Integrating Generative AI and IoT for Sustainable Smart Tourism Destinations
Abstract
:1. Introduction
1.1. Problem of the Study
1.2. Contribution
1.3. Objective
2. Literature Review
2.1. Generative Artificial Intelligence (Gen AI)
2.1.1. Gen AI Component
- Generative adversarial networks (GANs): Introduced by Goodfellow et al. [46], GANs are perhaps the most popular generative model. GANs consist of two neural networks—the generator and the discriminator—that compete against each other, thus improving the quality of the generated outputs [49]. The generator produces data, while the discriminator evaluates them against the real data [50]. This framework has been pivotal for advancements in realistic image generation [47,51].
- Variational autoencoders (VAEs): Developed by Kingma and Welling [52], VAEs are another key component of generative AI. VAEs are designed to compress data into a lower-dimensional space and then reconstruct them back into the original space, facilitating the generation of new data points. They are particularly known for their effectiveness in generating complex data structures like images [52].
- Transformer models: Recent years have seen a rise in the transformer models introduced by Vaswani et al. [50], which have revolutionized the field of natural language processing. Transformers use mechanisms like self-attention to process sequences of data, making them highly effective for generating coherent and contextually relevant text. They are the foundation of models like GPT (Generative Pre-trained Transformer) and BERT (Bidirectional Encoder Representations from Transformers) [50,53].
2.1.2. Gen AI Application
2.2. Natural Language Processing (NLP)
NLP for Tourism
- Customer service and chatbots: One of the prominent applications of NLP in tourism is in customer service, particularly through the use of chatbots. Chatbots leverage NLP to understand and respond to customer inquiries in real time, providing instant support and information [73].
- Sentiment analysis: Sentiment analysis, another critical NLP application, involves analyzing customer reviews and feedback to gauge sentiment and identify trends. By processing large volumes of text data from online reviews, social media posts, and surveys, sentiment analysis tools can help tourism businesses understand customer preferences and identify areas for improvement [74].
- Recommendation systems: NLP-powered recommendation systems are also revolutionizing the tourism sector. These systems analyze user preferences, past behaviors, and contextual information to provide personalized travel suggestions. For instance, systems can recommend destinations, activities, and accommodations tailored to individual tastes [75].
- Content generation: Using NLP technologies such as automated travel guides and blog posts is becoming increasingly popular. These tools can create engaging and informative content by extracting relevant information from various sources and presenting it coherently [76]. Automated content generation helps tourism companies maintain an active online presence and provide valuable information to potential travelers.
2.3. Internet of Things (IoT)
- Connectivity: This is crucial for enabling communication between IoT devices and the cloud or other data processing systems. Various communication protocols and technologies are used, including Wi-Fi, Bluetooth, Zigbee, and cellular networks [80]. Effective connectivity ensures that the data collected by sensors are transmitted reliably and efficiently to the appropriate processing units.
- Data processing: Once data are collected and transmitted, they need to be processed and analyzed to derive meaningful insights. Data processing can occur at the edge, closer to the data source, or in centralized cloud servers [81].
- User interfaces (UIs): These are the components through which users interact with IoT systems. UIs can take various forms, such as mobile apps, web dashboards, or voice assistants, allowing users to monitor and control IoT devices [82].
IoT for Tourism
- Smart destinations: The IoT plays a crucial role in developing smart destinations, which use interconnected devices to manage and optimize resources. For example, IoT-enabled sensors can monitor and manage tourist flows, reducing congestion and improving the visitor experience [84]. Smart destinations leverage IoT technology to provide real-time information about attractions, transportation, and local services, making travel more convenient and enjoyable.
- Enhanced customer experiences: The IoT significantly enhances customer experiences by providing personalized services. Wearable devices, such as smartwatches, can offer tourists personalized recommendations based on their preferences and real-time location data [85]. Hotels use IoT devices to automate room settings according to guest preferences, such as lighting, temperature, and entertainment systems, thereby increasing comfort and satisfaction.
- Efficient operations: The IoT improves operational efficiency in the tourism sector by enabling better resource management and reducing costs. For instance, IoT-enabled energy management systems can optimize energy usage in hotels and other tourist facilities, leading to significant cost savings and sustainability benefits [86].
- Data-driven insights: The vast amount of data generated by IoT devices provides valuable insights for tourism businesses. By analyzing these data, companies can understand customer behaviors, preferences, and trends, enabling them to tailor their services and marketing strategies more effectively [23].
2.4. Smart Tourism Destinations
- Integration of IoT: IoT technologies are cornerstones of smart tourism destinations. The IoT enables real-time data collection and monitoring through interconnected devices and sensors. These technologies can monitor tourist flows, optimize transportation systems, and provide personalized services [91].
- Big data analytics: This plays a crucial role in the functionality of smart tourism destinations. By analyzing vast amounts of data from various sources such as social media, mobile apps, and IoT devices [1], tourism managers can gain valuable insights into visitor behaviors and preferences [23,89].
- Smart infrastructure: This encompasses the physical and digital systems that support the operations of a smart tourism destination. This includes smart transportation networks, energy-efficient buildings, and digital information platforms. Smart infrastructure enhances the efficiency and sustainability of tourism operations. For example, smart grids and energy management systems reduce the environmental impact of tourist facilities [92].
- Impact on tourism management: Smart tourism destinations provide significant benefits for tourism management. Real-time data and predictive analytics enable proactive decision making and efficient resource allocation. This results in improved operational efficiency and better visitor management [89,92].
2.5. Research Hypothesis
2.5.1. Attention
2.5.2. Interest
2.5.3. Usage
2.5.4. Tourists’ Planning Prior to Trip
2.5.5. Tourism Experience
2.5.6. Emotion
2.5.7. Satisfaction
3. Research Methodology
3.1. Problem Statement
3.2. Research Design
- Handling diverse inquiries: The NLP module is designed to process a wide range of tourist inquiries, from simple questions about local attractions to more complex queries regarding travel logistics. It employs advanced algorithms to understand the context and intent behind each question, ensuring that relevant and accurate information is provided.
- Ensuring accuracy: To ensure high accuracy, the NLP system utilizes a combination of machine learning models and extensive linguistic databases. These models are trained on large datasets that include various dialects, slang, and idiomatic expressions, enabling the system to comprehend and respond accurately to different forms of natural language input.
- Cultural sensitivity: Cultural sensitivity is paramount in the responses generated by the NLP module. The system incorporates cultural context into its processing, considering local customs, traditions, and etiquette. This is achieved by integrating cultural databases and context-aware algorithms that adjust their responses to be respectful and appropriate for the cultural background of the user.
- Continuous learning and adaptation: The NLP module is designed to continuously learn and adapt from user interactions. Feedback mechanisms are in place to refine and improve the system’s understanding and response generation over time. Adaptive learning ensures that the system remains up to date with evolving language trends and cultural nuances.
3.3. Details of System Development
3.3.1. NLP Coding
3.3.2. IoT Development
- Initialize Wi-Fi credentials: set the SSID and password for the Wi-Fi network.
- Set up temperature sensor: configure the ADC (Analog-to-Digital Converter) on pin 34 with an attenuation of 11 dB for a wider input voltage range.
- Define Wi-Fi setup function: create the setup_wifi function to connect to the specified Wi-Fi network.
- Set up temperature sensor: configure the ADC (Analog-to-Digital Converter) on pin 34 with an attenuation of 11 dB for a wider input voltage range.
- Define temperature reading function: create the read_temp function to read the analog value from the temperature sensor, convert it to Celsius, and return it as a string.
- Define webpage generation function: create the web_page function to generate and return the HTML string containing the temperature and magnetic field data.
- Main function: create the main function to call the setup_wifi and http_server functions.
- Execute main function: ensure the main function runs when the script is executed.
3.3.3. Integration of Gen AI with IoT
3.3.4. System Implementation
3.3.5. Tourist System Utilization and User Evaluation
4. Experimental Results
4.1. Development of Integrated Gen AI with IoT System for Tourism
4.2. System Evaluation
4.2.1. Expert Evaluation
4.2.2. User Evaluation
5. Discussion
5.1. General Discussion
5.2. Theorical Implications
5.3. Tourism Business Implications
6. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
Appendix A. Variables
Att1 | Relevance | The relevance of using AI systems to support tourism/tourists see that AI can play a part in supporting tourism activities. |
Att2 | Authenticity | AI can present in-depth information well/tourists see that AI can provide detailed information on tourism activities. |
Att3 | Originality | AI can provide novel information directly to tourists/tourists see that AI can present new information. |
Att4 | Accessibility | AI can easily access information/tourists see that AI can easily access tourism information. |
In1 | Content | Tourists see that AI can present content-based information well. |
In2 | Inspiration | Tourists see that AI can inspire them to travel. |
In3 | Diversity | Tourists see that AI can create and present diverse information for planning tourism. |
In4 | Presentation | Tourists see that AI can present information in a perspective that is easy to use for tourism planning. |
Usa1 | Personalization | Tourists see that AI can create personalized information, such as individual planning for tourists. |
Usa2 | News | Tourists see that AI can provide up-to-date tourism information. |
Usa3 | Interaction | Tourists see that AI can interact well with tourists. |
Usa4 | Awareness | Tourists see that AI can present in-depth information and meet users’ needs more accurately. |
Usa5 | Utility | Tourists see that AI is useful for all groups and ages for tourism planning. |
Usa6 | Stability | Tourists see that AI is a stable system. |
Emo1 | Feedback | Tourists see that AI can provide good feedback from others. |
Emo2 | Empathy | Tourists see that AI can empathize with users’ emotions. |
Emo3 | Memories | Tourists see that AI can store good memories for tourists. |
Emo4 | Recommendation | Tourists see that AI can provide good recommendations for tourists. |
TPD1 | Tourists’ pre-trip planning | Tourists rely on AI for pre-trip planning. |
TPD2 | During the trip | Tourists rely on AI on during the trip. |
TPD3 | Post-trip feedback | Tourists provide feedback into the AI system to what extent. |
Sat1 | Overall satisfaction with AI | Overall satisfaction with AI in terms of data accuracy. |
Sat2 | Overall satisfaction with AI | Overall satisfaction with AI in terms of data updates. |
Sat3 | Overall satisfaction with AI | Overall satisfaction with AI in terms of data security. |
Exp1 | Tourists will use AI | Tourists will use AI for tourism in the future. |
Exp2 | Tourists will recommend AI | Tourists will recommend the use of AI for a good tourism experience to others. |
Exp3 | Tourists will choose AI support | Tourists will choose to use AI support systems for other matters in the future. |
INT1 | Interaction between experience | Interaction between experience and tourists’ planning prior to and during a trip. |
INT2 | Interaction between EMOTION | Interaction between emotion and tourists’ planning prior to and during a trip. |
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Indicator | Mean | Standard Deviation |
---|---|---|
Ease of use | 4.00 | 0.85 |
Ease of access | 4.00 | 0.77 |
System stability | 4.00 | 0.65 |
Network stability | 4.00 | 0.87 |
Runtime errors | 3.50 | 0.55 |
System accuracy | 4.50 | 0.89 |
System completeness | 4.00 | 0.98 |
Overall rating | 4.00 | 0.76 |
Latent Variables | Tourists’ Planning Prior to and During Trip | Experience | Satisfaction | ||||||
---|---|---|---|---|---|---|---|---|---|
TE | DE | IE | TE | DE | IE | TE | DE | IE | |
USAGE | 0.249 *** | 0.249 *** | _ | 0.226 *** | 0 | 0.226 *** | _ | _ | _ |
INTEREST | 0.221 *** | 0.221 *** | _ | 0.201 *** | 0 | 0.201 *** | _ | _ | _ |
ATTENTION | 0.079 ** | 0.079 ** | _ | 0.072 ** | 0 | 0.072 ** | _ | _ | _ |
EMOTION | 0.609 *** | 0.609 *** | _ | 0.554 *** | 0 | 0.554 *** | 0.144 ** | 0.144 ** | - |
Tourists’ planning prior to and during trip | _ | _ | _ | 0.909 *** | 0.909 *** | _ | 0.623 *** | _ | 0.623 *** |
Experience | _ | _ | _ | _ | _ | _ | 0.685 *** | 0.685 *** | _ |
INT1 | _ | _ | _ | _ | _ | _ | 0.421 *** | 0.421 *** | _ |
INT2 | _ | _ | _ | _ | _ | _ | 0.296 ** | 0.296 ** | _ |
Latent Variables | Observed Variables | Standardized Regression Weights () | se | t | |
---|---|---|---|---|---|
ATTENTION | Att1 | 0.8010 | - | - | 0.6380 |
Att2 | 0.8490 | 0.0570 | 19.0110 *** | 0.7210 | |
Att3 | 0.8740 | 0.0550 | 19.5270 *** | 0.7570 | |
Att4 | 0.7890 | 0.0610 | 17.4730 *** | 0.6290 | |
INTEREST | In1 | 0.7950 | - | - | 0.6470 |
In2 | 0.7900 | 0.0610 | 18.3020 *** | 0.6350 | |
In3 | 0.8630 | 0.0500 | 18.7990 *** | 0.7070 | |
In4 | 0.8250 | 0.0500 | 17.8080 *** | 0.6750 | |
USAGE | Usa1 | 0.7370 | - | - | 0.5530 |
Usa2 | 0.7100 | 0.0680 | 14.6900 *** | 0.5160 | |
Usa3 | 0.8340 | 0.0750 | 17.3510 *** | 0.6880 | |
Usa4 | 0.8820 | 0.0680 | 18.4960 *** | 0.7870 | |
Usa5 | 0.8020 | 0.0720 | 16.4810 *** | 0.6370 | |
Usa6 | 0.8410 | 0.0720 | 17.1960 *** | 0.6990 | |
EMOTION | Emo1 | 0.6580 | - | - | 0.4510 |
Emo2 | 0.6940 | 0.1070 | 12.3570 *** | 0.4850 | |
Emo3 | 0.8720 | 0.0910 | 14.4420 *** | 0.7320 | |
Emo4 | 0.7880 | 0.0880 | 13.8900 *** | 0.6470 | |
Tourists’ planning prior to and during trip | TPD1 | 0.9280 | - | - | 0.7990 |
TPD2 | 0.9480 | 0.0360 | 28.6560 *** | 0.8430 | |
TPD3 | 0.8590 | 0.0460 | 22.0040 *** | 0.6530 | |
Experience | Exp1 | 0.8860 | - | - | 0.7120 |
Exp2 | 0.9220 | 0.0460 | 23.7150 *** | 0.8020 | |
Exp3 | 0.7750 | 0.0550 | 16.6470 *** | 0.5090 | |
Satisfaction | Sat1 | 0.9010 | - | - | 0.8330 |
Sat2 | 0.8860 | 0.0320 | 29.3300 *** | 0.8060 | |
Sat3 | 0.8180 | 0.0450 | 24.7170 *** | 0.7000 | |
Experience tourists’ planning prior to and during trip | INT1 | 0.421 | 0.003 | 18.431 *** | 0.1030 |
EMOTION tourists’ planning prior to and during trip | INT2 | 0.296 | 0.003 | 3.427 ** | 0.0780 |
Moderator | BootMean (HIGH TPD) | BootSE (LOW TPD) | BootLLCI | BootULCI |
---|---|---|---|---|
Constant | −0.057 | 0.5164 | −1.1141 | 0.8632 |
Experience | 0.474 | 0.109 | 0.2673 | 0.6938 |
Moderator | BootMean (High TPD) | BootSE (Low TPD) | BootLLCI | BootULCI |
---|---|---|---|---|
Constant | −0.127 | 0.4414 | −1.0197 | 0.7437 |
EMOTION | 0.2707 | 0.0756 | −0.062 | 0.6101 |
Fit Index | Criterion | Value | Evaluation |
---|---|---|---|
χ2/df | Less than 2.00 | 1.154 | Pass |
p-value | Greater than 0.05 | 0.071 | Pass |
GFI (Goodness-of-Fit Index) | Greater than 0.95 | 0.965 | Pass |
AGFI (Adjusted Goodness-of-Fit Index) | Greater than 0.95 | 0.961 | Pass |
CFI (Comparative Fit Index) | Greater than 0.95 | 0.990 | Pass |
NFI (Normed Fit Index) | Greater than 0.95 | 0.977 | Pass |
RMR (Root Mean Square Residual) | Less than 0.05 | 0.032 | Pass |
RMSEA (Root Mean Square Error of Approximation) | Less than 0.05 | 0.040 | Pass |
Hypothesis | bb | Summary |
---|---|---|
H1 | 0.079 ** | Significant at the 0.01 level |
H2 | 0.221 *** | Significant at the 0.001 level |
H3 | 0.249 *** | Significant at the 0.001 level |
H4 | 0.609 *** | Significant at the 0.001 level |
H5 | 0.05 | Not significant |
H6 | 0.909 *** | Significant at the 0.001 level |
H7 | 0.685 *** | Significant at the 0.001 level |
H8 | 0.144 ** | Significant at the 0.01 level |
H9 | 0.296 ** | Significant at the 0.01 level |
H10 | 0.421 *** | Significant at the 0.001 level |
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Suanpang, P.; Pothipassa, P. Integrating Generative AI and IoT for Sustainable Smart Tourism Destinations. Sustainability 2024, 16, 7435. https://doi.org/10.3390/su16177435
Suanpang P, Pothipassa P. Integrating Generative AI and IoT for Sustainable Smart Tourism Destinations. Sustainability. 2024; 16(17):7435. https://doi.org/10.3390/su16177435
Chicago/Turabian StyleSuanpang, Pannee, and Pattanaphong Pothipassa. 2024. "Integrating Generative AI and IoT for Sustainable Smart Tourism Destinations" Sustainability 16, no. 17: 7435. https://doi.org/10.3390/su16177435