Nearly 34.6 million people traveled to Florida from July through September -- shattering another tourism record for the state.

TASHKENT, 5 November 2015 – Addressing and preventing human trafficking in the hospitality and tourism sector is the topic of a two-day OSCE-supported training course, launched today in Tashkent, for 25 representatives from Uzbekistan’s tourism infrastructure, airlines, airport services and police.

The course, organized by the OSCE Project Co-ordinator in Uzbekistan and the National Inter-agency Commission on Counteracting Human Trafficking, aims to contribute to preventing human trafficking through awareness raising and developing the anti-trafficking codes of conduct for personnel.

The OSCE Project Co-ordinator in Uzbekistan Gyorgy Szabo said the training course would help representatives from private businesses develop corporate responsibility for preventing human trafficking in the tourism infrastructure.  

Astrid Winkler, representative of the ECPAT International (End Child Prostitution, Child Pornography and Trafficking of Children for Sexual Purposes), said that “It is important that all stakeholders, including the tourism business, understand what human trafficking is and take a stance against it. Business can do a lot in terms of prevention.”

The training course is part of the OSCE Project Co-ordinator’s project to assist Uzbekistan in combating the trafficking in human beings.

Related Stories

• OSCE launches training seminar for Uzbek journalists on human trafficking
• OSCE-supported training course in Uzbekistan focuses on addressing and preventing human trafficking in the tourism sector
• OSCE Project Co-ordinator lights up central Tashkent with 20 solar-powered street lamps

For a country that has long prioritised a balanced lifestyle and good governance over economic gain, Bhutan is doing something it has never done before: embrace more independent, cost-conscious travellers in an urgent bid to revive its faltering economy.

[Ethiopian Herald] In step with global tourism trends, Ethiopia is revising its tourism policy and expanding destinations to unlock its vast potential and generate significant income, according to the Ministry of Tourism. The government plans to invest heavily in infrastructure and amend restrictive policies to fully realize the sector's promise.

[MAP] Dakhla -- Morocco has welcomed 14.6 million tourists by the end of this year's October, surpassing in just 10 months, the record for the entire year 2023, Minister of Tourism, Handicraft, and Social and Solidarity Economy, Fatim-Zahra Ammor, announced on Wednesday in Dakhla.

Thailand's landmark move to legalise same-sex marriage is set to boost “rainbow tourism” by attracting four million more visitors annually, and generate about US$2 billion in revenue, according to travel platform Agoda.

For the first time, the UN Climate Change conference of the parties will welcome Tourism Ministers, placing the sector firmly within the COP29 Action Agenda and providing a high-level platform for dialogue – at the initiative and joint leadership of the State Tourism Agency of the Republic of Azerbaijan and the World Tourism Organization (UN Tourism).

Sculptor had disassembled statue only to introduce minor alignment corrections as suggested by NITK, the MLA claims

Casinos would significantly boost tourism to Thailand, says the chief executive of the online travel agency Agoda, who is confident the country could set a record for foreign arrivals next year.

The city has repeatedly pressed for a special regulation from the national government

ఆంధ్రప్రదేశ్ రాష్ట్రంలో ఈ సీజన్లో అందాల అరకు పర్యాటకులకు కనువిందు చేస్తుంది. పచ్చని అడవి, ఎత్తైన కొండలు, జలపాతాల మధ్య మేఘాల దారుల్లో తిరుగుతున్నట్టు అక్కడికి వెళ్ళిన పర్యటకులందరికీ ఒక వింత అనుభూతి కలుగుతుంది. శీతాకాలంలో అరకులోని ప్రకృతి సోయగాలు మరింత రమణీయంగా పర్యాటకులను పిలుస్తున్నాయి. ఆంధ్ర ఊటీ అరకు ప్రకృతి ప్రేమికులను రారమ్మని పిలుస్తోంది. తగ్గుతున్న

Posting his journey on his Youtube channel, the Congress leader said that despite the recent challenges faced by the landslide, the area is filled with "incredible attractions."

Food tourism in Italy is a growing sector, notwithstanding the fact that Italians have been producing some of the best food in the world for centuries. Producing great food is one thing, making it accessible to visitors is another, and for many years it was not necessarily easy to plan your whole Italian holiday around […]

The post Food Tourism in Italy – A Guide appeared first on Three Monkeys Online Magazine.

Die Polizei in Irland ist einem geheimnisvollen polnischen Verkehrsrowdy auf die Spur gekommen, der landauf, landab die Straßen unsicher zu machen schien - denn gegen den seltsamen Herr Pravo Jazdy liefen Dutzende von Verfahren wegen Schnellfahrens und Parkverstößen. Und irgendwie schaffte es Pravo Jazdy immer, sich der Justiz zu entziehen, indem er eine falsche Adresse angab. Nun hat die Polizei dar Rätsel allerdings gelöst, wenn auch mit dem Ergebnis, dass sie die Bußgelder wohl in den Kamin schreiben kann. Zur Auflösung hier nur so viel: Es wäre nicht weiter verwunderlich, wenn auch ein französischer Adliger namens Permis de Conduire auf der Fahndungsliste stünde.

The city hopes the move will revive its status as an international business and travel hub, but the locals said the change may be too late.

Going on a vacation might seem like a rather unconventional way to try to improve your sleep habits.

In the era of influencers and search engine recommendations, locals have begun organizing efforts to mislead tourists and push back against the tide of visitors flocking to their favorite places.

Vallachira panchayat in Thrissur District of Kerala has held Onam celebrations without a break since 1962. The 10-day festival which highlights folk performances is now a showcase for tourists

Dozens of new tourist businesses are opening in the South of Scotland as visitors escape to the great outdoors.

Figures show 137,691 fewer people visited the island in the summer compared to 2019.

More than 30 public toilets in Ceredigion could close to cut costs.

A review of the front page stories from the daily and weekly newspapers in Wales.

The increased volume of accessible internet data creates an opportunity for researchers and practitioners to improve time series forecasting for many indicators. In our study, we assess the value of web traffic data in forecasting the number of short-term visitors travelling to Australia. We propose a feature-based model selection framework which combines random forest with feature ranking process to select the best performing model using limited and informative number of features extracted from web traffic data. The data was obtained for several tourist attraction and tourism information websites that could be visited by potential tourists to find out more about their destinations. The results of random forest models were evaluated over 3- and 12-month forecasting horizon. Features from web traffic data appears in the final model for short term forecasting. Further, the model with additional data performs better on unseen data post the COVID19 pandemic. Our study shows that web traffic data adds value to tourism forecasting and can assist tourist destination site managers and decision makers in forming timely decisions to prepare for changes in tourism demand.

This study inquires about the COVID-19-generated momentum and how it resulted in transformative opportunities for the hard-hit tourism industry in Hawai'i. It also investigates the type of sustainability-based management strategies that were favoured by actors from the industry to help navigate uncertain times and capture transformative opportunities. Findings indicate that actors from the tourism industry in Hawai'i perceived the COVID-19 pandemic as a huliau, or a point of transformation, to reflect and re-evaluate the tourism industry's responsibility and shift toward a recovery focused on sustainability. This research confirms that the pandemic-driven momentum accelerated opportunities for changing and transforming traditional business models and indicators of progress within the tourism industry in Hawai'i. Further research may explore additional Pacific Island countries to gain a deeper understanding of the problem within the region's context.

Aim/Purpose: The main goal of this systematic literature review was to look for studies that provide information relevant to business intelligence’s (BI) framework development and implementation in the tourism sector. This paper tries to classify the tourism sectors where BI is implemented, group various BI functionalities, and identify common problems encountered by previous research. Background: There has been an increased need for BI implementation to support decision-making in the tourism sector. Tourism stakeholders such as management of destination, accommodation, transportation, and public administration need a guideline to understand functional requirements before implementation. This paper addresses the problem by comprehensively reviewing the functionalities and issues that need to be considered based on previous business intelligence framework development and implementation in tourism sectors. Methodology: We have conducted a systematic literature review using the Preferred Reporting Items for Systematic Reviews and Guidelines for Meta-Analysis (PRISMA) method. The search is conducted using online academic database platforms, resulting in 543 initial articles published from 2002 to 2022. Contribution: The paper could be of interest to relevant stakeholders in the tourism industry because it provides an overview of the capabilities and limitations of business intelligence for tourism. To our knowledge, this is the first study to identify and classify the BI functionalities needed for tourism sectors and implementation issues related to organizations, people, and technologies that need to be considered. Findings: BI functionalities identified in this study include basic functions such as data analysis, reports, dashboards, data visualization, performance metrics, and key performance indicator, and advanced functions such as predictive analytics, trend indicators, strategic planning tools, profitability analysis, benchmarking, budgeting, and forecasting. When implementing BI, the issues that need to be considered include organizational, people and process, and technological issues. Recommendations for Practitioners: As data is a major issue in BI implementation, tourism stakeholders, especially in developing countries, may need to build a tourism data center or centralized coordination regulated by the government. They can implement basic functions first before implementing more advanced features later. Recommendation for Researchers: We recommend further studying the BI implementation barriers by employing a perspective of an adoption framework such as the technology, organization, and environment (TOE) framework. Impact on Society: This research has a potential impact on improving the tourism industry’s performance by providing insight to stakeholders about what is needed to help them make more accurate decisions using business intelligence. Future Research: Future research may involve collaboration between practitioners and academics in developing various BI architectures specific to each tourism industry, such as destination management, hospitality, or transportation.

Aim/Purpose: In this study, the research proposes and experiments with a new model of collecting, storing, and analyzing big data on customer feedback in the tourism industry. The research focused on the Vietnam market. Background: Big Data describes large databases that have been “silently” built by businesses, which include product information, customer information, customer feedback, etc. This information is valuable, and the volume increases rapidly over time, but businesses often pay little attention or store it discretely, not centrally, thereby wasting an extremely large resource and partly causing limitations for business analysis as well as data. Methodology: The study conducted an experiment by collecting customer feedback data in the field of tourism, especially tourism in Vietnam, from 2007 to 2022. After that, the research proceeded to store and mine latent topics based on the data collected using the Topic Model. The study applied cloud computing technology to build a collection and storage model to solve difficulties, including scalability, system stability, and system cost optimization, as well as ease of access to technology. Contribution: The research has four main contributions: (1) Building a model for Big Data collection, storage, and analysis; (2) Experimenting with the solution by collecting customer feedback data from huge platforms such as Booking.com, Agoda.com, and Phuot.vn based on cloud computing, focusing mainly on tourism Vietnam; (3) A Data Lake that stores customer feedback and discussion in the field of tourism was built, supporting researchers in the field of natural language processing; (4) Experimental research on the latent topic mining model from the collected Big Data based on the topic model. Findings: Experimental results show that the Data Lake has helped users easily extract information, thereby supporting administrators in making quick and timely decisions. Next, PySpark big data processing technology and cloud computing help speed up processing, save costs, and make model building easier when moving to SaaS. Finally, the topic model helps identify customer discussion trends and identify latent topics that customers are interested in so business owners have a better picture of their potential customers and business. Recommendations for Practitioners: Empirical results show that facilities are the factor that customers in the Vietnamese market complain about the most in the tourism/hospitality sector. This information also recommends that practitioners reduce their expectations about facilities because the overall level of physical facilities in the Vietnamese market is still weak and cannot be compared with other countries in the world. However, this is also information to support administrators in planning to upgrade facilities in the long term. Recommendation for Researchers: The value of Data Lake has been proven by research. The study also formed a model for big data collection, storage, and analysis. Researchers can use the same model for other fields or use the model and algorithm proposed by this study to collect and store big data in other platforms and areas. Impact on Society: Collecting, storing, and analyzing big data in the tourism sector helps government strategists to identify tourism trends and communication crises. Based on that information, government managers will be able to make decisions and strategies to develop regional tourism, propose price levels, and support innovative programs. That is the great social value that this research brings. Future Research: With each different platform or website, the study had to build a query scenario and choose a different technology approach, which limits the ability of the solution’s scalability to multiple platforms. Research will continue to build and standardize query scenarios and processing technologies to make scalability to other platforms easier.

Aim/Purpose: This study investigated factors influencing the intention to engage in green innovation among small and medium enterprises (SMEs) in the tourism sector, using an integrated approach from the pro-environmental planned behavior (PEPB) and technology organization environment (TOE) models. Background: Green innovation is a long-term strategy aimed at addressing environmental challenges in the Indonesian tourism sector, especially those related to SMEs in culinary, accommodation, transportation, and creative industries. While prior research primarily focused on innovation characteristics and various behavioral intentions towards new technologies, this study pioneered an approach to understanding green innovation practices among SMEs by examining behavioral intention and the influence of internal organizational and external environmental factors. This was achieved through the PEPB model, which extends the theory of planned behavior (TPB) by incorporating perceived authority support and perceived environmental concern and integrating it with the TOE model. This comprehensive approach was crucial for understanding SME motivations, needs, and challenges in adopting green innovation, thereby supporting environmental sustainability. Methodology: Data were collected through offline and online questionnaires and interviews with 405 SMEs that had implemented green innovation as respondents. The theoretical model was tested using partial least squares structural equation modeling (PLS-SEM) with top-level constructs. Contribution: This research contributed to the development and validation of an integrated model for green innovation in SMEs, offering insights and recommendations for all stakeholders in the tourism sector to formulate effective green innovation strategies. Findings: This research revealed that the integrated model of pro-environmental planned behavior and technology organization environment successfully explained 71% of the factors influencing the intention to engage in green innovation for SMEs in the tourism sector. Perceived authority support emerged as the strongest factor, while perceived behavioral control was identified as a weaker factor. Recommendations for Practitioners: The research findings recommended that SMEs in the tourism sector focus on customer satisfaction and operational efficiency and optimize the recruitment and training processes of resources to maximize success in adopting environmentally friendly innovations. Meanwhile, for the government, providing support, incentives, and stringent environmental regulations could encourage sustainable business practices. Recommendation for Researchers: The research findings recommended that SMEs in the tourism sector focus on customer satisfaction and operational efficiency and optimize the recruitment and training processes of resources to maximize success in adopting environmentally friendly innovations. Meanwhile, for the government, providing support, incentives, and stringent environmental regulations could encourage sustainable business practices. Impact on Society: Examining the factors influencing the intention to engage in green innovation among SMEs in the tourism sector carried significant social implications. The findings contributed to recommending strategies for businesses and stakeholders such as the government, investors, and tourists to collectively strive to minimize environmental damage in tourist areas through the implementation of green innovation. Future Research: There are several promising avenues to explore to enhance future research. Expanding the scope to include diverse regions and industries and using additional approaches, such as leadership theory and management commitment theories, can increase the R-squared value. Additionally, broadening the profile of interviewees to obtain a more comprehensive understanding of the intention to engage in green innovation should be considered.

Aim/Purpose: This study aims to develop a solution for personalized tourism recommendations that addresses information overload, data sparsity, and the cold-start problem. It focuses on enabling tourists to choose the most suitable tourism-related facilities, such as restaurants and hotels, that match their individual needs and preferences. Background: The tourism industry is experiencing a significant shift towards digitalization due to the increasing use of online platforms and the abundance of user data. Travelers now heavily rely on online resources to explore destinations and associated options like hotels, restaurants, attractions, transportation, and events. In this dynamic landscape, personalized recommendation systems play a crucial role in enhancing user experience and ensuring customer satisfaction. However, existing recommendation systems encounter major challenges in precisely understanding the complexities of user preferences within the tourism domain. Traditional approaches often rely solely on user ratings, neglecting the complex nature of travel choices. Data sparsity further complicates the issue, as users might have limited interactions with the system or incomplete preference profiles. This sparsity can hinder the effectiveness of these systems, leading to inaccurate or irrelevant recommendations. The cold-start problem presents another challenge, particularly with new users who lack a substantial interaction history within the system, thereby complicating the task of recommending relevant options. These limitations can greatly hinder the performance of recommendation systems and ultimately reduce user satisfaction with the overall experience. Methodology: The proposed User-based Multi-Criteria Trust-aware Collaborative Filtering (UMCTCF) approach exploits two key aspects to enhance both the accuracy and coverage of recommendations within tourism recommender systems: multi-criteria user preferences and implicit trust networks. Multi-criteria ratings capture the various factors that influence user preferences for specific tourism items, such as restaurants or hotels. These factors surpass a simple one-star rating and take into account the complex nature of travel choices. Implicit trust relationships refer to connections between users that are established through shared interests and past interactions without the need for explicit trust declarations. By integrating these elements, UMCTCF aims to provide more accurate and reliable recommendations, especially when data sparsity limits the ability to accurately predict user preferences, particularly for new users. Furthermore, the approach employs a switch hybridization scheme, which combines predictions from different components within UMCTCF. This scheme leads to a more robust recommendation strategy by leveraging diverse sources of information. Extensive experiments were conducted using real-world tourism datasets encompassing restaurants and hotels to evaluate the effectiveness of UMCTCF. The performance of UMCTCF was then compared against baseline methods to assess its prediction accuracy and coverage. Contribution: This study introduces a novel and effective recommendation approach, UMCTCF, which addresses the limitations of existing methods in personalized tourism recommendations by offering several key contributions. First, it transcends simple item preferences by incorporating multi-criteria user preferences. This allows UMCTCF to consider the various factors that users prioritize when making tourism decisions, leading to a more comprehensive understanding of user choices and, ultimately, more accurate recommendations. Second, UMCTCF leverages the collective wisdom of users by incorporating an implicit trust network into the recommendation process. By incorporating these trust relationships into the recommendation process, UMCTCF enhances its effectiveness, particularly in scenarios with data sparsity or new users with limited interaction history. Finally, UMCTCF demonstrates robustness towards data sparsity and the cold-start problem. This resilience in situations with limited data or incomplete user profiles makes UMCTCF particularly suitable for real-world applications in the tourism domain. Findings: The results consistently demonstrated UMCTCF’s superiority in key metrics, effectively addressing the challenges of data sparsity and new users while enhancing both prediction accuracy and coverage. In terms of prediction accuracy, UMCTCF yielded significantly more accurate predictions of user preferences for tourism items compared to baseline methods. Furthermore, UMCTCF achieved superior coverage compared to baseline methods, signifying its ability to recommend a wider range of tourism items, particularly for new users who might have limited interaction history within the system. This increased coverage has the potential to enhance user satisfaction by offering a more diverse and enriching set of recommendations. These findings collectively highlight the effectiveness of UMCTCF in addressing the challenges of personalized tourism recommendations, paving the way for improved user satisfaction and decision-making within the tourism domain. Recommendations for Practitioners: The proposed UMCTCF approach offers a potential opportunity for tourism recommendation systems, enabling practitioners to create solutions that prioritize the needs and preferences of users. By incorporating UMCTCF into online tourism platforms, tourists can utilize its capabilities to make well-informed decisions when selecting tourism-related facilities. Furthermore, UMCTCF’s robust design allows it to function effectively even in scenarios with data sparsity or new users with limited interaction history. This characteristic makes UMCTCF particularly valuable for real-world applications, especially in scenarios where these limitations are common obstacles. Recommendation for Researchers: The success of UMCTCF can open up new avenues in personalized recommendation research. One promising direction lies in exploring the integration of additional contextual information, such as temporal (time-based) or location-based information. By incorporating these elements, the model could be further improved, allowing for even more personalized recommendations. Furthermore, exploring the potential of UMCTCF in domains other than tourism has considerable significance. By exploring its effectiveness in other e-commerce domains, researchers can broaden the impact of UMCTCF and contribute to the advancement of personalized recommendation systems across various industries. Impact on Society: UMCTCF has the potential to make a positive impact on society in various ways. By delivering accurate and diverse recommendations that are tailored to individual user preferences, UMCTCF fosters a more positive and rewarding user experience with tourism recommendation systems. This can lead to increased user engagement with tourism platforms, ultimately enhancing overall satisfaction with travel planning. Furthermore, UMCTCF enables users to make more informed decisions through broader and more accurate recommendations, potentially reducing planning stress and leading to more fulfilling travel experiences. Future Research: Expanding upon the success of UMCTCF, future research activities can explore several promising paths. Enriching UMCTCF with various contextual data, such as spatial or location-based data, to enhance recommendation accuracy and relevance. Leveraging user-generated content, like reviews and social media posts, could provide deeper insights into user preferences and sentiments, improving personalization. Additionally, applying UMCTCF in various e-commerce domains beyond tourism, such as online shopping, entertainment, and healthcare, could yield valuable insights and enhance recommendation systems. Finally, exploring the integration of optimization algorithms could improve both recommendation accuracy and efficiency.

Aim/Purpose: This article takes ‘tourism decision-making behavior’ as an entry point, and deeply analyzes the factors influencing the travel decision-making of Chinese ‘American Travel’ tourists and their degree of influence, so as to provide a reference for the development of Chinese outbound tourism. Background: With the development of China’s economy and the improvement in people’s level, the outbound tourism market of Chinese residents has developed rapidly. The United States has become an important tourism destination country for Chinese residents’ outbound tourism, and China has also become one of the important tourist source countries of American tourism. However, the rapid development of ‘American tourism’ has also caused competition problems in China’s tourism industry. For example, prices and tourism products have become a means of competition among tourism enterprises. As the main body of consumption, tourists’ decision-making behavior will be affected by various factors. Methodology: Drawing lessons from previous scholars’ research results on tourism decision-making behavior, the influencing factors of tourism decision-making behavior are summarized. A theoretical model and index system of factors influencing tourism decision-making behavior of Chinese residents ‘Travel in the United States’ are established, research hypotheses are put forward, questionnaire data are collected, and SPSS and Amos are used to analyze and verify the theoretical model. Contribution: This research expands the literature on topics related to tourism decision-making in research and practice. It establishes a theoretical model and index system for the factors that influence the decision-making behavior of Chinese residents’ ‘American Travel’ tourism. In addition, we propose countermeasures for tourism products, enterprises, and the government. Findings: Prior knowledge and external information have a positive influence on tourism perception and value perception, and a negative influence on risk perception. Risk perception value perception has a positive and negative influence on tourism decision-making and tourism motivation, respectively. Tourism motivation has a positive influence on tourism decision-making and has a positive impact. Recommendation for Researchers: According to the research conclusions of this article, the following counter-measures and suggestions are put forward from three aspects of tourism: products, enterprises, and governments. On the basis of existing tourism products, relevant operating companies should pay more attention to the upgrading and transformation of tourism, leisure and entertainment products in scenic spots to increase the willingness of tourists to travel. When considering corporate marketing and promotion plans, tourism companies operating related businesses should increase the weight of their marketing budgets in online marketing, increase investment in online marketing, and develop mobile applications that meet the preferences of Chinese residents in the United States. Do a good job in the timely publication of safety reminders and local information. Safety is an important foundation for tourism development and the core concern of many tourists. Future Research: Due to the important research on the impact of tourism activities, the influencing factors are many and complex, and the psychological process of tourism decision-making is carried out directly. There are still unconsidered factors that need to be studied in depth. In the future, it is possible to compare multiple resource-featured themes, and increase the characteristics of potential tourists, and the factors affecting the selection behavior of regional cultural tourists, and so forth, in order to make the research more applicable and practical instructive significance.

CM Mahmood Khan orders early opening of provincial tourism authority

Children's Geographies; 02/01/2022
(AN 154441556); ISSN: 14733285
Academic Search Premier

Children's Geographies; 08/01/2024
(AN 178911401); ISSN: 14733285
Academic Search Premier

Great Tibet Tour Announces Booming Tibet Tourism

Discover Unique Experiences and Unforgettable Destinations to Ring in the New Year!

Seamless Integration of Healthcare and Travel for a Holistic Patient Experience

Filling The Labour Gaps The Key To A Thriving Tourism Industry In Canada jhammond@desti… Wed, 10/30/2024 - 15:35

International travel continued to lag behind pre-pandemic levels

U4SSC - Smart tourism: A path to more secure and resilient destinations

[Ethiopian Herald] ADDIS ABABA- A senior tourism researcher has called on the Ethiopian government to improve visa policies, streamline immigration processes, and reduce bureaucratic barriers to fully exploit the nation's untapped tourism potential.

[Nile Post] Uganda's tourism and cultural appeal dazzled thousands in China over the past two days as the Uganda Embassy in Beijing and the Consulate General in Guangzhou jointly hosted the "Explore Uganda" Independence celebrations.

[GroundUp] Heuningvlei was devastated by flooding for two consecutive years but the residents are determined to keep their town alive

[Daily Maverick] Once a popular tourism destination along Nelson Mandela Bay's beachfront, the 'Golden Mile', a hub of luxury hotels and guesthouses, has become a crime hotspot. The community is calling for decisive action, but the metro is still crafting its plan.

[ENA] Addis Ababa, Ethiopia's vibrant capital, known as the "political capital of Africa," is positioned to enhance its standing as a premier destination for conference tourism. As the headquarters of the African Union (AU) and the United Nations Economic Commission for Africa (UNECA), the city has long been a focal point for international diplomacy. Now, with new developments in tourist infrastructure and culturally rich attractions, Addis Ababa is prepared to host even more international conferences, summits,

[ENA] Addis Ababa -- Africans are urged to adequately use the potential of tourism for sustainable economic development and prosperity.

[Capital FM] London, UK -- The Global Tourism Resilience and Crisis Management Centre - Eastern Africa (GTRCMC-EA), based at Kenyatta University, has been honoured with the prestigious Tourism Resilience Award during the Global Resilience Council Hall of Fame Awards at Plaisterers' Hall in London.