
• Latency: Real-Time vs Batch Processing. Different travel scenarios require different response times. Real-time processing is essential for voice assistants, kiosks, and live support interactions. Batch processing is more suitable for call transcription, analytics, and post-processing tasks. The choice depends on how the system is used across the customer journey.
• Customization Capabilities. Travel-related vocabulary includes location names, brand terms, and service-specific phrases. Solutions that allow customization or adaptation can improve recognition quality in booking systems, hotel services, and customer interactions.
• Integration and Compatibility. Speech recognition should integrate smoothly with existing travel systems such as CRM platforms, PMS, booking engines, and mobile applications. APIs and SDKs simplify integration and reduce development time, which is important for fast deployment in competitive travel products.
• Security and Data Management. Travel companies process sensitive customer data, including personal details and booking information. It is important to ensure secure data handling, proper storage, and compliance with regulations such as GDPR. Deployment choice also affects how data is controlled.
• Total Cost of Ownership (TCO). Costs include not only implementation but also scaling, infrastructure, and ongoing usage. Travel platforms with high traffic or seasonal demand should consider how costs change with volume. A balanced approach helps avoid unexpected expenses over time.

There is no universal solution for all travel scenarios. The right choice depends on the specific use case, whether it is customer support, booking, in-room services, or airport operations, along with requirements for performance, cost, and data control.

Key Challenges and Limitations

Despite the growing adoption of speech recognition in travel and hospitality, its implementation comes with a set of practical challenges. These limitations become especially visible in real-world environments, where systems must operate with diverse users, high levels of background noise, and strict data requirements.

• Noisy Environments. Travel environments are rarely quiet. Airports, train stations, hotel lobbies, and streets introduce constant background noise that can interfere with speech recognition accuracy. Announcements, crowds, and overlapping conversations make it more difficult for systems to isolate and process speech correctly, especially in real-time scenarios.
• Accent and Language Variability. Travel platforms serve users from different countries, each with unique accents, speech patterns, and pronunciation. Even within the same language, variability can significantly impact recognition quality. Systems that are not properly trained or adapted may struggle to understand international users, leading to inconsistent performance.
• Recognition Errors and Impact on UX. Even with modern AI models, speech recognition is not error-free. Misinterpretations can lead to incorrect actions, failed bookings, or frustration during customer interactions. In high-impact scenarios such as customer support or navigation, these errors directly affect user satisfaction and trust in the system.
• Privacy and Compliance. Speech data often contains sensitive personal information, especially in travel scenarios involving bookings, identification, or customer support. Companies must ensure secure data processing and storage, as well as compliance with regulations such as GDPR. Deployment models and data handling strategies directly influence how these requirements are met.

In practice, these challenges do not limit adoption but define how speech recognition systems should be designed and deployed. Companies that account for real-world conditions, invest in model quality, and align their solutions with security and compliance requirements are more likely to achieve reliable performance and consistent user experience.

Speech Recognition Solutions for Travel and Hospitality: Lingvanex Example

For travel and hospitality companies, choosing a speech recognition provider directly affects data control, scalability, and the ability to operate in a global, multilingual environment.

Lingvanex is designed to address these challenges by combining flexible deployment, strong data privacy capabilities, and multilingual speech recognition tailored for real-world travel scenarios.

Full Data Control with On-Premise Deployment

Unlike many speech recognition providers that operate exclusively in the cloud, Lingvanex offers a fully on-premise deployment option.

This allows travel companies to:

• Process voice data entirely within their own infrastructure;
• Maintain full control over customer conversations and call recordings;
• Meet strict internal security policies and enterprise IT requirements.

This is especially important for airlines, hotel chains, and travel enterprises handling sensitive customer data.

GDPR Compliance and Data Privacy by Design

Travel businesses operating in Europe or serving EU customers must comply with strict data protection regulations such as GDPR.

Lingvanex supports compliance by:

• Enabling local data processing without sending audio to third-party servers;
• Reducing risks associated with cross-border data transfer;
• Giving companies full visibility and control over how voice data is stored and processed.

This makes Lingvanex particularly relevant for companies where data sovereignty and compliance are business-critical.

Multilingual Speech Recognition for Global Travel

Travel is inherently multilingual, and customer interactions often involve different languages, accents, and speech patterns.

Lingvanex provides:

• Support for multiple languages and accents;
• Consistent performance across international user bases;
• Seamless integration with translation technologies for real-time multilingual communication.

This enables travel platforms to deliver localized and inclusive experiences without expanding support teams.

Flexible Deployment for Different Travel Scenarios

Lingvanex supports multiple deployment models, allowing businesses to choose the architecture that fits their needs:

• On-premise → maximum control and compliance;
• Cloud API → fast integration and scalability.

This flexibility allows travel companies to adapt speech recognition to different use cases, from call center automation to mobile booking and in-room voice systems.

Built for Integration into Travel Ecosystems

Lingvanex is designed for seamless integration into existing travel infrastructure, including:

• Booking platforms and travel apps;
• CRM and customer support systems;
• PMS and hotel management systems.

With API-based integration and support for common audio formats, teams can deploy speech recognition without significant changes to their architecture.

This makes Lingvanex a practical choice for travel companies that require both scalability and strict data control.

How to Start Implementing Speech Recognition in Travel

Adopting speech recognition in travel and hospitality requires a structured approach that aligns technology with business goals and operational workflows. Instead of large-scale deployment from the start, companies typically achieve better results by focusing on targeted use cases and gradual implementation.

1. Define a Priority Use Case. The first step is to identify where speech recognition can deliver the highest impact. This could be call center automation, voice search in booking platforms, in-room assistants, or multilingual customer support. Prioritization should be based on expected ROI, operational pain points, and customer experience improvements.
2. Launch a Pilot Project. Before scaling, it is important to test the solution in a controlled environment. A pilot project allows teams to validate performance, integration, and user experience. This stage helps identify technical limitations, required customizations, and potential risks without affecting the entire system.
3. Measure Key Metrics. Evaluation should focus on measurable outcomes such as cost reduction, response time, and customer satisfaction. Additional metrics may include recognition accuracy, system latency, and operational efficiency. These insights help determine whether the solution meets business expectations.
4. Scale Across the Organization. Once the pilot proves successful, the solution can be expanded to additional use cases and departments. Scaling may involve integrating speech recognition into multiple systems, increasing workload capacity, and optimizing performance for different environments. A phased rollout helps maintain stability while maximizing impact.

In practice, successful implementation depends on aligning technology with real business needs and continuously refining the system based on performance and user feedback.

The Future of Speech Recognition in Travel

Speech recognition is evolving from a supporting technology into a core layer of digital interaction in travel and hospitality. As systems become more accurate and integrated, voice is expected to play a central role across the entire customer journey.

Voice-First Customer Journey

Travel experiences are gradually shifting toward voice-first interactions. From trip planning and booking to in-destination services, users will increasingly rely on voice instead of traditional interfaces. This reduces friction and enables faster, more natural interactions across devices and touchpoints.

Integration with AI Agents

Speech recognition is becoming a key component of AI-driven assistants and autonomous agents. These systems can handle complex, multi-step interactions such as booking changes, itinerary management, and customer support. In travel, this enables more efficient and scalable service models with reduced reliance on human agents.

Real-Time Speech and Translation

The combination of speech recognition and real-time translation is expected to redefine communication in global travel. Travelers will be able to interact with services, staff, and digital platforms in their native language, regardless of location. This reduces language barriers and improves accessibility across international markets.

Personalized Voice Interfaces

Future voice systems will become more personalized, adapting to user preferences, behavior, and travel history. This enables more relevant recommendations, tailored services, and context-aware interactions. In hospitality, this could include personalized room settings, service suggestions, and proactive assistance based on individual guest profiles.

Overall, speech recognition is moving toward deeper integration with AI systems and customer data, enabling more seamless, intelligent, and personalized travel experiences across the entire journey.

Conclusion

For companies evaluating speech recognition solutions, starting with a pilot or API integration is often the fastest way to validate ROI and performance in real travel scenarios.

Speech recognition is no longer an experimental technology in travel and hospitality. It is becoming part of the core digital infrastructure that supports customer interactions, operational efficiency, and service scalability. As voice interfaces continue to evolve, they are reshaping how travelers search, book, and interact with services across the entire journey.

For travel businesses, this shift directly impacts competitiveness. Companies that adopt speech technologies can streamline operations, reduce costs, and deliver faster, more intuitive customer experiences. At the same time, those that delay adoption risk falling behind in an industry where speed, convenience, and personalization are increasingly expected.

As the market continues to develop, the focus is not on whether to implement speech recognition, but on how to choose the right approach. Evaluating available solutions, deployment models, and use cases allows companies to align technology with their specific business needs and build a foundation for scalable, voice-enabled services.

References

1. PubMed (2020), In-room Voice-Based AI Digital Assistants Transforming On-Site Hotel Services and Guests’ Experiences.
2. ScienceDirect (2025), Voice Assistants in the Tourism Customer Journey: From Knowledge and Decision-Making to Ecotourism Loyalty in Family Vacations.
3. ResearchGate (2022), Voice Chatbot for Hospitality.