tourism online platform

How virtual tourism can rebuild travel for a post-pandemic world

The Faroe Islands is just one destination using new technologies to create a virtual tourism experience Image:  Knud Erik Vinding/Pixabay

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Stay up to date:, travel and tourism.

• The COVID-19 pandemic has upended the travel and tourism industries;
• Businesses in this sector must build infrastructure and practices that allow people to travel safely in a post-pandemic world and support local communities that benefit from tourism;
• Augmented, virtual and mixed reality technologies can offer alternative ways to travel the world and an exciting new model for the industry.

The tourism industry has hit a nadir owing to the COVID-19 pandemic. It will continue to feel the effects for at least the first three quarters of 2021 – according to a recent UN report , tourist arrivals globally in January 2021 were down 87% when compared to January 2020.

Travel will prevail over post-pandemic anxiety, making it incumbent on the aviation and tourism industry to build safer infrastructure and practices that take care of travellers’ well being.

Have you read?

International tourism is set to plunge by 80% this year – but some regions could recover more quickly, how global tourism can become more sustainable, inclusive and resilient, virtual reality adds to tourism through touch, smell and real people’s experiences.

After a year thwarted by the pandemic and with the future not looking too upbeat for the industry at this juncture, tourism business owners should look at alternative modes of interaction for holidaymakers that can also aid the people and economies who depend on tourism.

The COVID-19 pandemic has noticeably hastened the testing and rollout of forward-looking technologies. Technology has not only enabled citizens globally to interact with loved ones, but also helped industries such as healthcare, information technology, education and many more to work remotely.

In the last few decades, technology has helped travel and tourism industries increase their reach through travel booking websites, videos, blogs and travel photography. Digital tools and content are a vital source of information for vacationists organizing their next holiday or creating a destination wish list. Whilst remote or virtual tourism has been a futuristic theme within industry forums for some time, the world today, shaped by the COVID-19 pandemic, might now be ready to accept it.

A human-centric design that draws insights from cognitive behaviour, social psychology, neuroscience and behavioural economics applied with cutting edge technologies such as augmented, virtual or mixed reality (AR, VR, MR) could be a game-changer. AR, VR and MR can enable a seamless, uninterrupted interactive experience for viewers from their own private space. The design principles will create a frictionless digital user experience and construct a positive perception of a tourist destination.

There have been previous attempts to achieve this feat: if you are an aqua sightseer, you might be aware of a documentary exploring the Great Barrier Reef . Through an interactive website, one can view the clear, tranquil currents of the Pacific Ocean and the biodiversity of the reef, and experience the sounds of a healthy coral reef. Another much-discussed VR experience is Mission 828 which allows you to take a virtual parachute jump from the world’s tallest building, Burj Khalifa in Dubai. The Official Tourist Board of the Faroe Islands has also crafted a virtual experience to entice post-pandemic visitors from across the world.

Imagine a human-centric designed, interactive space online that makes a destination accessible and so real for a sightseer with sound captured by electro-acoustics researchers. You could view holiday sites in a video or through self-navigation using voice or joystick controls, interact with people using video-calling platforms, travel through the streets of said location, eavesdrop on local music and much more. This could be stitched together in a single platform individually or in silos on the internet and further enhanced by setting up physical experience tourism centres locally. Such a setup would allow tourist guides, artisans, craftspeople, hoteliers and transport business to create their own digital and virtual offerings and interact with possible customers.

Here’s how it might look: a vacationer starts their experience from the time their flight commences. The plane descends to the destination runway and pictures of the vicinity from the aircraft window pane are captured. The airport signage welcomes passengers and directs them to a pre-booked taxi. The vacationer gets to choose their first destination and travels through the streets in a chauffeur-driven car whose interactions en route become part of their cherished memories. On arrival, a tourist guide walks you through the destination all controlled with just a tap on your gadget. During the sightseeing, you hear random people speaking, posing for photographs and more. You take a photo to post on social media, go shopping and negotiate with a local vendor to purchase an artwork and get it delivered to your door. You learn how a local dish is prepared and get familiar with local customs.

A virtual platform could even provide an opportunity for people to explore areas that are affected by or fighting terrorism. For example, imagine seeing the diverse wildlife and snow leopard of the Gurez Valley, in the union territory of Jammu and Kashmir, India. It doesn’t stop there: if thought through, one could experience travelling to the South Pole, space and beyond. It could also serve as a learning portal for students to understand geographies, culture, art and history.

With technology improving lives globally, virtual tourism could reignite the tourism industry and its people and help build a more sustainable economic model. As a human-centric platform, it can establish local tourist guides, artisans and others as global citizens in the tourism industry.

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Digital Platforms and the Future of Tourism

Successful woman backpacker use digital tablet taking photo on mountain peak cliff. © lzf/Shutterstock

STORY HIGHLIGHTS

• The tourism industry is vital to the growth of the majority of developing economies, with the potential to create millions of jobs and promote entrepreneurship and innovation.
• Digital technologies have the potential to give small tourism businesses in emerging destinations direct access to a global market of travel consumers for the first time, vastly expanding their prospects.
• To celebrate World Tourism Day, the World Bank Group, together with partners, are exploring how digital advances can be used to benefit sustainable tourism for development.

Tourism is one of the fastest growing and most important economic sectors in the world providing benefits to both host communities and destination areas. In 2017, international tourist arrivals reached a new record high at over 1.3 billion according to the latest UNWTO Tourism Highlights . The sector has now seen uninterrupted growth in arrivals for eight straight years. It also represents 10.4% of GDP and in 2017 the tourism industry supported 313 million jobs or 1 in 10 jobs globally.

Tourism also provides better opportunities for women’s participation in the workforce, women’s entrepreneurship, and women’s leadership than other sectors of the economy.

That is why the World Bank Group continues to be committed to invest funds, knowledge and expertise to support tourism for development.

• As a part of the World Bank Growth & Competitiveness Project in the Gambia, a hospitality training center trained 1,235 students and helped increase arrivals by tourists from non-traditional markets by 71% between 2011 and 2015.
• In Peru, an IFC Advisory project financed by SECO (Swiss State Secretariat for Economic Affairs – Economic Cooperation and Development) helped streamline procedures for obtaining licenses and permits to start and operate tourism businesses in Cusco. The reforms eliminated 150 unnecessary processes and shaved three years off the business registration process, making it easier for both local and foreign investors to operate in Cusco.
• A World Bank Group report on the 20 Reasons Sustainable Tourism Counts for Development explains how sustainable tourism counts not just for travelers, but for tourism destinations and local residents.
• The World Bank Group developed the Abraham Path project to bring together fragmented communities along the path in the West Bank through many interventions like training and capacity, which were aimed at generating tourism revenue and jobs, and improving economic opportunities for women. The project has generated 137 jobs, 57% going to women.

Digital technologies and platforms are disrupting the way the tourism sector operates from end to end.   Many low-income economies can potentially benefit from this digital transformation and others are at risk of being left behind if they fail to embrace this moment.

The World Bank Group recently published two reports on the significance of this disruption on tourism and how countries can harness it for the benefit of all. According to the report, Tourism and the Sharing Economy , the annual growth rate for the global P2P accommodation is estimated at 31 percent between 2013 and 2025, six times the growth rate of traditional bed and breakfasts and hostels.

In addition, the second report, The Voice of Travelers , produced in collaboration with TripAdvisor explains how peer reviews and other forms of user-generated content (UGC), facilitated by digital platforms, have become the most important sources of travel information globally—more important than tourism boards and traditional outlets. This joint report is part of an MoU that was signed by the World Bank Group and TripAdvisor to promote the development of the tourism sector that highlights some of the key digital trends impacting the sector to countries and private sector partners.

This year, World Tourism Day is shining a light on “Tourism and the Digital Transformation” — as its theme. The UNWTO is calling on governments and the global community, to “support digital technologies that can transform the way we travel, reduce the ecological burden of tourism and bring the benefits of tourism to all.”  

They also can provide new opportunities for women and rural entrepreneurs to improve their market access and financial inclusion. However, many developing destinations have limited knowledge on how to take advantage of digital platforms and mitigate risks. Either constrained by the lack of understanding or knowhow and resources, these economies are not able to leverage digital tools to grow their tourism.

Responding to the global call to promote innovations in tourism through technology, the World Bank Group, will host a day-long event just prior to WTD2018 focusing on Digital Platforms and the Future of Tourism .

• Watch the Event Live on September 26th at 9:00 am
• Tourism and the Sharing Economy
• The Voice of Travelers

The best virtual tours to explore the world from home

Oct 8, 2020 • 5 min read

The Sistine Chapel is just one wonder offering a virtual museum tour ©Rajesh Gathwala/500px

We live in an age of unprecedented access to digital technology – and with it, brand new ways of exploring the world around us.

While it's not quite the same as seeing, say, the Mona Lisa or Christ the Redeemer in person, some of the world’s most popular and remote destinations have created libraries of online images and video, as well as 360 degree virtual tours that let you virtually explore museums, galleries, world wonders and even national parks.

Here a just a few of the best digital tours that let you wander the world from wherever you may be social distancing.

See the seven wonders of the world

If there’s anything capable of whetting your appetite for world travel, it is the new seven wonders of the world:  the Great Wall of China , the ancient city of Petra , the Taj Mahal , the Colosseum , Machu Picchu , Christ the Redeemer , and Chichen Itza . Thankfully there are impressive virtual tours of each from The New York Times , AirPano , Google , and Panoramas .

With modern technology, you can even see the last standing wonder of the ancient world— The Pyramids of Giza . There are a few other wonders that might not make it into to the top seven but are still worth a digital peek, like the Alhambra , Seville's La Giralda , and even Easter Island.

Best virtual museum tours

In recent years, Google has partnered with over 2,5000 art museums to upload high-resolution versions of millions of pieces of art. Highlights include New York’s MoMA , DC’s National Gallery of Art , Chicago’s Art Institute , the Casa Battl ó, and Amsterdam’s Van Gogh Museum to name a few.

In addition, The Louvre offers a virtual tour , as do The Vatican Museums , many of the Smithsonian Museums , the Russian Museum , the top-rated British Museum , the Minneapolis  Museum of Russian Art , and the Palace Museum in Beijing.

You may not be able to kiss the Blarney Stone right now, but you can tour the Blarney Castle from afar. You can also visit the Museum of Flight,  the Museum of Science, the Museum of Natural History,  the National Women's History Museum  and Boston's History of Science Museum .

While museums are often an inherently visual experience, there's a lot to be learned from archives of past lectures and tours like the ones preserved online by Nashville's Frist Museum , the Hunter Museum of American Art in Chattanooga, Tennessee, the Smithsonian American Art Museum,  the Frick , and others.

You might also like:  Broadway might be closed, but here’s how to stream the best performances from your home

Explore national parks

While travel to National Parks is best avoided for the time being, you don't need to miss out on the scenery. Virtual Yosemite is absolutely stunning and one of the best, replete with audio. Both Yellowstone National Park and Mount Rushmore offer virtual tours as well. 

Google has similar 360 degree audio-visual tours of five select national parks, including Kenai Fjords, Hawai'i Volcanos , Carlsbad Caverns, Bryce Canyon , and Dry Tortugas, as well as 31 more on Google Earth . You can also get an up-close look at almost 4,000 pieces of artwork, artifacts, and other treasures related to the history and culture of the national parks, and view online exhibits .

Digital safaris

Wildlife is a big draw for travelers, whether it's sighting some of the Big Five in Africa, glimpsing whales in North America, or introducing your children to new animals in person on a family safari . But if you're forays into the bush are grounded for now, many zoos and aquariums have created digital access to their habitats.

You can easily watch several live webcams of some of the nation’s greatest zoos and aquariums, including the  San Diego Zoo , Houston Zoo , Zoo Atlanta , the Tennessee Aquarium , and the Georgia Aquarium . Additionally you can see Canadian farm animals doing their thing , or you could watch Stella the Dog jump endlessly into huge piles of Maine leaves.

You may also like:  These nine wildlife web cams offer access to your favorite animals

Virtual hiking

Thanks to panoramic video, you can get a really good idea of what a hike looks like well before you arrive at the trailhead. For example, you can experience all of the following top-rated hikes right now from your computer or tablet:  Bryce Canyon , Grand Canyon , GR20 , Inca Trail , and the death-defying Angel's Landing . For even more great hikes, simply YouTube one of Lonely Planet’s top 10 treks or any other hike that suits your fancy. Bonus points if you follow along during a workout to enhance the realism.

Famous landmarks

You can visit many wonders of nature, including the Amazon Rainforest , Iguazu Falls , the Komodo Islands , or Table Mountain , using virtual tours. Or you can explore the Statue of Liberty , the Sahara Desert , Niagara Falls , or even a guided tour of the Eiffel Tower . For even more virtual tours, search your bucket list of adventures with  AirPano , Google Earth , or YouTube .

Travel to outer space

The moon hasn't made it to Lonely Planet's Best In Travel list (yet!), and even without self-isolation and shelter-in-place measures for COVID-19, many of us may never travel to space. But thanks to technology, now is as good of a time as any to do so virtually. Before blasting off, considering touring some of NASA’s offices first. Then relive the last lunar missions and moon walks in stunning HD. Or take a virtual tour of Mars with the help of Google. 

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This article was originally published March 2020 and was last updated October 2020.

This article was first published March 2020 and updated October 2020

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12 Best Apps & Websites for Booking Travel Online

by Elizabeth Gorga - Last updated on January 8, 2022

• Travel Apps

If there’s anything better than traveling, it’s the build up. Daydreaming of the perfect destination, scrolling through unique places to stay, and browsing travel guides for the best hole-in-the-wall restaurants and local must-sees are all opportunities to start wanderlusting before you’ve even packed your bags.

Be honest—how many Airbnb Stays do you have bookmarked?

True, it can be overwhelming to map out the nitty gritty flights, connections, accommodation, and pit stops when there are endless options available, but having something exciting to look forward to is one of the keys to happiness after all.

Lucky for you, there are countless apps and websites to help you with booking travel online, so you don’t have to feel overwhelmed when planning the travel adventure of your dreams.

Whether you’re an Apple or Android fan, you’ll have the time of your life in the lead up to your trip with the best travel booking apps and trip planners around!

What to look for in the best travel booking apps

You’re scrolling the app store wondering, what is the best trip planner app? There are hundreds of apps out there—how do you choose? The struggle is real. The truth is, many of them will be more useless than helpful. So how do you weed out the junk that’s clogging up your phone?

The best travel booking apps have these essential features.

1. They’re available offline.

If you’re traveling overseas, there’s no guarantee that you’ll have easy access to WiFi. Unless you have an international phone plan, a local SIM card, or don’t mind roaming charges, limited WiFi can be a killer if you want to access your travel apps in-country. The best travel booking apps have an offline version, so you can access them anywhere, anytime.

2. They include a booking service.

If you’re looking for the best travel booking app, it will definitely have a booking service included. Whether you’re looking for accommodation options or are wanting to book tickets and tours, the ability to book from the app makes everything easier. Let’s call it your one-stop-shop.

3. They have reviews.

The best way to find authentic experiences is to look for recommendations from other people, whether they’re locals or tourists who have stumbled upon hidden gems. Reviews can really make or break your travel plans. The best travel booking apps will definitely have you covered with experienced travelers’ tips and recommendations.

4. They have a translation function.

When you’re traveling between countries, it can be helpful to have a translation function on your travel apps, so you don’t have to switch between apps to get what you’re looking for. With a translation option, you can move about without language barriers, making travel easy even if you don’t speak the native tongue.

5. They have social media integration.

As simple as it sounds, the time it takes to sign up and register for a new app can be a deciding factor for many. In a world where we want information at our fingertips, it can be a turn off when it takes too much time to create a profile before you can use an app.

The best travel booking apps allow you to link your social media accounts—not only for easy access, but so you can share the highlights of your trip directly to your social accounts!

6. They have a travel planner feature.

We want more than just a place to book a bed for the night. We want an app to help us organize our itineraries, bookmark our favorite travel destinations, create schedules, and make travel easy. If you’re wondering what the best trip planner app is, you’ll know you’ve found it when you can plan your travel all in one place.

READ MORE: How to Plan a Trip When You’re Short on Time—And Cash

What is the best trip planner app 6 top contenders.

Searching for the best travel booking apps around? Here are the top apps to download before your next big trip.

1. LikeALocal

• Why it’s the best: If you’re into hole-in-the-wall restaurants and hidden spots that only the most seasoned travelers frequent, LikeALocal is the best travel booking app for you. With guides, tips, and tricks, all the content is created by true locals who have lived in the destination for years.
• The app allows you to browse destination guides, book tours, and even connect with residents through their Q&A feature—and it’s all available 100% offline.

• Why it’s the best: There’s no better way to settle into a new destination than staying in a local’s home. Airbnb is rising as one of the best travel booking apps for accommodation, offering an excellent in-between for hostels and hotels.
• Whether you want to book individual rooms or entire houses from local residents, there are options to suit most budgets. Through the app, you can easily communicate with your host, making it easy to check-in, out, and get the best tips on local hot spots.
• Available for iOS and Android.

3. HotelTonight

• Why it’s the best: While some travelers plan out every detail before leaving home, others live for spontaneity. Luckily, there’s an app for that, too. HotelTonight helps you find last-minute deals on nearby hotels, making it perfect for spur of the moment holidays or road trips with unplanned stops along the way.
• It’s easy to use, with filter options to help you find the right place to suit your needs, and booking a room for the night is just one click away.

• Why it’s the best: Are you a travel junkie? Love multi-destination travel? If you answered ‘ yes’ and you’re still wondering what the best trip planner app is, look no further than TripIt. All you have to do is forward your travel confirmation details to [email protected], and the app creates a master document for all of your travels so you can stay organized!
• You can access it any time, WiFi or dead zone. Upgrade to their paid version to receive real-time flight alerts, a currency converter, socket requirements for your destination, tipping advice, alternative routes for canceled flights, and even track your reward points.

5. Skyscanner

• Why it’s the best: If you’re looking for flights, Skyscanner is where it’s at! The app gives you access to flights from over 1,200 different sources so you can find the best options out there, whether you’re looking for the cheapest options, the fastest routes, or the most affordable days to fly.
• If you’re in the daydreaming stages and open to new possibilities, Skyscanner also has an “anywhere” option to help you find affordable flights you never knew were possible. You can even set up alerts for price dips to find the best airfare rates around.
• Available for IOS.

6. PackPoint

• Why it’s the best: For some travelers, packing can be the biggest headache when preparing for a trip. PackPoint will keep you organized and do the hard work for you. The app creates a custom packing list based on your gender, travel destination, dates and duration of travel, and the type of trip you’re planning. It even checks the weather for you!
• You can add or remove items to make it more personal and check them off as you go, making the packing experience quick and easy.

6 best travel booking websites

Prefer to sit down in front of a computer to do some brainstorming for your next trip? Then you’re probably wondering what the best travel booking website is. Here’s a round-up of the best places for booking travel online.

1. Tripadvisor

No trip is complete without a visit to Tripadvisor. Whether you’re looking for tours, hotels, transportation, restaurants, or just a little travel inspo, Tripadvisor is one of the best websites for browsing and booking travel online.

You can read reviews, save your favorites, get organized using the map features, and book travel all in one place. This site is a must when planning your next trip.

2. Intrepid

Adventure junkies love Intrepid. With small group tours to more than 100 countries, Intrepid is one of the top tour companies for solo travelers who want an adrenaline rush and connection to like-minded people. While most tours attract young travelers under 30, there is no age limit for tours through Intrepid.

Search for unique, niche experiences, from cycling the Middle East to trekking the world’s tallest mountains. With great deals all year round, you can score discounts of up to 50% and reserve your next tour for as low as $1.

3. Hostelworld

Who doesn’t love a good hostel? Backpackers on a budget can find some of the most affordable accommodation options through Hostelworld. Search for your destination, filter by price range or ratings, and find accommodation descriptions, reviews, and booking policies for hostels in over 170 countries worldwide.

The full-screen interactive map makes it easy to see if the hostel is near where you want to be—whether that’s down the street from night clubs or nature.

4. Couchsurfing

If you want a truly unique travel experience at the absolute lowest cost, why not try couch surfing? Through Couchsurfing, you can connect to locals and stay in their homes for free, saving you money and giving you access to true local experiences.

If you don’t feel comfortable staying in someone’s home, Couchsurfing also has a Hangouts feature for you to use to meet and socialize with other travelers—perfect for solo travelers!

If you’re booking travel online, Momondo is worth a browse. This flight search engine is unique to other sites, showing prices of smaller airlines and travel companies that are often overlooked by other popular search engines.

With Momondo, you can find the best times to fly using their monthly matrix, featuring the best prices and routes, no matter where you want to go. Check out the flight insights page for trends and analytics, which will give you insight into how far in advance you need to book your flights to save the most money.

6. GoAbroad

Last, but certainly not least on the list of best sites for booking travel online, is the one and only GoAbroad (that’s us!). Find your perfect program for meaningful travel abroad, with over 15,000 programs, destinations worldwide, scholarships, and insider tips.

If you want to travel abroad with purpose, GoAbroad can undoubtedly help you discover the program to help you smash your goals and make your wildest dreams come true! You can browse our Travel Resource Hub right now to get a jump start on your travel planning.

Booking travel online is fast and easy!

If you’ve been bitten by the travel bug, there’s really no excuse not to start planning your next getaway. Check out the best travel booking apps and websites to stay organized, score great deals, and quench your wanderlust. Booking travel online has never been easier! You’re just a click away from your next big adventure abroad.

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• Course Catalog

Welcome to the Tourism Online Academy

The Tourism Online Academy is an online learning platform that will provide self-paced, 100% online courses that mainly focus on concepts, areas of interest and fundamental principles related to the tourism sector, addressing the challenges it faces such as globalization, digital revolution, travel marketing and sustainability, among others. These flexible courses allow participants to reconcile academic, professional and other personal commitments.

IE University and the World Tourism Organization of the United Nations have partnered to launch the Online Tourism Academy with the support of different partners – and we are expecting many more to join this initiative in the future.

This high-quality learning experience is available for everyone and caters to those who are looking to improve or supplement their current skillset in order to adapt to this fast-growing and evolving sector. Through these first programs, participants will acquire managerial skills in digital marketing, finance, strategy, operations, innovation and digital transformation.

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Comparing online travel review platforms as destination image information agents

• Original Research
• Open access
• Published: 22 April 2021
• Volume 23 , pages 159–187, ( 2021 )

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• Xinxin Guo   ORCID: orcid.org/0000-0002-7904-3040 1 ,
• Juho Pesonen 1 &
• Raija Komppula 1  

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Online travel reviews have been extensively used as an important data source in tourism research. Typically, data for online travel review research is collected only from one platform. However, drawing definite conclusions based on single platform analyses may thus produce biases and lead to erroneous conclusions and decisions. Therefore, this research verifies whether or not there are discrepancies and commonalities between different travel review platforms. In this study, five native Chinese travel review platforms were selected: Ctrip; Qyer; Mafengwo; Tuniu; and Qunar. Using a mixed content analysis method, the destination image of Finland was extracted from 10,197 travel reviews in Simplified Chinese as the destination image is a popular topic in online review research. Results show Finland’s destination image representation varies between Chinese travel review platforms. This discrepancy is especially prominent in the dimension of functional and mixed functional-psychological destination attributes. Significant theoretical contributions and managerial implications for the analysis of online travel reviews and destination image research are discussed.

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1 Introduction

Today, online travel reviews (OTRs) have a huge influence on the tourist decision-making process, because they are often used when tourists compare various options and make travel-related decisions. OTRs are also an indicator of a destination’s post-visit destination image (DI) because tourists write reviews of their experiences based on the image they have after the trip (González-Rodríguez et al. 2016 ; Park et al. 2007 ). OTRs are therefore gaining increasing attention in tourism research and destination marketing. Meanwhile, DI is increasingly analyzed using online textual data instead of other data collection methods such as interviews (Lu and Stepchenkova 2015 ). New analysis methods based on big data allow us to gain in-depth knowledge from this vast social media data ocean (Fazzolari and Petrocchi 2018 ).

Earlier studies involving OTRs have relied on a single data source (Xiang et al. 2017 ). In using a single data source for OTR research, researchers ignore platform-specific biases such as differences in platform design, user base, platform-specific behavior, and storage strategy (Pfeffer 2014 ). Using a single platform is also a potential source of sampling bias that potentially complicates the interpretation of the research findings (Tufekci 2014 ). This study aims to explore whether or not platform-specific biases in OTRs should be accounted for in tourism research and practice, and if so how. Moreover, earlier studies have mainly used statistical analyses, natural language processing techniques, or algorithms to explore the length of the review, frequently words, topics, and review sentiment (Xiang et al. 2017 ; Zhang and Cole 2016 ), or have analyzed the functional features of different websites (Pai et al. 2014 ).

Additionally, research on OTR platforms in tourism studies is still based largely on the Western context (Xiang et al. 2017 ). With the rapid growth of Chinese outbound tourists in recent years, scholars are increasingly focusing on China and other Asian countries. The exploration of Chinese social media platforms has become an important research venue (Sotiriadis and Sotiriadis 2017 ). However, cultural and language barriers mean research on Chinese OTR platforms is rarely published in English. Data from OTR platforms may, therefore, provide a new approach to destination image research among Chinese tourists.

To address these gaps in the previous research, this study makes an in-depth comparison of various native Chinese OTR platforms to identify their potential differences and universal attributes. The differences are analyzed by comparing the DI between platforms as the DI concept is an important topic in OTR analysis (Marine-Roig and Ferrer-Rosell 2018 ). Additionally, the aim was to explore the reasons for discrepancies and commonalities in the representation of the DI. An instrumental case study approach (Mills et al. 2013 ) was used in this study as we are interested in the differences of online platforms in representing DI instead of the case itself. Since the research team is familiar with Finland and tourism in the country, it was chosen as the case destination. Finland is also a relatively new destination for Chinese tourists, but growing fast before COVID-19 pandemic. This development aspect makes Finland an interesting case to study the phenomenon. However, we acknowledge that the context is secondary for this research compared to the phenomenon itself and the destination could have been virtually any another destination. The data was collected from five Chinese OTR platforms and analyzed using a mixed content analysis approach focusing on data referring to Finland as a tourist destination. A qualitative content analysis was used to formulate a DI coding manual (for use in the analysis) from part of the samples. A quantitative content analysis was then conducted to objectively extract Finland’s DI from the other OTRs’ data, based on the coding manual.

The structure of the paper is as follows: Chapter 2 presents previous social media studies of OTR platforms. Chapter 3 introduces the theoretical background of the DI framework. The methodology and results are presented in Chapters 4 and 5. A theoretical discussion and practical implications based on the results are presented in Chapter 6. The final chapter includes a conclusion, a discussion of the study’s limitations, and suggestions for future study.

2 Social media analytics on online travel review platforms

In 2018, the number of Chinese outbound tourists exceeded 149 million (iResearch 2019 ). The increasing outbound travel has also led to the increasing use of online travel review platforms in China. With the development of information technologies, China’s tourism information services now cover the pre-travel, on-travel, and post-travel processes (Pan et al. 2019 ). OTR platforms are especially prominent: 51.4% of outbound tourists obtain travel recommendations and information from Chinese OTR platforms (iResearch 2019 ). Besides, 71.6% of Chinese outbound tourists share travel experiences on Chinese social media, and 39.9% of tourists share travel experiences on OTR platforms (iResearch 2019 ). All the evidence indicates that OTR platforms are very important in any attempt to understand outbound Chinese tourists.

There is a vast amount of online information on OTR platforms, commonly known as “big data”. When researchers conduct DI studies based on shared online travel experiences, OTRs are regarded as a form of electronic word-of-mouth communication (eWOM) (Marine-Roig 2017 ). Although online reviews may be seen as unsolicited and unbiased online information that reflects the realistic tourist perception of the destination (Marine-Roig 2017 ), the OTR content given by different tourist segments has different focuses (Van der Zee and Bertocchi 2018 ). Nowadays, the application of OTRs in tourism research has received increasing attention, and researchers usually collect data from a single Western OTR platform, especially TripAdvisor, Yelp, or Expedia (Xiang et al. 2017 ).

Many researchers have adopted a single OTR platform approach in tourism studies (Xiang et al. 2017 ). The platform-specific biases of different OTR platforms mean that multi-platform data sources may be more valid in researching tourism phenomena. These biases are not only reflected in the platform design itself (Pai et al. 2014 ), but in the posting behavior of tourists and managers (Pfeffer 2014 ). The research already demonstrates that the major Western OTR platforms differ regarding the cost of reviewing (Chevalier et al. 2018 ; Zhuang et al. 2018 ), the review content posting behavior in terms of the number of reviews, the review length, customer preference, and sentiment, for example (Proserpio and Zervas 2017 ; Wang and Chaudhry 2018 ; Xiang et al. 2017 ).

Researchers are thus well aware of the differences between major Western OTR platforms. However, although we often use OTR platforms for destination image analytics (Marine-Roig and Ferrer-Rosell 2018 ), how the OTR platform itself affects DI analytics remains unknown. It is imperative to understand how the DI differs between different platforms or whether there is a difference at all. We, therefore, compare Chinese OTR platforms and analyze the results of online DI analytics from major Chinese OTR platforms.

3 The framework of the destination image

The usual definition of DI is the sum of a person’s beliefs, ideas and impressions of a destination (Crompton 1979 ). It is formed in a process in which personal, sociocultural, and information technology factors (Beerli and Martin 2004 ; Josiassen et al. 2015 ; Kislali et al. 2016 ; San Martín and Del Bosque 2008 ), as well as stimulus factors (e.g., information sources, previous experience of the destination) affect the formation of the image (Gartner 1993 ). According to Gartner ( 1993 ), destination information can be regarded as a continuum of various image formation agents, ranging from traditional forms of the induced agent to autonomous and organic image agents. “Induced agents” refers to the information provided by commercial destination actors representing the supply-side view of DI as the projected image (Mak 2017 ). “Autonomous image agents” refers to information sources which are not under the control of the destination organizations, referring to news, movies, and documentaries, for example (Gartner 1993 ). “Organic image agents” refers to information sources based on a visit to the area (Gartner 1993 ).

With the development of information technology, induced and organic image formation agents are not necessarily mutually exclusive but may complement each other (Selby and Morgan 1996 ). One view is that the Internet can be seen as an induced information agent in the image formation process (Beerli and Martin 2004 ). The opposing view is that the previous point is outdated in the modern online environment, and the different online travel platforms (such as official tourism websites, travel blog platforms, or travel review platforms) on the Internet can be classified as induced, autonomous, or organic information agents (Llodrà-Riera et al. 2015 ). Online destination information can, therefore, be regarded as an agent of induced or organic image formation, both of which play a significant role in the image formation process (Llodrà-Riera et al. 2015 ). Besides, when tourists obtain destination information from different online travel platforms, there may be a discrepancy between the destination images based on official tourism website content (induced), travel blog platform content (autonomous), and travel review platform content (organic) (Mak 2017 ; Marine-Roig and Ferrer-Rosell 2018 ). Perceptions of official tourism website content (induced) and travel blog platform content (autonomous) differ less from each other (Marine-Roig and Ferrer-Rosell 2018 ).

Due to the OTRs’ source credibility and information quality, travel review platforms as organic information agents are more unbiased and trustworthy than the induced information agents of official tourism websites (Filieri et al. 2015 ). The assessment of DI formation based on OTR data is, therefore, becoming increasingly popular. In particular, understanding DI based on different OTR platform content may assist in exploring whether there is a DI discrepancy between different organic information agents. Mak ( 2017 ) used the term online destination image to depict “the online representation of the collective beliefs, knowledge, ideas, feelings and overall impressions of a destination.”

There are two main approaches to defining DI construction. One considers DI as a multidimensional construct with two main components: the cognitive image and the affective image of destinations (Baloglu and McCleary 1999 ). These two images respectively represent a tourist’s knowledge of the destination and their emotions based on their destination knowledge (Baloglu and McCleary 1999 ; Gartner 1993 ). The other most-cited construction is considering DI as a person’s overall evaluation of the destination, which includes attribute-based and holistic components (Echtner and Ritchie 1991 , 1993 ). Each component can be further subdivided into functional-psychological; or common-unique characteristics (Echtner and Ritchie 1991 , 1993 ). The attribute-holistic continuum illustrates whether the representation of DI is from the perspective of an individual attribute or a holistic aggregate. The functional-psychological continuum refers to functional (directly observable or measurable) or psychological (less tangible, difficult to measure) attributes. The common-unique continuum also refers to common characteristics, attributes, and impressions according to which destinations are commonly compared, or it refers to unique or destination-specific features (Echtner and Ritchie 1991 , 1993 ). By introducing a three-dimensional DI framework, Echtner and Ritchie ( 1993 ) developed a 35-item destination attribute scale, ranging from more functional attributes (including tourist sites/activities, national parks, and historic sites) and mixed destination attributes (including crowdedness, cleanliness, and political stability) to more psychological destination attributes (including hospitality, place atmosphere, and reputation).

Subsequently, some studies have proposed various scales to determine the destination attributes and measure the DI (Beerli and Martin 2004 ; Choi et al. 2007 ; Enright and Newton 2005 ; Gallarza et al. 2002 ; Marine-Roig 2017 ; Rodrigues et al. 2015 ; Vinyals-Mirabent 2019 ). In this study, we have combined Echtner and Ritchie’s ( 1993 ) functional-psychological attribute scales and Beerli and Martín’s ( 2004 ) attribute classification as an adapted framework (see Appendix 1 ) for data analysis. Echtner and Ritchie’s ( 1993 ) study placed 35-item destination attributes into a functional-psychological scale, which does not cover all the universal attributes in the destination. Therefore, another often cited destination attribute study by Beerli and Martín ( 2004 ) was applied for the adapted framework. Beerli and Martín’s ( 2004 ) study classified destination attributes into nine dimensions, but they did not distinguish the functional or psychological features of these attributes. For this reason we developed an adapted attribute framework which combines the advantages of Echtner and Ritchie’s ( 1993 ) and Beerli and Martín’s ( 2004 ) studies.

In order to build the adapted attribute framework, the first step was to place Echtner and Ritchie’s ( 1993 ) 35 identified destination attributes into Beerli and Martín’s ( 2004 ) destination attribute classifications. Then, according to the functional-psychological definition of the attribute given in Echtner and Ritchie’s ( 1993 ) study, the functional and psychological feature of the attribute classification were determined. For example, tourist sites, tourist activities, sports activities, national parks, and tourist entertainment were regarded as functional destination attributes in Echtner and Ritchie’s study ( 1993 ). In Beerli and Martín’s ( 2004 ) study, these attributes were classified as a tourism leisure dimension. Therefore, the tourism leisure dimension was considered a functional attribute after some research group discussion. Moreover, in the tourism leisure dimension, architecture and buildings, which were not covered in Echtner and Ritchie’s ( 1993 ) study but were identified in Beerli and Martín’s ( 2004 ) study, were also considered as a functional destination attribute. The adapted DI framework comprehensively illustrates the destination attributes from continuous functional to psychological characteristics in nine dimensions. The tourism leisure and recreation, natural resources, and tourism infrastructure dimensions are more related to the functional level. On the other hand, the dimensions of culture, history, art, general infrastructure, and natural environment belong to the mixed functional-psychological level. The abstract psychological attributes include politics and economics, the social environment, and the atmosphere of the place in question.

As socio-demographic and sociocultural factors (Beerli and Martin 2004 ; Josiassen et al. 2015 ; Kislali et al. 2016 ; San Martín and Del Bosque 2008 ) play an important role in the image formation process. It can be assumed that tourists with different cultural backgrounds may perceive the same destination attribute differently (Nakayama and Wan 2019 ). As most of the academic research on destination image analytics has been conducted Using western platforms, a short review of the literature focusing on Chinese tourists’ perceived images of Western destinations may highlight the dimensions of the image the Chinese tourists’ highlight. Chinese tourists retain different preferences for domestic and Western destinations (Li and Stepchenkova 2012 ; Wang and Hsu 2010 ). In domestic travel, the service quality attribute is the most important factor in shaping the DI (Wang and Hsu 2010 ). However, most Chinese tourists visiting Western countries share travel experiences concerning natural resources and local cultures (Huang and Gross 2010 ; Li and Stepchenkova 2012 ; Sun et al., 2015 ). Chinese tourists are also willing to discuss political and economic issues affecting Western destinations (Li and Stepchenkova 2012 ). To confirm judgements about Western destinations, Chinese outbound tourists tend to compare differences between a Western destination’s social systems and China’s (Huang and Gross 2010 ). Additionally, Chinese cultural norms play an important role in the process of perception formation and the interpretation of Western destinations (Sun et al. 2015 ). These cultural norms include the desire for harmony and respect for the authorities. The different cultural backgrounds of Chinese and Western tourists mean there may be significant differences in perceptions of the same destination (Tang et al. 2009 ). According to Kim and Morrison ( 2005 ), Chinese outbound tourists are more likely to change their perception of destinations in a short period than Western tourists.

4 Methodology

Adopting the mixed qualitative and quantitative content analysis approach, this study compared the representation of the image of Finland in different Chinese OTRs, to interpret the commonalities and discrepancies between various platforms. China has become one of the largest source markets in international tourism (UNWTO 2018 ), and this growth has also been witnessed in Finland. Between 2011 and 2018, the number of Chinese tourists visiting Finland increased by 323% (Statistics Finland 2019 ). In 2012, the Finnish national tourism office (VisitFinland) and the Finnish airline company Finnair established digital marketing strategies on Weibo, in China. Although the data reveals that the Nordic countries have great potential in the Chinese market, gaps and deficiencies remain in DI research in the Nordic countries (Andersen et al. 2018 ). Today, China has the largest market of Internet users, accounting for 21% of the worldwide total (Meeker 2019 ). With an increasing number of Chinese tourists sharing travel experiences online, the massive amount of information they generate provides researchers with a way of studying the DI of a European destination from the perspective of Chinese tourists. Even though the study uses Finland and China as examples, the results can be generalized to other market combinations.

In China, travel websites with review functions can be classified in two main categories: travel vertical platforms, such as Mafengwo, Qyer; and online tour agents (OTA), such as Ctrip, Tuniu, and Qunar (Graff and Parulis-Cook 2019 , p. 53). Vertical travel websites rely heavily on user-created content, and provide tourists with generated travel information and related travel-specific services (Graff and Parulis-Cook 2019 ; Kizmaz 2018 ). Chinese OTA websites provide many travel-related services including visa arrangements, tax refunds, and financial services, as well as travel information. Many Chinese OTA websites now also have a review function for users to share their comments about destinations. Before entering the detailed introduction of the research method, a flowchart (Fig.  1 ) summarizing the key information of the research process is shown below.

The flowchart of the research process

The flowchart shows the four main parts of the research process, namely data collection, data pre-processing, qualitative content analysis, and quantitative content analysis. The following chapters will introduce each of these steps in detail.

4.1 Data collection and pre-processing

Baidu is the largest online search engine in China. Its information center ( http://site.baidu.com/ ) displays 23 popular Chinese tourism websites. Manually typing “芬兰” (Finland) into the search engine of each tourism information website resulted in six websites with Chinese OTRs for Finland. These OTRs were on Qyer, Ctrip, Mafengwo, Tuniu, Qunar, and Maotuying (the Chinese version of TripAdvisor). On Maotuying, the OTRs in Chinese are translated from other languages. This platform was therefore excluded from the study. Table 1 displays background information about these five platforms. Figure  2 shows the format of the OTRs on different platforms. OTRs are basically comprised of four components: linguistic features; semantic features; sentiment; and reviewer information (Xiang et al. 2017 ). The differences in travel platform design mean that not all these features can be found on each platform (Xiang et al. 2017 ). For example, except for Tuniu, tourists can attach photos to OTRs on the other four platforms. On Qyer, Mafengwo, Tuniu, and Qunar tourists can comment on others’ OTRs. However, all the platforms contain basic review features (textual review content, ratings, and the release time) and reviewer’s information (nickname, profile photo).

The format of the OTRs on Qyer, Ctrip, Mafengwo, Tuniu, and Qunar

In Fig.  2 , the red box indicates the content of the OTRs. The black circle indicates the reply function.

Data collection was conducted using a web crawler, Octoparse, which was used to extract the required data information from the hypertext markup language on the travel review webpages. In this study, we collected only the textual review content, release date, and reviewer’s nickname. The collection process took place between early October and the end of December 2018. A total of 10,389 OTRs related to Finland were crawled by the setting crawling process. The textual OTR content includes descriptions of attractions, hotels, restaurants, entertainment activities, and others. Furthermore, only Simplified Chinese OTRs were considered in this study. After deleting 192 English and Traditional Chinese OTRs, a total of 10,197 Simplified Chinese OTRs were applied for further analysis. As Table 2 shows, Qyer had the largest number (3570) of OTRs, followed by Ctrip and Qunar. Mafengwo and Tuniu had a nearly equal number of OTRs and are the smallest platforms.

4.2 Data analysis

The content analysis approach is commonly adopted to analyze textual messages (Stepchenkova and Mills 2010 ). It can be used to compress many words into categories based on explicit coding rules (Harwood and Garry 2004 ). Most of the existing literature used either the qualitative or quantitative content analyses to study the perceived destination from OTRs, and the quantitative approach seems to be the mainstream choice (Marine-Roig and Ferrer-Rosell 2018 ; Qi et al. 2018 ; Zhang and Cole 2016 ). Applying the computerized quantitative content analysis approach to OTR-based image studies includes two basic steps, data pre-processing and attribute identification (Marine-Roig and Clavé 2016 ; Xiang et al. 2017 ). Data pre-processing generally involves some operations, including tokenization (means breaking the review text into words, phrases, or other meaningful elements), and removing stop words (e.g. a, the, so, or other words do not contribute to the meanings of the text) (Xiang et al. 2017 ). Attribute identification in a quantitative content analysis aims to detect the frequency, density and weight of keywords or key phrases in the content by computer program, and then aggregate keywords or key phrases into destination attribute categories (Marine-Roig and Clavé 2016 ). Because a quantitative content analysis often focuses on searching for keywords, adopting a quantitative computerized approach alone often results in ignoring valuable contextual information embedded in the OTR data (Stepchenkova et al. 2009 ; Zhang and Cole 2016 ).

In contrast to the quantitative content analysis approach, the qualitative content analysis approach is the subjective interpretation of textual content, and used to manually extract the DI from a small number of tourists’ narrative descriptions (Sun et al. 2015 ; Tegegne et al. 2018 ). Using the qualitative content analysis approach could extract the valuable contextual information embedded in the textual content. The systematic classification process of encoding the destination attributes and identifying attribute categories is the core of the qualitative content analysis approach in DI studies (Lian and Yu 2017 ). In addition, inter-coder reliability must be carefully considered, which means that different coders need to produce the same encoding results in the same way (Lian and Yu 2017 ). Although a qualitative content analysis focuses on valuable contextual information embedded in the text content, manual coding is quite time-consuming to apply for large-scale text analysis. In order to solve the two problems of extracting valuable information embedded in review content, and processing large amounts of OTR data, thus, a novel approach combining both qualitative and quantitative methods was applied in this study.

The qualitative content analysis in this study was conducted first to identify Finland’s destination attributes and build up a coding manual. In this process, the coding of the destination attributes and categorization followed the adapted attribute framework from previous studies (see Appendix 1 ). Basically, the adapted attribute framework ensured the validity of encoding destination attributes and identifying attribute categories. Therefore, two coders randomly chose 10% of the travel reviews to formulate a coding manual of Finland’s destination attributes. All data coding was conducted on Chinese-language texts using the computer-assisted text analysis software NVivo 12 Plus. The data reached a saturation point when adding additional OTRs failed to reveal novel aspects or issues (Papathanassis and Knolle 2011 ). The coding manual was built by using the following steps: (a) an OTR was read carefully and destination attribute were identified based on the context of review content, (b) the identified attribute was verified in the adapted attribute framework, (c) the code was confirmed if the identified attribute existed in the framework, (d) if the identified attribute did not exist in the framework, the coders discussed and decided on the attribute code and its classification. Furthermore, in order to ensure the reliability of the coding manual, a second reviewer with tourism expertise in China and Finland was asked to review the codes.

In the process of formulating the coding manual, several operations were performed on the selected reviews. First, the coders made efforts to unify the spelling of the names of attractions on different review platforms. For example, the description of Kamppi Chapel and the Silent Church pointed to the same attraction, which was coded as “Kamppi Chapel” under the destination attribute code “churches”. Second, universal terms of destination attributes were applied in the cases that Chinese tourists mentioned general infrastructure without giving a specific name. For instance, Chinese tourists gave descriptions of Finland’s libraries without referring to a certain place, thus the general terms “libraries” were applied to these descriptions.

Based on the coding manual from the qualitative content analysis, a computerized quantitative content analysis was applied to the remaining data by using the “automatic coding using existing coding patterns” function in NVivo 12 Plus. The premise of using pattern-based auto-coding is that the coder needs to manually code part of the material. When using the identified codes for automatic coding, NVivo compares each text part (e.g., a sentence or paragraph) with the review content already coded into the destination attribute. If the content of the text paragraph is similar in wording to the content already coded for the destination attribute, the text paragraph will be coded for that identified attribute. In doing so, the quantitative content analysis results can then reveal Finland’s image on the various Chinese OTR platforms. This study further used the post hoc z test to verify whether the differences between Finland’s image on different platforms are significant. The chi-square post hoc z-test using adjusted residuals is applied to detect differences between groups data (Zhang et al. 2017 ). The premises of using the z-test are that the variance is known and the sample size is large enough (sample size ≥), as is in this case (Table 4 ). The test shows the cells in the chi-square table that have significantly lower or higher adjusted residuals on the 95% confidence interval.

5 Results of the destination image analysis

5.1 qualitative analysis results for finland’s destination image categories.

Compared with the adapted DI framework (see Table 3 ), the qualitative content analysis results proved that Finland’s Chinese OTR data-based DI covered all nine destination attribute dimensions, from the functional to the psychological levels. However, within every attribute dimension, while some universal destination attributes did not appear, other new destination attributes were identified in the data.

The following paragraphs demonstrate some of the aspects identified with some quotations from the source data to illustrate the points.

At the functional level, attributes identified in the adapted frameworks such as beaches and the richness of the countryside did not appear in the Chinese OTRs. However, other attributes were identified in the selected OTR samples, for example, the new attributes of city parks (Quotation 1) and payment methods (Quotation 2) were identified at the functional level. This can be seen in the following quotations:

Quotation 1 “ Sibelius Park is located about 1.5 km northwest of Temppeliaukio Church. It was built to commemorate the great Finnish musician Sibelius. The park is full of flowers and green grass, …”—Reviewer (Case number: 5143) from Ctrip.

Quotation 2 “… At the terminal, we bought a round-trip ticket for 5 euros at the self-service ticket vending machine. It seems that only cash is accepted, and no credit card was accepted. ….”—Reviewer (Case number: 3474) from Qyer.

At the mixed functional-psychological level, attributes concerning the development of health services and telecommunications and traffic congestion which were in the adapted framework were missing. However, a few new attributes, such as educational facilities (Quotation 3), and national industry (Quotation 4) were identified at the mixed functional-psychological level. The following quotations illustrate these aspects.

Quotation 3: “The informatization of Finnish libraries is very good. Finns can borrow materials from the public libraries… and return books to another library which is near their home, …”—Reviewer (Case number: 9583) from Mafengwo.

Quotation 4: “… Known for its technology-intensive industries, it has become a leading technology center in the Nordic region, where Nokia's headquarters is located.”—Reviewer (Case number: 5700) from Qyer.

At the psychological level, aspects from the adapted framework including the degree of urbanization, economic development, the opportunity for adventure, the mystic vs prosaic aspect, the luxurious vs impoverished nature of the destination, or fashionable vs outdated elements could not be discerned. This did not mean that Chinese tourists were not aware of Finnish destination attributes at the psychological level. On the contrary, Chinese outbound tourists seem to have an abundant and unique psychological perception of Finland, especially concerning the atmospheric dimensions. The following quotations (Quotation 5, 6) emphasize the “harmonious” atmosphere.

Quotation 5 “…there are a lot of people of different skin color sitting on the steps, sunbathing and chatting, and the whole atmosphere is very harmonious and enjoyable.”—Reviewer (Case number: 9172) from Qyer

Quotation 6 “There are also food stalls in the market, there are fruit sellers and handicraft sellers. This free market is across the road from the presidential palace and other government buildings—what a great harmonious society scene!”—Reviewer (Case number: 10030) from Tuniu.

Table 3 shows Finland’s destination attributes identified in Chinese OTRs. As can be seen most of the attributes appeared at the psychological level including attributes concerning ethnic origins, an ancient and historic atmosphere, an artistic atmosphere, as well as desolate, depressing, harmonious, majestic, fairytale, magical and solemn attributes.

5.2 Quantitative analysis results of Finland’s image on five review platforms

Based on the coding manual created in the qualitative analysis phase, a quantitative content analysis for the remaining 90% of OTR data was conducted. The results presented in Table 4 show that Chinese tourists visiting Finland generally perceived Finland as a leisure destination with various cultural, historic, and artistic elements. The culture, history, and art dimensions had the largest amount of coding references, accounting for 20.32% of the total, followed by the tourism leisure and recreation dimensions, accounting for 19.51%. These two attribute dimensions accounted for a large share of the Finland’s DI at the functional level. The largest dimension at the psychological level was the place atmosphere, with a 19.25% share of mentions. Furthermore, the shares of the dimensions encompassing Finnish natural resources, natural environment, political and economic, and social environment were 5.52, 5.69, 3.53, and 2.35%. The proportions of the latter three dimensions were much lower than the proportions of the first three dimensions.

As can be seen from Table 4 , the results therefore also reveal a discrepancy in Finland’s DI based on different platform OTRs. From the number of destination attribute references, the total number of destination attribute references came to 4961 on Qyer, the largest of the other four platforms, followed by Ctrip, with 3579, Mafengwo, with 2493, Qunar, with 2019, and Tuniu, with 1437 references. Qyer had a greater number of attribute references than the other four platforms concerning the natural resources dimension (320), tourism infrastructure dimension (651), general infrastructure dimension (977), natural environment dimension (257), political and economic dimension (234), social environment dimension (120), and the place atmosphere dimension (889). Ctrip had a greater number of destination attributes references concerning the tourism leisure and recreation dimension (827), and the culture, history, art dimension (823). The remaining three platforms Qunar, Mafengwo and Tuniu did not have the largest number of references for any destination attribute dimension.

The z test showed that the differences between the five travel review platforms were significant. Reviews on Qyer discuss natural resources, tourism infrastructure, general infrastructure, and political and economic situation significantly more than what could be expected. However, there is significantly less information about tourism leisure and recreation, and culture, history and art compared to other platforms. These two destination image dimensions are more prominent on Ctrip, as well as tourism infrastructure. However, Ctrip lacks reviews on general infrastructure, as does Qunar and Mafengwo. Tuniu seems to focus the most on culture, history, and art as well as the atmosphere of the place. The results are interesting also in the destination image dimensions. It seems that the most significant differences are in functional dimensions whereas psychological dimensions are relatively similar between different platforms.

6 Discussion

There is no doubt that big data has had a major impact on tourism research (Li et al. 2018 ). Whereas most previous studies have used a quantitative content analysis approach with a single OTR platform (Lalicic et al. 2021 ; Tseng et al. 2015 ; Xiang et al. 2017 ), we explored the differences and universal attributes of various OTR platforms with a mixed qualitative and quantitative content analysis approach. This approach allowed us to extract valuable contextual information embedded in a large amount of OTR data. Additionally, in applying Simplified Chinese OTR content from multiple Chinese-based platforms, this study interpreted Finland’s image in terms of nine destination attribute dimensions of a functional and psychological destination attribute scale. In this study, we also identified statistically significant differences between the review topics on various Chinese OTR platforms. Users of different platforms discuss different issues in their reviews. These differences can have a significant effect on what kind of conclusions are drawn from destination image studies based on OTRs. We also found that Chinese online travel reviews of Finland focus on functional and mixed psychological-functional destination attributes.

6.1 Differences in destination image between OTR platforms

First of all, this study aimed to explore if and how the destination image differed on various OTR platforms (Table 4 ). The results of this study show that there are indeed many differences between platforms. This is an important observation for researchers as well as tourism managers. OTRs are often analyzed to understand DI (Lalicic et al. 2021 ; Marine-Roig and Clavé 2016 ; Marine-Roig and Ferrer-Rosell 2018 ). The results demonstrated how the conclusions made about DI can differ depending on where the OTR analyzed come from. For example, if only Ctrip or Mafengwo reviews are analyzed the results would show that the Chinese tourists focus on tourism leisure and recreation. However, if only Tuniu reviews were analyzed the results would show that Chinese tourists focus and pay attention mainly to culture, history, and art. Only by including a wide range of sources and data is it possible to form a comprehensive picture of the actual DI.

Previous research has shown that Western OTR platforms (TripAdvisor, Expedia, and Yelp) have discrepancies in their displays of the hotel product, and each platform has its own characteristics (Xiang et al. 2017 ). According to the results of this study, platform-based specific characteristics in the representation of Finland’s image exist based on different Chinese platforms’ OTR content. Tourism studies, especially DI research, should take into account platform-specific biases, and data collected from multiple OTR platform can better reflect DI compared to individual platforms. These platform-specific characteristics are especially prominent in the functional and mixed functional-psychological dimensions.

Earlier studies have also argued that these platform-specific content characteristics were explained by the different user groups on each OTR platform (Xiang et al. 2017 ). However, based on the Chinese OTR platforms, it seems that the differences cannot be directly reflected by their target user groups. For example, both Ctrip and Tuniu focus on middle- and high-income tourists, but the platform-specific characteristics in their OTR content still differ. These differences, therefore, seem to be the result of multiple factors, including not only the user group but their online product, marketing, or market positioning. It is critical to understand these differences when conducting social media analytics research for destination image analyses. Where the data is collected matters. Various platforms’ destination image analyses provide different results. Drawing definite conclusions based on single platform analyses can thus produce biases and lead to incorrect conclusions on what the customers thinkg about the destination (Pfeffer 2014 ).

6.2 OTRs as organic image information agents

Second, this study demonstrates that OTRs, as organic image information agents, can contribute significantly to functional and psychological destination attributes, and are especially prominent at the functional and mixed functional-psychological level. This result is interesting when compared to the earlier literature. Earlier studies have shown that organic image formation agents (e.g., word-of-mouth) affect the perception of psychological destination attributes, whereas induced (advertising) and autonomous (non-promotional) sources contribute to the formation of functional destination attributes (Baloglu 2000 ; Mak 2017 ). In this study, the results show that, overall, the functional attributes and mixed functional-psychological attributes had more descriptions than the psychological attributes in Chinese OTRs. This was especially true for the tourism leisure and recreation attribute dimension; the and culture, history, art dimension. The reason for this may be related to the development of information technology. The application of portable communication devices and the popularization of information networks have made it easy for tourists to share all aspects of their travel experience through social media platforms (Huertas and Marine-Roig 2016 ).

This study also contributes to the tourism literature by revealing the relational nature of DI and its formation by organic image information agents referring to OTR platforms. The results of this study confirm that each destination may have its own destination attribute scale, which is consistent with Beerli and Martín’s ( 2004 ) study. Although, Finland was perceived as a leisure destination with various cultural, historic, and artistic elements, and all nine destination attribute dimensions were recognized in travel reviews, some universal destination attributes such as beaches and the wealth of the countryside did not appear and some new attributes such as city parks and payment methods were identified (Table 3 ). The results expand our knowledge on the topics that tourists pay attention to when they are travelling and what factors can affect the perceived destination image.

Furthermore, according to the results of this study, most Chinese tourists pay less attention to the political attributes in Finland, including safety issues, the political environment, and political stability. However, more than ten years ago, when OTR platforms were rarely used by Chinese outbound tourists, most considered safety one of the most important destination attributes (Kim et al. 2005 ). The development of information technology may explain this: tourists now have more diverse ways of obtaining destination information through social media, leading to a more comprehensive understanding of destinations and thus minimize their risk perception. Tourists assist each other especially through OTR platforms, which may be beneficial for risk reduction (Jacobsen and Munar 2012 ).

6.3 Practical implications

Chinese tourists use organic image information agents, OTRs, as influential destination information sources (iResearch 2019 ). Due to the discrepancy in the representation of DI on different OTR platforms, this study provides at least four implications for all DMOs. Western DMOs could use the knowledge concerning destination image analysis to position themselves in the Chinese market and modify their service design and marketing processes.

First, these Chinese platforms provide not only OTR functions to tourists but also travel products to consumers, including flights, accommodation, and tour guide services. Based on the background of each platform and the DI it represents, DMOs may need to develop suitable product strategies for each OTR platform and different target groups. The most efficient marketing combinations come from marketing and selling the correct tourism service on the correct channel.

Second, DI directly affects the travel intentions of potential tourists (Chaulagain et al. 2019 ). Today, induced and organic sources may complement each other (Selby and Morgan 1996 ). DMOs should focus on the image displayed by OTR content and try to increase interactivity with tourists (Huertas and Marine-Roig 2016 ), especially on opinion leaders’ OTRs. As OTRs become increasingly influential, a DI represented on OTR platforms will increasingly affect the perceived DI of tourists searching for travel-related information (Marine-Roig and Ferrer-Rosell 2018 ). Indubitably, opinion leaders’ OTRs have a great impact on the DI of potential tourists (Jalilvand 2017 ). Their OTRs may enhance DI and make the information recipient feel the destination is attractive. Their comments may also weaken the DI and discourage tourists from visiting a destination. Given that most Chinese OTR platforms offer a comment function (see Fig.  1 ), DMOs could supplement destination information or repair damaged DI through opinion leaders’ OTRs (Chevalier et al. 2018 ).

Third, DMOs should aim to reduce the gap between tourists’ perceived and the destination’s projected images. Tourists will thus have satisfying experiences and perceive a strong destination brand (Marine-Roig and Ferrer-Rosell 2018 ). Understanding tourists’ perceived images are crucial here. In reducing the gap between these two kinds of image, DMOs need to adjust marketing strategies, and the entire destination may even need to be developed in tune with the realities of the general infrastructure and social and political environments (Marine-Roig and Ferrer-Rosell 2018 ).

Fourth, DMOs should pay great attention to the influence of tourists’ cultural backgrounds and demographic characteristics on the perception of the destination. The image formation process is not only affected by destination information sources (induced, autonomous, and organic) but also by sociocultural and socio-demographic characteristics (Josiassen et al. 2015 ; Nakayama and Wan 2019 ; San Martín and Del Bosque 2008 ). Chinese outbound tourists emphasize Chinese cultural norms in the process of image perception and interpretation (Sun et al. 2015 ). Therefore, Chinese tourists seem to have an abundant and unique psychological perception in the place atmosphere dimension (Table 3 ), the “harmonious atmosphere” attribute was especially identified in the review content. In addition, most users of China’s OTR platforms are white-collar workers (Table 1 ), and their average age is around 35 (iResearch 2019 ). Young Chinese outbound tourists’ emphasize local culture and novel travel experiences (Sparks and Pan 2009 ), and consequently Chinese tourists have a strong perception of a destination’s culture, history, and art attributes. These findings prove that understanding the characteristics of tourists is of great help to grasp the reasons behind DI formation.

In addition to the contributions this study has for all destinations, this study also provides three insights for Finnish DMOs. First, to improve Chinese tourists’ awareness of Finland, Finnish DMOs should strengthen their cooperation with OTR platforms like Tuniu, Qunar, and Mafengwo, or find Chinese agents to launch Finnish tourism products on them. Of the nine dimensions of DI analyzed, Chinese tourists seem to lack an awareness of the Finnish social and political environments. Perhaps the Finnish DMOs should consider making more efforts in these areas on Chinese social media to increase Chinese tourists’ familiarity with Finnish destinations. Finally, overall, Chinese tourists perceive Finland as “a cultural, history, art, and leisure destination”, which differs from the Finnish DMOs’ promotion of “a nature destination” (VisitFinland 2019 ). Finnish DMOs, therefore, need to interview Chinese tourists more comprehensively to identify why Chinese tourists do not mention nature-based attributes in their reviews. Are they not interested in nature? Do they fail to access it when they visit? Are they unaware of Finland’s natural attractions? It may be that Chinese tourists prefer to stay in cities and do not venture into nature tourism areas. Thus, the Chinese market might not be the right market to position Finland as a nature destination.

7 Conclusion and limitations

In the new era, big data provides a new stream for tourism research (Lu and Stepchenkova 2015 ). Many earlier studies adopted a single platform as a data source, but they ignored that using single data sources may induce a sampling bias that potentially complicates the interpretation of the research findings. Therefore, in this study, we explored Chinese outbound tourists’ perception of Finland, and compared the discrepancies and commonality of Finland’s image between different OTR platforms. An analysis of Simplified Chinese OTRs proved that different Chinese OTR platforms had a DI discrepancy. The results showed that all nine destination attribute dimensions could be identified in the Chinese perception of the destination. However, when the destination attributes of each dimension were examined more closely, major differences in the destination attributes could be observed on the OTR platforms. In this study, the DI based on different OTR platform content revealed discrepancies at the functional and mixed functional-psychological levels. These differences may be the result of a variety of factors, such as the platform’s target group, market positioning, or other factors. At the psychological destination attribute level, different OTR platforms showed consistency in their representation of the social image, political and economic, and place atmosphere dimensions.

Additionally, rich OTR content could cover all destination attribute dimensions, but each destination is unique when it comes to the scale of the destination attribute. With the development of information technology, OTR platforms have gradually become an important channel for tourists to obtain destination information (Marine-Roig 2017 ). Especially in the introduction to the functional and the mixed functional-psychological destination attributes, OTRs provide a more comprehensive understanding of destinations. At the psychological level of destination attributes, although Chinese OTRs show an abundant and unique psychological perception of the place atmosphere attribute, less attention was paid to the political and social environment, such as safety issues. The reason for this result may be related to the advantages of OTRs, in that tourists can assist each other on OTR platforms to reduce the risk of travel (Jacobsen and Munar 2012 ).

Although this study used mixed methods to conduct a comparative analysis of DI represented by various OTR platforms, it has some limitations that may inspire future research. First, an adapted DI framework was applied in this study, and the Finnish attribute coding manual was defined by tourism researchers proficient in Chinese and Finnish culture. However, there is an inevitable degree of subjectivity during the qualitative content analysis process. In this study, OTRs were crawled from Chinese OTR platforms, ignoring the significance of their Western equivalents. To further understand the discrepancies in the same destination’s representation by different OTR platforms, other Western OTR platforms like TripAdvisor, Yelp, or Google Reviews might also be included in future research. Meanwhile, in this study, the discrepancies in the DI on OTR platforms were discussed based on the basic information of the platforms. In order to explain the results of the discrepancies more accurately, other indicators could be considered in future research, such as the actual market and actual product positioning.

The study’s most important limitation is that the analysis process does not calculate the length of the review text, or split the sentences when analyzing the attributes of the phrases in the text, which is common to computerized methods that clean, debug, and analyze large-scale OTR data. Future research could use detailed computerized methods, such as those proposed by Marine-Roig and Clavé ( 2016 ), to compare whether the computerized analytical results are consistent with the results of this study method. It is also possible to apply the compositional data analysis approach (CoDa) (Coenders and Ferrer-Rosell 2020 ; Lalicic et al. 2021 ) to analyze multiple destinations’ image, thus verifying whether the reasons for the discrepancies between different platforms are the same for different destinations. Besides, although this study has conducted a detailed analysis of each of the destination attributes of the image, due to word limitations, this paper only shows the discrepancies between the various platforms from the perspective of the main DI dimensions. For future research, given the improved performance of big data analysis software, different analysis methods and software could be used to verify the results of this research, such as performing a quantitative analysis using Python. Alternatively, photos could be considered for use as a data source to verify whether discrepancies exist between various OTR platforms.

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Guo, X., Pesonen, J. & Komppula, R. Comparing online travel review platforms as destination image information agents. Inf Technol Tourism 23 , 159–187 (2021). https://doi.org/10.1007/s40558-021-00201-w

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Measuring sustainable tourism with online platform data

• Felix J. Hoffmann 1 ,
• Fabian Braesemann   ORCID: orcid.org/0000-0002-7671-1920 2 , 3 &
• Timm Teubner   ORCID: orcid.org/0000-0002-5927-3770 1  

EPJ Data Science volume  11 , Article number:  41 ( 2022 ) Cite this article

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Sustainability in tourism is a topic of global relevance, finding multiple mentions in the United Nations Sustainable Development Goals. The complex task of balancing tourism’s economic, environmental, and social effects requires detailed and up-to-date data. This paper investigates whether online platform data can be employed as an alternative data source in sustainable tourism statistics. Using a web-scraped dataset from a large online tourism platform, a sustainability label for accommodations can be predicted reasonably well with machine learning techniques. The algorithmic prediction of accommodations’ sustainability using online data can provide a cost-effective and accurate measure that allows to track developments of tourism sustainability across the globe with high spatial and temporal granularity.

1 Introduction

The tourism industry is of tremendous economic relevance, accounting for an estimated 10% of global GDP in the years before the Covid-19 pandemic [ 1 ]. Though strongly affected by restrictions and other uncertainties in international travel, the sector is expected to resume growth and fully recover throughout the coming years [ 2 ]. Tourism is also of high importance for economic development, which is underlined by its inclusion in the United Nations’ Sustainable Development Goals (SDGs), where it is directly mentioned in three of the 17 goals [ 3 ]. Between 2008 and 2018, the relative importance of tourism for the respective country’s GDP increased in 43 out of 70 countries that report to the UN [ 4 ]. At the same time, it has been criticized to have adverse environmental and social effects [ 5 ], causing 8% of the global carbon emissions in 2013 [ 6 ]. To balance the economic, environmental, and social impacts of tourism, the relevance of sustainable tourism becomes evident [ 7 ]. In order to monitor and manage tourism in view of sustainability, granular and accurate spatio-temporal data is needed. There is a growing number of indicator frameworks for the tourism sector that aim to measure sustainability, with the majority of successfully implemented projects focusing on the European market. Current data collection methods, however, are often costly and yield piecemeal results. Ideally, improvements would allow for a cost-efficient implementation in both high income and developing countries, where tourism is growing faster than in more mature markets [ 8 ]. In the past, data collection could often be improved by means of tapping into alternative data sources. Examples include the assessment of the digital gender gap based on social network data or the assessment of poverty using mobile phone records [ 9 , 10 ]. Besides lowering the cost of data collection, such approaches allow for the calculation of indicators in near real-time, rather than relying on year-long cycles.

The present paper expands on such approaches by exploring the feasibility of using online platform data to assess the sustainability of tourism throughout Europe. Specifically, we pose the following research question:

Research Question : Can statistical learning techniques using data from an online tourism platform predict tourist accommodations as sustainable, as indicated by a sustainability label?

We use a machine learning approach on online platform data alone to answer the research question. Thus, our study is different from others that discuss rule-based classification systems used by traditional sustainability labels. These labels require detailed information about waste, water use, and other factors to determine an accommodations’ level of sustainability. While highly accurate and true to the causal relationships of sustainability, the corresponding data collection procedures are expensive and not feasible quickly at scale. The classifiers introduced below rely on correlated but not necessarily causal factors. They can hence not fully replace the physical measurement of factors determining sustainability. Instead, the models’ wide applicability and low cost of calculation can serve to complement existing labels, allow for nowcasting of sustainability indicators, and increase the geographical coverage of such indicators.

The analysis is based on a unique dataset of TripAdvisor.com accommodations and the platform’s GreenLeader award. We contribute to the literature in two ways. First, we identify and outline systematic differences between award-holding and non-holding accommodations using public platform data. Secondly, making use of supervised machine learning techniques, we identify sustainable accommodations with reasonable accuracy also in regions that have not implemented the platform’s GreenLeader award. In doing so, we show that large-scale monitoring of sustainable tourism using online platform data in near real-time is feasible. The approach presented here provides a cost-effective and accurate measure with high spatial and temporal granularity, which could be rolled out to track sustainable tourism across the globe.

The remainder of the paper is structured as follows. Section  2 provides an overview of related work and past projects making use of alternative data sources for development statistics in general, and for the assessment of sustainable tourism in particular. Following this, Sect.  3 introduces our methodology, the data set, as well as criteria for model evaluation. Next, Sect.  4 presents our results. In Sect.  5 , we then discuss our findings in view of practical and theoretical implications and conclude the paper with limitations and suggestions for further research.

2 Background and related work

2.1 measuring sustainability in tourism.

The majority of current sustainability practices in tourism result from regulation and economic incentives rather than intrinsic motivation [ 11 ]. Accordingly, policy makers need to define and monitor sustainability in tourism to achieve change. A number of frameworks aim to supply this information by means of indicators (which represent a core element of development research and a central pillar of the SDGs). Next to the UN’s SDGs, tourism-specific indicators were devised by, among others, the World Tourism Organization (UNWTO), Footnote 1 the Global Sustainable Tourism Council (GSTC), Footnote 2 the European Commission, Footnote 3 and the European Environmental Agency (EEA). Footnote 4 Tourism finds direct mention in Goal 8 (‘Decent work and economic growth’), Goal 12 (‘Responsible consumption and production’), and Goal 14 (‘Life below water’) [ 3 ]. Note that the use of indicators for the measurement of sustainability in tourism dates back almost three decades, when the World Tourism Organization began to promote their use for policy-making and destination management [ 12 ]. Today, the Global Sustainable Tourism Council sets a widely used and accepted standard for sustainability of private companies in tourism based on performance indicators [ 13 ]. Moreover, the European Commission first published its ‘European Tourism Indicator System’ (ETIS) in 2013 (with several revisions in the subsequent years). Building on 27 core and 40 optional indicators, ETIS provides the most detailed approach to measure sustainable tourism [ 14 ]. At the same time, the EEA has developed a ‘Tourism and Environment Reporting Mechanism’ (TOUERM), monitoring the environmental impact of tourism similar to other industries. Note that the nine TOUERM indicators are similar/overlapping with those of the ETIS framework.

2.2 Sustainability labels

While the aforementioned indicators are mostly geared towards policy making, sustainability labels also serve as a source of information for consumers. Naturally, these labels can also be used to gather information about the state of sustainability in a region or country. Sustainability labels are deemed a suitable means to facilitate ecological progress, especially with regard to clean water and energy, sustainable consumption, and climate protection [ 11 ]. Consequently, such labels are considered both by the ETIS and TOUERM frameworks. ETIS indicator A.2.1 can thus be used to gather information relevant for policymakers while relying on third parties’ assessments. It is important to note that there is great variety of sustainability labels, oftentimes leaving consumers left to wonder about their exact meaning, the applied standards, as well as their credibility in view of control mechanisms and enforcement [ 15 ].

Beyond institutional labels such as the EU Ecolabel (introduced by the European Commission to highlight low waste, energy efficiency and other sustainability factors [ 16 ], online platforms have introduced indicative labels as well. TripAdvisor’s GreenLeader award, for instance, was introduced in the US in 2013 and has been consistently expanded to other countries. Touristic accommodations interested in the award can apply through a questionnaire and must fulfil several standards to become a ‘GreenLeader’. These standards include, among others, towel re-use, recycling, and green roofing [ 17 ]. While institutional labels inherit credibility from the sponsoring institution, it is worth taking a closer look at the GreenLeader scheme: The award was devised in cooperation with the UN Environment Programme and has been critically acclaimed. The German Consumer Association highlights its high standards, independence, and transparency. The potential for widespread application stemming from TripAdvisor’s global presence makes the label a useful point of reference. Award-holding listings are decorated with a visual label on TripAdvisor, incentivising accommodations to apply [ 18 ].

2.3 Problems of sustainable tourism indicators

The compilation of indicators for sustainable tourism comes with several difficulties, as outlined in the European Tourism Indicator System (ETIS). While some of the data used in the ETIS is readily available from national statistics offices, it is complemented by additional data from surveys and other sources. Rasoolimanesh et al. (2020), for example, reviewed and assessed 97 academic studies on sustainable tourism in terms of relevance for the SDGs, related governance, stakeholders, and the subjectivity of the indicators [ 7 ]. Governance-related indicators were found in less than a quarter of all studies, stressing the importance of strong institutions to push evidence-based decision making. Another identified problem is access to and accuracy of data, which play an important role for implementing robust and evidence-based indicators. The European Commission is aware of the time and cost intensity of this approach and suggests not to collect annual data for all indicators, but rather to rely on three-year cycles [ 14 ].

Moreover, data reliability is an issue. Modica et al. (2018) asses the initial implementation of the ETIS in the Sardinian region of Cagliari and found that up to 52% of indicator data was missing [ 19 ]. Such issues and questions regarding definitions and measurability have, for example, led to the abandonment of indicator 8.9.2 (‘Proportion of jobs in sustainable tourism out of total tourism jobs’) from the Sustainable Development Goals framework [ 20 ]. These issues in data collection exist despite high-quality census data and experienced institutions and researchers in the OECD countries. Moreover, there are only few studies on sustainable tourism indicators outside Europe and North America [ 7 ]. This means that the data gap on sustainable tourism is increasing, as tourism industries in the Middle East, North Africa, South Asia, and South-East Asia are growing faster than those in Europe and North America [ 2 ]. Scholars in development studies suggest yearly or even quarterly reporting of data instead of relying on multiple-year resolution [ 21 ]. With traditional methods of data collection, however, this goal seems unattainable.

2.4 Sustainable tourism indicators and online data

To overcome such limitations, online data, which is often created as a byproduct of digital business operations, may complement existing data sources. For example, search engines’ primary function is not the accumulation of popular search terms and their development over time, but this byproduct offers valuable insights for areas such as disease control [ 22 , 23 ], unemployment statistics [ 24 , 25 ], or sales forecasting [ 26 ].

Through utilizing the above-mentioned strengths and under careful consideration of the associated risks, the use of big data from online sources can increase the quality of existing development evaluations and allow for assessing previously unmeasured outcomes [ 27 ]. To better understand how different research approaches aim to achieve this goal, an overview of research facilitating methods of online data collection and analysis in tourism research and related phenomena is presented in Table  1 .

For example, GPS trackers and mobile phone applications can be employed to identify patterns of tourist movements in great detail. In combination with app data, Buning and Lulla (2020) were able to differentiate rental bike use between local users and tourists [ 28 ]. Furthermore, segments of tourists that are likely to use specific trails and visit at peak times could be identified via matching app-generated location and demographic survey data [ 29 ]. Big data can also help for improved touristic capacity management. For instance, tourist flights, overnight stays, and sightseeing crowds can be analyzed and forecasted using big data. This also holds for air passenger demand based on flight price search/comparison websites [ 30 ], as well as for the nights spent at certain destinations by tourists with high spatial resolution [ 31 ]. Using booking and travel platform data, the density of touristic stays could be estimated for the area of the European Union and Great Britain [ 32 ]. Similarly, the number of Airbnb offers per tract in the United States could be calculated [ 33 ]. At the level of single touristic sights, visitor flows to World Heritage sites were successfully estimated using Instagram posts [ 34 ]. Focusing on the quality of businesses, geographic clusters of similarly rated restaurants and bars were determined using Yelp review data [ 35 ].

Furthermore, text sources such as online customer reviews offer the possibility to analyze tourists’ concerns and preferences in real-time. For example, attributes that are important to environmentally aware tourists have been identified using text mining techniques on Airbnb comments [ 36 ]. The presence and depth of environmental discourses can be assessed based on booking and travel platform data [ 37 ]. TripAdvisor comments, in particular, have been used to understand sustainable practices introduced by accommodations [ 38 ]. Twitter data has been used to assess the attractiveness of popular touristic sites [ 39 ]. Advanced analytics can also be applied to traditional data sources, for instance, to assess a destination’s potential for ecotourism using artificial neural networks [ 40 ].

In addition, online data has found application in the measurement and nowcasting of several other phenomena. For example, Twitter data has been used to assess damages after earthquakes [ 41 ]. The estimated size of Facebook ad audiences was used to calculate a wealth index at high spatial granularity [ 42 ]. Professional social network data was used to estimate gender gaps within industries and seniority levels [ 43 ]. Lastly, the effect of the Covid-19 pandemic on the property sector was estimated using property listing website data [ 44 ].

3 Methodology

Having reviewed the literature on approaches to measure sustainable tourism, we now introduce the methodology used for the algorithmic identification of GreenLeader accommodations based on publicly displayed data from TripAdvisor.

After establishing the methodological steps of the analysis in the following paragraphs, we investigate the research question whether statistical learning algorithms are able to reveal systematic differences between online profiles of sustainable and non-sustainable accommodations that allow for predicting the existence of a sustainability label.

3.1 Data collection

Data collection took place in November and December of 2020 via web-scraping. For each of the countries included in the analysis, links to all cities with TripAdvisor listings were extracted from the starting page. To obtain direct links to all listings, the algorithm looped through the city URLs and searched for hotel listings in each city. Using this approach, we collected a total of 260,348 individual accommodation listings from 37 European countries. These include all 27 EU member states as well as England, Northern Ireland, Scotland, Wales, Iceland, Liechtenstein, Monaco, Norway, San Marino, and Switzerland.

TripAdvisor provides a broad range of information about accommodations. There are three main sources of data: Page owners (i. e., the accommodation’s operators), consumers, and other/external websites. Next to basic information such as an accommodation’s name and location, page owners can detail their hotel’s size and class, provide contact information, and publish room features and property amenities. Consumers can rate the accommodation on several quality metrics and give an overall rating. They can further provide written reviews, comments, questions, tips, or upload photos. TripAdvisor supplements this information in two ways. First, the website incorporates information from other webpages (e. g., average prices from other websites such as Booking.com and Opodo ). The platform further calculates a score for the accommodation’s location in the city and counts the number of close-by restaurants and attractions using geographic data from Google. Secondly, TripAdvisor accumulates and publishes background data of commentators such as their chosen language as well as trip times and durations. In addition, the website uses customer feedback to create an accommodation ranking within each city.

From the individual listings’ web pages, we collected a total of 102 features, covering five categories of data: the hotel description, its class and ratings (a), prices and information about the size (b), scores calculated by TripAdvisor about the hotel and its location (c), measures of customer interaction (d), and hotel amenities (e).

These features comprise all readily available numeric variables of a listing as well as its binary and ordinal labels. Of the collected information, 15 features relate to hotel description and ratings (a), six variables inform about price segment and hotel size (b), three features relate to location (c). In addition, customer interaction (d) is included through 16 variables for text and image interactions (reviews, uploaded photos). Please note that these variables refer to the amount of user interaction (i. e. number of photos uploaded, number of reviews). The content of photos or reviews is not analyzed. Finally, 62 features inform about the availability of specific amenities and hotel features (e). A detailed summary and description of all variables is provided in Additional file 1 sections I and II.

After splitting the dataset into observations stemming from countries using the TripAdvisor GreenLeader award and those without, 215,806 labelled listings remain available for classifier training. Of these observations, almost 30% have complete information about all variables of interest; 70% have at least one missing value. The variables with most missing values are hotel class (49% missing), TripAdvisor-generated location scores (34% missing) and the number of available rooms (26% missing). The amount of missing data for these variables is critically large – imputation of missing data is not possible here. To make sure not to introduce any bias due to imputation, we therefore exclude all observations with missing values. This leaves 65,515 complete and labelled observations for model training. Comparisons between the full data set (including missing values) and the final data set are provided in Additional file 1 section III. Furthermore, we excluded four observations that contained erroneous records on the number of rooms available (for details, see Additional file 1 section IV).

3.2 Data processing

We undertook several steps of data pre-processing before further analysis. Some variables had to be transformed to deal with skewness. This allowed for the selection and final application of transformations offering the greatest improvement in classifier performance. As dependent variable, we focus on the TripAdvisor GreenLeader Award as a (binary) proxy for an accommodation’s sustainability. A detailed account of all variables’ distributions is provided in Additional file 1 section V.

3.3 Classification

As the next step, we set out to distinguish sustainable and non-sustainable accommodations. To do so, we employ a grid search of preparatory methods and algorithms to find the models with best predictive performance. In total, we ran 360 models based on 3 dimensionality reduction techniques × 5 resampling approaches × 6 data transformations × 4 classifiers. The models are evaluated using three metrics suitable for imbalanced classification tasks. In the following, the components of the analyzed modelling processes and the applied classification metrics are listed. This allows for an understanding of the grid of methods used. A more detailed overview of the pre-processing techniques used in the grid search is provided in Additional file 1 section VI.

Dimensionality reduction (i. e. principal component analysis) of the input data enables the chosen classifiers to work more efficiently and avoids issues of excess dimensionality. Three options are compared in the grid search: Use of the full dataset, use of the first four, and use of the first eight principal components created from all variables.

Moreover, resampling alleviates issues related to imbalanced data by altering the training dataset to have a more equal distribution of labels. The grid search compares modelling processes without resampling with processes using one of four resampling strategies. These are random oversampling, random undersampling, and S ynthetic M inority O versampling TE chnique ( SMOTE ), as well as the combination of SMOTE and undersampling.

Additionally, the grid search considered six types of data transformation to adjust the distribution of the input data. The use of the original data is compared to three straightforward (normalization, standardization and robust scaling) and two distribution-dependent transformations (Yeo-Johnson and Box-Cox).

Finally, four classifiers are compared across the grid: Logistic Regression, Linear Discriminant Analysis, Quadratic Discriminant Analysis, and the Random Forest. More details on the classifiers are provided in Additional file 1 section VII.

The metrics used to compare the modelling processes pay special attention to the imbalanced distribution of class labels. In particular, standard accuracy is not a viable metric in this case since a useless model predicting the majority class label for all observations would score highly (with accuracy equalling the proportion of majority class observations in the data, in our case 96%). Thus, the final metrics chosen for comparison are recall, the F2-measure, and the R eceiver O perating C haracteristic A rea U nder C urve ( ROC AUC ). This choice of metrics reflects the importance of recognizing sustainable accommodations despite the comparatively few available cases in the dataset. All metrics are calculated using tenfold cross-validation.

The TripAdvisor GreenLeader label is awarded to accommodations that fulfil requirements regarding specific sustainability practices. These accommodations make up 4% of all listings in the sample. Clear standards and thorough checks of the accommodations’ claims make the award a reliable source of information. As a first step of our analysis, we compare the relevant variables conditional on the accommodations’ award status (Additional file 1 section VIII). In the following, we highlight several key findings of this analysis.

4.1 Descriptive statistics

There are large differences between GreenLeader accommodations and others in terms of size and type of accommodation as well as user interaction variables (Fig.  1 ). For example, the median number of reviews received by GreenLeader accommodations (normalized by the number of rooms offered by the accommodation) is 10, while the median is only 6 for other accommodations (Fig.  1 (A)). GreenLeader accommodations also tend to be larger, with a median of 96 rooms vs. 28 rooms for other accommodations (Fig.  1 (B)). GreenLeaders also differ with regard to the number of uploaded photos (Fig.  1 (C)), the number of languages spoken by their staff (Fig.  1 (D)), the distribution of accommodation types (Fig.  1 (E)), hotel class (Fig.  1 (F)), and amenities (Fig.  1 (G)). In general, GreenLeader accommodations tend to be larger hotels with more stars, more and diverse amenities, and a higher level of user interaction as measured by reviews and uploaded photos. The variables displayed in Fig.  1 show only a subset of the more than 100 variables that could be derived from the platform data, but they illustrate the differences in publicly available features that appear to be correlated with an accommodation’s sustainability (see Additional file 1 section V for correlation matrices between the GreeanLeader badge and (a) numeric variables, (b) binary variables in the data set). In the following, we illustrate how unsupervised and supervised statistical learning techniques reveal structure in the data to distinguish groups of hotels and other accommodations that show higher or lower shares of sustainability.

Differences between GreenLeader and other accommodations in TripAdvisor data. ( A )–( D ) Distributions of continuous variables: reviews per room, Number of rooms, total number of photos per room and languages spoken by staff in GreenLeader (blue) and other (red) accommodations. ( E )–( F ) Proportion of accommodation types and hotel class (stars) in the groups of GreenLeader (left) and other (right) accommodations. ( G ) Proportion of amenities in the groups of GreenLeader (top) and other (bottom) accommodations. GreenLeader accommodations tend to be larger, have more user interactions, are of higher quality, and offer more amenities than other accommodations

4.2 Unsupervised statistical learning

Figure  2 displays the results of dimensionality reduction (principal component analysis) and cluster analysis (k-means) applied to the 33 continuous variables in the data set. The exploratory dimensionality reduction reveals that four components capture a large share of the variation in the data (see Additional file 1 section IX). Figure  2 (A) illustrates the loadings of the first four components. We have used hierarchical clustering to identify groups of variables with similar loadings (see dendrogram in Fig.  2 (A)). This analysis reveals that the variables tend to group into four clusters, which represent distinct types of information available about each accommodation. The variables Walker score, Restaurant score, and Attractions score all describe the location around the accommodation. A second group of variables describes quality indicators, such as the number of languages spoken by staff, hotel class (stars), and price. Variables related to the user rating (e. g., value, service, and average rating) form a third distinct group, while the fourth cluster describes aspects related to the size of the accommodation (number of rooms, number of reviews). As shown in Fig.  2 (A), the data has a structure that can be detected by unsupervised learning algorithms, which reflects interpretable concepts and trust cues known from other domains of the platform economy [ 45 ].

Unsupervised Learning techniques applied to TripAdvisor data. ( A ) Heatmap of principal component loadings of the four main principal components based on dimensionality reduction of the 33 continuous variables in the data set. The algorithm identifies four main dimensions in the data: accommodation size and user interaction (PC1), user rating (PC2), location (PC3), and quality (PC4). ( B ) Summary statistics of four clusters identified by k-means clustering. The accommodations can be grouped according to quality and user interaction variables. The clusters show different proportions of the GreenLeader outcome variable, varying from 2% to 19%. ( C ) Two-dimensional representation (PC1, PC2) of TripAdvisor data (10% sample) grouped in four clusters (panels) and GreenLeader/other accommodations (color). The unsupervised learning algorithms are able to split the data into distinct groups with varying proportions of GreenLeader accommodations

In addition to dimensionality reduction, we also use k-means clustering to identify groups of similar accommodations. At this point we want to underline that the cluster analysis is used as an exploratory statistical analysis in this study only. It serves as a way to illustrate that accommodations, which share certain features (among them the sustainability label), tend to co-occur in the data.

Note that many choices on the number of clusters are possible and justifiable (Additional file 1 section X). Here, we used four clusters for separating the data into prototypical groups. Figure  2 (B) provides summary statistics of the four groups. Cluster 1 represents a small subset of the data containing only 2% of all accommodations, 19% of which are GreenLeader accommodations. The group is characterized by high quality hotels with many rooms, a lot of user interactions (reviews and photos), many international guests (high share of English language reviews), and high prices. Cluster 2 also contains a disproportionately large share of GreenLeaders, high-quality, and expensive hotels. In contrast to Cluster 1, however, this Cluster’s accommodations are significantly smaller. The largest share of the data is captured by Clusters 3 and 4, which account for 83% of all accommodations but with an average share of only 2% GreenLeader accommodations. Compared to the other two clusters, the accommodations in these groups are substantially less expensive and smaller, they have significantly fewer user interactions, a lower share of English language reviews, and lower quality. The main differences between Cluster 3 and 4 are, again, size and quality: Cluster 3 accommodations have, on average, 50% more rooms than those in Cluster 4, but they score lower on price and quality characteristics (stars), internationality, and user interactions.

In summary, the total dataset can be split into (at least) four groups of hotels: large, high-class hotels in cluster 1, small, high-class hotels in cluster 2, low-price hotels in cluster 3, and other accommodations in cluster 4. Clusters 1 and 2 show the highest share of GreenLeader accommodations.

The cluster differences and the differences between GreenLeader and other accommodations are shown in Fig.  2 (C) in the dimensionality-reduced space of the first two principal components. Each panel represents a cluster from the table in Fig.  2 (B); circles indicate the position of the majority of the data points of the two accommodation types (GreenLeader vs. non-GreenLeader) in each panel. The plot shows that the different clusters take distinct positions within the two-dimensional space. Accommodations in Cluster 1 – the high-quality hotels with many rooms – score high on the first principal component (which loads heavily on variables related to a hotel’s size) but stretch along the second axis. Similarly, Cluster 2 shows a relatively high loading on the first principal component, but in contrast to Cluster 1, the data tend to show higher values on the second component (reflecting, for instance, better user ratings). Clusters 3 and 4 – the relatively low-price accommodations – both score low on the first component, that is, they represent smaller accommodations. Their main difference is the second principal component: Cluster 3 accommodations seem to be characterized by low user ratings, while Cluster 4 comprises more accommodations with a positive rating. Note, however, that while there are some differences in the positioning within each panel, there is also a large overlap between both groups. The differences are most pronounced in Clusters 3 and 4, which indicates that the GreenLeader accommodations differ more strongly from other accommodations in the realm of lower quality, low-price accommodations.

Overall, the application of the unsupervised learning techniques in this section reveals structures in the data on hotel descriptions, amenities, user ratings, and customer interactions that seem to be correlated with the presence of the GreenLeader sustainability label. We do not make any claims as to whether there exists any causal relationship between these features and the sustainability label; we only observe that they tend to co-occur in the data.

4.3 Classification performance and extrapolation

The aim of the study is to investigate whether algorithmic machine learning algorithms trained on publicly available data can identify and predict the extent of sustainable tourism with high temporal and spatial accuracy. To do so, we tested large sets of models and preprocessing techniques and their combinations. To compare the prediction performance of the models, we report cross-validated average effects on three established metrics: F2 score, Recall, and ROC AUC. In Fig.  3 , we provide the main results. Detailed results are provided in Additional file 1 section XI.

Classification performance and extrapolation. ( A ) Comparison of 360 classification models (90 models per classifier and panel) regarding three performance metrics: F2 score, Recall, and ROC AUC (each dot represents a model). QDA model (1) shows the highest F2 score, QDA model (2) achieves the highest Recall, and Random Forest model (3) the best ROC AUC score. ( B ) Confusion matrices of the three best performing models (1) to (3) according to the performance metrics (top panels) and a random draw model (4) (lowest panel). Inset: performance comparison between machine learning models (red) and 20,000 random draws (blue) according to F2 score, Recall, and ROC AUC. The machine learning models show a significantly better prediction performance than the random draw models. ( C ) Predicted share of GreenLeader accommodations in Europe (NUTS-2) according to the QDA model (1). The model predicts urban centres and several regions in West and North Europe to have the high shares of sustainable tourist accommodations

The grid search comprised a total of 360 models (i. e., 4 classifiers × 3 dimensionality reduction techniques × 5 resampling methods × 6 data transformations) and resulted in widely varying performances (Fig.  3 (A)). Depending on the assessment metric, several model specifications show similar performance. However, many models are not competitive. For example, a large number of Random Forest models score low on the Recall metric.

Fig.  3 (A) shows the best performing models according to three assessment matrices: QDA model (1) according to the F2 score (F2 score = 0.404), QDA model (2) according to Recall (Recall = 0.867), and Random Forest model (3) according to the ROC AUC score (ROC AUC = 0.887). To give an intuition into the quality of the prediction, Fig.  3 (B) shows the confusion matrices of the cross-validated average prediction accuracy of these models in comparison to a random draw model (4). Please note that each of the listed models uses the same set of variables. They differ with respect to the specification of the full machine learning pipeline, i. e. model choice and preprocessing steps.

High performance on the F2 score as seen for QDA model (1) indicates both, high degrees of precision in correctly identifying non-sustainable hotels and recall of sustainable accommodations. As the latter is at the core of this project, we also look at recall individually. Here, QDA model (2) is able to identify the largest percentage of true GreenLeader listings. Finally, Random Forest model (3) has the highest ability of discriminating between the two classes and hence scores highest in the ROC AUC metric.

From comparing the confusion matrices, but also the overall prediction performance of the different models displayed in Fig.  3 (A), it is obvious that there is not one single best choice. This is due to statistical uncertainty, but also because of the fuzziness of the sustainability concept and data used here. Furthermore, as Figs.  1 and 2 show, the data vary in terms of quality, user ratings, location, and interaction metrics. GreenLeader and other accommodations overlap in this regard. Hence, it is not surprising that machine learning models will not be able to perfectly differentiate between the groups.

Nonetheless, it becomes clear that the data allows for the training of statistical learning models that assess the sustainability of touristic accommodations with a level of accuracy far beyond random draw (Fig.  3 (B): confusion matrix of model (4) in the lowest panel). The overall predictive capability of the machine learning models trained on the TripAdivsor data is highlighted in the inset of Fig.  3 (B). It compares the distributions of the prediction performance of 20,000 random draws using the unconditional probability of 4% GreenLeader accommodations (blue) in the data with the performance of all machine learning models from Fig.  3 (A) (red). Since the employed metrics of F2 score, Recall and ROC AUC are less intuitive than simple accuracy, we use random draw as a comparative baseline. The median performance of the machine learning models outperforms random draw substantially: by a factor of 8 with regard to the F2 score, by a factor of 17 with regard to recall, and by a factor of 1.67 with regard to the ROC AUC. In other words, the publicly available TripAdvisor data is informative with respect to the sustainability of touristic accommodations. Footnote 5

To illustrate the granularity of the derived touristic sustainability measure, Fig.  3 (C) shows the predicted share of GreenLeader accommodations in the European NUTS-2 regions for countries with and without TripAdvisor’s GreenLeader program. Touristic sustainability follows a particular spatial distribution that seems to be related to the socio-economic structure in Europe. While it is beyond the scope of this study to explain the geography of touristic sustainability in detail, several observations can readily be made. First, the predicted share of GreenLeader accommodations is higher in metropolitan than in rural areas. For example, Berlin, Hamburg, Birmingham, London, Stockholm, Helsinki, Copenhagen, Vienna, Warsaw, Prague, Bratislava, Budapest, Zagreb, Madrid, and Sofia all seem to host much higher share of sustainable accommodations than the surrounding countryside. Moreover, sustainable tourism seems to be more widespread in North and West Europe than in East and South Europe. Footnote 6

In summary, our findings illustrate that the automatized and algorithmic prediction of sustainable tourism indicators is feasible. This can contribute to providing a cost-effective, accurate, and spatially granular assessment and tracking of sustainable tourism over time. The method showcased here can also have positive effects on transparency and thus support informed customer decisions. Moreover, it can help platforms and other organizations to identify sustainable accommodations.

5 Discussion

Tourism plays an important role in economic development across the globe and indicators are crucial to understand its development in different regions over time. Though heavily affected by the Covid-19 pandemic, the sector is expected to resume its growth path soon. With it, the environmental and social impacts of tourism will also continue to grow. Measuring and fostering sustainable tourism through effective indicators is thus a topic of global interest. Today, the global use of sustainable tourism indicators is limited by implementation costs and difficulties in data collection. In other areas, the inclusion of alternative data sources has been proven to be beneficial. This paper sets out to test the applicability of an alternative data source for the measurement of sustainable tourism.

5.1 Summary of the results

In collecting and analyzing data from TripAdvisor – one of the globally leading online tourism platforms – we show that it is possible to create a cost-effective, granular, and accurate measure of sustainable tourism based on publicly available online data. We compare differences between touristic accommodations holding TripAdvisor’s GreenLeader award and other accommodations regarding hotel quality metrics, user interaction, user rating, and location features. We conduct a grid search on a total of 360 machine learning pipelines to differentiate sustainable from non-sustainable hotels based on the high-dimensional platform data. The performance of the machine learning models is substantially better in identifying sustainable hotels than the baseline model of unconditional expectation. Footnote 7 In other words, machine learning models trained on online platform data can make a contribution in assessing the state of sustainable tourism in countries and regions as the presence of the sustainability award shows correlation with various other characteristics. Note that some caution is due since these correlates are – in most cases – not formative for the accommodation’s sustainability.

For example, we find that more expansive and larger hotels with more rooms and customer reviews (see Fig.  2 (B)) tend to have a higher share of GreenLeader badges. We want to state explicitly that we do not assume such features to be causal for an accommodation’s sustainability. However, both could, nonetheless be related. It might be the case that larger hotels have more resources available to deal with the requirements of sustainability labels. It could also be that larger hotels are more dependent on web traffic from online platforms and therefore invest more resources in obtaining badges from the platform. Furthermore, hotels in the premium segment with a focus on quality and user satisfaction might want to utilise the sustainability label as an additional quality criterion. While such factors are not displayed in the large-scale online platform data, the observable correlations between prices or hotel size and the sustainability label seem to capture such patterns.

The approach presented here reveals factors that correlate with the sustainability label, but it should not be employed to assess individual accommodations’ degree of sustainability based on the correlations alone. However, the approach may well serve to statistically assess countries’ and regions’ degree of sustainable tourism with high temporal and spatial granularity, for example for the purpose of nowcasting sustainability indicators or for extending the geographical coverage of such indicators to places without ‘ground-truth’ data on sustainable tourism.

It is important to highlight that the purpose of the analysis is not in predicting and identifying individual hotels as sustainable, but on providing a probabilistic assessment about the distribution of sustainable hotels in a region (as shown in Fig.  3 (C)) derived from the prediction model, which uses individual-level data. In that sense, our analysis is similar to medical studies that aim to quantify population-wide health risks. Such assessment consider individual-level risk factors such as age, obesity or nutrition to calculate an estimate of the share of the population being at risk of cardiovascular diseases, but they do not aim to make predictions on the level of individuals patients.

5.2 Theoretical implications for the applicability of big data in tourism research

Past studies have employed text analysis to understand user preferences and discussions [ 36 , 37 ]. Accommodation-specific data has been used for the estimation of visitor capacities in neighborhoods [ 32 ]. In contrast, this paper focuses on the classification of accommodations by sustainability. We add to the literature by utilizing accommodations’ own presentation and associated user interactions to gain information about their sustainability practices. The creation of a large numeric dataset allows for the training of common machine learning algorithms. Using available ground truth data for classifier training, the quality of the analyzed classifiers could be assessed in detail. This approach was followed in prior work where it allowed for comprehensive model assessment using true values and labels [ 9 , 31 ]. In doing so, we were able to confirm the applicability of travel platform data for use in tourism statistics. In particular, the low cost of data collection and high spatial resolution of the data could be shown. It should be noted that the estimated models do not attempt to create an alternate definition of sustainability through using new causal factors. Instead, the true label is determined trough physical measurements of energy, waste and water. Here, it is estimated using correlated factors available in the online platform data. The legitimacy of using of private company sources for the collection of data for policy making will remain an important open question. Our study helps to underline the potential of the large-scale analysis of online data as a valuable method for research in sustainable tourism, and sustainability studies in general.

5.3 Implications for tourism practitioners and policy makers

Tourism platforms can make use of our findings in multiple ways. Listings, which are predicted to employ sustainability practices but do not (yet) carry the award, can be actively approached, and be made aware of the GreenLeaders program. These accommodations can, in turn, benefit from increased visibility and increase their attractiveness for environmentally conscious consumers. More visibly communicating sustainability efforts could hence become a competitive advantage. This could in turn increase pressure on competing businesses to also invest in sustainable practices.

For platforms, cooperation with researchers and statistics departments is an effective way of underlining environmental and social efforts. Policy makers can benefit from the availability of inexpensive, granular, and up-to-date data. For policy makers in countries with established frameworks for sustainable tourism statistics, the higher frequency and granularity of reporting can offer important supplementary information. Through the comparison with traditional data sources, model accuracy can be monitored, and models can be adjusted where needed. In countries without established frameworks, the proposed methodology can offer estimates when traditional methods of data collection are prohibitively costly to implement, or where important infrastructure is not available.

These estimates can guide policy makers towards initial interventions and allow for detailed monitoring of the associated effects. In relation to existing frameworks of sustainable tourism indicators, the implications are twofold: For the ETIS framework, which collects data on the percentage of accommodations using a voluntary sustainability label under its indicator A.2.1., the described models can offer a remedy for difficulties in data collection for this indicator. For other frameworks, inclusion of the presented indicator can be discussed to create a more complete picture without significantly increasing data collection efforts.

5.4 Practical implications and limitations

It is important to note that the proposed methodology cannot replace accommodation surveys and other statistically robust modes of data collection. It should instead be considered as a complementary source of information or a first estimate when no other data is available. The presented methodology is heavily reliant on the quality of the big data sample. Systematic differences between accommodations listed on TripAdvisor and those that are not should hence be a focal point of further research. Another limitation might be potential confounding factors on the regional level that we could not control for. Our analysis solely uses variables on the level of individual accommendations. For example, it might be that legal requirements or cultural values in some regions affect the share of hotels with a sustainability label.

In addition, omitting incomplete observations may further limit the validity of the training sample and alternatives should be explored in greater detail. Future research should furthermore explore the feasibility of other, freely available data sources. Both, accommodation characteristics and the sustainability label, are taken from TripAdvisor. Characteristics could be collected from a range of other travel platforms. An alternative label for classifier training could be created from other well-established sustainability programs. Although a broad range of classifiers and preparatory steps was compared, other approaches may yet outperform the methods included in the analysis. Lastly, this paper treats sustainability as a binary variable, separating accommodations into those following any sustainability practices and those following none. Additional research could explore whether the degree of sustainability practices, expressed for example by the differentiated TripAdvisor GreenLeader labels from ‘partner’ to ‘platinum’ level, can also be modelled.

On a more general note, the methodology suffers from shortcomings common to all big data approaches. Although new forms of data collection and analysis have filled data gaps and increased our understanding of social, economic, and touristic activity, there are justifiable concerns about the use of such (alternative) data sources. Machine learning methods have the outstanding ability of combining many weak signals into predictions for labels or variables. These signals do not need to be in line with theoretical groundwork and in practice will often not be. For this reason, some algorithmically derived signals would not have been included as relevant explanatory variables in traditional modelling [ 21 ]. Algorithmic prediction models also reproduce existing biases in the data. In our case, not all hotels that would fulfil the criteria to obtain a GreenLeader badge might have actually applied for the label – a bias that our model cannot control for. Additionally, the use of big data and machine learning did cause concerns regarding privacy issues and the possibility that algorithms might pick up unethical or discriminatory practices present in historical data sets [ 46 ]. However, with the advantage of improved detection of patterns in the data comes the risk of disregarding underlying theory altogether [ 47 ].

6 Concluding note

This paper set out to analyze whether online platform data can be used to inform about sustainable tourism. Sustainability in tourism describes the goal of balancing economic, social, and environmental factors. The complexity of this goal requires diverse sources of information to monitor progress and inform decision-makers. The corresponding data collection processes offer room for improvement with regards to both cost and frequency of reporting. Research is often focused on Europe, the world’s largest tourism market, but despite freely available national census data and experienced practitioners there are difficulties in the implementation of existing indicator frameworks and the collection of relevant data.

In this paper, we offer an alternative to existing methodologies through the use of travel platform data. In this extended pilot, the platform TripAdvisor was used as the sole source of data. Tourist accommodation data was collected through automated scraping of TripAdvisor listings from 37 European countries. Following several data exploration steps, we developed a supervised learning model for the assessment of accommodations’ sustainability. Ground truth data was sourced from TripAdvisor (where the GreenLeader award is available in 27 of the 37 countries). The final model was chosen from a set of four supervised learning techniques, each building upon combinations of dimensionality reduction, resampling, and data transformation methods. The imbalanced nature of the classification task added difficulty. With less than 4% of training data belonging to the sustainable class, use of the accuracy metric would have been misleading. Model comparison was hence performed using the F2-metric. Recall and the Receiver Operating Characteristic Area Under Curve metric. A classifier using quadratic discriminant analysis was chosen as the final model. Overall, prediction quality was high but not excellent, with all methods struggling to successfully recognize observations from the positive class without significantly increasing the proportion of false positive predictions.

All findings are subject to limitations, the most important being the yet unconfirmed validity of the collected sample for the population of accommodations in each country. This representativeness of the sample and the use of other readily available platform data sources should be the focus of further research.

Availability of data and materials

The datasets analysed during the current study and code produced for the analysis are available in the author’s GitHub repository: https://github.com/felixjhoffmann/SustainableTourism/ .

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In this study, we make the comparison of the performance of our models with a simple random draw model only to illustrate that there is an informative signal contained in the online platform data, which has predictive capacity that can be identified via statistical learning algorithms. We do not want to claim that a random draw model would be the only alternative modelling strategy. In fact, many different types of models can lead to good predictions, as we show by using a set of 360 different models in total.

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The performance of the models is compared to a random draw only for the purpose of illustrating the predictive capacity in the data, see footnote  5 .

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Authors’ information

Felix Hoffmann holds a MSc Industrial Economics from Technische Universität Berlin and a BSc Economics from the University of Amsterdam. His research revolves around the use of alternative data sources in public economics. He works in industry as an analytics and data consultant. Fabian Braesemann is a Departmental Research Lecturer in AI & Work at the Oxford Internet Institute, University of Oxford. In his research, he applies social data science methodologies on large-scale online data to understand market and information dynamics in a digitally connected world. Furthermore, he is Founder of the Datenwissenschaftliche Gesellschaft Berlin , a company focused on applying data science to economic, policy, and development problems. Timm Teubner is Professor at the Einstein Center Digital Future at Technische Universität Berlin. He holds a Diploma degree in Industrial Engineering & Management and a doctoral degree in Information Systems from Karlsruhe Institute of Technology (KIT). His research interests include online platforms and multi-sided markets, reputation systems, trust in digital services, online auctions, as well as Internet user behavior. His research has been published in journals such as the Journal of the Association for Information Systems, Information & Management, Electronic Markets, International Journal of Electronic Commerce, Business & Information Systems Engineering, and Economics Letters.

The authors received no financial support for the research, authorship, and publication of this article. Open Access funding enabled and organized by Projekt DEAL.

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FH and FB contributed equally to this study. FH produced the code for web-scraping, unsupervised learning and model comparison. He created the initial draft of the article. FB conceptualised the research, supported the statistical analysis and produced the enclosed visualizations, and. TT guided the writing process and structured the final presentation of results. All authors wrote and approved the final manuscript.

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Correspondence to Timm Teubner .

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Felix J. Hoffmann and Fabian Braesemann contributed equally to this work.

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Hoffmann, F.J., Braesemann, F. & Teubner, T. Measuring sustainable tourism with online platform data. EPJ Data Sci. 11 , 41 (2022). https://doi.org/10.1140/epjds/s13688-022-00354-6

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BOOKING VS. EXPEDIA, THE FIGHT OF TWO E-TOURISM PLATFORMS

Booking and Expedia dominate the field of global tourism on the Internet. Their domain is online booking of flights, hotels, apartments, cars etc. via different e-tourism platforms.

Booking Holdings owns Booking.com. It has 23,000 employees, earns nearly $12.7 billion in sales and had a net income of $2.3 billion in 2017.

The challenger is Expedia with 22,000 employees, nearly $10 billion in sales and a net income of $378 million in 2017. According to a recent survey from Hotrec , Booking Holdings is the leader in online booking of hotel rooms in Europe with a 60% market share in 2016, followed by Expedia Group with 20%.

Both e-tourism platforms have an impressive galaxy of brands. With nearly 200 travel booking sites in nearly 75 countries, Expedia Group works in almost all fields. Either as an online travel agency (Expedia), hotel comparison (Trivago), hotel reservation website (Hotels.com) or business travel (Egencia, Traveldoo).

The same goes for Booking Holdings, which is represented as a booking site Booking.com or as a search engine Kayak as well as an online travel agency Priceline. If we focus on the last quarters, Booking Holdings wins the round of profits. In the second quarter of 2018, the group registered a net income of nearly $1 billion, compared to 1 million for Expedia Group.

This difference in profitability can be explained in particular by significantly different economic models. At Booking Holdings, agency revenue is dominant, with nearly $9.7 billion out of a total of 12.7 billion in 2017.

That is to say Booking Holdings is defined mainly as a third platform that connects customers and tourism professionals as a pure marketplace. This highly profitable model requires above all marketing and technological investments. At Expedia Group, merchant revenues are in the majority, with nearly $5.4 billion out of a total of $10 billion last year. Thus said, Expedia Group works mainly in terms of resale, like a distributor.

The main asset of Booking Holdings remains Booking.com. Its hotel reservation platform concentrates the majority of its revenues and has a strong hitting power.

“We have 29 million rooms available around the world, spread across 140,000 destinations and 230 countries, and Booking.com allows nearly 1.5 million reservations per day in 43 languages,” says Vanessa Heydorff, Executive Director of Booking.com in France, Spain and Portugal.

According to reports, Booking.com made more than 2 million hotel partners in 2017. In comparison, Expedia Group claims “only” 590,000. Moreover, when we compare the number of nights reserved, the victory comes back to Booking Holdings, which achieved double numbers in 2017 than Expedia Group.

However, Expedia Group has a secret weapon: packaging. The Expedia Travel Agency combines its hotel offerings with those of its 550 partner airlines, 25,000 travel-related activities and cruise companies.

“We can provide a complete trip for online customers, from flights to hotel rooms to car reservations, and we can go further than securing a dry booking,” says Jean-Philippe Monod, vice president of Expedia Group.

In June 2018, the Expedia brand launched its advantage add-on service, which saves up to 42% on the price of a hotel booked alone and offline, provided you book a flight, a car or another package at the same time.

The commissions charged as an agency are between 10 and 20 percent on average. Booking and Expedia are often accused by tourism professionals to cut their margins. “Some hoteliers complain, but they just have to calculate how much it would cost them to do it all by themselves. Visibility on the Internet is not free. We innovate efficiently to enable them to generate transactions and we buy a lot of advertising from Google,” Jean Philippe Monod replied.

Similarly, Booking Holdings is investing heavily in Google’s and Facebook’s targeted advertising. Its web marketing budget reached $3.4 billion in 2016.

Finally, these two e-tourism platforms are constantly investing in the AI and personalization of offers, the heart of their business. Expedia Group has announced an investment of nearly 1.3 billion euros in 2018 in these areas.

They are also forming strategic alliances. In June 2018, Booking.com invested $500 million in Didi Chuxing, the Chinese Uber, to interconnect their respective platforms. The company also invested $450 million in the Chinese marketplace dedicated to Meituan-Dianping services (a diversified online platform, in the hotel, catering, entertainment, with group purchasing service) in October 2017.

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THE HOLIDAY TRAVEL INDUSTRY IN CHINA IS FLOURISHING

Cruise ships are back on track in germany, istanbul airport welcomed 70% of visitors, hotel technologies are reshaping the industry, latest reviews, tourism review online magazine 1 / 2023, tourism review online magazine 1 / 2022, tourism review online magazine 1 / 2021.

Agoda to Promote Tourism in Goa Under New Deal – India Report

Bulbul Dhawan , Skift

April 8th, 2024 at 11:00 PM EDT

Goa is seeking to reinvent itself as a year-round destination. Its partnership first with MakeMyTrip and now Agoda is a step towards being known for more than just its beaches.

Bulbul Dhawan

The Skift India Newsletter is your go-to platform for all news related to travel, tourism, airlines, and hospitality in India.

Online travel platform Agoda has signed an agreement with Goa Tourism to promote the coastal state. The collaboration aims to highlight various experiences that the state can offer to different travelers. 

As part of the partnership, Agoda will be creating custom pages and travel guides that would highlight Goa and its hidden gems. It will also be using its social media platforms for the promotion of the destination. 

Goa beyond beaches: In February this year, the government of Goa partnered with online travel agency MakeMyTrip . As part of the agreement, MakeMyTrip will focus on the inland offerings of the state, promoting it as a year-round destination. 

Goa has been focusing on promoting itself beyond its beaches as part of its regenerative tourism initiative and ‘Goa Beyond Beaches’ vision. 

Ooty Most-Searched Leisure Destination for Domestic Summer Travel

Travel insights by online booking platform Booking.com has revealed that Ooty is the most-searched domestic leisure destination this year among Indian travelers. Booking.com Travel Predictions 2024 have revealed that 57% of the Indian travelers are looking to undertake long domestic trips between April 15 and July 15 this year. 

Dubai continues to be the most-favored international destination for summer, followed by Singapore, London, Paris, and Bangkok. 

Santosh Kumar, country manager for India, Sri Lanka, Maldives and Indonesia at Booking.com said that there has been a significant change in the way Indians are approaching summer travel in recent years. “While Indians are exploring both domestic and overseas destinations, we are seeing travelers gravitating towards culturally significant or leisure-oriented destinations with a growing desire for immersive experiences,” he added. 

IndiGo Announces Three Routes in Kerala, Lakshadweep

Budget airline IndiGo has announced three new routes that are set to be operational from May this year. The new routes will connect Kozhikode with Kochi in Kerala; Kochi in Kerala with Agatti in the Lakshadweep archipelago; and Kozhikode with Agatti via Kochi, the airline has said. 

Agatti is the latest destination to be added in IndiGo’s network, after the carrier began operating daily flights there from Bengaluru on March 31 . Vinay Malhotra, head of global sales, IndiGo, said that these new flights would contribute to travel, tourism, and trade in the region. 

The airline’s move is in line with the government of India’s push to tourism in Lakshadweep recently.

Jayaraj Shanmugam Appointed Head of Global Airport Operations at Air India

Air India has appointed Jayaraj Shanmugam as its head of global airport operations, and the appointment is set to come into effect on April 15. Shanmugam is set to join the carrier from Bangalore International Airport Limited (BIAL), where he is the Chief Operating Officer. 

He has previously worked with airlines such as Singapore Airlines, Qatar Airways, and Jet Airways. At Air India, he will oversee the airline’s airport operations worldwide and ensure coordination and efficiency to make the passenger journey smooth. 

RCI Launches First-Ever Cruise Exchange Program in India

Timeshare exchange company RCI has launched its first-ever cruise exchange program in India on the occasion of its 50th anniversary. The program will allow members to use their timeshare ownerships in order to access discounted rates on cruise bookings worldwide. 

As part of the program, active members of RCI would be able to book from a range of cruise options, including different destinations, cruise lengths, and cabin categories, RCI said in a statement. 

Air India Express Launches Bag Track and Protect Services

Air India Express has launched ‘Bag Track and Protect’ services that would let passengers track their baggage. The services have been launched in partnership with Blue Ribbon Bag and would need to be pre-booked by passengers as add-ons. 

This service would also facilitate expedited return of delayed baggage and allow passengers to get compensation in case of delayed baggage not being delivered within 96 hours of landing at the destination. 

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Tags: agoda , air india , Air India Express , airlines , aviation , aviation industry , aviation news , booking.com , cruise , domestic leisure travel , dubai , goa tourism , hotels , india , india outbound , india travel , indian airlines , indigo , indigo airlines , Leisure , leisure travel , leisure travelers , makemytrip , online , online booking , online travel , online travel agencies , online travel companies , skift india report , tourism , Travel Trends

Photo credit: Goa has been a popular Indian destination for a long time, known mostly for its beaches. Incredible India website

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10 Disappointing Travel Destinations, According to Travelers

Posted: April 6, 2024 | Last updated: April 6, 2024

Traveling is an adventure that many people dream of. Exploring new places, trying different foods, and immersing oneself in different cultures can enrich experiences. However, some travel destinations may not meet the hype or expectations. Recently on an online platform, people have shared several overrated travel destinations worldwide that may need more time and money.

1. Gatlinburg, Tennessee, USA

Gatlinburg, the misunderstood gem of travel destinations, has been misjudged by many. The city’s popularity is often credited to its thrilling carnival rides and famous attractions, but there’s more to Gatlinburg than meets the eye. A seasoned traveler, having witnessed the beauty of the surrounding mountains, urges visitors to look beyond the city and behold the exquisite natural wonder. Diverse and vibrant lush forests, winding trails that lead to mystical destinations, and awe-inspiring views that leave one spellbound are hidden treasures waiting to be discovered.

2. Baltimore, Maryland, USA

Per one user, Baltimore fell short of their expectations and was deemed overrated. Anticipating a more exhilarating and dangerous adventure, the user was left wanting as all they did was visit the aquarium.

3. Zurich, Switzerland

As one traveler noted, Zurich, a city with a reputation that precedes itself, is only some of it’s cracked up to be. According to the person, there are specific guidelines to follow in this urban landscape, and the key to accessing all that the city has to offer is enough money. Sundays can be especially challenging, as most places outside the tourist hotspots near Hauptbahnhof will be shuttered, leaving visitors with limited options.

4. Cairo, Egypt

Egypt, as described by one traveler, has its ups and downs. Cairo, unfortunately, found itself at the bottom of the list, with the traveler finding it polluted, congested, and lacking in attractions. Disappointingly, even the Giza pyramids’ base had fast-food chains like KFC and Pizza Hut. However, the traveler’s three-day voyage up the Nile shone brightly as they explored the temples and ancient sites that embody the essence of Egypt’s allure.

5. Dubai, United Arab Emirates

One individual who has spent their entire life in Dubai views the city differently. They deem it grossly overrated, citing a need for more enthralling activities besides indulging in shopping, which is the city’s primary focus. Their memory of a hailstorm that ravaged the city several years ago still stands out as the most intriguing occurrence they can recount.

6. Los Angeles, California, USA

Someone’s assessment of Los Angeles leaves no room for ambiguity. To them, it is an overrated tourist spot with an unwieldy urban landscape that resembles a “Mad Max apocalyptic hellhole the size of England.” The remark conveys a sense of disappointment and frustration with the city’s over crowdedness and navigational challenges, all of which underscore their belief that the city is not as glamorous as its reputation suggests.

7. Miami, Florida, USA

Beneath the glitz and glamour of Miami lies a stark reality – at least, that’s what one user thinks. While the city boasts breathtaking beaches, a melting pot of cultures, and sunny skies, it is marred by a high crime rate and an air of snobbery. The club scene and shopping districts may be alluring, but the prices often leave one feeling robbed. Despite loving Miami and having lived in other states and even Europe, the user believes the city is only some of what it’s hyped up to be for the average person.

8. Boise, Idaho, USA

Boise, Idaho – it’s been hailed as a hidden gem of the West by many, but one user begs to differ. In their opinion, the city is nothing to write home about, with its hot and drab landscape lacking any noteworthy highlights. And if that’s not enough to dampen your spirits, brace yourself for the crazy Mormons and Republicans that supposedly run the city, along with an invasion of creepy crawly spiders. According to the user, Boise is anything but underrated and is, in fact, a tad worse than what most people may imagine.

9. Damascus, Syria

Some travelers expressed their worries about the ongoing conflicts and security issues in Syria, particularly in Damascus, which could make the trip unsafe or unpleasant. Other comments mentioned that the city might not have enough tourist infrastructure, such as hotels, restaurants, and transportation, to accommodate large numbers of visitors.

Some travelers noted that the city’s cultural heritage sites had been heavily restored, which can diminish the authenticity and charm of the experience. Additionally, the city may be heavily influenced by tourism, making it feel less genuine. A few comments also mentioned that the city could be polluted and overcrowded, particularly in the peak tourism season, which could be a turn-off for some visitors.

10. New Delhi, India

New Delhi – is a city full of vibrant colors, rich history, and mouth-watering cuisine. Well, not according to this tourist. In a fiery rant, they express their profound disappointment with the city, branding it as an overrated travel destination. Crowded streets, a lack of fun activities, and the constant stench of burning garbage, cow dung, and open sewers – it’s a far cry from the idyllic picture most people have in mind. And don’t even get them started on the food, which they claim is cooked in unhygienic conditions, and the relentless hustle and bustle of cab drivers, merchants, and restaurant owners preying on unsuspecting tourists. All in all, it could have been a better experience, in their opinion.

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Guesty snaps up $130M at $900M valuation to help property managers list on Airbnb and beyond

Travel and tourism are very much back on the map for consumers and the business world. Now, to underscore that surge, one of the startups building software in the space has closed a big round of funding. Guesty , a platform that lets accommodation managers manage their business online, including on platforms like Airbnb and Vrbo, has raised $130 million.

Sources confirmed to TechCrunch that the Series F values Guesty at around $900 million post-money.

The company, based out of New York with roots in Israel, says its revenue has increased 5x in the last three years, and it expects to turn profitable this year. The company did not specify actual revenue figures.

KKR is leading this round, with Apax Funds, Inovia, BDT & MSD Partners and Sixth Street also participating.

To put the funding into some context: Post-COVID, the global travel and tourism sector has been on a strong rebound, and is expected to generate record-high sales of $11.1 trillion in 2024, according to the World Tourism and Travel Council . That would be despite tourism in the U.S. and China still catching up to pre-pandemic levels.

For Guesty and its competitors, this upswing has played out in the form of a number of nine-figure funding rounds. Guesty last raised a Series E of $170 million that valued it at $690 million in August 2022. Guesty's close competitor, Hostaway, raised $175 million last May, marking its first big funding round. Within a day of that news, GetYourGuide raised a monster $194 million at a $2 billion valuation.

Mews, which like Guesty builds SaaS but for hoteliers, raised $110 million at a $1.2 billion valuation in March. This trend is a strong reminder that investors are still willing to sign term sheets in the right circumstances.

"It's definitely a tough market. In every round I've raised, I would always get 40 no's for every yes," Amiad Soto, Guesty’s CEO, told TechCrunch. Now, with Guesty "closing in on becoming profitable this year," he joked that "I still got 40 no's, but also a lot more yes's."

Soto, who co-founded Guesty with his brother Koby (who is no longer with the company), plans to deploy the funding across a few different areas.

First of all, the company wants to continue expanding its existing platform for current customers. That business today already covers "hundreds of thousands" of properties, and it will double down on the one-stop-shop concept that a lot of other B2B tech companies are pursuing today, Soto said. He declined several times to give me a more specific figure on the number of properties its platform covers.

The platform provides the basics of listing and booking management software, analytics, accounting tools, the ability to manage multiple properties and CRM features. More recently, it added enhanced payment services and capital advances (built in-house, not white-labeled from third parties, Soto said), damage protection services (dipping into the area of insurance), website building tools and price optimization services that all integrate with the dozens of interfaces where a property manager might list a room or home for travelers to book.

Second of all, the main focus to date for Guesty has been short-term lets — properties booked typically for less than a month — but the company now wants to expand into the medium-term space. This will open it up to more people who might be living temporarily in a location for a specific work assignment, for example.

Third of all, Soto said Guesty wants to consider more acquisitions. The market may not be looking favorable for all startups right now, but that is less a comment on the strength of startups (talent and innovations) than it is on the state of venture capital right now. There are a lot of very interesting companies out there that might be ready to entertain acquisition offers that provide less bullish valuations.

Stephen Shanley, partner and head of Europe Tech Growth at KKR; Lauriane Requena, a principal at KKR Tech Growth; and Dennis Kavelman, a partner at Inovia Capital, are all joining the board with this round. “Guesty is a best-in-class operator and one of the clear leaders in the property management sector," Shanley said in a statement. "There has been a significant shift towards the short-term rental market, and this investment will support the company as it continues to meet that growing customer need."

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Measuring sustainable tourism with online platform data

Felix j. hoffmann.

1 Trust in Digital Services, Technische Universität Berlin, 10623 Berlin, Germany

Fabian Braesemann

2 Oxford Internet Institute, University of Oxford, OX1 3JS Oxford, UK

3 Datenwissenschaftliche Gesellschaft Berlin, 10117 Berlin, Germany

Timm Teubner

Associated data.

The datasets analysed during the current study and code produced for the analysis are available in the author’s GitHub repository: https://github.com/felixjhoffmann/SustainableTourism/ .

Sustainability in tourism is a topic of global relevance, finding multiple mentions in the United Nations Sustainable Development Goals. The complex task of balancing tourism’s economic, environmental, and social effects requires detailed and up-to-date data. This paper investigates whether online platform data can be employed as an alternative data source in sustainable tourism statistics. Using a web-scraped dataset from a large online tourism platform, a sustainability label for accommodations can be predicted reasonably well with machine learning techniques. The algorithmic prediction of accommodations’ sustainability using online data can provide a cost-effective and accurate measure that allows to track developments of tourism sustainability across the globe with high spatial and temporal granularity.

Supplementary Information

The online version contains supplementary material available at 10.1140/epjds/s13688-022-00354-6.

Introduction

The tourism industry is of tremendous economic relevance, accounting for an estimated 10% of global GDP in the years before the Covid-19 pandemic [ 1 ]. Though strongly affected by restrictions and other uncertainties in international travel, the sector is expected to resume growth and fully recover throughout the coming years [ 2 ]. Tourism is also of high importance for economic development, which is underlined by its inclusion in the United Nations’ Sustainable Development Goals (SDGs), where it is directly mentioned in three of the 17 goals [ 3 ]. Between 2008 and 2018, the relative importance of tourism for the respective country’s GDP increased in 43 out of 70 countries that report to the UN [ 4 ]. At the same time, it has been criticized to have adverse environmental and social effects [ 5 ], causing 8% of the global carbon emissions in 2013 [ 6 ]. To balance the economic, environmental, and social impacts of tourism, the relevance of sustainable tourism becomes evident [ 7 ]. In order to monitor and manage tourism in view of sustainability, granular and accurate spatio-temporal data is needed. There is a growing number of indicator frameworks for the tourism sector that aim to measure sustainability, with the majority of successfully implemented projects focusing on the European market. Current data collection methods, however, are often costly and yield piecemeal results. Ideally, improvements would allow for a cost-efficient implementation in both high income and developing countries, where tourism is growing faster than in more mature markets [ 8 ]. In the past, data collection could often be improved by means of tapping into alternative data sources. Examples include the assessment of the digital gender gap based on social network data or the assessment of poverty using mobile phone records [ 9 , 10 ]. Besides lowering the cost of data collection, such approaches allow for the calculation of indicators in near real-time, rather than relying on year-long cycles.

The present paper expands on such approaches by exploring the feasibility of using online platform data to assess the sustainability of tourism throughout Europe. Specifically, we pose the following research question:

Research Question : Can statistical learning techniques using data from an online tourism platform predict tourist accommodations as sustainable, as indicated by a sustainability label?

We use a machine learning approach on online platform data alone to answer the research question. Thus, our study is different from others that discuss rule-based classification systems used by traditional sustainability labels. These labels require detailed information about waste, water use, and other factors to determine an accommodations’ level of sustainability. While highly accurate and true to the causal relationships of sustainability, the corresponding data collection procedures are expensive and not feasible quickly at scale. The classifiers introduced below rely on correlated but not necessarily causal factors. They can hence not fully replace the physical measurement of factors determining sustainability. Instead, the models’ wide applicability and low cost of calculation can serve to complement existing labels, allow for nowcasting of sustainability indicators, and increase the geographical coverage of such indicators.

The analysis is based on a unique dataset of TripAdvisor.com accommodations and the platform’s GreenLeader award. We contribute to the literature in two ways. First, we identify and outline systematic differences between award-holding and non-holding accommodations using public platform data. Secondly, making use of supervised machine learning techniques, we identify sustainable accommodations with reasonable accuracy also in regions that have not implemented the platform’s GreenLeader award. In doing so, we show that large-scale monitoring of sustainable tourism using online platform data in near real-time is feasible. The approach presented here provides a cost-effective and accurate measure with high spatial and temporal granularity, which could be rolled out to track sustainable tourism across the globe.

The remainder of the paper is structured as follows. Section  2 provides an overview of related work and past projects making use of alternative data sources for development statistics in general, and for the assessment of sustainable tourism in particular. Following this, Sect.  3 introduces our methodology, the data set, as well as criteria for model evaluation. Next, Sect.  4 presents our results. In Sect.  5 , we then discuss our findings in view of practical and theoretical implications and conclude the paper with limitations and suggestions for further research.

Background and related work

Measuring sustainability in tourism.

The majority of current sustainability practices in tourism result from regulation and economic incentives rather than intrinsic motivation [ 11 ]. Accordingly, policy makers need to define and monitor sustainability in tourism to achieve change. A number of frameworks aim to supply this information by means of indicators (which represent a core element of development research and a central pillar of the SDGs). Next to the UN’s SDGs, tourism-specific indicators were devised by, among others, the World Tourism Organization (UNWTO), 1 the Global Sustainable Tourism Council (GSTC), 2 the European Commission, 3 and the European Environmental Agency (EEA). 4 Tourism finds direct mention in Goal 8 (‘Decent work and economic growth’), Goal 12 (‘Responsible consumption and production’), and Goal 14 (‘Life below water’) [ 3 ]. Note that the use of indicators for the measurement of sustainability in tourism dates back almost three decades, when the World Tourism Organization began to promote their use for policy-making and destination management [ 12 ]. Today, the Global Sustainable Tourism Council sets a widely used and accepted standard for sustainability of private companies in tourism based on performance indicators [ 13 ]. Moreover, the European Commission first published its ‘European Tourism Indicator System’ (ETIS) in 2013 (with several revisions in the subsequent years). Building on 27 core and 40 optional indicators, ETIS provides the most detailed approach to measure sustainable tourism [ 14 ]. At the same time, the EEA has developed a ‘Tourism and Environment Reporting Mechanism’ (TOUERM), monitoring the environmental impact of tourism similar to other industries. Note that the nine TOUERM indicators are similar/overlapping with those of the ETIS framework.

Sustainability labels

While the aforementioned indicators are mostly geared towards policy making, sustainability labels also serve as a source of information for consumers. Naturally, these labels can also be used to gather information about the state of sustainability in a region or country. Sustainability labels are deemed a suitable means to facilitate ecological progress, especially with regard to clean water and energy, sustainable consumption, and climate protection [ 11 ]. Consequently, such labels are considered both by the ETIS and TOUERM frameworks. ETIS indicator A.2.1 can thus be used to gather information relevant for policymakers while relying on third parties’ assessments. It is important to note that there is great variety of sustainability labels, oftentimes leaving consumers left to wonder about their exact meaning, the applied standards, as well as their credibility in view of control mechanisms and enforcement [ 15 ].

Beyond institutional labels such as the EU Ecolabel (introduced by the European Commission to highlight low waste, energy efficiency and other sustainability factors [ 16 ], online platforms have introduced indicative labels as well. TripAdvisor’s GreenLeader award, for instance, was introduced in the US in 2013 and has been consistently expanded to other countries. Touristic accommodations interested in the award can apply through a questionnaire and must fulfil several standards to become a ‘GreenLeader’. These standards include, among others, towel re-use, recycling, and green roofing [ 17 ]. While institutional labels inherit credibility from the sponsoring institution, it is worth taking a closer look at the GreenLeader scheme: The award was devised in cooperation with the UN Environment Programme and has been critically acclaimed. The German Consumer Association highlights its high standards, independence, and transparency. The potential for widespread application stemming from TripAdvisor’s global presence makes the label a useful point of reference. Award-holding listings are decorated with a visual label on TripAdvisor, incentivising accommodations to apply [ 18 ].

Problems of sustainable tourism indicators

The compilation of indicators for sustainable tourism comes with several difficulties, as outlined in the European Tourism Indicator System (ETIS). While some of the data used in the ETIS is readily available from national statistics offices, it is complemented by additional data from surveys and other sources. Rasoolimanesh et al. (2020), for example, reviewed and assessed 97 academic studies on sustainable tourism in terms of relevance for the SDGs, related governance, stakeholders, and the subjectivity of the indicators [ 7 ]. Governance-related indicators were found in less than a quarter of all studies, stressing the importance of strong institutions to push evidence-based decision making. Another identified problem is access to and accuracy of data, which play an important role for implementing robust and evidence-based indicators. The European Commission is aware of the time and cost intensity of this approach and suggests not to collect annual data for all indicators, but rather to rely on three-year cycles [ 14 ].

Moreover, data reliability is an issue. Modica et al. (2018) asses the initial implementation of the ETIS in the Sardinian region of Cagliari and found that up to 52% of indicator data was missing [ 19 ]. Such issues and questions regarding definitions and measurability have, for example, led to the abandonment of indicator 8.9.2 (‘Proportion of jobs in sustainable tourism out of total tourism jobs’) from the Sustainable Development Goals framework [ 20 ]. These issues in data collection exist despite high-quality census data and experienced institutions and researchers in the OECD countries. Moreover, there are only few studies on sustainable tourism indicators outside Europe and North America [ 7 ]. This means that the data gap on sustainable tourism is increasing, as tourism industries in the Middle East, North Africa, South Asia, and South-East Asia are growing faster than those in Europe and North America [ 2 ]. Scholars in development studies suggest yearly or even quarterly reporting of data instead of relying on multiple-year resolution [ 21 ]. With traditional methods of data collection, however, this goal seems unattainable.

Sustainable tourism indicators and online data

To overcome such limitations, online data, which is often created as a byproduct of digital business operations, may complement existing data sources. For example, search engines’ primary function is not the accumulation of popular search terms and their development over time, but this byproduct offers valuable insights for areas such as disease control [ 22 , 23 ], unemployment statistics [ 24 , 25 ], or sales forecasting [ 26 ].

Through utilizing the above-mentioned strengths and under careful consideration of the associated risks, the use of big data from online sources can increase the quality of existing development evaluations and allow for assessing previously unmeasured outcomes [ 27 ]. To better understand how different research approaches aim to achieve this goal, an overview of research facilitating methods of online data collection and analysis in tourism research and related phenomena is presented in Table  1 .

Table 1

Tabular overview of empirical approaches to measure sustainable tourism, sustainable development indicators and related phenomena with online platform data

For example, GPS trackers and mobile phone applications can be employed to identify patterns of tourist movements in great detail. In combination with app data, Buning and Lulla (2020) were able to differentiate rental bike use between local users and tourists [ 28 ]. Furthermore, segments of tourists that are likely to use specific trails and visit at peak times could be identified via matching app-generated location and demographic survey data [ 29 ]. Big data can also help for improved touristic capacity management. For instance, tourist flights, overnight stays, and sightseeing crowds can be analyzed and forecasted using big data. This also holds for air passenger demand based on flight price search/comparison websites [ 30 ], as well as for the nights spent at certain destinations by tourists with high spatial resolution [ 31 ]. Using booking and travel platform data, the density of touristic stays could be estimated for the area of the European Union and Great Britain [ 32 ]. Similarly, the number of Airbnb offers per tract in the United States could be calculated [ 33 ]. At the level of single touristic sights, visitor flows to World Heritage sites were successfully estimated using Instagram posts [ 34 ]. Focusing on the quality of businesses, geographic clusters of similarly rated restaurants and bars were determined using Yelp review data [ 35 ].

Furthermore, text sources such as online customer reviews offer the possibility to analyze tourists’ concerns and preferences in real-time. For example, attributes that are important to environmentally aware tourists have been identified using text mining techniques on Airbnb comments [ 36 ]. The presence and depth of environmental discourses can be assessed based on booking and travel platform data [ 37 ]. TripAdvisor comments, in particular, have been used to understand sustainable practices introduced by accommodations [ 38 ]. Twitter data has been used to assess the attractiveness of popular touristic sites [ 39 ]. Advanced analytics can also be applied to traditional data sources, for instance, to assess a destination’s potential for ecotourism using artificial neural networks [ 40 ].

In addition, online data has found application in the measurement and nowcasting of several other phenomena. For example, Twitter data has been used to assess damages after earthquakes [ 41 ]. The estimated size of Facebook ad audiences was used to calculate a wealth index at high spatial granularity [ 42 ]. Professional social network data was used to estimate gender gaps within industries and seniority levels [ 43 ]. Lastly, the effect of the Covid-19 pandemic on the property sector was estimated using property listing website data [ 44 ].

Methodology

Having reviewed the literature on approaches to measure sustainable tourism, we now introduce the methodology used for the algorithmic identification of GreenLeader accommodations based on publicly displayed data from TripAdvisor.

After establishing the methodological steps of the analysis in the following paragraphs, we investigate the research question whether statistical learning algorithms are able to reveal systematic differences between online profiles of sustainable and non-sustainable accommodations that allow for predicting the existence of a sustainability label.

Data collection

Data collection took place in November and December of 2020 via web-scraping. For each of the countries included in the analysis, links to all cities with TripAdvisor listings were extracted from the starting page. To obtain direct links to all listings, the algorithm looped through the city URLs and searched for hotel listings in each city. Using this approach, we collected a total of 260,348 individual accommodation listings from 37 European countries. These include all 27 EU member states as well as England, Northern Ireland, Scotland, Wales, Iceland, Liechtenstein, Monaco, Norway, San Marino, and Switzerland.

TripAdvisor provides a broad range of information about accommodations. There are three main sources of data: Page owners (i. e., the accommodation’s operators), consumers, and other/external websites. Next to basic information such as an accommodation’s name and location, page owners can detail their hotel’s size and class, provide contact information, and publish room features and property amenities. Consumers can rate the accommodation on several quality metrics and give an overall rating. They can further provide written reviews, comments, questions, tips, or upload photos. TripAdvisor supplements this information in two ways. First, the website incorporates information from other webpages (e. g., average prices from other websites such as Booking.com and Opodo ). The platform further calculates a score for the accommodation’s location in the city and counts the number of close-by restaurants and attractions using geographic data from Google. Secondly, TripAdvisor accumulates and publishes background data of commentators such as their chosen language as well as trip times and durations. In addition, the website uses customer feedback to create an accommodation ranking within each city.

From the individual listings’ web pages, we collected a total of 102 features, covering five categories of data: the hotel description, its class and ratings (a), prices and information about the size (b), scores calculated by TripAdvisor about the hotel and its location (c), measures of customer interaction (d), and hotel amenities (e).

These features comprise all readily available numeric variables of a listing as well as its binary and ordinal labels. Of the collected information, 15 features relate to hotel description and ratings (a), six variables inform about price segment and hotel size (b), three features relate to location (c). In addition, customer interaction (d) is included through 16 variables for text and image interactions (reviews, uploaded photos). Please note that these variables refer to the amount of user interaction (i. e. number of photos uploaded, number of reviews). The content of photos or reviews is not analyzed. Finally, 62 features inform about the availability of specific amenities and hotel features (e). A detailed summary and description of all variables is provided in Additional file 1 sections I and II.

After splitting the dataset into observations stemming from countries using the TripAdvisor GreenLeader award and those without, 215,806 labelled listings remain available for classifier training. Of these observations, almost 30% have complete information about all variables of interest; 70% have at least one missing value. The variables with most missing values are hotel class (49% missing), TripAdvisor-generated location scores (34% missing) and the number of available rooms (26% missing). The amount of missing data for these variables is critically large – imputation of missing data is not possible here. To make sure not to introduce any bias due to imputation, we therefore exclude all observations with missing values. This leaves 65,515 complete and labelled observations for model training. Comparisons between the full data set (including missing values) and the final data set are provided in Additional file 1 section III. Furthermore, we excluded four observations that contained erroneous records on the number of rooms available (for details, see Additional file 1 section IV).

Data processing

We undertook several steps of data pre-processing before further analysis. Some variables had to be transformed to deal with skewness. This allowed for the selection and final application of transformations offering the greatest improvement in classifier performance. As dependent variable, we focus on the TripAdvisor GreenLeader Award as a (binary) proxy for an accommodation’s sustainability. A detailed account of all variables’ distributions is provided in Additional file 1 section V.

Classification

As the next step, we set out to distinguish sustainable and non-sustainable accommodations. To do so, we employ a grid search of preparatory methods and algorithms to find the models with best predictive performance. In total, we ran 360 models based on 3 dimensionality reduction techniques × 5 resampling approaches × 6 data transformations × 4 classifiers. The models are evaluated using three metrics suitable for imbalanced classification tasks. In the following, the components of the analyzed modelling processes and the applied classification metrics are listed. This allows for an understanding of the grid of methods used. A more detailed overview of the pre-processing techniques used in the grid search is provided in Additional file 1 section VI.

Dimensionality reduction (i. e. principal component analysis) of the input data enables the chosen classifiers to work more efficiently and avoids issues of excess dimensionality. Three options are compared in the grid search: Use of the full dataset, use of the first four, and use of the first eight principal components created from all variables.

Moreover, resampling alleviates issues related to imbalanced data by altering the training dataset to have a more equal distribution of labels. The grid search compares modelling processes without resampling with processes using one of four resampling strategies. These are random oversampling, random undersampling, and S ynthetic M inority O versampling TE chnique ( SMOTE ), as well as the combination of SMOTE and undersampling.

Additionally, the grid search considered six types of data transformation to adjust the distribution of the input data. The use of the original data is compared to three straightforward (normalization, standardization and robust scaling) and two distribution-dependent transformations (Yeo-Johnson and Box-Cox).

Finally, four classifiers are compared across the grid: Logistic Regression, Linear Discriminant Analysis, Quadratic Discriminant Analysis, and the Random Forest. More details on the classifiers are provided in Additional file 1 section VII.

The metrics used to compare the modelling processes pay special attention to the imbalanced distribution of class labels. In particular, standard accuracy is not a viable metric in this case since a useless model predicting the majority class label for all observations would score highly (with accuracy equalling the proportion of majority class observations in the data, in our case 96%). Thus, the final metrics chosen for comparison are recall, the F2-measure, and the R eceiver O perating C haracteristic A rea U nder C urve ( ROC AUC ). This choice of metrics reflects the importance of recognizing sustainable accommodations despite the comparatively few available cases in the dataset. All metrics are calculated using tenfold cross-validation.

The TripAdvisor GreenLeader label is awarded to accommodations that fulfil requirements regarding specific sustainability practices. These accommodations make up 4% of all listings in the sample. Clear standards and thorough checks of the accommodations’ claims make the award a reliable source of information. As a first step of our analysis, we compare the relevant variables conditional on the accommodations’ award status (Additional file 1 section VIII). In the following, we highlight several key findings of this analysis.

Descriptive statistics

There are large differences between GreenLeader accommodations and others in terms of size and type of accommodation as well as user interaction variables (Fig.  1 ). For example, the median number of reviews received by GreenLeader accommodations (normalized by the number of rooms offered by the accommodation) is 10, while the median is only 6 for other accommodations (Fig.  1 (A)). GreenLeader accommodations also tend to be larger, with a median of 96 rooms vs. 28 rooms for other accommodations (Fig.  1 (B)). GreenLeaders also differ with regard to the number of uploaded photos (Fig.  1 (C)), the number of languages spoken by their staff (Fig.  1 (D)), the distribution of accommodation types (Fig.  1 (E)), hotel class (Fig.  1 (F)), and amenities (Fig.  1 (G)). In general, GreenLeader accommodations tend to be larger hotels with more stars, more and diverse amenities, and a higher level of user interaction as measured by reviews and uploaded photos. The variables displayed in Fig.  1 show only a subset of the more than 100 variables that could be derived from the platform data, but they illustrate the differences in publicly available features that appear to be correlated with an accommodation’s sustainability (see Additional file 1 section V for correlation matrices between the GreeanLeader badge and (a) numeric variables, (b) binary variables in the data set). In the following, we illustrate how unsupervised and supervised statistical learning techniques reveal structure in the data to distinguish groups of hotels and other accommodations that show higher or lower shares of sustainability.

Differences between GreenLeader and other accommodations in TripAdvisor data. ( A )–( D ) Distributions of continuous variables: reviews per room, Number of rooms, total number of photos per room and languages spoken by staff in GreenLeader (blue) and other (red) accommodations. ( E )–( F ) Proportion of accommodation types and hotel class (stars) in the groups of GreenLeader (left) and other (right) accommodations. ( G ) Proportion of amenities in the groups of GreenLeader (top) and other (bottom) accommodations. GreenLeader accommodations tend to be larger, have more user interactions, are of higher quality, and offer more amenities than other accommodations

Unsupervised statistical learning

Figure  2 displays the results of dimensionality reduction (principal component analysis) and cluster analysis (k-means) applied to the 33 continuous variables in the data set. The exploratory dimensionality reduction reveals that four components capture a large share of the variation in the data (see Additional file 1 section IX). Figure  2 (A) illustrates the loadings of the first four components. We have used hierarchical clustering to identify groups of variables with similar loadings (see dendrogram in Fig.  2 (A)). This analysis reveals that the variables tend to group into four clusters, which represent distinct types of information available about each accommodation. The variables Walker score, Restaurant score, and Attractions score all describe the location around the accommodation. A second group of variables describes quality indicators, such as the number of languages spoken by staff, hotel class (stars), and price. Variables related to the user rating (e. g., value, service, and average rating) form a third distinct group, while the fourth cluster describes aspects related to the size of the accommodation (number of rooms, number of reviews). As shown in Fig.  2 (A), the data has a structure that can be detected by unsupervised learning algorithms, which reflects interpretable concepts and trust cues known from other domains of the platform economy [ 45 ].

Unsupervised Learning techniques applied to TripAdvisor data. ( A ) Heatmap of principal component loadings of the four main principal components based on dimensionality reduction of the 33 continuous variables in the data set. The algorithm identifies four main dimensions in the data: accommodation size and user interaction (PC1), user rating (PC2), location (PC3), and quality (PC4). ( B ) Summary statistics of four clusters identified by k-means clustering. The accommodations can be grouped according to quality and user interaction variables. The clusters show different proportions of the GreenLeader outcome variable, varying from 2% to 19%. ( C ) Two-dimensional representation (PC1, PC2) of TripAdvisor data (10% sample) grouped in four clusters (panels) and GreenLeader/other accommodations (color). The unsupervised learning algorithms are able to split the data into distinct groups with varying proportions of GreenLeader accommodations

In addition to dimensionality reduction, we also use k-means clustering to identify groups of similar accommodations. At this point we want to underline that the cluster analysis is used as an exploratory statistical analysis in this study only. It serves as a way to illustrate that accommodations, which share certain features (among them the sustainability label), tend to co-occur in the data.

Note that many choices on the number of clusters are possible and justifiable (Additional file 1 section X). Here, we used four clusters for separating the data into prototypical groups. Figure  2 (B) provides summary statistics of the four groups. Cluster 1 represents a small subset of the data containing only 2% of all accommodations, 19% of which are GreenLeader accommodations. The group is characterized by high quality hotels with many rooms, a lot of user interactions (reviews and photos), many international guests (high share of English language reviews), and high prices. Cluster 2 also contains a disproportionately large share of GreenLeaders, high-quality, and expensive hotels. In contrast to Cluster 1, however, this Cluster’s accommodations are significantly smaller. The largest share of the data is captured by Clusters 3 and 4, which account for 83% of all accommodations but with an average share of only 2% GreenLeader accommodations. Compared to the other two clusters, the accommodations in these groups are substantially less expensive and smaller, they have significantly fewer user interactions, a lower share of English language reviews, and lower quality. The main differences between Cluster 3 and 4 are, again, size and quality: Cluster 3 accommodations have, on average, 50% more rooms than those in Cluster 4, but they score lower on price and quality characteristics (stars), internationality, and user interactions.

In summary, the total dataset can be split into (at least) four groups of hotels: large, high-class hotels in cluster 1, small, high-class hotels in cluster 2, low-price hotels in cluster 3, and other accommodations in cluster 4. Clusters 1 and 2 show the highest share of GreenLeader accommodations.

The cluster differences and the differences between GreenLeader and other accommodations are shown in Fig.  2 (C) in the dimensionality-reduced space of the first two principal components. Each panel represents a cluster from the table in Fig.  2 (B); circles indicate the position of the majority of the data points of the two accommodation types (GreenLeader vs. non-GreenLeader) in each panel. The plot shows that the different clusters take distinct positions within the two-dimensional space. Accommodations in Cluster 1 – the high-quality hotels with many rooms – score high on the first principal component (which loads heavily on variables related to a hotel’s size) but stretch along the second axis. Similarly, Cluster 2 shows a relatively high loading on the first principal component, but in contrast to Cluster 1, the data tend to show higher values on the second component (reflecting, for instance, better user ratings). Clusters 3 and 4 – the relatively low-price accommodations – both score low on the first component, that is, they represent smaller accommodations. Their main difference is the second principal component: Cluster 3 accommodations seem to be characterized by low user ratings, while Cluster 4 comprises more accommodations with a positive rating. Note, however, that while there are some differences in the positioning within each panel, there is also a large overlap between both groups. The differences are most pronounced in Clusters 3 and 4, which indicates that the GreenLeader accommodations differ more strongly from other accommodations in the realm of lower quality, low-price accommodations.

Overall, the application of the unsupervised learning techniques in this section reveals structures in the data on hotel descriptions, amenities, user ratings, and customer interactions that seem to be correlated with the presence of the GreenLeader sustainability label. We do not make any claims as to whether there exists any causal relationship between these features and the sustainability label; we only observe that they tend to co-occur in the data.

Classification performance and extrapolation

The aim of the study is to investigate whether algorithmic machine learning algorithms trained on publicly available data can identify and predict the extent of sustainable tourism with high temporal and spatial accuracy. To do so, we tested large sets of models and preprocessing techniques and their combinations. To compare the prediction performance of the models, we report cross-validated average effects on three established metrics: F2 score, Recall, and ROC AUC. In Fig.  3 , we provide the main results. Detailed results are provided in Additional file 1 section XI.

Classification performance and extrapolation. ( A ) Comparison of 360 classification models (90 models per classifier and panel) regarding three performance metrics: F2 score, Recall, and ROC AUC (each dot represents a model). QDA model (1) shows the highest F2 score, QDA model (2) achieves the highest Recall, and Random Forest model (3) the best ROC AUC score. ( B ) Confusion matrices of the three best performing models (1) to (3) according to the performance metrics (top panels) and a random draw model (4) (lowest panel). Inset: performance comparison between machine learning models (red) and 20,000 random draws (blue) according to F2 score, Recall, and ROC AUC. The machine learning models show a significantly better prediction performance than the random draw models. ( C ) Predicted share of GreenLeader accommodations in Europe (NUTS-2) according to the QDA model (1). The model predicts urban centres and several regions in West and North Europe to have the high shares of sustainable tourist accommodations

The grid search comprised a total of 360 models (i. e., 4 classifiers × 3 dimensionality reduction techniques × 5 resampling methods × 6 data transformations) and resulted in widely varying performances (Fig.  3 (A)). Depending on the assessment metric, several model specifications show similar performance. However, many models are not competitive. For example, a large number of Random Forest models score low on the Recall metric.

Fig.  3 (A) shows the best performing models according to three assessment matrices: QDA model (1) according to the F2 score (F2 score = 0.404), QDA model (2) according to Recall (Recall = 0.867), and Random Forest model (3) according to the ROC AUC score (ROC AUC = 0.887). To give an intuition into the quality of the prediction, Fig.  3 (B) shows the confusion matrices of the cross-validated average prediction accuracy of these models in comparison to a random draw model (4). Please note that each of the listed models uses the same set of variables. They differ with respect to the specification of the full machine learning pipeline, i. e. model choice and preprocessing steps.

High performance on the F2 score as seen for QDA model (1) indicates both, high degrees of precision in correctly identifying non-sustainable hotels and recall of sustainable accommodations. As the latter is at the core of this project, we also look at recall individually. Here, QDA model (2) is able to identify the largest percentage of true GreenLeader listings. Finally, Random Forest model (3) has the highest ability of discriminating between the two classes and hence scores highest in the ROC AUC metric.

From comparing the confusion matrices, but also the overall prediction performance of the different models displayed in Fig.  3 (A), it is obvious that there is not one single best choice. This is due to statistical uncertainty, but also because of the fuzziness of the sustainability concept and data used here. Furthermore, as Figs.  1 and ​ and2 2 show, the data vary in terms of quality, user ratings, location, and interaction metrics. GreenLeader and other accommodations overlap in this regard. Hence, it is not surprising that machine learning models will not be able to perfectly differentiate between the groups.

Nonetheless, it becomes clear that the data allows for the training of statistical learning models that assess the sustainability of touristic accommodations with a level of accuracy far beyond random draw (Fig.  3 (B): confusion matrix of model (4) in the lowest panel). The overall predictive capability of the machine learning models trained on the TripAdivsor data is highlighted in the inset of Fig.  3 (B). It compares the distributions of the prediction performance of 20,000 random draws using the unconditional probability of 4% GreenLeader accommodations (blue) in the data with the performance of all machine learning models from Fig.  3 (A) (red). Since the employed metrics of F2 score, Recall and ROC AUC are less intuitive than simple accuracy, we use random draw as a comparative baseline. The median performance of the machine learning models outperforms random draw substantially: by a factor of 8 with regard to the F2 score, by a factor of 17 with regard to recall, and by a factor of 1.67 with regard to the ROC AUC. In other words, the publicly available TripAdvisor data is informative with respect to the sustainability of touristic accommodations. 5

To illustrate the granularity of the derived touristic sustainability measure, Fig.  3 (C) shows the predicted share of GreenLeader accommodations in the European NUTS-2 regions for countries with and without TripAdvisor’s GreenLeader program. Touristic sustainability follows a particular spatial distribution that seems to be related to the socio-economic structure in Europe. While it is beyond the scope of this study to explain the geography of touristic sustainability in detail, several observations can readily be made. First, the predicted share of GreenLeader accommodations is higher in metropolitan than in rural areas. For example, Berlin, Hamburg, Birmingham, London, Stockholm, Helsinki, Copenhagen, Vienna, Warsaw, Prague, Bratislava, Budapest, Zagreb, Madrid, and Sofia all seem to host much higher share of sustainable accommodations than the surrounding countryside. Moreover, sustainable tourism seems to be more widespread in North and West Europe than in East and South Europe. 6

In summary, our findings illustrate that the automatized and algorithmic prediction of sustainable tourism indicators is feasible. This can contribute to providing a cost-effective, accurate, and spatially granular assessment and tracking of sustainable tourism over time. The method showcased here can also have positive effects on transparency and thus support informed customer decisions. Moreover, it can help platforms and other organizations to identify sustainable accommodations.

Tourism plays an important role in economic development across the globe and indicators are crucial to understand its development in different regions over time. Though heavily affected by the Covid-19 pandemic, the sector is expected to resume its growth path soon. With it, the environmental and social impacts of tourism will also continue to grow. Measuring and fostering sustainable tourism through effective indicators is thus a topic of global interest. Today, the global use of sustainable tourism indicators is limited by implementation costs and difficulties in data collection. In other areas, the inclusion of alternative data sources has been proven to be beneficial. This paper sets out to test the applicability of an alternative data source for the measurement of sustainable tourism.

Summary of the results

In collecting and analyzing data from TripAdvisor – one of the globally leading online tourism platforms – we show that it is possible to create a cost-effective, granular, and accurate measure of sustainable tourism based on publicly available online data. We compare differences between touristic accommodations holding TripAdvisor’s GreenLeader award and other accommodations regarding hotel quality metrics, user interaction, user rating, and location features. We conduct a grid search on a total of 360 machine learning pipelines to differentiate sustainable from non-sustainable hotels based on the high-dimensional platform data. The performance of the machine learning models is substantially better in identifying sustainable hotels than the baseline model of unconditional expectation. 7 In other words, machine learning models trained on online platform data can make a contribution in assessing the state of sustainable tourism in countries and regions as the presence of the sustainability award shows correlation with various other characteristics. Note that some caution is due since these correlates are – in most cases – not formative for the accommodation’s sustainability.

For example, we find that more expansive and larger hotels with more rooms and customer reviews (see Fig.  2 (B)) tend to have a higher share of GreenLeader badges. We want to state explicitly that we do not assume such features to be causal for an accommodation’s sustainability. However, both could, nonetheless be related. It might be the case that larger hotels have more resources available to deal with the requirements of sustainability labels. It could also be that larger hotels are more dependent on web traffic from online platforms and therefore invest more resources in obtaining badges from the platform. Furthermore, hotels in the premium segment with a focus on quality and user satisfaction might want to utilise the sustainability label as an additional quality criterion. While such factors are not displayed in the large-scale online platform data, the observable correlations between prices or hotel size and the sustainability label seem to capture such patterns.

The approach presented here reveals factors that correlate with the sustainability label, but it should not be employed to assess individual accommodations’ degree of sustainability based on the correlations alone. However, the approach may well serve to statistically assess countries’ and regions’ degree of sustainable tourism with high temporal and spatial granularity, for example for the purpose of nowcasting sustainability indicators or for extending the geographical coverage of such indicators to places without ‘ground-truth’ data on sustainable tourism.

It is important to highlight that the purpose of the analysis is not in predicting and identifying individual hotels as sustainable, but on providing a probabilistic assessment about the distribution of sustainable hotels in a region (as shown in Fig.  3 (C)) derived from the prediction model, which uses individual-level data. In that sense, our analysis is similar to medical studies that aim to quantify population-wide health risks. Such assessment consider individual-level risk factors such as age, obesity or nutrition to calculate an estimate of the share of the population being at risk of cardiovascular diseases, but they do not aim to make predictions on the level of individuals patients.

Theoretical implications for the applicability of big data in tourism research

Past studies have employed text analysis to understand user preferences and discussions [ 36 , 37 ]. Accommodation-specific data has been used for the estimation of visitor capacities in neighborhoods [ 32 ]. In contrast, this paper focuses on the classification of accommodations by sustainability. We add to the literature by utilizing accommodations’ own presentation and associated user interactions to gain information about their sustainability practices. The creation of a large numeric dataset allows for the training of common machine learning algorithms. Using available ground truth data for classifier training, the quality of the analyzed classifiers could be assessed in detail. This approach was followed in prior work where it allowed for comprehensive model assessment using true values and labels [ 9 , 31 ]. In doing so, we were able to confirm the applicability of travel platform data for use in tourism statistics. In particular, the low cost of data collection and high spatial resolution of the data could be shown. It should be noted that the estimated models do not attempt to create an alternate definition of sustainability through using new causal factors. Instead, the true label is determined trough physical measurements of energy, waste and water. Here, it is estimated using correlated factors available in the online platform data. The legitimacy of using of private company sources for the collection of data for policy making will remain an important open question. Our study helps to underline the potential of the large-scale analysis of online data as a valuable method for research in sustainable tourism, and sustainability studies in general.

Implications for tourism practitioners and policy makers

Tourism platforms can make use of our findings in multiple ways. Listings, which are predicted to employ sustainability practices but do not (yet) carry the award, can be actively approached, and be made aware of the GreenLeaders program. These accommodations can, in turn, benefit from increased visibility and increase their attractiveness for environmentally conscious consumers. More visibly communicating sustainability efforts could hence become a competitive advantage. This could in turn increase pressure on competing businesses to also invest in sustainable practices.

For platforms, cooperation with researchers and statistics departments is an effective way of underlining environmental and social efforts. Policy makers can benefit from the availability of inexpensive, granular, and up-to-date data. For policy makers in countries with established frameworks for sustainable tourism statistics, the higher frequency and granularity of reporting can offer important supplementary information. Through the comparison with traditional data sources, model accuracy can be monitored, and models can be adjusted where needed. In countries without established frameworks, the proposed methodology can offer estimates when traditional methods of data collection are prohibitively costly to implement, or where important infrastructure is not available.

These estimates can guide policy makers towards initial interventions and allow for detailed monitoring of the associated effects. In relation to existing frameworks of sustainable tourism indicators, the implications are twofold: For the ETIS framework, which collects data on the percentage of accommodations using a voluntary sustainability label under its indicator A.2.1., the described models can offer a remedy for difficulties in data collection for this indicator. For other frameworks, inclusion of the presented indicator can be discussed to create a more complete picture without significantly increasing data collection efforts.

Practical implications and limitations

It is important to note that the proposed methodology cannot replace accommodation surveys and other statistically robust modes of data collection. It should instead be considered as a complementary source of information or a first estimate when no other data is available. The presented methodology is heavily reliant on the quality of the big data sample. Systematic differences between accommodations listed on TripAdvisor and those that are not should hence be a focal point of further research. Another limitation might be potential confounding factors on the regional level that we could not control for. Our analysis solely uses variables on the level of individual accommendations. For example, it might be that legal requirements or cultural values in some regions affect the share of hotels with a sustainability label.

In addition, omitting incomplete observations may further limit the validity of the training sample and alternatives should be explored in greater detail. Future research should furthermore explore the feasibility of other, freely available data sources. Both, accommodation characteristics and the sustainability label, are taken from TripAdvisor. Characteristics could be collected from a range of other travel platforms. An alternative label for classifier training could be created from other well-established sustainability programs. Although a broad range of classifiers and preparatory steps was compared, other approaches may yet outperform the methods included in the analysis. Lastly, this paper treats sustainability as a binary variable, separating accommodations into those following any sustainability practices and those following none. Additional research could explore whether the degree of sustainability practices, expressed for example by the differentiated TripAdvisor GreenLeader labels from ‘partner’ to ‘platinum’ level, can also be modelled.

On a more general note, the methodology suffers from shortcomings common to all big data approaches. Although new forms of data collection and analysis have filled data gaps and increased our understanding of social, economic, and touristic activity, there are justifiable concerns about the use of such (alternative) data sources. Machine learning methods have the outstanding ability of combining many weak signals into predictions for labels or variables. These signals do not need to be in line with theoretical groundwork and in practice will often not be. For this reason, some algorithmically derived signals would not have been included as relevant explanatory variables in traditional modelling [ 21 ]. Algorithmic prediction models also reproduce existing biases in the data. In our case, not all hotels that would fulfil the criteria to obtain a GreenLeader badge might have actually applied for the label – a bias that our model cannot control for. Additionally, the use of big data and machine learning did cause concerns regarding privacy issues and the possibility that algorithms might pick up unethical or discriminatory practices present in historical data sets [ 46 ]. However, with the advantage of improved detection of patterns in the data comes the risk of disregarding underlying theory altogether [ 47 ].

Concluding note

This paper set out to analyze whether online platform data can be used to inform about sustainable tourism. Sustainability in tourism describes the goal of balancing economic, social, and environmental factors. The complexity of this goal requires diverse sources of information to monitor progress and inform decision-makers. The corresponding data collection processes offer room for improvement with regards to both cost and frequency of reporting. Research is often focused on Europe, the world’s largest tourism market, but despite freely available national census data and experienced practitioners there are difficulties in the implementation of existing indicator frameworks and the collection of relevant data.

In this paper, we offer an alternative to existing methodologies through the use of travel platform data. In this extended pilot, the platform TripAdvisor was used as the sole source of data. Tourist accommodation data was collected through automated scraping of TripAdvisor listings from 37 European countries. Following several data exploration steps, we developed a supervised learning model for the assessment of accommodations’ sustainability. Ground truth data was sourced from TripAdvisor (where the GreenLeader award is available in 27 of the 37 countries). The final model was chosen from a set of four supervised learning techniques, each building upon combinations of dimensionality reduction, resampling, and data transformation methods. The imbalanced nature of the classification task added difficulty. With less than 4% of training data belonging to the sustainable class, use of the accuracy metric would have been misleading. Model comparison was hence performed using the F2-metric. Recall and the Receiver Operating Characteristic Area Under Curve metric. A classifier using quadratic discriminant analysis was chosen as the final model. Overall, prediction quality was high but not excellent, with all methods struggling to successfully recognize observations from the positive class without significantly increasing the proportion of false positive predictions.

All findings are subject to limitations, the most important being the yet unconfirmed validity of the collected sample for the population of accommodations in each country. This representativeness of the sample and the use of other readily available platform data sources should be the focus of further research.

Below is the link to the electronic supplementary material.

Acknowledgements

Not applicable.

Authors’ information

Felix Hoffmann holds a MSc Industrial Economics from Technische Universität Berlin and a BSc Economics from the University of Amsterdam. His research revolves around the use of alternative data sources in public economics. He works in industry as an analytics and data consultant. Fabian Braesemann is a Departmental Research Lecturer in AI & Work at the Oxford Internet Institute, University of Oxford. In his research, he applies social data science methodologies on large-scale online data to understand market and information dynamics in a digitally connected world. Furthermore, he is Founder of the Datenwissenschaftliche Gesellschaft Berlin , a company focused on applying data science to economic, policy, and development problems. Timm Teubner is Professor at the Einstein Center Digital Future at Technische Universität Berlin. He holds a Diploma degree in Industrial Engineering & Management and a doctoral degree in Information Systems from Karlsruhe Institute of Technology (KIT). His research interests include online platforms and multi-sided markets, reputation systems, trust in digital services, online auctions, as well as Internet user behavior. His research has been published in journals such as the Journal of the Association for Information Systems, Information & Management, Electronic Markets, International Journal of Electronic Commerce, Business & Information Systems Engineering, and Economics Letters.

Author contributions

FH and FB contributed equally to this study. FH produced the code for web-scraping, unsupervised learning and model comparison. He created the initial draft of the article. FB conceptualised the research, supported the statistical analysis and produced the enclosed visualizations, and. TT guided the writing process and structured the final presentation of results. All authors wrote and approved the final manuscript.

The authors received no financial support for the research, authorship, and publication of this article. Open Access funding enabled and organized by Projekt DEAL.

Availability of data and materials

Declarations.

The authors declare that they have no competing interests.

1 https://www.unwto.org

2 https://www.gstcouncil.org

3 https://ec.europa.eu

4 https://www.eea.europa.eu

5 In this study, we make the comparison of the performance of our models with a simple random draw model only to illustrate that there is an informative signal contained in the online platform data, which has predictive capacity that can be identified via statistical learning algorithms. We do not want to claim that a random draw model would be the only alternative modelling strategy. In fact, many different types of models can lead to good predictions, as we show by using a set of 360 different models in total.

6 For average sampled and predicted values by country, please refer to Additional file 1 setion XII.

7 The performance of the models is compared to a random draw only for the purpose of illustrating the predictive capacity in the data, see footnote  5 .

Felix J. Hoffmann and Fabian Braesemann contributed equally to this work.

Contributor Information

Fabian Braesemann, Email: [email protected] .

Timm Teubner, Email: ed.nilreb-ut@renbuet .

Reinsurance News

Onebefore launches travel insurance product on aneevo’s broker platform.

12th April 2024 - Author: Kassandra Jimenez-Sanchez

Specialist managing general agent (MGA) OneBefore has launched its travel insurance product, ‘Beyond Travel Insurance’ on Aneevo, the travel insurance broker distribution platform formerly known as Just Travel Cover wholesale.

Partnering with Aneevo, Vered Lobel, CEO of OneBefore noted, facilitates the instant and mass distribution of OneBefore’s new product to hundreds of UK insurance brokers.

She added: “We believe Beyond Travel Insurance provides these brokers with a quality product to strengthen their offering to their customers.

“In just three months, we’ve been able to launch a new product on the Aneevo broker distribution platform. Available now, this rapid launch was made possible thanks to the collaborative approach of the OneBefore and Aneevo teams.”

Daley Gore, Managing Director of Aneevo, said: “We’re delighted to be partnering with OneBefore and adding a new insurer to our panel.

“The new scheme is now live across all of our broker partners, putting them in a strong position ahead of the peak summer period to cater for more risks with access to a wider selection of products for their clients.”

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More From Forbes

10 online job sites you should know about in 2024.

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If you're not experiencing any success with the most popular job boards, you need to explore some ... [+] unconventional routes and tap into less popular job sites

You've heard of Indeed, Glassdoor ZipRecruiter, and of course, LinkedIn. These are the typical online job sites we all resort to when we're looking for a new role.

But what happens when you've been searching for months and these job boards are ineffective for your job search strategy? How can you secure an interview—and a job offer—when these sites are swarmed with thousands of candidates, all vying for the same role?

Luckily, there are other, not-so-popular online job sites which have hundreds of employers on them, eager for their next candidate. These sites tend to be more niche, and as such, you'll have enhanced visibility to opportunities within your specific industry and sector. And since they are industry or sector-specific, employers may prefer to hire candidates there than on any other platform—meaning you have an advantage here if you find a good employer on these sites.

This is why you need to have an outside-the-box approach for your job search, because these sources may not automatically come to mind if you've found yourself laid off.

10 Types Of Online Job Sites

Generally speaking, there are 10 main categories of online sites in which you can find your next job—perhaps a little unconventionally.

1. Government Job Boards

Government job boards, such as USA Jobs, are where you will find a wide range of public sector opportunities listed.

2. Company Career Pages

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The vast majority of employers post job adverts directly on their own websites via their careers section You may be able to find roles here that are not listed anywhere else.

3. Freelance, Gig, And Side Hustle Platforms

Remember a job does not necessarily need to be within the confines of a 9 to 5 or handed out by an employer for it to be called a job there are many opportunities In the gig economy While the freelance job market Which are Only by your imagination creativity and innovation. You can find gigs and promote yourself on platforms such as Fiverr or Upwork.

4. Industry Job Boards

Industry specific websites can be useful resources for finding a multitude of listings within your field. For example, ArtJobs is an online job site dedicated to roles within theatre production arts culture and education.

5. Remote Job Sites

If you're looking specifically for remote work or other flexible opportunities such as part time and job shares, try remote job boards such as Remote.co, or FlexiJobs.

6. Professional Associations

Professional associations are another fantastic industry specific resource, because they are designed to cater to the individual professional development needs of their members and peers within the industry. For instance, the Club Management Association of America is a professional association for club managers across all disciplines.

In addition to offering their own certifications, they also provide their own job listings for senior management roles paying over $100,000, mid-level management, and entry-level and internship opportunities. This is available to members and non-members.

7. Salary Comparison Websites

Company transparency platforms, including salary comparison sites such as Salary.com, also post unique job listings, along with detailed compensation data for your industry.

8. Start-Up Directories

Crunchbase , for example has a live list of hot start-ups and companies that have completed fresh funding rounds, who are actively hiring. AngelList and Work In Startups are two other start-up job boards you might want to consider, if you enjoy working in a buzzing environment where you are meaningfully contributing to growth.

9. Social Media (LinkedIn, Facebook, etc.)

Other than LinkedIn's own job board, you can use LinkedIn and other social media platforms such as Facebook to find roles through networking and engagement with key decision makers, who may be in your connections or friends list, and of course, through word of mouth referrals. You should also join industry-specific LinkedIn and Facebook groups.

10. Community Forums

Join industry-specific online forums such as GitHub or Stack Overflow. You may also find jobs through Reddit threads dedicated to your field.

Industry associations are fantastic resources for finding your next dream role

When you initially started reading this article, you probably assumed it would list a plethora of traditional job boards. Hopefully, by now, the few minutes you've spent reading this have opened your eyes to discover that there are more opportunities available than you initially conceived. Not every job board needs to have "job board" in its name, or have obvious job search features at first glance. Through getting creative and innovative, you can secure numerous career development opportunities and stay ahead of the curve.

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