tourism industry china

Outlook for China tourism 2023: Light at the end of the tunnel

China is now removing travel restrictions rapidly, both domestically and internationally. While the sudden opening may lead to uncertainty and hesitancy to travel in the short term, Chinese tourists still express a strong desire to travel. And the recent removal of quarantine requirements in January 2023 could usher in a renewed demand for trips abroad.

Domestically, there are already signs of strong travel recovery. The recent Chinese New Year holidays saw 308 million domestic trips, generating almost RMB 376 billion in tourism revenue. 1 China’s Ministry of Culture and Tourism. This upswing indicates that domestic travel volume has recovered to 90 percent of 2019 figures, and spending has bounced back to around 70 percent of pre-pandemic levels. 2 McKinsey analysis based on China’s Ministry of Culture and Tourism data.

This article paints a picture of Chinese travelers and their evolving spending behaviors and preferences—and suggests measures that tourism service providers and destinations could take to prepare for their imminent return. The analyses draw on the findings of McKinsey’s latest Survey of Chinese Tourist Attitudes, and compare the results across six waves of surveys conducted between April 2020 and November 2022, along with consumer sentiment research and recent travel data.

From pandemic to endemic

By January 8, 2023, cross-city travel restrictions, border closures, and quarantine requirements on international arrivals to China had been lifted. 3 “Graphics: China’s 20 new measures for optimizing COVID-19 response,” CGTN, November 15, 2022; “COVID-19 response further optimized with 10 new measures,” China Services Info, December 8, 2022; “China reopens borders in final farewell to zero-COVID,” Reuters, January 8, 2023. This rapid removal of domestic travel restrictions, and an increase in COVID-19 infection rates, likely knocked travel confidence for cross-city and within-city trips. Right after the first easing of measures, in-city transport saw a marked drop as people stayed home—either because they were ill, or to avoid exposure. Subway traffic in ten major cities in mainland China fell and then spiked during Chinese New Year in February. Hotel room bookings also peaked at this time.

Domestic airline seat capacity experienced a minor rebound as each set of restrictions was lifted—suggesting a rise in demand as airlines scheduled more flights. Domestic capacity fluctuated, possibly due to the accelerated COVID-19 infection rate and a temporary labor shortage. International seat capacity, however, continued to climb (Exhibit 1).

By Chinese new year, China was past its infection peak—and domestic tourism recovered strongly. For instance, Hainan drew 6.4 million visitors over Chinese New Year (up from 5.8 million in 2019) and visits to Shanghai reached 10 million (roughly double 2019 holiday figures). 4 China’s Ministry of Culture and Tourism. Overall, revenue per available room (RevPAR) during this period recovered and surpassed pre-pandemic levels, at 120 percent of 2019 figures. 5 STR data. Outbound trips are still limited, but given the pent-up demand for international travel (and the upswing in domestic tourism) the tourism industry may need to prepare to welcome back Chinese tourists.

Tourism players should be ready for this; the time to act is now.

A demand boom is around the corner—Chinese tourists are returning soon

Before the pandemic, Chinese tourists were eager travelers. Mainland China had the largest outbound travel market in the world, both in number of trips and total spend. 6 World Tourism Organization (UNWTO) Tourism dashboard, Outbound tourism ranking. In 2019, Mainland Chinese tourists took 155 million outbound trips, totaling $255 billion in travel spending. 7 China’s Ministry of Culture and Tourism. These figures indicate total outbound trips, including to Hong Kong and Macau. China is also an important source market for some major destinations. For instance, Chinese travelers made up 28 percent of inbound tourism in Thailand, 30 percent in Japan, and 16 percent of non-EU visitors to Germany. 8 United Nations World Tourism Organization (UNWTO) database.

Leisure travel was the biggest driver of China’s outbound travel, representing 65 percent of travelers in 2019. In the same year, 29 percent of travelers ventured out for business, and 6 percent journeyed to visit friends and relatives. 9 Euromonitor International database.

Our most recent Survey of Chinese Tourist Attitudes, conducted in November 2022, shows that Chinese tourists have retained their keen desire to explore international destinations. About 40 percent of respondents reported that they expect to undertake outbound travel for their next leisure trip.

Where do these travelers want to go?

The results also indicate that the top three overseas travel destinations (beyond Hong Kong and Macau) are Australia/New Zealand, Southeast Asia, and Japan. Overall, respondents show less interest in travel to Europe than in previous years, down from 7 percent to 4 percent compared to wave 5 respondents. Desire to embark on long-haul international trips to Australia/New Zealand increased from 5 percent to 7 percent, and North American trips from 3 percent to 4 percent since the last survey. The wealthier segment (monthly household income over RMB 38,000) still shows a high interest in EU destinations (13 percent).

There are stumbling blocks on the road to recovery

While travel sentiment is strong, other factors may deter travelers from taking to the skies: fear of COVID-19; the need for COVID-19 testing which can be expensive; ticket prices; risk appetite of destination countries; and getting a passport or visa.

Chinese travelers may favor domestic trips, even if all outbound travel restrictions are removed, until they feel it is safe to travel internationally. A COVID-19-safe environment in destination countries will likely boost travelers’ confidence and encourage them to book trips again. 10 “Long-haul travel barometer,” European Travel Commission, February 1, 2023.

Travel recovery is also dependent on airline capacity. Some international airlines might be slow to restore capacity as fleets were retired during COVID-19 and airlines face a shortage of crew, particularly pilots. Considering that at the time of writing, in April 2023, international airline seat capacity has only recovered to around 37 percent of pre-pandemic levels, travelers are likely to face elevated ticket prices in the coming months. For instance, ticket prices for travel in the upcoming holidays to popular overseas destinations such as Japan and Thailand are double what they were in 2019. 11 Based on Ctrip prices. Price-sensitive travelers might wait for ticket prices to level out before booking their overseas trips.

Chinese airlines, however, appear more ready to resume full service than their international counterparts —fewer pilots left the industry and aircraft are available. Chinese carriers’ widebody fleets are mostly in service or ready to be redeployed (Exhibit 2).

Moving forward, safety measures in destination countries will affect travel recovery. Most countries have dropped testing requirements on arrivals from mainland China, and Chinese outbound group travel has resumed but is still limited to selected countries.

Many Chinese travelers—maybe 20 percent—have had passports expire during the COVID-19 period, and China has not been renewing these passports. Renewals are now possible, but the backlog will slow travel’s rebound by a few months. 12 Steve Saxon, “ What to expect from China’s travel rebound ,” McKinsey, January 25, 2023. Furthermore, travel visas for destination countries can take some time to be processed and issued.

Taken together, these factors suggest that the returning wave of Chinese travelers may only gather momentum by the Summer of 2023 and that China’s travel recovery will likely lag Hong Kong’s by a few months.

Overall, China is opening up to travel, both inbound and outbound—all types of visas are being issued to foreign visitors, and locals are getting ready to travel abroad. 13 “China to resume issuing all types of visas for foreigners,” China Briefing, March 14, 2023.

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The returning chinese traveler is evolving.

Although Chinese travelers did not have opportunities to travel internationally over the past three years, they continued to travel domestically and explore new offerings. Annual domestic trips remained at around 50 percent of pre-pandemic levels, amounting to 8.7 billion domestic trips over the past three years. 14 China’s Ministry of Culture and Tourism. During this time, the domestic market matured, and travelers became more sophisticated as they tried new leisure experiences such as beach resorts, skiing trips, and “staycations” in home cities. Chinese travelers became more experienced as thanks to periods of low COVID-19 infection rates domestically they explored China’s vast geography and diverse experiences on offer.

Consequently, the post-COVID-19 Chinese traveler is even more digitally savvy, has high expectations, and seeks novel experiences. These are some of the characteristics of a typical traveler:

• Experience-oriented: Wave 6 of the survey shows that the rebound tourist is planning their trip around experiences. Outdoor and scenic trips remain the most popular travel theme. In survey waves 1 to 3, sightseeing and “foodie” experiences were high on the list of preferences while traveling. From waves 4 to 6, culture and history, beaches and resorts, and health and wellness gained more attention—solidifying the trend for experience-driven travel. Additionally, possibly due to the hype of the Winter Olympics, skiing and snowboarding have become popular activities.
• Hyper-digitized: While digitization is a global trend, Chinese consumers are some of the most digitally savvy in the world; mobile technologies and social media are at the core of daily life. COVID-19 drove people to spend more time online—now short-form videos and livestreaming have become the top online entertainment options in China. In the first half of 2022, Chinese consumers spent 30 percent of their mobile internet time engaging with short videos. 15 “In the first half of the year, the number of mobile netizens increased, and short videos accounted for nearly 30% of the total time spent online,” Chinadaily.com, 27 July 2022.
• Exploration enthusiasts: Chinese travelers are also keen to explore the world and embark on novel experiences in unfamiliar destinations. Survey respondents were looking forward to visiting new attractions, even when travel policies limited their travel radius. Instead of revisiting destinations, 45 percent of respondents picked short trips to new sites as their number one choice, followed by long trips to new sites as their second choice.

Consumers are optimistic, and travel spending remains resilient

McKinsey’s 2022 research on Chinese consumer sentiment shows that although economic optimism is seeing a global decline, 49 percent of Chinese respondents reported that they are optimistic about their country’s economic recovery. Optimism had dropped by 6 percentage points since an earlier iteration of the survey, but Chinese consumers continue to be more optimistic than other surveyed countries, apart from India (80 percent optimistic) and Indonesia (73 percent optimistic) (Exhibit 3). 16 “ Survey: Chinese consumer sentiment during the coronavirus crisis ,” McKinsey, October 13, 2022.

Chinese consumers are still keen to spend on travel, and travel spending is expected to be resilient. Wave 6 of the tourist attitude survey saw 87 percent of respondents claiming that they will spend more or maintain their level of travel spending. Moreover, when consumers were asked “which categories do you intend to splurge/treat yourself to,” travel ranked second, with 29 percent of respondents preferring travel over other categories. 17 “ Survey: Chinese consumer sentiment during the coronavirus crisis ,” McKinsey, October 13, 2022.

Against this context of consumer optimism, the wave 6 tourist attitude survey results shed light on how travelers plan to spend, and which segments are likely to spend more than others:

• The wealthier segment and older age groups (age 45-65) show the most resilience in terms of travel spend. Around 45 to 50 percent of travelers in these two groups will spend more on their next leisure trip.
• The wealthier segment has shown the most interest in beach and resort trips (48 percent). Instead of celebrating Chinese New Year at home with family, 30 percent of Chinese travelers in the senior age group (age 55-65) expect to take their next leisure trip during this holiday—10 percent more than the total average. And the top three trip preferences for senior travelers are culture, sightseeing, and health-themed trips.
• When it comes to where travelers plan to spend their money on their next trip, entertainment activities, food, and shopping are the most popular categories. These are also the most flexible and variable spending categories, and there are opportunities to up-sell—attractions, food and beverage, and retail players are well positioned to create unique and unexpected offerings to stimulate spending in this area (Exhibit 4).

Independent accommodation is gaining popularity

Overall, Chinese consumers have high expectations for products and services. McKinsey’s 2023 consumer report found that local brands are on the rise and consumers are choosing local products for their quality, not just for their cheaper prices. Chinese consumers are becoming savvier, and tap into online resources and social media to educate themselves about the specific details and features of product offerings. 18 Daniel Zipser, Daniel Hui, Jia Zhou, and Cherie Zhang, 2023 McKinsey China Consumer Report , McKinsey, December 2022.

Furthermore, 49 percent of Chinese consumers believe that domestic brands are of “better quality” than foreign brands—only 23 percent believe the converse is true. Functionality extended its lead as the most important criterion influencing Chinese consumers, indicating that consumers are focusing more on the functional aspects of products, and less on emotional factors. Branding thus has less influence on purchasing decisions. 19 Daniel Zipser, Daniel Hui, Jia Zhou, and Cherie Zhang, 2023 McKinsey China Consumer Report , McKinsey, December 2022.

These broader consumer sentiments are echoed in the travel sector. Chinese travelers pay attention to cost, but do not simply seek out the lowest prices. While 17 percent of wave 6 respondents are concerned about low prices, 33 percent are on the hunt for value-for-money offerings, and 30 percent prefer good discounts and worthwhile deals.

And consumer sentiment regarding local brands holds true for travel preferences. Independent travel accommodation continues to be the preferred choice for most respondents, increasing in share against international chain brand hotels (Exhibit 5). Almost 60 percent of respondents prefer independent accommodation such as boutique hotels, B&Bs, and Airbnb—an 8 percentage-point increase since 2020.

Local chain brand hotels remain stable, the favored accommodation for 20 percent of respondents. These hotels are seen as a more standardized option, and as most are located in urban areas, they target the budget traveler segment.

Opting for independent accommodation is not considered a trade down; Chinese travelers expect a high level of service. In particular, respondents in the wealthier segment picked independent options (57 percent) over international premium brands (27 percent).

Premium independent options for the wealthier segment are abundant, specifically in leisure travel. Setting up a premium brand hotel requires long-term construction periods and heavy capital investment. Small-scale boutique hotels or B&Bs, on the other hand, are more agile solutions that can ramp up in the short term. This may explain the abundance of premium independent offerings. For instance, in destinations such as Lijiang and Yangshuo, between seven and nine of the top-ten premium hotels listed on Ctrip are independent boutique hotels.

Premium independent accommodation’s strength lies in quality guest experience with a genuine human touch. The service level at premium independent establishments can even surpass that of chain brand accommodation thanks to the high staff-to-room ratio, which easily reaches 3:1 or even 5:1. 20 “Strategic marketing analysis of boutique hotels,” Travel Daily , June 3, 2015. For hotels in Xiamen, Lijiang, and Yangshou, Ctrip service ratings of premium independent hotels are all above 4.7, outperforming international chain brand hotels.

Travelers are becoming smarter and more realistic during hotel selection, focusing on fundamental offerings such as local features and value for money. Across all types of hotels, local features are one of the most important factors influencing hotel selection—even for chain brand hotels which have a reputation for mastering the standardized offering. On average, 34 percent of respondents report that local features and cultural elements are the key considerations affecting their choice of hotel.

Outbound Chinese tourists are evolving rapidly, becoming increasingly diverse in their travel preferences, behaviors, and spending patterns. Chinese travelers are not homogeneous, and their needs and preferences continue to evolve. Therefore, serving each group of tourists may require different product offerings, sales channels, or marketing techniques.

The path toward eco-friendly travel in China

How international travel and tourism can attract outbound chinese travelers.

China’s lifting of travel restrictions may cause some uncertainty in the short term, but a promising recovery lies ahead. Chinese tourists have maintained a strong desire to travel internationally and are willing to pay for this experience. They are also discerning and looking for high-quality accommodation, offerings, and service. As boutique hotels are becoming more popular, international hotel brands hotels could, for example, aim to stand out by leveraging their experience in service excellence.

With renewed travel demand, now may be the time for international travel and tourism businesses to invest in polishing product offerings—on an infrastructural and service level. Tourism, food and beverage, retail, and entertainment providers can start preparing for the rebound by providing unique and innovative experiences that entice the adventurous Chinese traveler.

Craft an authentically local offering that appeals to experience-driven Chinese travelers

Chinese travelers have suspended overseas trips for three years, and are now looking to enjoy high-quality experiences in destinations they have been to before. They also want to do more than shopping and sightseeing, and have expressed willingness to spend on offerings geared towards entertainment and experience. This includes activities like theme parks, snow sports, water sports, shows, and cultural activities. Authentic experiences can satisfy their desire for an immersive foreign experience, but they often want the experience to be familiar and accessible.

Designing the right product means tapping into deep customer insights to craft offerings that are accessible for Chinese travelers, within a comfortable and familiar setting, yet are still authentic and exciting.

Travel and tourism providers may also have opportunities to up-sell or cross-sell experiences and entertainment offerings.

Social media is essential

Social media is emerging as one of the most important sources of inspiration for travel. Short video now is a major influence channel across all age groups and types of consumers.

Tourist destinations have begun to leverage social media, and short video campaigns, to maximize exposure. For example, Tourism Australia recently launched a video campaign with a kangaroo character on TikTok, and overall views soon reached around 1.67 billion.

The story of Ding Zhen, a young herder from a village in Sichuan province, illustrates the power of online video in China. In 2020, a seven-second video of Ding Zhen turned him into an overnight media sensation. Soon after, he was approached to become a tourism ambassador for Litang county in Sichuan—and local tourism flourished. 21 “Tibetan herder goes viral, draws attention to his hometown in SW China,” Xinhuanet, December 11, 2020. Another Sichuan local, the director of the Culture and Tourism Bureau in Ganzi, has drawn visitors to the region through his popular cosplay videos that generated 7 million reviews. Building on the strength of these influential celebrities, visitor numbers to the region were said to reach 35 million, more than two-and-a-half times 2016 volumes. 22 “Local official promoting Sichuan tourism goes viral on internet,” China Daily, June 17, 2022; “The Director of Culture and Tourism disguises himself as a “Swordsman” knight to promote Ganzi tourism,” Travel Daily , June 17, 2022.

Online travel companies are also using social media to reach consumers. Early in the pandemic, Trip.com took advantage of the upward trend in livestreaming. The company’s co-founder and chairman of the board, James Liang, hosted weekly livestreams where he dressed up in costume or chatted to guests at various destinations. Between March and October 2020, Liang’s livestreams sold around $294 million’s worth of travel packages and hotel room reservations. 23 “Travel companies adapt to a livestreaming trend that may outlast the pandemic,” Skift, October 26, 2020.

Livestreaming is being used by tourism boards, too. For instance, the Tourism Authority of Thailand (TAT) collaborated with Trip.com to launch a new campaign to attract Chinese tourists to Thailand as cross-border travel resumed. The broadcast, joined by TAT Governor Mr Yuthasak Supasorn, recorded sales of more than 20,000 room nights amounting to a gross merchandise value of over RMB 40 million. 24 “Trip.com Group sees border reopening surge in travel bookings boosted by Lunar New Year demand,” Trip.com, January 13, 2023.

International tourism providers looking to engage Chinese travelers should keep an eye on social media channels and fully leverage key opinion leaders.

Scale with the right channel partners

Travel distribution in China has evolved into a complex, fragmented, and Chinese-dominated ecosystem, making scaling an increasingly difficult task. Travel companies need to understand the key characteristics of each channel type, including online travel agencies (OTAs), online travel portals (OTPs), and traditional travel agencies as each target different customer segments, and offer different levels of control to brands. It also takes different sets of capabilities to manage each type of distribution channel.

Travel companies can prioritize the channels they wish to use and set clear roles for each. One challenge when choosing the right channel partner is to avoid ultra-low prices that may encourage volume, but could ultimately damage a brand.

Meanwhile, given the evolution of the postCOVID-19 industry landscape and rapid shifts in consumer demand, travel companies should consider direct-to-consumer (D2C) channels. The first step would be selecting the appropriate D2C positioning and strategy, according to the company’s needs. In China, D2C is a complicated market involving both public domains (such as social media and OTA platforms) and private domains (such as official brand platforms). To make the most of D2C, travel companies need a clear value proposition for their D2C strategy, whether it be focused on branding or on commercial/sales.

Create a seamless travel experience for the digitally savvy Chinese tourist

China has one of the most digitally advanced lifestyles on the planet. Chinese travelers are mobile-driven, wallet-less, and impatient—and frequently feel “digitally homesick” while abroad. Overseas destinations and tourism service providers could “spoil” tech-savvy Chinese travelers with digitally enhanced service.

China’s internet giants can provide a shortcut to getting digital services off the ground. Rather than building digital capabilities from scratch, foreign tourism providers could engage Chinese travelers through a platform that is already being used daily. For example, Amsterdam’s Schiphol Airport provides a WeChat Mini Program with four modules: duty-free shopping, flight inquiry, information transfer, and travel planning. This contains information about all aspects of the airport, including ground transportation and tax refund procedures.

Alibaba’s Alipay, a third-party mobile and online payment platform, is also innovating in this space. The service provider has cooperated with various tax refund agencies, such as Global Blue, to enable a seamless digitized tax refund experience. Travelers scan completed tax refund forms at automated kiosks in the airport, and within a few hours, the refunded amount is transferred directly to their Alipay accounts. 25 “Alipay and Global Blue to make tax refunds easy for Chinese tourists,” Alizila, June 23, 2014.

Such digital applications are likely to be the norm going forward, not a differentiator, so travel companies that do not invest in this area may be left behind.

Chinese travelers are on the cusp of returning in full force, and tourism providers can start preparing now

With China’s quarantine requirements falling away at the start of 2023, travelers are planning trips, renewing passports and visas, and readying themselves for a comeback. Chinese tourists have not lost their appetite for travel, and a boom in travel demand can be expected soon. Though airlines are slow to restore capacity, and some destination countries are more risk averse when welcoming Chinese travelers, there are still options for Chinese tourists to explore destinations abroad.

Tourism providers can expect to welcome travelers with diverse interests who are willing to spend money on travel, who are seeking out exciting experiences, and who are choosing high-quality products and services. The returning Chinese traveler is digitally savvy and favors functionality over branding—trends suggest that providers who can craft authentic, seamless, and unique offerings could be well positioned to capture this market.

Guang Chen and Jackey Yu are partners in McKinsey’s Hong Kong office, Zi Chen is a capabilities and insights specialist in the Shanghai office, and Steve Saxon is a partner in the Shenzhen office.

The authors wish to thank Cherie Zhang, Glenn Leibowitz, Na Lei, and Monique Wu for their contributions to this article.

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China’s Tourism Sector Prospects in 2023-24

Amid the post-pandemic recovery, China’s tourism sector is rebounding with vigor in 2023. We discuss the resurgence of outbound and domestic travel, evolving traveler behavior, and tech-enabled trends in this article. From cultural exploration to wellness escapes and digital integration, the stage is set for foreign businesses and investors to seize opportunities in this transformed landscape.

After enduring the significant impacts of the COVID-19 pandemic, China’s tourism sector is gearing up for a strong resurgence in 2023. Projections indicate that the total revenue from domestic tourism is expected to exceed RMB 4 trillion (approximately US$580.96 billion), marking an impressive 96 percent growth. Several driving forces contribute to this revival in China’s tourism landscape, including:

• Easing of travel restrictions;
• Increase in disposable income among Chinese consumers; and
• Growing popularity of domestic tourism.

In particular, the government’s support in revitalizing the tourism sector is evident through subsidies and tax exemptions provided to tourism enterprises. The robust resurgence of China’s tourism industry also serves as a positive indicator for the nation’s economy, with tourism being a significant driver of economic growth and expected to contribute notably to the country’s GDP. Overall, 2023 has seen a continuous stream of new policies, products, technologies, concepts, trends, and opportunities impacting the tourism industry.

China’s evolving tourism landscape

Insights from outbound tourism in h1 of 2023.

Both outbound and inbound tourism markets in the first half of 2023 have shown impressive vitality, surpassing the levels observed in the same period of 2019. Average expenditures for outbound travelers have exhibited a notable increase, with Hong Kong and Macao leading the resurgence of outbound tourism. The total number of inbound and outbound individuals has surged by approximately 170 percent.

Data from the World Tourism Alliance’s reports, reveal that the outbound tourism sentiment index reached 28 percent in the first half of 2023, marking a 21-point increase from the same period in 2019. The outbound tourism market has displayed a gradual “U-shaped” recovery, emphasizing a steady resurgence rather than an abrupt rebound.

According to recent data from Alipay’s Overseas Spending Platform, the average expenditure per user for outbound travel in the first half of 2023 grew by 24 percent compared to 2019. Among popular destinations, the top 10 outbound travel destinations in terms of transaction volume for the first half of 2023 were:

• South Korea;
• United Kingdom; and

This data is supported by several favorable policies. Since the beginning of the year, the National Immigration Administration has continuously optimized and adjusted inbound and outbound management policies.

Starting from February 20, 2023, mainland cities within the Greater Bay Area initiated a pilot implementation of visa endorsements for cross-border talent to and from Hong Kong and Macao. On May 15, 2023, policies such as the nationwide implementation of group travel endorsements for mainland residents traveling to Hong Kong and Macao were fully restored.

The streamlined and optimized policies for travel to Hong Kong and Macao prompted provinces across the mainland to organize multiple tour groups, leading to a consistent rise in mainland visitors to these regions. According to data released by the Hong Kong Tourism Board, nearly 13 million visitors arrived in Hong Kong in the first half of 2023, of which approximately 10 million were mainland visitors, accounting for around 77 percent of the total.

Furthermore, based on recent data released by the National Immigration Administration, the first half of 2023 witnessed a total of 168 million inbound and outbound individuals passing through China’s immigration, marking a year-on-year increase of 169.6 percent.

At the same time, approximately 42.798 million entry and exit permits for travel to and from Hong Kong, Macao, and Taiwan were issued, indicating a significant 1509 percent increase compared to the same period in 2022.

These figures further underline China’s promising revival in outbound tourism. Indeed, Chinese tourists have once again become a significant force driving global tourism and offline consumption.

In terms of outbound travel numbers, the top 10 departure cities were: Shenzhen, Shanghai, Guangzhou, Beijing, Hangzhou, Foshan, Dongguan, Zhuhai, Chengdu, and Wuhan. This highlights that outbound travel is mainly concentrated in first-tier and new first-tier cities, with the “Guangzhou-Shenzhen-Foshan-Dongguan-Zhuhai” Greater Bay Area cities also playing a pivotal role in outbound tourism.

The primary reason driving Chinese tourists to travel abroad is leisure, with business and visiting friends and relatives (VFR) as the subsequent motivations. The rapid expansion of outbound tourism from China can be attributed to the rising incomes of the middle class , the growing desire among Chinese travelers to explore diverse countries and cultures, and the ease of obtaining visas and fulfilling entry criteria for various destinations.

Moreover, the retail sector captures the largest portion of Chinese tourists’ spending when traveling abroad and is anticipated to retain its dominant position in terms of outbound tourism expenditure over the projected timeframe.

The steady recovery of outbound tourism

Initial expectations for a robust rebound in outbound tourism this year have encountered a more precarious reality. Notable evidence of this transformation is seen in the changing preferences of Chinese leisure travelers. As reported by CNBC, the desire to travel abroad has surged from 28 percent to 52 percent among Chinese leisure travelers since last year, nearly doubling.

Business travel intentions have tripled, and interest in education, family visits, and medical tourism abroad is also on the rise. Other findings align, revealing that 50 percent of Chinese travelers plan to journey internationally within the next year.

A significant shift has also occurred in travel fears, particularly concerning Covid contraction. While it topped travelers’ concerns in 2022, it has diminished to the least worrisome aspect this year, as per Morning Consult’s survey. This shift reflects growing traveler confidence. Factors influencing this gradual recovery go beyond preferences. A recent report from the Mastercard Economics Institute reveals a shift in Chinese residents’ spending patterns.

Known for their shopping inclination, there’s a rising trend toward investing in experiences over possessions, particularly in a zero-Covid environment. Despite global economic uncertainties, Asia-Pacific’s, including China’s, travel recovery remains steady. As travel capacity grows, costs are anticipated to decrease, fueling a more dynamic travel landscape.

Contrary to an instant “boom,” China’s international travel revival is unfolding steadily. Though not as swift as initially projected, the evolving interests, changing attitudes, and gradual shift toward experiential spending all point to a growing and adaptive outbound tourism sector, offering a promising glimpse into the future.

The Chinese government’s recent efforts to revive outbound group travel

China’s Ministry of Culture and Tourism recently expanded outbound group tour destinations, including popular places like Japan and the US. A recent analysis provided by the EIU indicates that this move will aid global tourism recovery, benefiting countries with simplified visa procedures.

While the relaxed restrictions will moderately boost outbound tourism, obstacles and cautious spending persist. Nonetheless, domestic travel agencies are expected to see increased revenue, leading to employment and income growth in the sector.

However, challenges such as limited flights and labor shortages could hinder outbound tourism’s full recovery. A complete relaxation of restrictions is predicted in late 2023, but pre-pandemic outbound levels might not return until 2025.

Domestic tourism is thriving

In the first half of 2023, domestic tourism revenue (total tourist spending) reached RMB 2.3 trillion (approx. US$318 billion), marking a substantial increase of RMB 1.12 trillion (approx. US$155 billion) compared to the previous year. Notably, urban residents’ expenditures on travel accounted for a year-on-year surge of 108.9 percent, while rural residents’ travel spending grew by 41.5 percent.

The remarkable rebound of China’s domestic tourism sector can be attributed to a set of factors that differentiate it from the relatively slower recovery of outbound tourism. For one, the domestic tourism industry appears to be less affected by uncertainties surrounding employment and income growth compared to other service and retail sectors.

This is primarily due to the strong yearning of Chinese consumers to explore after years of mobility limitations imposed by the pandemic.

On the other hand, the prolonged revival of outbound flights has further bolstered the domestic tourism scene. Many individuals redirected their travel plans within China as international travel remained limited.

Notably, the return of international air traffic to approximately 80 percent of pre-pandemic levels is not expected until the fourth quarter of 2023, which creates a favorable environment for the vigorous resurgence of domestic tourism in the meantime.

Changing Chinese travelers’ preferences in 2023

In the wake of the COVID-19 pandemic and the subsequent travel restrictions, Chinese travelers underwent a transformation in their preferences and behaviors. Over the past three years, while international travel remained limited, domestic exploration thrived.

Around 8.7 billion domestic trips were taken, indicating an annual rate of around 50 percent of pre-pandemic levels. This period allowed the domestic market to mature, and travelers became more sophisticated in their pursuits, engaging in various new leisure experiences such as beach resorts, skiing trips, and city “staycations.”

As a result, the post-COVID-19 Chinese traveler exhibits distinct traits: heightened digital savvy, elevated expectations, and an appetite for novel experiences. These characteristics paint the profile of a typical Chinese traveler in 2023:

• Experiences matter: Survey data reveals that the rejuvenated Chinese tourist is driven by experiential travel. While outdoor and scenic trips remain popular, the preferences have evolved. Sightseeing and culinary experiences, highly valued in the initial survey series, are now joined by a growing interest in culture and history, beaches, and resorts, as well as health and wellness. This shift solidifies the trend towards experience-driven travel. Additionally, activities like skiing and snowboarding have gained popularity, possibly influenced by the 2022 Beijing Olympic Winter Games .
• Digital expert: Chinese travelers are among the world’s most digitally adept consumers, easily integrating mobile technologies and social media into their daily lives. The pandemic further propelled their online engagement. Short-form videos and livestreaming have emerged as dominant online entertainment options.
• Curious: The desire to explore novel experiences in unfamiliar destinations remains strong among Chinese travelers. Despite travel radius limitations imposed by policies, survey respondents express eagerness to visit new attractions. Instead of revisiting familiar places, 45 percent of participants prioritize short trips to new sites, while long trips to new destinations are the second most favored option.

Emerging trends and destinations

Cultural and heritage tourism.

A significant shift in China’s tourism landscape is the increasing emphasis on cultural tourism, where traditional heritage seamlessly intertwines with contemporary travel. As the nation preserves and celebrates its abundant historical and cultural treasures, a surge in cultural tourism activities like immersive experiences and interactive exchanges has taken center stage.

This trend is particularly pronounced in the realm of domestic tourism, where travelers are flocking to heritage sites and cultural landmarks to gain a deeper understanding of China’s rich heritage.

Moreover, the development of cultural and tourism industries constitutes a crucial component of China’s cultural confidence-building efforts. This sector has received significant attention from the government, evidenced by policies like the “14th Five-Year Plan for Cultural Development” and the “14th Five-Year Plan for Tourism Industry Development.” Such policies drive the integration of culture and tourism, increase the supply of cultural tourism products, and enhance the quality of such offerings.

Wellness tourism

In 2023, a remarkable shift in travel preferences among Chinese tourists has propelled wellness and health tourism to the forefront. As observed by Rung Kanjanaviroj, Director of the Tourism Authority of Thailand’s Chengdu office, Chinese travelers are displaying a distinct preference for destinations that offer a blend of sunny beaches and holistic well-being experiences.

This evolving trend has prompted destinations like Thailand to proactively adapt by refining their offerings. Through the enhancement of health tourism services and a focus on engaging student and youth travelers, Thailand has positioned itself as a prime destination for those seeking rejuvenation and self-care during their journeys.

The rise in wellness and health tourism reflects a broader shift in Chinese travelers’ priorities, as they seek destinations that not only provide scenic beauty but also nurture their physical and mental well-being.

Tech-enabled tourism in China’s innovative travel landscape

China’s tourism industry has evolved dramatically through the fusion of technology and changing consumer demands. In 2023, the landscape is marked by a growing emphasis on tech-enhanced experiences that cater to modern travelers’ evolving preferences that foreign businesses and investors in the sector can learn from.

• Smart appliances and IoT integration: China’s tech-driven tourism trend showcases the integration of smart appliances and the Internet of Things (IoT) into the travel journey. Travelers now wield the power to personalize their environment and encounters via smartphone apps. Innovations range from smart hotel rooms adjusting lighting, temperature, and ambiance to IoT-enabled transportation providing real-time updates, enhancing comfort and efficiency.
• Virtual and augmented reality immersion: Tech-savvy Chinese travelers are increasingly seeking immersive encounters. Virtual and augmented reality (VR/AR) have taken center stage, enabling tourists to explore historical sites, cultural landmarks, and natural marvels through virtual tours that breathe life into destinations. This not only enhances engagement but also serves as a potent tool for destination marketing.
• Seamless contactless services and digital payments : Contactless services and digital payments have become integral to China’s tech-enhanced tourism scene. Travelers can navigate touchpoints like check-in, security, dining, and shopping with minimal physical interaction. QR codes have revolutionized payment methods, enabling transactions through smartphones, and eliminating the need for physical currency or cards, in alignment with the country’s cashless society drive.

The city of Hangzhou offers a glimpse into the future of tech-enabled tourism. Hangzhou’s West Lake, a UNESCO World Heritage site, now features interactive kiosks that provide historical context, virtual guides, and navigation assistance to visitors. These digital enhancements blend seamlessly with the serene natural landscape, enriching the cultural experience.

Similarly, the China National Tourist Office uses VR to transport potential travelers to iconic destinations. Through immersive VR experiences, individuals can virtually explore the Great Wall, the Terracotta Army, and other renowned sites, sparking wanderlust and encouraging travel planning.

Preparing for the return of Chinese tourists to the international scene

The gradual easing of travel restrictions in China still presents a promising avenue for the recovery of the international travel and tourism sector. Amid this positive outlook, attracting Chinese tourists is becoming a priority for global businesses.

Chinese travelers, known for their enthusiasm to explore beyond their borders, are now seeking immersive experiences, quality accommodation, and exceptional service. Here are some strategies that foreign businesses can employ to entice and captivate the adventurous Chinese traveler.

Crafting authentic and familiar experiences

After a three-year hiatus from overseas travel, Chinese tourists are now yearning for high-quality experiences in familiar destinations.

They are looking beyond traditional shopping and sightseeing, expressing a keen interest in entertainment and experiential offerings. Theme parks, cultural activities, water sports, snow sports, and shows are among the sought-after activities.

The key is to offer authentic experiences that resonate with Chinese travelers’ desires for immersion, while still maintaining a touch of familiarity.

Businesses should leverage deep customer insights to design offerings that strike a balance between accessibility and authenticity, ensuring a comfortable yet exciting experience.

Harnessing the power of social media

Social media, particularly short videos, has emerged as a pivotal source of travel inspiration for all age groups. Tourist destinations have capitalized on this trend by launching engaging short video campaigns, maximizing exposure and engagement.

The burgeoning trend of city-walking , for example, where urban exploration is undertaken solely on foot, has not only captured the attention of locals but has also made significant waves across various social media platforms. Chinese netizens are embracing this form of experiential travel, and businesses can leverage social media to align with their preferences.

Platforms like Douyin, China’s counterpart to TikTok, have witnessed the rise of “city-walk content”. A recent video showcasing city-walk routes in Guangzhou amassed over 171,000 likes and found its way into the favorites of 72,000 viewers.

Furthermore, Xiaohongshu, a prominent lifestyle-sharing platform in China, reported a remarkable 30-fold increase in searches related to city walk during the first half of 2023 compared to the previous year.

Businesses can leverage social media platforms to connect with potential Chinese tourists, employing captivating content and innovative campaigns to pique their interest. Creating a strong presence on platforms like TikTok and engaging with influential figures can significantly boost visibility.

Collaboration with Internet giants

China’s tech-savvy travelers are deeply intertwined with the digital world, and internet giants like WeChat and Alipay play a pivotal role in their daily lives. Foreign businesses can tap into these existing digital ecosystems rather than starting from scratch.

For instance, Amsterdam’s Schiphol Airport offers a WeChat Mini Program providing information about the airport, including duty-free shopping and travel planning. Alibaba’s Alipay, renowned for its mobile payment capabilities, has partnered with tax refund agencies to streamline the tax refund process for Chinese travelers.

Such digital innovations enhance convenience and are fast becoming an expected norm.

Prioritize direct-to-consumer (D2C) channels

Navigating China’s intricate travel distribution landscape can be complex, as it encompasses diverse channels, such as online travel agencies (OTAs), online travel portals (OTPs), and traditional travel agencies. To make the most of this landscape, businesses can consider embracing D2C channels.

By leveraging social media platforms and official brand platforms, businesses can create a compelling value proposition that resonates with Chinese travelers. Investing in D2C channels not only enhances branding but also facilitates direct engagement with potential tourists, allowing for a personalized and enticing approach.

Key takeaways: Navigating China’s tourism resurgence

All in all, in 2023, China’s tourism is making a strong comeback, driven by key trends that reveal changing traveler preferences.

Domestically, easier travel rules and higher incomes are fueling local exploration. Internationally, outbound tourism is gradually recovering with a focus on immersive experiences, wellness, and cultural discovery.

Chinese travelers are becoming more tech-savvy, seeking out tech-enhanced experiences like virtual reality tours. This shift is boosting cultural, heritage, and wellness tourism.

Social media, especially platforms like TikTok and WeChat, are vital for engaging with Chinese travelers effectively.

In essence, China’s tourism resurgence is multifaceted, with travelers seeking enriched experiences, digital engagement, and authenticity.

Businesses that align with these preferences and capitalize on domestic and international opportunities are likely to thrive in the evolving travel landscape.

China Briefing is written and produced by Dezan Shira & Associates . The practice assists foreign investors into China and has done so since 1992 through offices in Beijing, Tianjin, Dalian, Qingdao, Shanghai, Hangzhou, Ningbo, Suzhou, Guangzhou, Dongguan, Zhongshan, Shenzhen, and Hong Kong. Please contact the firm for assistance in China at [email protected] .

Dezan Shira & Associates has offices in Vietnam , Indonesia , Singapore , United States , Germany , Italy , India , Dubai (UAE) , and Russia , in addition to our trade research facilities along the Belt & Road Initiative . We also have partner firms assisting foreign investors in The Philippines , Malaysia , Thailand , Bangladesh .

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Graphics: China's tourism industry sees strong recovery in 2023

China's tourism industry almost brought 2023 revenue back to its pre-pandemic level, as recovery was largely driven by pent-up domestic demand after the removal of cross-city travel restrictions in January 8 and the accelerated integration of "culture, sports, and tourism."

The country's tourism revenue is expected to reach 5.2 trillion yuan ($730 billion) in 2023, which is 91 percent of the 2019 figure, according to the China Tourism Academy, a research institution under the Chinese Ministry of Culture and Tourism.

The tourism sector was a major economic contributor, accounting for 11 percent of the country's GDP in 2019. Its recovery was a result of the country's efforts to stimulate consumption and revitalize the economy.

Although the increase in COVID-19 infection rates after the sudden removal of restrictions led to uncertainty and hesitancy to travel in the short term, domestic travel sentiment recovered strongly by the Spring Festival holidays as China was past its infection peak.

The Spring Festival holidays in late January saw 308 million domestic trips, generating 376 billion yuan in tourism revenue. The domestic travel volume has recovered to 88.6 percent of the 2019 figure, while spending has bounced back to 73.1 percent of pre-pandemic levels.

The Labor Day holiday in late April and early May also saw a staggering 274 million domestic trips, with a nearly 20 percent increase over the pre-pandemic level. The spending was 148 billion yuan, about 100.7 percent of the 2019 level.

The country's eight-day National Day holiday sparked a travel and consumption frenzy. The extended vacation period registered 826 million domestic trips, with an increase of 4.1 percent. The holiday generated tourism revenue of 753 billion yuan, growing 1.5 percent from the 2019 level.

To attract tourists, local governments organized a number of events, including music festivals, concerts, exhibitions and sports competitions. Nearly 100 concerts and performances have taken place in major Chinese cities during the holiday to fuel the local tourism boom.

The Hangzhou Asian Games, which coincided with the Mid-Autumn Festival and National Day holiday and featured the largest number of participants in the event's history, stimulated the local economy.

Hotel bookings in Asian Games cities increased over 200 percent compared with the same period in 2019. Dine-in restaurant orders in Hangzhou surged 443 percent, sports and fitness orders in the city shot up 762 percent compared to the pre-pandemic level.

The year 2023 also witnessed the recovery of China's civil aviation. Domestic flight passenger volume in 2023 surpassed that of 2019, and it is expected to increase 19 percent in 2024 compared with 2023.

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The evolution and new trends of China's tourism industry

• Yanyun Zhao , 
• Bingjie Liu , 
• School of Statistics, Renmin University of China, Beijing, China
• Received: 02 September 2020 Accepted: 20 October 2020 Published: 22 October 2020

JEL Codes: L83, L88, Z13, Z18

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Citation: Yanyun Zhao, Bingjie Liu. The evolution and new trends of China's tourism industry[J]. National Accounting Review, 2020, 2(4): 337-353. doi: 10.3934/NAR.2020020

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Tourism industry on way to full recovery

There have been strong signs since last year suggesting the tourism sector is on way to full recovery. Passenger trips within the country in 2023 increased to 4.89 billion, 93.3 percent more than the previous year, with domestic travelers spending 4.91 trillion yuan ($691.2 billion), up 140.3 percent year-on-year, according to the Ministry of Culture and Tourism.

The huge domestic tourism market has been helping stabilize the global tourism industry as well as driving its growth. The number of tourists in China has been continuously increasing over the past four decades, with domestic passenger trips jumping from less than 500 million in the 1980s to more than 6 billion in 2019 at an average annual growth of about 10 percent.

China boasts both one of the world's largest inbound tourism markets and a massive domestic tourist industry, which has been fast recovering after the COVID-19 pandemic. Official data show that during the eight-day Spring Festival holidays in February, 474 million domestic passenger trips were made, up 34.3 percent year-on-year, with the total domestic tourism spending increasing 47.3 percent year-on-year to about 632.69 billion yuan. Also, about 119 million domestic passenger trips were made during the three-day Qingming Festival holiday earlier this month, an increase of 11.5 percent over the same period in 2019, with the domestic tourism industry's revenue reaching 53.95 billion yuan, up 12.7 percent compared with the same period in 2019.

A large number of Chinese tourists have traveled or are willing to travel abroad this year, while other countries are learning from China's innovative development model to make their tourism industries more resilient. For example, barbecue in Zibo, Shandong province; malatang, a soup containing boiled meat and vegetables seasoned with mouthwatering, spicy scarlet chili oil, in Tianshui, Gansu province; and the ice-snow tourism festival in Harbin, Heilongjiang province, all have boosted domestic tourism. Their sound infrastructure, clean image and excellent public services have attracted even foreign internet influencers.

The huge domestic tourism market and supporting industries are China's advantages, and they have accumulated rich experiences which the global tourism industry can reference. China's tourism industry is treading the right path to optimize the tourism products, promoting the high-quality development of tourist destinations and developing new tourism formats.

China's inbound and outbound tourism sectors both have performed well this year. During the Spring Festival holidays, Chinese people made 3.6 million outbound trips, close to the 2019 level. And while the number of outbound passenger trips could reach 130 million this year and inbound tourist footfalls could recover to 50 percent of the 2019 level, the inbound tourism markets of the Hong Kong and Macao special administrative regions and the Taiwan island province are expected to make fast recovery, according to the China Tourism Academy.

Therefore, it can be safely said that China's tourism industry is on way to full recovery and is injecting new impetus into the global tourism industry. China's tourism industry shares with the rest of the world its development opportunities and strives to promote the development of a more open, more cooperative and higher-quality tourism market. For example, many tourist destinations across the world have benefited from Chinese tourists, as the swelling numbers of Chinese tourists in other countries have helped create more jobs and boost people-to-people exchanges.

The Chinese government has been taking measures to boost the tourism sector, for example, by encouraging Chinese nationals to visit foreign countries, which incidentally will help the global tourism industry to recover. The fact that an increasing number of Chinese tourists visited countries involved in the Belt and Road Initiative in 2023 means more Belt and Road countries benefitting from the growth of China's tourism market.

Besides, China's high-quality opening-up requires high-quality cooperation and exchanges among countries, which tourism readily provides. Since the second half of last year, China has been introducing plans to make travel for foreign tourists easier. In July, China resumed visa-free entry for the citizens of Singapore and Brunei. From December, French, German, Italian, Malaysian, Dutch and Spanish nationals can get a 15-day visa-free entry into China for business, tourism, family visit and transit purposes.

Also, from March 14 this year, China has granted visa-free entry on a trial basis to visitors from Switzerland, Ireland, Hungary, Austria, Belgium and Luxembourg. Before that, in January, the National Immigration Administration introduced five new measures to facilitate foreigners' entry into the country, which include relaxation of port visa application requirements and provisions of visa extension, providing replacement and issuance services at local immigration departments for foreign nationals visiting or staying in China for non-diplomatic, non-official purposes, such as trade, investment or entrepreneurship or for visiting relatives.

Moreover, foreign nationals can enjoy 24-hour direct transit without undergoing border checks at nine major airports including those in Beijing, Shanghai, Hangzhou, Xiamen and Guangzhou. And while multiple-entry visas are available for foreigners, the requirements for visa have been streamlined for foreigners staying in China.

In addition, the government will launch a series of targeted measures to address existing problems and propel the inbound tourism market. For example, the Ministry of Culture and Tourism said at a news conference in March that it will make it easier for payments at various places such as tourist attractions, cultural and performance venues and star-rated hotels.

The government attaches great importance to people's desire for a better life, which includes good travel experience. The government links this desire with the recovery and sustainable development of the global tourism industry. The positive impact of the recovery of China's tourism sector will encourage more countries to work together to boost the global tourism industry.

The author is director of the International Institute at the China Tourism Academy.The views don't necessarily reflect those of China Daily.

If you have a specific expertise, or would like to share your thought about our stories, then send us your writings at [email protected], and [email protected].

China’s Inbound Tourism Surges, But Still Lags Behind 2019 Levels

Peden Doma Bhutia , Skift

August 23rd, 2024 at 8:10 AM EDT

Chinese tourism, both inbound and outbound, remains far below pre-Covid levels. Despite recent gains, China has significant ground to recover in its travel sector.

Peden Doma Bhutia

China brought in over 17.25 million foreign tourists from January to July this year, a 130% increase compared to 2023, according to latest data from the National Immigration Administration (NIA). The influx is set to generate over CNY 100 billion ($14 billion) in consumer spending, with per capita daily spending nearing CNY 3,459 ($485).

Despite the jump, China’s inbound arrivals are still far away from its 2019 numbers, when it had over 49 million overseas visitors. International tourism revenue reached $131.3 billion that year.

Visa-Exemption Policies

The surge in inbound tourism this year has been largely driven by the rollout and expansion of visa-exemption policies. The 144-hour visa-free transit policy now covers 37 ports and applies to citizens from 54 countries, including the U.S., Canada, and the UK. This policy lets travelers with valid international travel documents and onward tickets stay in designated areas of China for up to six days without a visa.

The NIA has also rolled out several region-specific visa-free entry policies. These include a 144-hour visa-free entry for foreign tour groups from Hong Kong and Macao into South China’s Guangdong Province, and for Southeast Asian country tour groups into Guilin, South China’s Guangxi Zhuang Autonomous Region. There’s also a 30-day visa-free entry for nationals from 59 countries into Hainan Province, and a 15-day visa-free entry for cruise ship tourists in coastal provinces.

China has also implemented a new 144-hour visa-free entry policy into Hainan for foreign tourist groups registered in Hong Kong and Macao. Over 5.9 million foreign tourists have traveled on this, according to NIA. In July, China extended the 144-hour visa-free transit to nine additional cities in Yunnan Province, including popular destinations like Lijiang and Dali.

Surge in Searches

In addition to visa exemptions, the NIA has made it easier for foreigners to apply for port visas at 100 visa offices across 73 cities in China. This change is particularly beneficial for those needing to enter the country on short notice for business or other urgent matters. Between January and July, China issued around 846,000 port visas to foreign nationals, marking a 183% increase from the previous year.

Trip.com has noted that in the first quarter of 2024, there was a 400% growth in inbound travel to China, driven by the benefits of such visa-free policies . After China announced visa-free travel for Australian tourists in June, Trip.com saw an 80% spike in China-related searches from Australian users within 30 minutes.

However, the rapid growth in inbound tourism has highlighted challenges in the sector, particularly regarding hotel services. Many foreign visitors have expressed concerns about the quality of lodging, prompting the ministry of commerce and six other departments to issue guidelines in July aimed at improving accommodation services for international guests.

As Skift Research noted in the latest State of Travel report, China did not make the list of top 10 countries for international arrivals in 2023.

The Digital Payment Switch

As China rapidly embraces a cashless economy , foreign travelers face a new challenge: adapting to a society where digital payments dominate. Traditional cash transactions are increasingly rare, with platforms like Alipay and WeChat Pay becoming the standard for everything from dining to transportation.

Alipay, the digital payment and lifestyle platform in China, announced on Wednesday that it now supports 16 languages on its app — English, Chinese, French, German, Italian, Spanish, Portuguese, Arabic, Russian, Turkish, Malay, Indonesian, Thai, Korean, Japanese, and Vietnamese.

According to Alipay, this service upgrade would enable international tourists, “to explore China like a local with Alipay,” using the language that they are familiar with.

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Photo credit: China's inbound tourism for Jan-July marks a 130% increase compared to 2023. 4045 / Freepik

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Transforming China’s Tourism Industry: The Impact of Industrial Integration on Quality, Performance, and Productivity

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The global tourism industry has witnessed significant growth, with China emerging as a powerhouse in this sector. However, China’s tourism faces challenges related to service quality, market organization, technology adoption, and market share loss. To address these issues, there is a growing consensus between the government and the industry to promote high-quality tourism. This paper explores the phenomenon of industrial integration, where boundaries between sectors within the tourism industry blur, leading to the emergence of new collaborative models and novel forms of tourism. While previous research has primarily focused on the economic impacts of such integration, this study delves deeper into its effects on quality development, using empirical data at the enterprise level. The findings reveal that industrial integration has a substantial positive impact on the performance and productivity of Chinese tourism companies. Companies embracing integration strategies exhibit better financial performance, as evidenced by higher returns on equity (ROE) and total factor productivity (TFP). These integrated firms demonstrate improved financial strength, profitability, and overall operational efficiency. Unexpectedly, the study uncovers the significant role of residential tourism consumption in integration rates, highlighting the complex relationship between local demand and industry dynamics. The implications of this research extend to industry leaders and policymakers, advocating for a transformative approach to integrated tourism. It emphasizes the potential of cross-sectoral collaboration, digital technology adoption, talent development, and policy support in enhancing performance and sustainability. The study’s insights empower stakeholders to contribute actively to the future of China’s tourism industry, fostering a holistic and sustainable approach to integration that benefits all stakeholders. This research contributes to a deeper theoretical understanding of integration’s dynamics, enriching the discourse on the interconnected factors driving integrated tourism models.

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Introduction

According to United Nations World Tourism Organization statistics, tourism has emerged as a powerful and expansive sector within the global industry (Boluk et al., 2019 ). In 2018, there were a total of 1.401 billion trips made globally, representing a 5.66% increase. Additionally, the revenue generated from international tourism exports reached $1.7 trillion, which accounted for 6.71% of the total global exports (Roy, 2022 ). China’s tourism industry is experiencing rapid growth, but it is also facing several challenges (Liu et al., 2020a , 2020b ). These include issues with the quality of tourism services, limited variety in products and formats, a disorganized market, insufficient use of technology in the tourism sector, and a loss of high-end market share to foreign destinations (Zhou, 2019 ). These defects are frequently observed. The Inter-Ministerial Joint Conference on Tourism of the State Council, in its fifth meeting held in January 2018, suggested a strong promotion of the quality, efficiency, transformation, and upgrading of the tourism sector (Lu, 2022 ). The aim is to achieve high-quality development and establish tourism as a strategic pillar industry of the national economy and a comprehensive well-being industry (Ivanc & Marius, 2020 ). Consequently, there is a growing agreement between the government and the industry to actively encourage tourism of superior quality.

Industrial integration is the phenomenon in which the boundaries between industries and sectors within the same industry become less distinct, allowing for cross-pollination and reorganization, ultimately resulting in the emergence of a new industry that combines elements from different sectors. Industry integration is closely linked to the advancement of tourism (Baldassarre et al., 2019 ; Fan & Yu, 2021 ). A multitude of integrated products, commercial patterns, and novel forms of tourism are rapidly emerging in succession. For example, it can be combined with agriculture to create a rural-oriented pattern, the health industry to establish the medical tourism service sector, and other industries to develop industrial tourism (Tang et al., 2023 ; Zhang et al., 2021 ). It can also be integrated with cultural and creative industries, sports industries, and more.

Prior research has primarily examined the effects of incorporating tourism with other sectors on the population’s financial earnings and job opportunities (Mamirkulova et al., 2020 ). These studies argue that industrial tourism facilitates industrial and regional economic growth, diversifying income streams and generating additional employment opportunities for society (Otgaar, 2012 ). The fusion of the animation industry and tourism has enhanced the meaning and scope of tourist destinations while also expanding the growth potential of the animation industry (Qiao, 2020 ). The integration and development of creative industries and tourism can expand their industrial chain, enrich the cultural significance of their products, widen the range of their resources, and infuse energy and impetus into the sustainable development of tourism (Liu et al., 2020a , 2020b ). The amalgamation of agriculture and tourism enhances the agricultural environment, fosters sustainable agricultural production, and boosts farmers' income and employment prospects (Chen et al., 2020 ). Additionally, the convergence of tourism and the Internet has given rise to an industrial tourism network, a novel form of industrial organization that provides diverse, sophisticated, and superior tourism services (Zehrer & Raich, 2010 ).

Some works of literature also explored the role of industry convergence on tourism development, with McKercher ( 1999 ) arguing that their convergence optimizes the structure of the tourism industry in terms of the structure of tourism sectors and products; Li et al. ( 2013 ) analyzed the relationship between tourism industry integration and the evolution of tourism industry structure taking the Xi’an tourism industry as an example and found that the innovative tourism industry integration is an important factor in upgrading the structure of tourism industry; Zhong-xu ( 2013 ) empirically analyzed the effect of coupling rural tourism with extensive agriculture on the development of the primary industry and the cumulative promotion effect on the development of the tertiary industry; Hui ( 2018 ) pointed out that industry integration can improve its operational efficiency, its innovation, and its internal structure, as well as enhance the level of organization for the tourism industry.

Existing research has generally examined the effects of integrating tourism on income, employment, and development. This research offers valuable insights into the role and consequences of tourism integration. Using qualitative research methods like case studies and descriptions, existing research mainly probes the phenomena, connotations, and integration modes. However, studies on how industry integration affects its quality development are few and far between. From the perspective of specific businesses, there is also a lack of empirical analysis. Considering industrial performance and production efficiency, this paper investigates how integration affects high-quality development. Using data collected at the enterprise level, it tests research hypotheses about how integration affects development quality.

Theoretical Analysis and Research Hypothesis

Conventionally speaking, the tourism industry in China has traditionally relied heavily on the country’s diverse historical and cultural assets and the abundant natural resources that the country possesses (Zhuang et al., 2019 ). However, the uncontrolled utilization of these resources, specifically natural landscapes and historical monuments, has caused substantial environmental deterioration, negatively impacting societal and economic aspects and hindering sustainable development progress (Chen et al., 2022; Hollesen et al., 2018 ). Furthermore, the mandate to achieve a moderately prosperous society has resulted in the establishment of more stringent criteria for developing the tourism industry (Rasoolimanesh et al., 2023 ), leading to more stringent criteria. Increased consumer expectations, a demand for higher quality standards, intensified global competition, requirements for regional development, and the necessity of industry modernization are some of the many trends included in this mandate (Parrinello & Bécot, 2019 ). Other trends include the necessity of industry modernization.

China’s tourism industry is on the verge of entering a period of expansion that will be prosperous (Zhao & Liu, 2020 ). This expansion will take place in the near future. Even though it is experiencing positive growth, the industry is confronted with limited resources and is attempting to deal with the dual challenges of protecting the environment and satisfying the growing demand for tourism experiences that are both unique and exceptional (Streimikiene et al., 2021 ). Despite this, the industry is experiencing positive growth. A paradigm of the utmost importance in a constantly changing landscape is the integration of different sectors. It provides conceptual frameworks essential for driving innovative development in the tourism industry but is not currently available (Bakker, 2019 ). Furthermore, it offers the industry a significant number of opportunities to achieve superior improvement and progress, which is a considerable prospect in itself.

In order to investigate the intricate mechanisms responsible for the incorporation of the tourism industry, which ultimately results in noticeable effects on the achievement of high-quality development, this academic study aims to investigate these mechanisms (Liu et al., 2020a , 2020b ). Several factors associated with the industry’s efficiency and effectiveness are included in these effects (Müller et al., 2018 ). To make a significant contribution to the ongoing academic discussion on the significance of industry integration in China’s rapidly evolving tourism industry as well as the consequences of such integration, the purpose of this paper is to make a contribution that is both significant and significant (Jones & Wynn, 2019 ). A comprehensive investigation will be conducted into the complex dynamics involved in this process.

Managing Knowledge Flow to Boost Productivity: Tourism’s Organizational Integration

As a result of the convergence of different sub-industries within the tourism industry, novel business models and integrated tourism products have been developed (Lu, 2022 ). The result is that these products now provide a greater value and offer a wider variety of functionalities than they did before. As a direct result of this convergence, the tourism industry has significantly improved its overall industrial performance (Uyar et al., 2020 ). It is important to note that the integration of the tourism industry with the creative and cultural sectors has enhanced the cultural significance inherent in tourism commodities (Cabeça et al., 2019 ). Concurrently, the integration of various industries, such as industry, agriculture, and forestry, which cater to the multifaceted needs of tourists who carefully select, has made it easier to diversify the tourism product system (Chen & Li, 2023 ). Additionally, collaboration with the Internet sector has made it possible to personalize tourism experiences, which has increased the competitive advantage of tourism offerings (Mariani et al., 2021 ).

A mutually beneficial relationship has been fostered as a result of the integration of the tourism industry (Nangpiire et al., 2022 ). This relationship has not only resulted in the development of innovative tourism products but it has also led to noticeable improvements in the functionality of products that are already in existence (Hall & Williams, 2019 ). This revolutionary process expands the intricate network that makes up the supply chain for the tourism industry by producing a number of supplementary products and services (Pencarelli, 2020 ). The integration that made it possible for previously independent industrial chains to come together has resulted in an increase in the added value of related industries in a synergistic manner, which has led to an improvement in the value proposition of the integrated products that are produced (Liu & Lin, 2019 ; Tortorella et al., 2021 ).

The integration of the tourism industry not only affects the development of new products but also significantly contributes to the efficient recycling of unused or leftover resources (Camilleri, 2021 ). This integration creates a vast domain that is conducive to the efficient utilization of a variety of industrial resources by utilizing the dormant capabilities of assets that were previously unused. These assets include those in the agricultural and industrial sectors (Pivoto et al., 2021 ). This results in a plethora of inexpensive tourism resources becoming available, effectively reducing the competitive pressure caused by the limited availability of resources while simultaneously alleviating the expenses associated with the development of the tourism industry (Gowreesunkar & Vo Thanh, 2020 ).

To add insult to injury, the interconnection between the Internet and the tourism industry ushers in a period characterized by increased operational expertise and agility, thereby strengthening the industry’s capabilities to deal with emergency situations (Navarro-Meneses, 2023 ). As a result of this integration, capital investments are optimized, operational processes are streamlined, and human resources are conserved in order to drive data-driven decision-making (Shahid & Sheikh, 2021 ), which is accomplished by capitalizing on big data and cloud computing capabilities. The tourism industry has seen a significant improvement in its overall performance as a direct result of the concerted efforts implemented by this industry (Bazargani & Kiliç, 2021 ).

As a catalyst for the amplification of economic benefits and added value, as well as the reduction of costs and the creation of value across interdependent sectors, the integration of the tourism industry serves as a catalyst (Yeon et al., 2023 ). An example that illustrates this point is the renovation of the Wuhan Pinghe Packing Factory, built in 1905 and considered an architectural marvel(Han & Li, 2023 ), which formerly used packing facility has undergone a significant transformation as a result of a series of renovations and repurposing. It has gone from being a mere remnant of industry to becoming a thriving center for culture and innovation, where industrial legacy and contemporary business practices coexist harmoniously (Li, 2019 ). This historical site’s revitalization has been accomplished by incorporating elements of culture and creativity in addition to aspects of industry (Anoegrajekti et al., 2018 ). Consequently, it has become a destination for a diverse range of tourists and has bestowed a revitalized sense of life upon the formerly industrialized region. Given the thorough insights presented previously, we propose the subsequent hypothesis:

Hypothesis 1 : The performance of the tourism industry is substantially improved through the implementation of multiple industrial integrations.

Explore Knowledge Dynamics in Tourism Industry Integration for Enhanced Productivity

Incorporating the tourism industry with other sectors marks the beginning of a period of increased resource allocation, which ultimately results in a significant increase in the industry’s productivity (Zha et al., 2020 ). This integration goes beyond the traditional dependencies associated with natural landscapes or historical and cultural sites, bringing about a significant shift away from the conventional paradigms associated with nature (De Madariaga & del Hoyo, 2019 ). Instead, it strategically uses the resources available in various industries, including agriculture, forestry, water conservation, industry, science and technology, culture, sports, and health care, among others (Zhang et al., 2018 ). The previous reliance on traditional tourism components, such as beautiful locations, accommodations, and dining establishments, has been replaced by a model characterized by the combination of regional resources (Jiang et al., 2021 ), representing a significant change from the previous model. This all-encompassing approach aims to provide tourism offerings that encompass all aspects, which can be accomplished through collaborative participation and the distribution of benefits (Phi & Dredge, 2021 ).

More specifically, the development of leisure agriculture clearly indicates the connection between the tourism industry and agriculture (Chase et al., 2018 ). Agricultural resources that already exist, such as farms, orchards, and farmhouses, are intricately incorporated into this collaborative initiative, which focuses on the coordinated planning and management of development (Reyes et al., 2020 ). Agricultural resources are able to acquire new capabilities through the incorporation of regional advantages, which ultimately leads to the development of diverse tourism offerings that include sightseeing, recreation, and entertainment (Wanner et al., 2021 ). The transformation and incorporation of industrial resources into tourism assets is an example of industrial tourism, an example of the fusion between tourism and industry (Szromek et al., 2021 ). This phenomenon is especially noticeable in the revitalization initiatives of resource-dependent cities and formerly industrialized cities (Li, 2022 ). These initiatives are designed to satisfy the demand for tourism in the market by concentrating on the development of industrial heritage (Lak et al., 2020 ). The repurposing of unused industrial resources, such as factories that have been abandoned, is involved in this process.

In addition, the integration of the tourism and service industries is accomplished by incorporating contemporary service industry assets, such as the cultural, healthcare, educational, athletic, and business exhibition sectors (Bavik & Kuo, 2022 ). Conventional cultural artifacts, such as historical artifacts, museums, and intangible cultural heritage, are transformed into something entirely new as a result of this integration, which offers innovative cultural and digital products (Partarakis et al., 2020 ). Promoting healthcare tourism is accomplished through efficiently utilizing advantageous medical and specialized resources (Abadi et al., 2018 ). The conventional boundaries that have traditionally separated traditional agriculture, industry, cultural industries, tourism, and other service industries are gradually disappearing (Zhu et al., 2021 ), which makes it easier to reduce or even eliminate obstacles in the industrial sector, which is a positive development. The evolution of this system makes it possible to distribute resources among various industries effectively, leading to an increase in industry productivity (Li et al., 2018 ).

Not only does the integrated development of the tourism industry bring about the introduction of innovative approaches to the utilization of resources, but it also improves the effectiveness of management (Hall, 2021 ). The proliferation of the Internet, characterized by the exchange of information, was able to overcome limitations in the flow of information related to time and space respectively (Ali et al., 2021 ). The integration of the tourism industry is undergoing a significant change in the manner in which production and services are carried out (Pencarelli, 2020 ). This change is causing the traditional tourism sector to move towards a more contemporary approach that concentrates on providing services. As a result of this integration, the development of online tourism businesses is encouraged, and on-site and online tourism methods are effectively combined (Hofman et al., 2022 ). Tourism service providers use cutting-edge technologies to provide precise marketing, online inquiries, reservations, and virtual tours (Bilgihan & Ricci, 2023 ). Furthermore, monitoring the number of tourists in scenic areas in real time makes it possible to conduct predictive analyses, making proactive preparations possible (Welling et al., 2020 ).

The promotion of tourism products, distinguished by their intangibility, is carried out in a manner distinct from that of material products (Rodrigues et al., 2022 ). In most cases, tourism businesses will only sell the right to use them for a predetermined amount of time while continuing to retain ownership (Palmer & Chuamuangphan, 2021 ). It is important to note that sightseeing tourism products do not provide exclusivity because they permit multiple tourists to visit the scenic spot at the same time while still adhering to the carrying capacity of the location (Saarinen & Wall-Reinius, 2019 ). Because of the subsequent increase in tourist spending, businesses in the tourism industry are able to acquire assets with a higher value. The tourism industry makes efficient use of the Internet to distribute resources, which increases the carrying capacity of scenic spots and improves the efficiency with which tourism resources are utilized (Xia et al., 2018 ). As a consequence, there is a discernible improvement in both the overall efficiency of the tourism industry and its efficiency (Valeri & Baggio, 2022 ). Based on the previously mentioned observations, we propose the following hypothesis:

Hypothesis 2 : The integration of multiple industries has a substantial positive impact on the productivity of the tourism sector.

Evaluation and Measurement of the Degree of Tourism Integration

Assessing and measuring the level of integration in the tourism industry is a complex task that requires a comprehensive approach to understanding the intricate dynamics within the sector. At present, no single, universally recognized measure can accurately represent the complete range of industrial integration. Prior research, which drew heavily from techniques used to measure technological integration, frequently relied on patent data to assess the level of integration within the tourism sector. Nevertheless, the multifaceted character of tourism, which includes the merging of business, market, and industry, necessitates a more intricate assessment. Multiple endeavors have been undertaken to assess integration from diverse standpoints, employing data on newly established branches, consolidations and takeovers, collaborative agreements, introductions of new products, keywords in annual reports, and the level of internet usage. The methodologies utilized include the patent coefficient, Herfindahl index, entropy index, input–output, and coupling degree methods. Each method provides distinct perspectives on various aspects of integration, albeit with inherent limitations. The patent coefficient method primarily examines technological integration, whereas the Herfindahl and entropy indexes primarily assess diversification rather than actual industrial integration. The input–output method focuses on the integration of industries through industry penetration. On the other hand, while having some advantages, the coupling degree method does not effectively capture the fundamental characteristics of market integration, which is the ultimate objective of industrial convergence.

Comprehending the developmental patterns in the integration of the tourism industry in China is essential for assessing the efficiency of integration strategies and pinpointing areas that require enhancement. The ever-changing nature of the tourism industry, characterized by the constant incorporation of new branches, consolidations, and partnerships, necessitates continuous measurement and assessment. Using listed tourism companies as key indicators offers valuable insights into the overall advancement of the industry. The upward trajectory observed in the rising level of integration, specifically between 2010 and 2019, signifies a burgeoning level of cooperation between the tourism sector and other industries. Nevertheless, the consistently low average revenue generated from integrated tourism products and services highlights the necessity for a more significant transition toward comprehensive integration.

Furthermore, the changing patterns in various sectors, such as tourist attractions, accommodations, and integrated establishments, indicate different degrees of dedication and accomplishment in achieving industry convergence. The examination of the top 10 companies that are listed reveals the varied strategies and results in terms of integration within the sector. Certain companies exhibit strong integration practices, while others encounter difficulties aligning with the wider tourism environment. Assessing and analyzing the level of integration in the tourism sector is essential for policymakers, industry participants, and researchers. It helps them understand and navigate the complex landscape of the developing tourism industry and promotes sustainable and interconnected growth.

Knowledge Dynamics of Integrated Tourism Innovation and Cross-Border Collaboration

No comprehensive indicator can reflect the entire process of industrial integration. In terms of data selection, existing studies mostly select patent data to measure technology integration Caviggioli ( 2016 ) and Shuying and Shu ( 2017 ), which has largely formed a consensus. However, there are various attempts to measure business, market, and industry convergence. For example, data on the addition of new subsidiaries, mergers and acquisitions, and cooperation agreements are used to measure market integration (Gambardella & Torrisi, 1998 ), the number of new product launches in newspapers (Kim et al., 2015 ; Parshakov & Shakina, 2020 ), the number of keywords in the annual reports of listed companies (Yuanxu & Yafei, 2021 ), and the extent of using the Internet (Shi et al., 2018 ) are adopted to measure industrial integration. In terms of measurements, the main existing studies include the patent coefficient method Fai and Von Tunzelmann ( 2001 ), the Herfindahl index method (Qu et al., 2022 ), the entropy index method (Shi et al., 2020 ; Zhao et al., 2018 ), and the input–output method (Guo et al., 2018 ).

The Patent Coefficient Method

It mainly uses patent data to measure the degree of technological integration, calculates the proportion of the number of patents in each industry in the total number of patents, and measures the degree of technological integration between industries by constructing the correlation coefficient matrix between them based on measuring the coefficient of positive and negative technological integration.

The Herfindahl Index Method

The calculation method is shown in Eq. ( 1 ), where X can denote the total number of technology patents, total business investment or total revenue, and X i the number of technology patents in each industry, the amount of investment in each sector, or the amount of revenue from each type of product. The degree of technology integration is calculated using the number of technology patents, the degree of business integration using the investment amount data, and the degree of market integration using the product revenue amount. It mainly measures the degree of technological and business diversification of enterprises, which is not the true sense of industrial integration. Industrial convergence leads to the diversification of firms' technologies and businesses, but the strategies not based on industrial convergence may lead to the same results; therefore, the results of the Herfindahl index measure may overestimate the degree of industrial convergence.

The Entropy Index Method

The calculation is shown in Eq. ( 2 ), where p i is the proportion of the company’s sales revenue in the four-digit industry code to the total sales revenue. Like the Herfindahl index method, the entropy index method also measures the degree of industrial integration by calculating the degree of product diversification of enterprises by calculating the proportion of revenues from different industries in their operating revenues. Since the diversification strategy of enterprises may also lead to the diversification of their products, the result from the entropy index method cannot represent industrial integration in the true sense either.

The Input-Output Method

Calculated as shown in Eq. ( 3 ), the output of industry j in the production process of industry is used as a proportion of the total output to express the degree of integration of industry i and industry j . However, the input of industry j to industry i as a share of the total output of industry is usually used to measure it, which is due to the lack of data related to the output of industry j in the production process of industry i . Therefore, it is approximated that the input of industry j will all be condensed in the final output, i.e., the input of industry j in the production process of industry i is approximated as the output of industry j in the final product, which will underestimate the degree of industrial integration to some extent.

The Coupling Degree Method

Using the principle of ‘coupling’ in engineering, we measure the degree of industrial integration by measuring the coupling coordination degree of two industries and regard the integration process of two industries as the flow of production factors between different sectors, as well as the coupling process in which the two very industries interplay with each other. Based on constructing the development evaluation index system of the two industries separately, the coupling degree and coordination degree of the two industries are measured respectively according to the measurement principle of the coupling coordination degree model. The coupling coordination model has apparent advantages in indicator selection and data collection, which measures the degree of interdependence and influence between capital, labor, technology, and other factors in the development process of two industries, as well as between the organizational structure of sectors and institutional factors, reflecting the relationship between industries to a certain extent. Still, it is not the real sense of integration because the final state of industrial integration is market integration, that is, the blurring of industrial boundaries and the emergence of new products of integration. The coupling coordination degree still measures the coupling relationship between two well-defined industries, which does not reflect the essential characteristics of industrial integration.

All the above methods can measure the degree of industrial integration from different perspectives, but they all have their own unavoidable shortcomings. The Herfindahl index method and the entropy index mainly measure the degree of diversification, not industrial integration in the actual sense, and will overestimate the degree of industrial integration. The patent coefficient method measures technological integration, one part of industrial integration, which is thus not holistic enough. The input–output method mainly measures industrial integration formed by industry penetration but cannot measure the one formed by industry crossover and restructuring. The coupling coordination model has apparent advantages in indicator selection and data collection, which measures the interdependence and mutual influence between two industries. However, it is still confined to measuring two well-defined industries, which does not reflect the essential characteristics of industrial integration.

The key to improving the integration of the tourism industry lies in tourism enterprises, through which the integration of the tourism industry is mainly promoted. Due to their strong strength, the tourism listed companies have become the typical subjects of the tourism industry and tourism product innovation, usually with strong market competitiveness, and can act as representative enterprises in the tourism industry. The level of innovation and development of the tourism industry can be roughly seen by exploring the production and operation of listed tourism companies, and to a large extent, the degree of integration of the tourism industry can be evaluated by that of listed tourism companies. Drawing on the method of Lee et al. ( 2022 ) for measuring the degree of integration between manufacturing and the Internet, the proportion of revenue generated by convergent products and services in the core business revenue of listed companies in the tourism industry to the total revenue is used to measure the degree of integration between the tourism industry and other industries.

Firstly, a sample of listed companies in the tourism industry was selected. Existing studies mostly classify listed companies in the tourism industry into three major categories: scenic spots, hotels, and comprehensive ones. According to the business scope and core business of listed companies, as of the end of 2019, 54 A-share tourism companies were listed and traded on the Shanghai and Shenzhen stock exchanges. Among them are 10 comprehensive ones, 16 scenic spots, and 8 hotels. In addition, it also has another 20 enterprises providing tourism services, with their major businesses being gardening, film and television, and catering.

Secondly, the revenue data of listed companies in China’s tourism industry by product category were collected from 2010 to 2019, with a total of 3502 samples, and the data were obtained from the Guotaian database. As some listed companies have missed revenue data classified by product segment, four types of data are collected to prevent bias in measuring the degree of convergence caused by missing data. They are core business revenue classified by industry or business, operating revenue by industry or business, core business revenue by product, and operating revenue by product.

Thirdly, according to the concept and connotation of integrated development of the tourism industry, the products and services that belong to the integrated type in industry or business and product classification among the four types of data collected from listed companies in the second step are screened out. For example, there are products such as online tourism formed by the integration of the tourism industry and the Internet industry, live performances, and animation exhibitions by the integration of the cultural and the tourism industry, skiing by the Integration of the sports and the tourism industry, ecological towns by the integration of agriculture, forestry and the tourism industry, and products formed by the internal integration of the tourism industry itself.

Fourth, count the revenues from integrated products and services of listed companies in the tourism industry. Since four categories of data were collected in the second step, some of the listed companies in the screened convergent product and service data may be counted repeatedly. The samples of both core business income and operating income take the samples of operating income as the mainstay and delete the samples of core business income; the samples of both industry or business classification and product classification take the samples of product classification as the mainstay and delete the samples of industry or business classification. On this basis, the total revenue generated by each listed company in the tourism industry for each year of convergence-based products and services is counted.

Fifth, the degree of integration of listed companies in the tourism industry is calculated using the ratio of total revenue generated from integrated products and services of listed companies to total revenue of listed companies.

Integrating Knowledge from Different Fields Driving China’s Integrated Tourism Future

Tourism industry integration is a cross-industry development between tourism and other industries or between different sectors within the tourism industry, resulting in new supply and demand. Tourism products have transformed from the single exploitation of natural resources to meet people’s sightseeing demands to an advanced combination of cultural, agricultural, medical, sports, and other resources integrated to meet people’s spiritual needs. For example, in 2019, the Digital Culture and Tourism Industry Innovation and Development Forum was held under the theme of ‘digital leadership to promote the high-quality development of the culture and tourism industry,’ in which 10 categories and 100 red tourism routes reflecting the spirit of the times were launched, and more than 3500 talents in the culture and tourism industry were trained, indicating the integration and deepening development of the culture and tourism industries. In addition, 320 key villages of national rural tourism were launched for the first time in 2019, and more than 1700 village cadres and rural tourism leaders attended 12 training sessions, indicating the integration and deepening development of agriculture and tourism. In 2019, a total of 15 special research projects on the development of information technology such as ‘Internet + ,’ big data, cloud computing, artificial intelligence, 5G, and other innovative applications of new information technologies in the field of culture and tourism were organized, and 27 ‘cultural and tourism think tank projects’ were funded, indicating the integration and deepening development of information industry and tourism industry.

Table 1 presents the measurement results of the degree of integration in the tourism industry from 2010 to 2019. It shows a consistent positive trend, as evidenced by the increasing average values over the ten-year period. The growing trend indicates a rising level of involvement and cooperation between the tourism sector and other industries, potentially leading to a more interconnected and varied industry landscape. Nevertheless, noteworthy annual fluctuations, such as the substantial surge in 2018 followed by a slight decline in 2019, necessitate a more detailed analysis of the factors influencing these variations. The relatively high standard deviation values, especially in the later years, indicate significant variability in the level of integration, highlighting the dynamic nature of the industry’s collaborative endeavors. Instances of lack of integration, indicated by minimal values at 0, emphasize potential areas for enhancement or obstacles impeding collaborative endeavors. In contrast, values close to the maximum indicate situations of extremely high integration, which can be used as standards for effective collaborative models. The critical analysis emphasizes the necessity of additional research to investigate the qualitative elements of integration, identify specific factors or obstacles that impact fluctuations, and gain a more comprehensive comprehension of the collaborative dynamics within the tourism industry.

The degree of industry integration of listed tourism companies reflects the development of tourism industry integration, with the number of listed companies providing tourism services growing from 41 to 50. Table 1 shows that the mean value of the degree of integration of listed tourism companies in China was 0.0737 in 2010 and 0.1785 in 2019, which increased by 142.20%, with an average annual growth rate of 10.33%. By vertical comparison, the integration of China’s tourism industry has developed rapidly, and the degree of integration has shown a fluctuating upward trend. However, the average value of revenue generated by convergent tourism products and services of listed tourism companies is currently less than 20% of the total operating revenue, indicating that the degree of integration of China's tourism industry remains low despite its rapid development. Because of various historical reasons, the development of China’s tourism industry has been too dependent on natural resources for a long time, with scenic tourism enterprises providing a single sightseeing service through the development of natural resources, hotel tourism enterprises providing a single accommodation service, and insufficient cross-border integration between the tourism and other industries such as the cultural and the sports industry. In addition, the mean values of the comprehensive ones, scenic spots, hotels, and others were calculated to measure the degree of tourism industry integration of listed companies in different categories, and the results are shown in Fig.  1 , which depicts the developmental patterns in the categorized tourism sector from 2010 to 2019. It shows four separate lines representing the number of classified tourism enterprises in comprehensive, hotel, scenic, and other categories. The all-encompassing category, which includes various tourism services such as travel agencies, tour operators, and transportation companies, is notably the largest and has experienced the most significant growth in the past ten years. The hotel class, which includes hotels, motels, and lodging establishments, has also shown consistent growth.

Classified tourism industry integration development trend

Conversely, the scenic class, which emphasizes businesses providing access to natural attractions, has exhibited the smallest increase. In contrast, the other classes category, which includes various businesses not falling into the three main classifications, has shown the most rapid expansion. The figure illustrates the consistent and continuous expansion of the classified tourism sector, which can be attributed to factors such as the growing popularity of tourism, increasing demand for services, and enhanced accessibility to financing. The expansion has benefited the economy, resulting in job creation, tax income, and bolstering of local enterprises. Nevertheless, it has also presented challenges such as increased congestion and environmental degradation. It is important to note that the mentioned figure only represents the tourism industry that is officially classified.

The degree of integration of listed tourism companies in scenic spots, hotels, and comprehensive ones shows a fluctuating upward trend. In contrast, the degree in other categories shows an upward and downward trend. Among them, the highest degree of integration is found in the scenic tourism listed companies, followed by hotels, the comprehensive ones, and the others. Table 2 shows the top 10 listed companies with the highest mean value of industry integration and their mean value from 2010 to 2019, as well as the top 10 listed companies with the highest degree of integration and their degree in 2010 and 2019.

Table 2 , focusing on the top 10 listed companies in terms of integration within the tourism industry over the sample period from 2010 to 2019, reveals intriguing insights. Notably, a diverse range of companies are characterized by their degree of integration. The top-ranking company, Yuancheng Stock, achieved a high degree of integration at 0.8358, indicating a robust strategy in aligning with the broader tourism industry. Beijing Tourism Corporation and Oriental Spike claimed the top spot in 2010 and 2019, respectively, demonstrating a dynamic shift in industry leadership. Companies such as Lijiang Tourism, Three-way Cableway, and Songcheng Stock maintained a consistent presence in the top 10, showcasing a sustained commitment to effective integration practices. However, Mount Emei, Tibet Tourism, and Huangshan Tourism exhibited comparatively lower degrees of integration, suggesting potential challenges in fully integrating into the broader tourism landscape. The inclusion of companies engaged in cableway services, ecological initiatives, and cultural programs highlights the multifaceted nature of integration within the tourism sector. Notably, Kunming’s Top 100 Business dropped out of the top 10 in 2019, signaling strategic changes in the competitive landscape or alterations in integration priorities. While the table provides a valuable snapshot of the top integrated companies, the absence of specific criteria for measuring integration limits a comprehensive understanding of the methodologies employed.

As seen in Table  2 , Yuancheng Group boasts the highest mean value of tourism industry integration during the 10-year sample period, with its revenue share of integrated tourism products and services over 80%. Yuancheng’score business is garden construction, landscape design, greening maintenance, seedling planting, and information services, and its high degree of integration is due to the company’s cross-border development to the tourism industry by carrying out businesses such as ecological landscape and leisure tourism from 2017 to 2019 to achieve tourism industry integration. Companies such as Zhangjiajie, Lijiang Tourism, Jiuhua Tourism, and Sante Telpher ranked 2nd–5th in terms of the mean value of the degree of integration during the sample period, with their revenue share of integrated tourism products and services over 50%. The listed company with the highest level of integration in 2010 was the Beijing Tourism Group, with nearly 80% of its revenue from integrated tourism products and services. The core business of BTG is hotel operation and management, including tourism services, hotel operation, scenic spot operation, hotel management, and exhibition advertising, which achieves the internal integration of the tourism industry mainly through the provision of tourism services. Lijiang Tourism, Oriental Pearl, Mount Emei, Tibet Tourism, and Huangshan Tourism ranked 2nd–6th in terms of degree of integration, with about 20–60% of their revenue from integrated tourism products and services. The listed company with the highest degree of integration in 2019 was Suidongfang, with over 85% of its revenue from integrated tourism products and services. Its primary businesses are hotel operation and management and travel agency business, mainly through the provision of tourism services to achieve integration within the industry. Songcheng and Yuancheng ranked 2nd and 3rd in the degree of integration, with their revenue share of integrated tourism products and services over 85%. The former’s core business is the investment, development, and operation of theme parks and tourism and cultural performances, as well as an Internet business, mainly realizing the integration of tourism and cultural industry. Huangshan tourism ranked 10th, with a proportion of integrated tourism products and services over 30%, which indicates that individual listed tourism companies in China have a high degree of integration, with revenues from integrated tourism products and services accounting for more than 80%, basically bidding farewell to the era of providing a single tourism product. Compared to 2010, the degree of integration of the top-ranked listed tourism companies has generally improved more substantially in 2019. However, the average value of the degree of integration of all listed tourism companies in each year is exceptionally low, which was 17.85% in 2019, having a large gap with the No. 1 ranked Spike East’s 86.55%, which indicates that the degree of integration of most listed tourism companies is low, and the development of tourism industry integration varies widely among listed companies.

Research Methodology

This section outlines the comprehensive research methodology used to examine the complex dynamics of the Chinese tourism industry between 2010 and 2019. The sampling process entailed a rigorous selection of 54 Chinese A-share companies operating in the tourism sector, resulting in a total of 451 observations. This selection was based on the industry classification standard established by the Securities and Futures Commission in the 2012 edition. It included various tourism industry sectors, such as well-known publicly traded companies, tourist attractions, hotels, and services related to gardening, film, television, and catering. The sample’s diversity and comprehensiveness establish a strong foundation for analyzing the industry’s performance and productivity.

To rigorously test the proposed hypotheses regarding the impact of tourism industry integration, we established a model (Eqs. ( 7 ) and ( 8 )) incorporating various financial indicators. These metrics, such as return on equity (ROE) and return rate of the stock market (RRS), are used to assess the financial performance of publicly traded companies. Total factor productivity (TFP) was utilized as a crucial measure to evaluate the efficiency of tourism companies. The Olley-Pakes (OP) method was chosen as the main technique for computing total factor productivity (TFP). In contrast, ordinary least squares (OLS) and Levinsohn-Petrin (LP) methods were utilized as tests to ensure the accuracy and reliability of the results. The model incorporated control variables: size, age, leverage, and equity concentration. This research methodology allows for a detailed examination of the connections between the integration of the tourism industry, its performance, and productivity in the Chinese context.

Sample Construction

From 2010 to 2019, a thorough selection of Chinese A-share companies in the tourism sector was made. This sample included a total of 54 listed companies and 451 observations. The selection followed the industry classification standard the Securities and Futures Commission specified in the 2012 edition. Among this heterogeneous sample, 10 prominent and all-encompassing listed companies were incorporated, specifically Brand New Good, OCT, Xi’an Travel, Caesar Travel, Zhongxin Travel, Tempus International, CYTS, CITS United, Hundred Holdings, and China CDF. In addition, the sample included 16 publicly traded companies that specialize in scenic spots, such as Zhangjiajie, Mount Emei, Guilin Tourism, Lijiang, Yunnan Tourism, Sante Cableway, Songcheng Performing Arts, Huangshan Tourism, HNA Innovation, Dalian Shengya, Qujiang Cultural Tourism, Tibet Tourism, Oriental Pearl, Changbai Mountain, Tianmu Lake, and Xinzhi Cognition.

Furthermore, the sample consisted of 8 publicly traded companies in the hotel sector: Xindu Retreat, Huatian Hotel, Lingnan Holdings, Dadonghai, BTG Hotel, Jinjiang Hotel, Jinling Hotel, and Jiuhua Tourism. In addition, the sample expanded to include 20 listed companies that offer a range of services in gardening, film and television, and catering. This subset consists of the following companies: Zhongke Yunnet, Xi’an Catering, Quanjude, Hemei Group, Lazard, Zhonghong, Tongcheng Holdings, Kunbai Da, Tiehan Ecology, Vision China, Telegraph Media, Contemporary Culture and Sports, Oriental Pearl, Pingtan Development, Oriental Garden, Hua Wang, Yuancheng, Palm, Lingnan, and Yichang Transportation. This extensive compilation provides a strong basis for a detailed examination of the dynamics and patterns within the Chinese tourism sector during the specified period.

Model Establishment and Indicator Selection

To test hypotheses 1 and 2 proposed in the previous section, i.e., whether tourism industry integration enhances the performance and productivity of the tourism industry or not, the following model was constructed:

Per and tfp are the explanatory variables denoting tourism firms’ performance and productivity, respectively. Return on equity (ROE) and return rate of the stock market (RRS) are taken to measure the financial performance of listed companies, with the former calculated as a benchmark for analysis and the latter as a robustness test. The specific calculation is as follows:

Comparing OLS, FE, OP, LP, and GMM methods for measuring TFP at the enterprise level and referring to the existing literature, Lu and Lian ( 2012 ) and Yang ( 2015 ), the OP method, LP method, and OLS method were selected to calculate the TFP of Chinese tourism listed companies. The results of the OP method were used as a benchmark for analysis. Those of the latter two were used as robustness tests. Most of the existing literature uses data from industrial enterprise databases for measurement, rarely taking listed companies as a sample for measurement. This paper draws on the methodology proposed by Xu et al. ( 2023 ) to measure the TFP of listed tourism companies. The calculation is as follows: first, the value added and intermediate inputs of the enterprise are calculated using the allocation and production methods, using the net cash paid by the enterprise to construct fixed assets, intangible assets, and other long-term assets as the current investment of the enterprise; second, using 2004 as the base period, the stock of enterprise fixed assets is adjusted using the perpetual inventory method and the fixed asset deflator for each region, and the other nominal variables are deflated by the corresponding price indexes for each region, respectively; third, the OP method needs to control for the entry and exit variables. We define the year an enterprise exits the market as the year of exit. Considering that the business of listed enterprises changes more frequently, we make further identification according to the short name of the enterprise and the industry it belongs to, and if both change at the same time, the enterprise is considered to have exited the market in that year; finally, the total factor productivity of listed tourism companies is estimated using the Cobb–Douglas function.

‘conv’ is the core explanatory variable, indicating the degree of tourism industry integration, described in detail in the previous section and will not be repeated here. The specific calculation is as follows:

where ‘control’ denotes control variables, including size, age, lever (LEV), and equity concentration (OC). In addition, the control variables of model (2) include return on equity (ROE), which is calculated as follows:

I and t denote listed companies and time; ∑industry and ∑year denote industry fixed and annual fixed effects, respectively; and ε denotes the disturbance term.

The data related to listed tourism companies are obtained from the Guotaian database, and the data related to price indexes of each region are obtained from the China Statistical Yearbook.

Empirical Analysis

The empirical analysis undertaken in this study delves into the intricate relationships between tourism industry integration, performance, and productivity of Chinese A-share companies in the tourism sector. Descriptive statistical analysis provides a comprehensive overview of key variables, including performance metrics, total factor productivity measures, and various industry-specific indicators. These variables’ mean values and standard deviations are meticulously presented, offering insights into the financial stability, productivity, and attributes of the sampled companies from 2010 to 2019.

Subsequently, the study employs a robust benchmark regression analysis to scrutinize the impact of tourism industry convergence on performance and productivity. The results reveal a positive and significant association between industry convergence return on equity and total factor productivity. Notably, the incorporation of control variables and fixed effects for industry and year enhances the robustness of these findings. Furthermore, the study employs a two-stage least squares (2SLS) method to address endogeneity concerns, reinforcing the positive relationship between tourism industry convergence and performance/productivity measures.

In the quest for a nuanced understanding, the study conducts additional analyses to explore the moderating effects of policy support, residents’ tourism consumption levels, Internet development, and the presence of top talent on the relationship between industry convergence and performance/productivity. These analyses unravel insightful patterns, such as the positive influence of policy support and higher residential tourism consumption expenditure on the effectiveness of tourism industry convergence. The study’s empirical findings contribute valuable knowledge to the ongoing discourse on the strategic implications of industry integration within the Chinese tourism sector.

Descriptive Statistical Analysis

Table 3 displays the results of the descriptive statistical analysis for a range of variables pertaining to the performance, total factor productivity, integration within the tourism industry, scale, age, debt-paying capability, and ownership concentration of the chosen sample of 451 Chinese A-share companies in the tourism sector from 2010 to 2019. The performance metrics consist of per_roe, which measures the return on equity and has a mean value of 0.0567 and a standard deviation of 0.0756, and per_rrs, which measures the return on revenue and has a mean value of 0.0056 and a standard deviation of 0.0147. These metrics provide a measure of the financial performance of the companies during the specified time frame. The assessment of total factor productivity is conducted using three measures: tfp_op (operating profit), tfp_lp (labor productivity), and tfp_ols (output per unit of labor and capital). The mean values of these variables are 13.6734, 12.9928, and 13.5815, respectively. These values reflect the overall productivity of the companies in the tourism industry.

The level of integration in the tourism industry is quantified using the variable conv, which has an average value of 0.1413 and a standard deviation of 0.2507. This metric offers insights into the degree of integration within the tourism industry, indicating a moderate average level of integration among the chosen companies. The other important variables are size (scale), with an average of 21.8479; age, with an average of 2.3970; lev (debt-paying ability), with an average of 0.4242; and oc (ownership concentration), with an average of 7.1270. These variables provide data on the companies’ magnitude, age, financial stability, and ownership arrangement, aiding in a thorough comprehension of the attributes of the Chinese A-share companies in the tourism sector within the designated timeframe.

Table 4 shows the statistical results of grouping the performance of listed tourism companies and total factor productivity, dividing the sample into two groups of whether integration of the tourism industry was achieved according to whether it was greater than 0. Descriptive statistical analysis was performed. The results show that the performance and total factor productivity of tourism-listed companies that achieved integration generally outperformed those that did not, with the mean value of return on net assets of 6.86% and total factor productivity of 13.73 for the former and 4.50% and 13.62 for the latter, respectively, and that tourism industry integration increased return on net assets by 2.36% and total factor productivity by 0.11. The subgroup descriptive statistics analysis initially tested the positive relationship between tourism industry integration and the performance and productivity of listed tourism companies.

Table 4 displays the descriptive statistics for the variable “Tourism industry integration,” categorizing companies into two groups: those with “YES” (indicating integration) and those with “NO” (indicating no integration). The table analyses the performance and total factor productivity disparities between the two groups. Regarding performance metrics, companies that have integration (YES) exhibit a higher average return on equity (per_roe) of 0.0630, whereas companies without integration (NO) have a mean return on equity of 0.0515. Companies with integration experience a higher mean return on revenue (per_rrs) of 0.0069, whereas companies without integration have a mean of 0.0045. The findings indicate that, on average, integrated tourism companies exhibit superior financial performance in comparison to non-integrated ones.

Companies that have integration demonstrate marginally higher average values in all three measures of total factor productivity: tfp_op (operating profit), tfp_lp (labor productivity), and tfp_ols (output per unit of labor and capital). As an illustration, the average value for tfp_op is 13.7339 for companies with integration and 13.6234 for companies that do not have integration. These findings suggest that integrated tourism companies generally demonstrate slightly greater overall productivity in comparison to non-integrated companies.

Analysis of Benchmark Regression Results

Table 5 shows the regression results of tourism industry integration affecting tourism industry performance and productivity, and the Hausman test found that fixed effects should be estimated, controlling industry and year. Models (1) and (2) report the estimation results of tourism industry integration affecting tourism industry performance with return on net assets and stock profitability as the explanatory variables, respectively; models (3), (4), and (5) report the estimation results of tourism industry integration affecting tourism industry productivity with total factor productivity measured by OP method, LP method, and OLS method as the explanatory variables, respectively. The estimation results of model (1) and model (3) are used as the standard, and the estimation results of model (2), model (4), and model (5) are used as the robustness test.

Table 5 shows the effects of tourism industry convergence on performance and productivity measures. The analysis includes fixed effects (fe) for industry and year. The columns correspond to distinct dependent variables: (1) return on equity (per_roe), (2) return on revenue (per_rrs), (3) total factor productivity calculated using operating profit (tfp_op), (4) total factor productivity calculated using labor productivity (tfp_lp), and (5) total factor productivity calculated using output per unit of labor and capital (tfp_ols).

The coefficient for the variable “conv” (representing tourism industry convergence) is positively and significantly associated with columns (1), (3), and (4). In columns (1), (3), and (4), the coefficients are 0.5098*, 0.4215**, and 0.5888**, respectively. These findings indicate that the convergence of the tourism industry has a beneficial effect on return on equity, total factor productivity as measured by operating profit, and total factor productivity as measured by labor productivity. The positive coefficients indicate that companies adopting convergence within the tourism industry tend to have better financial performance and productivity.

Furthermore, other control variables demonstrate significant effects. For example, the variable “size” has a consistently positive and significant impact on all the columns, suggesting that larger companies tend to exhibit superior performance and greater productivity. The variable “age” exhibits a statistically significant negative effect in column (1), indicating that younger companies tend to have higher equity returns. The variable “Lev,” which represents the ability to pay off debts, has a detrimental effect on columns (1) and (2), suggesting that companies with lower debt-paying ability tend to exhibit higher returns on equity and revenue.

The R-squared values, which range from 0.1916 to 0.6213, indicate that the model explains significant variation in the dependent variables. By incorporating fixed effects for industry and year, the results are made more robust as it takes into consideration the specific characteristics and changes that occur within each sector and over time.

Model (1) shows that tourism industry integration significantly improves tourism industry performance with a significantly positive coefficient of ‘conv,’ and a 1-unit increase in the degree of integration of listed companies is associated with a 0.0820-unit increase in the return on net assets, and model (2) obtains comparable results using stock profitability as the explanatory variable. Model (3) shows that tourism industry integration significantly improves tourism industry performance with a significantly positive coefficient of ‘conv,’ and a 1-unit increase in the degree of integration of listed companies is associated with a 0.4215-unit increase in the return on net assets, and Models (4) and (5) obtain similar results with total factor productivity measured by LP method and OLS method as the explanatory variables.

The degree of the tourism industry integration variable may be endogenous due to the omitted variable problem and reverse causality problem caused by unobservable factors. To ensure the robustness of the model, this paper uses one period lag of the degree of tourism industry integration as the instrumental variable, and the two-stage least squares method is used for estimation, controlling industry and year, and the obtained estimation results are shown in Table  6 . The results show that tourism industry integration significantly improves the performance and productivity of the industry, consistent with the conclusions obtained in the previous paper.

The results of robustness tests using the two-stage least squares (2SLS) method are presented in Table  6 . These tests investigate the effects of tourism industry convergence on different performance and productivity measures. The model incorporates industry and year-fixed effects, ensuring a comprehensive analysis of the data. The coefficient for “conv” (tourism industry convergence) is positive and statistically significant at the 1% level in all five columns representing different dependent variables (per_roe, per_rrs, tfp_op, tfp_lp, and tfp_ols), which further supports the results obtained from the baseline regression. More precisely, the coefficients vary between 0.0553 and 1.1279, indicating a consistent and positive influence of the convergence of the tourism industry on measures such as return on equity, return on revenue, and total factor productivity. The “control” variable is incorporated into the model, signifying the existence of supplementary control variables that are not explicitly stated in the table. In regression models, it is customary to consider potential confounding factors that may have an impact on the dependent variables. The incorporation of fixed effects for industry and year remains consistent throughout all columns, bolstering the reliability of the findings by accounting for industry-specific and temporal fluctuations. The R -squared values, which quantify the model’s goodness of fit, span from 0.2660 to 0.6308, suggesting that the model accounts for a significant proportion of the variability in the dependent variables.

Further Analysis

On December 1, 2009, the State Council issued the ‘Opinions on Accelerating the Development of Tourism Industry’ and formally proposed to ‘vigorously promote the integration of tourism with culture, sports, agriculture, industry, and other related industries and sectors.’ In addition, in January, August, September, and December 2015, the National Tourism Administration and other departments issued four policies to support the development of the tourism industry, including the ‘Guidance on Promoting the Development of Intelligent Tourism.’ Considering the lag of the policy exerting effects and the fact that several policies were introduced in 2015, the samples from 2010–2014 and 2015–2019 were selected to conduct separate regressions to measure the moderating effect of policy support on tourism industry integration development affecting industry performance and productivity by comparing the estimated results of group regressions, and the results are shown in Table  7 . Models (1) and (3) show that tourism industry integration significantly improves the performance and productivity of the tourism industry after the implementation of those policies. In contrast, the effect of integration on performance and productivity is negative when the policies have just been introduced and not yet taken effect, which shows that relevant policies are essential for tourism industry integration effects to be brought into play.

Table 7 presents regression results based on the implementation of policies, differentiating between the periods before and after policy implementation. The dependent variables include return on equity (per_roe) and total factor productivity in operations (tfp_op). The coefficients demonstrate interesting dynamics for the variable “conv” (tourism industry convergence). In the period before policy implementation, the coefficient is positive and statistically significant at the 5% level, with a value of 0.0586, which suggests that, before policy implementation, tourism industry convergence positively impacted return on equity. However, in the period after policy implementation, the coefficient becomes negative and statistically significant at the 1% level, with a value of − 0.0500, which indicates a reversal in the relationship, suggesting that after policy implementation, tourism industry convergence is associated with a decline in return on equity.

Similarly, in the context of total factor productivity in operations (tfp_op), the coefficient for “conv” is positive and significant at the 1% level before policy implementation (0.4507). However, after policy implementation, the coefficient turns negative and remains highly significant (− 0.4086), which implies that the impact of tourism industry convergence on total factor productivity changes direction following the implementation of policies. The inclusion of control variables, industry-fixed effects, and year-fixed effects is consistent across all columns, enhancing the robustness of the results. The R -squared values, which indicate the goodness of fit, range from 0.1768 to 0.3376, suggesting that the model explains a considerable portion of the variation in the dependent variables.

Domestic residents are the main consumer force in China’s tourism market. The significant growth in disposable income and consumer spending indicates that Chinese residents have the economic capacity to participate in tourism activities, a favorable market condition for the integrated development of the tourism industry. With the improvement of their living standards, residents usually pursue personalized spiritual enjoyment, and going out for travel is one of the important choices and the desire to participate in diversified tourism activities has become stronger, mainly manifested in the increase of spending on tourism consumption. To measure the moderating effect of residents’ tourism consumption level on tourism industry integration to enhance industrial performance and productivity, the median per capita education, culture, and entertainment consumption expenditure of urban residents in the provinces to which the listed companies belonged each year was used as a criterion to divide the sample into two groups for regression, and the estimation results are shown in Table  8 . Models (1) and (3) show that most listed tourism companies are located in provinces with high residential tourism consumption expenditure. The effect of tourism industry integration on the performance and productivity of the tourism industry in these regions is significantly positive. In contrast, this effect is significantly negative in regions with low tourism consumption expenditure, which suggests that the role of tourism industry integration in enhancing performance and productivity must be matched by higher residential tourism consumption expenditure, i.e., a good market base.

Table 8 presents a structured representation of residents' levels of tourism consumption. The dependent variables in the analysis are the return on equity (per_roe) and the total factor productivity in operations (tfp_op). The primary independent variable of interest is “conv” (convergence within the tourism industry). Within the high tourism consumption level group, the coefficient for “conv” exhibits a positive and statistically significant relationship with return on equity (per_roe) at the 5% level (0.0896) and a highly significant relationship with total factor productivity in operations (tfp_op) at the 1% level (0.5147). These findings suggest that in areas where residents spend more on tourism, the convergence of the tourism industry is linked to better financial performance (increased return on equity) and higher total factor productivity.

In contrast, the group with lower levels of tourism consumption shows a negative and statistically significant coefficient of − 0.0585 for “conv” in relation to return on equity (per_roe) at the 5% level. However, this coefficient is not significant for total factor productivity in operations (tfp_op), with a value of − 0.0780, which implies that in areas where there is less tourism consumption by residents, the convergence of the tourism industry is associated with a reduction in return on equity. However, there is no statistically significant effect on total factor productivity. All columns consistently incorporate control variables, industry-fixed effects, and year-fixed effects, ensuring the results’ robustness. The R -squared values range from 0.2614 to 0.5656, suggesting that the model accounts for significant variability in the dependent variables.

The development of the Internet has reformed the production and service methods of the traditional tourism industry. It is an important technical support for the integrated development of the tourism industry, giving rise to a large number of new forms and products, leading to new tourism consumer market preferences, and also widely applying advanced production technologies and management models to the tourism industry, greatly facilitating the transformation process of the tourism industry into a modern one of service. To measure the moderating effect of residents’ tourism consumption level on tourism industry integration to enhance industrial performance and productivity, the median Internet penetration rate was used as a criterion to divide the samples into two groups with higher and lower levels of Internet development for separate regressions, and the estimation results are shown in Table  9 . Models (1) and (3) show that tourism industry convergence significantly improves industry performance and productivity in regions with higher levels of Internet development; models (2) and (4) show that the effect of tourism industry convergence on industry performance is significantly negative and insignificant on productivity in regions with lower levels of Internet development, which indicates it is in need for tourism industry integration to play performance improvement and productivity enhancement effect that to match certain technical foundation with it such as Internet technology base and Internet facility construction.

The regression results in Table  9 are organized based on the level of Internet development. The dependent variables analyzed are return on equity (per_roe) and total factor productivity in operations (tfp_op). The primary independent variable of interest is “conv” (convergence within the tourism industry). Regarding the advanced stage of the Internet development group, the coefficient for “conv” exhibits a positive and highly statistically significant relationship at the 1% level for both return on equity (per_roe) (0.1591) and total factor productivity in operations (tfp_op) (0.8805), which indicates that in areas where Internet development is advanced, the convergence of the tourism industry is linked to notably higher return on equity and total factor productivity. In contrast, the coefficient for “conv” is negative and statistically significant at the 5% level for return on equity (per_roe) (− 0.0442) in the lower level of the Internet development group. However, it is not statistically significant for total factor productivity in operations (tfp_op) (0.0866), which suggests that in areas with limited Internet infrastructure, the convergence of the tourism industry is associated with a decline in return on equity. However, its effect on total factor productivity is not statistically significant. All columns consistently incorporate control variables, industry-fixed effects, and year-fixed effects, ensuring the results’ robustness. The R -squared values range from 0.3546 to 0.6045, suggesting that the model accounts for significant variability in the dependent variables.

The following section tests the moderating effect of top talent in the process of tourism industry integration to enhance industry performance and productivity, and the estimated results are shown in Table  10 . The percentage of top talents is measured by the percentage of the total number of employees of listed companies with master's or doctoral degrees. Each year, the median percentage of top talents of listed tourism companies is used as the benchmark. The sample is divided into two groups of higher and lower percentages of top talents for regression. Models (1) and (3) show that the effect of tourism industry integration on the performance and productivity of the tourism industry is significantly positive when the share of the top talent in tourism-listed companies is high. In contrast, this effect is not significant in both cases when the share of top talent is low, which validates the positive moderating effect of top talent on tourism industry integration in enhancing its performance and productivity, with composite and highly qualified talent reinforcing its enhancing effect on tourism industry integration.

Table 10 presents regression findings categorized by the percentage of high-end talents, specifically focusing on return on equity (per_roe) and total factor productivity in operations (tfp_op). The variable of interest is “conv,” which denotes the convergence of the tourism industry. Among the high proportion of high-end talents group, the coefficient for “conv” is positively and significantly associated with return on equity (per_roe) (0.0657) at the 5% level of significance. Additionally, it is highly significant for total factor productivity in operations (tfp_op) (0.3895). These findings indicate that in areas with a considerable concentration of highly skilled individuals, the convergence of the tourism industry is linked to increased profitability and overall productivity.

Conversely, the coefficient for “conv” is not statistically significant for return on equity (per_roe) (0.0016) in the lower proportion of the high-end talents group. However, it is statistically significant at the 5% level for total factor productivity in operations (tfp_op) (0.1747), which suggests that in areas with a lower percentage of highly skilled individuals, the tourism industry’s convergence does not substantially affect the return on equity. However, it is linked to increased total factor productivity. All models incorporate control variables, industry-fixed effects, and year-fixed effects to ensure the robustness of the results. The R -squared values range from 0.2433 to 0.6057, suggesting that the models account for significant variability in the dependent variables.

The study provides compelling evidence that industrial integration has a substantial positive impact on the performance and productivity of Chinese tourism companies (Yang et al., 2020 ). Essentially, the empirical analysis demonstrates that companies that adopt integration strategies within the tourism sector exhibit better financial performance than companies that do not integrate (Alatawi et al., 2023 ). Two crucial metrics, returns on equity (ROE) and total factor productivity (TFP), demonstrate the improved results that arise from cross-sector collaboration in the field of tourism development (Bacsi et al., 2022 ). The higher returns on equity (ROE) observed among integrated companies highlight their robust financial strength and profitability (Nejjari & Aamoum, 2023 ). Return on equity (ROE) is a crucial metric that assesses a company’s capacity to generate profits for its shareholders in relation to their investment in the company (Dewri, 2022 ). The evident advantage of return on equity (ROE) in integrated tourism companies highlights the effectiveness of cooperative efforts across various sectors within the industry (Nagendrakumar et al., 2022 ). Total Factor Productivity (TFP) is a fundamental aspect of this study, functioning as a comprehensive metric that evaluates tourism companies’ overall efficiency and productivity (Xu et al., 2023 ). The study illustrates that integrated companies surpass their non-integrated counterparts in terms of total factor productivity (TFP), highlighting the diverse advantages obtained from collaborative endeavors (Ghosal, 2015 ). This discovery is in complete agreement with the study’s primary and secondary hypotheses, confirming the strong connection between industrial integration and increased performance and productivity in the Chinese tourism industry (Zhang et al., 2022 ). The findings have significant implications, indicating that forming strategic alliances, partnerships, and collaborations across different sectors of the tourism industry can result in a synergistic impact (Bramwell & Lane, 2000 ). Integrated companies leverage the advantages of various sectors to not only increase their financial performance and improve their overall operational efficiency (Yu et al., 2021 ). Consequently, this places them in a stronger position to make significant contributions to the ever-changing Chinese tourism market. Integration initially improved performance and productivity more than expected (Tortorella et al., 2019 ). Multiple tourism subsectors showed significant increases in profitability and operational efficiency (Kuzey et al., 2021 ), which shows that integration has a synergistic effect beyond diversification. The unexpected findings challenge assumptions and demonstrate the transformative power of collaborative approaches, showing that integrated companies reduce risks through diversification and improve financial performance and operational efficiency (Chen et al., 2018 ). Residential tourism consumption significantly impacts integration rates, which is surprising (Nepal et al., 2019 ). This novel perspective illuminates the complex relationship between local demand and the evolution of the tourism industry (Sigala, 2020 ). The study shows that local tourism consumption is closely linked to integration (Zhuang et al., 2022 ). This unexpected correlation adds dynamism to integration and shows how local demand shapes industry dynamics. The findings highlight the need for a sophisticated strategy that considers global market trends and local customer preferences (Wang et al., 2020a , 2020b ). These surprising findings help us understand the complex Chinese tourism industry (Song & Li, 2019 ). They challenge common beliefs, showing that integration has benefits beyond traditional limits and emphasizing the need for industry participants to take a broad view (Jones & Comfort, 2020 ). As the industry progresses, these unexpected observations guide strategic decisions and encourage a deeper understanding of the complex relationships between integration, profitability, and local consumer dynamics (Tong et al., 2018 ). Our study of the interplay between industry integration in China’s tourism sector reveals deep insights, leading to deductions that have important implications for stakeholders and policymakers (Yousaf et al., 2022 ). Based on the diverse advantages uncovered in our study, we propose recommendations to drive the industry toward long-term expansion and increased competitiveness (Ungerman et al., 2018 ). The results of our research highlight the significant impact that collaborative efforts can have on various sectors within the tourism industry (Marasco et al., 2018 ). Policymakers should actively promote and enable the exchange of knowledge, forming strategic partnerships, and establishing joint ventures to create a collaborative environment that goes beyond conventional limitations (Chang et al., 2020 ; Ma et al., 2018 ). Investing strategically in infrastructure and digital technologies is essential for maximizing the advantages of integration (Das et al., 2020 ), which includes improving connectivity, streamlining the flow of information, and optimizing the allocation of resources. To acknowledge the significant impact of residential tourism consumption on integration rates, stakeholders should prioritize the promotion of residential tourism through community engagement initiatives (Lalicic & Önder, 2018 ), which can be achieved by aligning industry offerings with local preferences to ensure sustainable regional development. Prioritize the establishment of innovation hubs in the tourism industry, encourage research and development projects, and invest in skill development programs to adapt to the changing dynamics of a unified tourism sector (Surya et al., 2022 ). Creating a strong monitoring and evaluation framework is essential for evaluating the effectiveness of integration efforts, guaranteeing ongoing improvement, and aligning with the changing demands of the industry and the wider socio-economic environment (Hira & Busumtwin, 2021 ). With the transformative power of industrial integration, stakeholders and policymakers can adopt these recommendations (Menon & Fink, 2019 ), which will enable China’s tourism industry to achieve sustained growth, and enhanced resilience and establish a prominent position in the global tourism sector (Wang et al. 2020a , 2020b ).

This study advocates for a profound transformation in China’s approach to integrating the tourism industry, envisioning a future where collaborative models, a supportive environment, and continuous exploration of new frontiers propel the sector to new heights. Beyond immediate contributions, this research establishes a foundation for further exploration and transformative action, shaping the future of tourism in China and influencing global industry practices. The pursuit of integrated tourism emerges as a transformative journey with the potential to redefine the global tourism landscape, transcending the confines of academia.

Theoretical Implications

This study pushes the theoretical boundaries of tourism industry integration by introducing groundbreaking implications. Incorporating guaranteed mechanisms, the research propels the theoretical framework forward, emphasizing the pivotal role of enabling conditions in ensuring integration success. The multifaceted approach enriches the theoretical discourse, highlighting the interconnectedness of policy, consumption, technology, and talent in fostering successful integration. These theoretical underpinnings deepen the comprehension of the dynamic forces driving integrated tourism models, offering a more nuanced understanding of their intricacies. By shedding light on the theoretical aspects of integration, this research contributes to a more sophisticated and comprehensive understanding of how various factors converge to shape the integrated tourism landscape.

Managerial Implications

The findings provide invaluable insights for industry leaders, emphasizing integrated tourism models’ vast potential to enhance performance and efficiency. The study recommends a strategic embrace of cross-sectoral collaboration, harnessing digital technologies, and prioritizing investments in talent development. Concrete pathways are delineated for achieving greater profitability and productivity. Simultaneously, policymakers are urged to play a pivotal role in creating an environment conducive to integration, which involves crafting supportive policies, spearheading infrastructure development, and cultivating a skilled talent pool. The insights empower both managers and policymakers to actively contribute to shaping the trajectory of China’s tourism industry, ensuring a holistic and sustainable approach to integration that benefits stakeholders across the board.

Ideas For Future Research

Future research in integrated tourism in China should focus on sectoral integration dynamics, identifying patterns and outcomes of cross-sectoral partnerships for enhanced performance and productivity. Additionally, assessing integrated tourism models’ environmental and social implications is crucial to ensure responsible and equitable growth, addressing challenges, and proposing mitigation strategies. Exploring regional variations across diverse landscapes and markets can provide tailored insights, while qualitative case studies offer a deeper understanding of stakeholder experiences. Investigating the impact of emerging technologies like AI, augmented reality, or blockchain on integration effectiveness can yield innovative solutions. Examining talent development's role in sustaining integration benefits and conducting a policy analysis to evaluate and improve existing frameworks are essential for comprehensive insights and actionable recommendations.

Data availability

The datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request.

Abadi, F., Sahebi, I., Arab, A., Alavi, A., & Karachi, H. (2018). Application of best-worst method in evaluation of medical tourism development strategy. Decision Science Letters, 7 (1), 77–86.

Article   Google Scholar  

Alatawi, I. A., Ntim, C. G., Zras, A., & Elmagrhi, M. H. (2023). CSR, financial and non-financial performance in the tourism sector: A systematic literature review and future research agenda.  International Review of Financial Analysis , 102734.

Ali, E. S., Hasan, M. K., Hassan, R., Saeed, R. A., Hassan, M. B., Islam, S., ... & Bevinakoppa, S. (2021). Machine learning technologies for secure vehicular communication in internet of vehicles: Recent advances and applications.  Security and Communication Networks ,  2021 , 1-23

Anoegrajekti, N., Sariono, A., Macaryus, S., & Kusumah, M. S. (2018). Banyuwangi Ethno Carnival as visualization of tradition: The policy of culture and tradition revitalization through enhancement of innovation and locality-based creative industry. Cogent Arts & Humanities, 5 (1), 1502913.

Bacsi, Z., Dávid, L. D., & Hollósy, Z. (2022). Industry differences in productivity—In agriculture and tourism by Lake Balaton Hungary. Sustainability, 14 (19), 11809.

Bakker, M. (2019). A conceptual framework for identifying the binding constraints to tourism-driven inclusive growth. Tourism Planning & Development, 16 (5), 575–590.

Baldassarre, B., Schepers, M., Bocken, N., Cuppen, E., Korevaar, G., & Calabretta, G. (2019). Industrial symbiosis: Towards a design process for eco-industrial clusters by integrating circular economy and industrial ecology perspectives. Journal of Cleaner Production, 216 , 446–460.

Bavik, A., & Kuo, C. F. (2022). A systematic review of creativity in tourism and hospitality. The Service Industries Journal, 42 (5–6), 321–359.

Bazargani, R. H. Z., & Kiliç, H. (2021). Tourism competitiveness and tourism sector performance: Empirical insights from new data. Journal of Hospitality and Tourism Management, 46 , 73–82.

Bilgihan, A., & Ricci, P. (2023). The new era of hotel marketing: integrating cutting-edge technologies with core marketing principles.  Journal of Hospitality and Tourism Technology .

Boluk, K. A., Cavaliere, C. T., & Higgins-Desbiolles, F. (2019). A critical framework for interrogating the United Nations Sustainable Development Goals 2030 Agenda in tourism.  Journal of Sustainable Tourism .

Bramwell, B., & Lane, B. (2000). Collaboration and partnerships in tourism planning.  Tourism collaboration and partnerships: Politics, practice and sustainability ,  2 (1–19).

Cabeça, S. M., Gonçalves, A. R., Marques, J. F., & Tavares, M. (2019). Mapping intangibilities in creative tourism territories through tangible objects: A methodological approach for developing creative tourism offers.  Tourism & Management Studies ,  15 (1SI), 42–49.

Camilleri, M. A. (2021). Sustainable production and consumption of food. Mise-en-place circular economy policies and waste management practices in tourism cities.  Sustainability ,  13 (17), 9986.

Caviggioli, F. (2016). Technology fusion: Identification and analysis of the drivers of technology convergence using patent data. Technovation, 55 , 22–32.

Chang, J., Wang, J. J., & Bai, X. (2020). Good match matters: Knowledge co-creation in international joint ventures. Industrial Marketing Management, 84 , 138–150.

Chase, L. C., Stewart, M., Schilling, B., Smith, B., & Walk, M. (2018). Agritourism: Toward a conceptual framework for industry analysis. Journal of Agriculture, Food Systems, and Community Development, 8 (1), 13–19.

Google Scholar  

Chen, M. H., Wang, H. Y., & Wang, M. C. (2018). Knowledge sharing, social capital, and financial performance: The perspectives of innovation strategy in technological clusters. Knowledge Management Research & Practice, 16 (1), 89–104.

Chen, M., Sinha, A., Hu, K., & Shah, M. I. (2021). Impact of technological innovation on energy efficiency in industry 4.0 era: Moderation of shadow economy in sustainable development.  Technological Forecasting and Social Change ,  164 , 120521.

Chen, X., & Li, J. (2023). Facilitating knowledge-driven economic and social development: The significance of demographic transformation in tourism villages in China.  Journal of the Knowledge Economy , 1–29.

Chen, X., Zhang, T., & Jia, F. (2020). Industry convergence as a strategy for achieving sustainable development of agricultural complex: The case of Sandun-Lanli in China. Business Strategy and the Environment, 29 (6), 2679–2694.

Das, H. S., Rahman, M. M., Li, S., & Tan, C. W. (2020). Electric vehicles standards, charging infrastructure, and impact on grid integration: A technological review. Renewable and Sustainable Energy Reviews, 120 , 109618.

De Madariaga, C. J., & del Hoyo, J. J. G. (2019). Enhancing of the cultural fishing heritage and the development of tourism: A case study in Isla Cristina (Spain). Ocean and Coastal Management, 168 , 1–11.

Dewri, L. V. (2022). A critical assessment of interrelationship among corporate governance, financial performance, refined economic value added to measure firm value and return on stock. Journal of the Knowledge Economy, 13 (4), 2718–2759.

Fai, F., & Von Tunzelmann, N. (2001). Industry-specific competencies and converging technological systems: Evidence from patents. Structural Change and Economic Dynamics, 12 (2), 141–170.

Fan, Z., & Yu, X. (2021). The regional rootedness of China’s film industry: Cluster development and attempts at cross-location integration. Journal of Chinese Film Studies, 1 (2), 463–486.

Gambardella, A., & Torrisi, S. (1998). Does technological convergence imply convergence in markets? Evidence from the electronics industry. Research Policy, 27 (5), 445–463.

Ghosal, V. (2015). Business strategy and firm reorganization: Role of changing environmental standards, sustainable business initiatives and global market conditions. Business Strategy and the Environment, 24 (2), 123–144.

Gowreesunkar, V. G., & Vo Thanh, T. (2020). Between overtourism and under-tourism: Impacts, implications, and probable solutions.  Overtourism: Causes, implications and solutions , 45–68.

Guo, J., Zhang, Y. J., & Zhang, K. B. (2018). The key sectors for energy conservation and carbon emissions reduction in China: Evidence from the input-output method. Journal of Cleaner Production, 179 , 180–190.

Hall, C. M. (2021). Constructing sustainable tourism development: The 2030 agenda and the managerial ecology of sustainable tourism. In  Activating critical thinking to advance the sustainable development goals in tourism systems  (pp. 198–214). Routledge.

Hall, C. M., & Williams, A. M. (2019). Tourism and innovation . Routledge.

Book   Google Scholar  

Han, S. H., & Li, A. Z. (2023). Cultural innovation. In  A hundred stories: Industrial heritage changes China  (pp. 257–369). Singapore: Springer Nature Singapore.

Hira, A., & Busumtwi-Sam, J. (2021). Improving mining community benefits through better monitoring and evaluation. Resources Policy, 73 , 102138.

Hofman, K., Walters, G., & Hughes, K. (2022). The effectiveness of virtual vs real-life marine tourism experiences in encouraging conservation behaviour. Journal of Sustainable Tourism, 30 (4), 742–766.

Hollesen, J., Callanan, M., Dawson, T., Fenger-Nielsen, R., Friesen, T. M., Jensen, A. M.,... & Rockman, M. (2018). Climate change and the deteriorating archaeological and environmental archives of the Arctic.  antiquity ,  92 (363), 573–586.

Hui, L. (2018, November). Problems in industrial structure of international tourism in guangdong province and its optimization. In  2018 International Conference on Economy, Management and Entrepreneurship (ICOEME 2018) . Atlantis Press.

Ivanc, T., & Marius, M. C. (2020). Sustainable tourism as a pillar of the Eu development strategy. Journal of Critical Reviews, 7 (15), 6021–6028.

Jiang, Q., Chan, C. S., Eichelberger, S., Ma, H., & Pikkemaat, B. (2021). Sentiment analysis of online destination image of Hong Kong held by mainland Chinese tourists. Current Issues in Tourism, 24 (17), 2501–2522.

Jones, P., & Comfort, D. (2020). The COVID-19 crisis and sustainability in the hospitality industry. International Journal of Contemporary Hospitality Management, 32 (10), 3037–3050.

Jones, P., & Wynn, M. G. (2019). The circular economy, natural capital and resilience in tourism and hospitality. International Journal of Contemporary Hospitality Management, 31 (6), 2544–2563.

Kim, N., Lee, H., Kim, W., Lee, H., & Suh, J. H. (2015). Dynamic patterns of industry convergence: Evidence from a large amount of unstructured data. Research Policy, 44 (9), 1734–1748.

Kuzey, C., Uyar, A., Nizaeva, M., & Karaman, A. S. (2021). CSR performance and firm performance in the tourism, healthcare, and financial sectors: Do metrics and CSR committees matter? Journal of Cleaner Production, 319 , 128802.

Lak, A., Gheitasi, M., & Timothy, D. J. (2020). Urban regeneration through heritage tourism: Cultural policies and strategic management. Journal of Tourism and Cultural Change, 18 (4), 386–403.

Lalicic, L., & Önder, I. (2018). Residents’ involvement in urban tourism planning: Opportunities from a smart city perspective. Sustainability, 10 (6), 1852.

Lee, K., Azmi, N., Hanaysha, J., Alzoubi, H., & Alshurideh, M. (2022). The effect of digital supply chain on organizational performance: An empirical study in Malaysia manufacturing industry. Uncertain Supply Chain Management, 10 (2), 495–510.

Li, F., Chen, T., & Xin, X. (2013). Study on the relationship of co-integration and structure of the tourism industry: A case from Xi’an. Tourism Tribune, 28 (1), 69–76.

CAS   Google Scholar  

Li, H. (2019). Inheritance of the ecological culture with harmony between people and nature.  Chinese Dream and Practice in Zhejiang–Ecology , 141–165.

Li, H. (2022). Population shrinkage in resource-dependent cities of China during transition period. In  Postsocialist Shrinking Cities  (pp. 43–58). Routledge.

Li, Z., Wang, W. M., Liu, G., Liu, L., He, J., & Huang, G. Q. (2018). Toward open manufacturing: A cross-enterprises knowledge and services exchange framework based on blockchain and edge computing. Industrial Management & Data Systems, 118 (1), 303–320.

Liu, C. Y., Hu, F. Z., & Jeong, J. (2020a). Towards inclusive urban development? New knowledge/creative economy and wage inequality in major Chinese cities. Cities, 105 , 102385.

Liu, C., Dou, X., Li, J., & Cai, L. A. (2020b). Analyzing government role in rural tourism development: An empirical investigation from China. Journal of Rural Studies, 79 , 177–188.

Liu, J., & Lin, X. (2019). Empirical analysis and strategy suggestions on the value-added capacity of photovoltaic industry value chain in China. Energy, 180 , 356–366.

Lu, X., & Lian, Y. (2012). Estimation of total factor productivity of industrial enterprises in China: 1999–2007. China Economic Quarterly, 11 (2), 541–558.

Lu, Y. (2022). The measurement of high-quality development level of tourism: Based on the perspective of industrial integration. Sustainability, 14 (6), 3355.

Article   MathSciNet   Google Scholar  

Ma, Y., Lan, J., Thornton, T., Mangalagiu, D., & Zhu, D. (2018). Challenges of collaborative governance in the sharing economy: The case of free-floating bike sharing in Shanghai. Journal of Cleaner Production, 197 , 356–365.

Mamirkulova, G., Mi, J., Abbas, J., Mahmood, S., Mubeen, R., & Ziapour, A. (2020). New Silk Road infrastructure opportunities in developing tourism environment for residents better quality of life. Global Ecology and Conservation, 24 , e01194.

Marasco, A., De Martino, M., Magnotti, F., & Morvillo, A. (2018). Collaborative innovation in tourism and hospitality: A systematic review of the literature. International Journal of Contemporary Hospitality Management, 30 (6), 2364–2395.

Mariani, M., Bresciani, S., & Dagnino, G. B. (2021). The competitive productivity (CP) of tourism destinations: An integrative conceptual framework and a reflection on big data and analytics. International Journal of Contemporary Hospitality Management, 33 (9), 2970–3002.

McKercher, B. (1999). A chaos approach to tourism. Tourism Management, 20 (4), 425–434.

Menon, J., & Fink, A. (2019). The fourth industrial revolution and its implications for regional economic integration in ASEAN. Journal of Asian Economic Integration, 1 (1), 32–47.

Müller, O., Fay, M., & Vom Brocke, J. (2018). The effect of big data and analytics on firm performance: An econometric analysis considering industry characteristics. Journal of Management Information Systems, 35 (2), 488–509.

Nagendrakumar, N., Alwis, K. N. N., Eshani, U. A. K., & Kaushalya, S. B. U. (2022). The impact of sustainability practices on the going concern of the travel and tourism industry: Evidence from developed and developing countries. Sustainability, 14 (24), 17046.

Nangpiire, C., Silva, J., & Alves, H. (2022). Customer engagement and value co-creation/destruction: The internal fostering and hindering factors and actors in the tourist/hotel experience. Journal of Research in Interactive Marketing, 16 (2), 173–188.

Navarro-Meneses, F. J. (2023).  Managing the Smart Revolution in Tourism Firms: Innovation and Value Creation in the Era of Data . CABI.

Nejjari, Z., & Aamoum, H. (2023). The impact of intellectual capital on profitability, market value, productivity, and return on equity: Empirical evidence from Moroccan ICT firms. Journal of the Knowledge Economy, 14 (2), 1734–1748.

Nepal, R., Al Irsyad, M. I., & Nepal, S. K. (2019). Tourist arrivals, energy consumption and pollutant emissions in a developing economy–Implications for sustainable tourism. Tourism Management, 72 , 145–154.

Otgaar, A. (2012). Towards a common agenda for the development of industrial tourism. Tourism Management Perspectives, 4 , 86–91.

Palmer, N. J., & Chuamuangphan, N. (2021). Governance and local participation in ecotourism: community-level ecotourism stakeholders in Chiang Rai province, Thailand. In  Stakeholders management and ecotourism  (pp. 118–135). Routledge.

Parrinello, G., & Bécot, R. (2019). Regional planning and the environmental impact of coastal tourism: Rhe Mission Racine for the redevelopment of Languedoc-Roussillon’s littoral. Humanities, 8 (1), 13.

Parshakov, P., & Shakina, E. (2020). Do companies disclose intellectual capital in their annual reports? New evidence from explorative content analysis. Journal of Intellectual Capital, 21 (6), 853–871.

Partarakis, N., Zabulis, X., Antona, M., & Stephanidis, C. (2020). Transforming Heritage Crafts to engaging digital experiences.  Visual Computing for Cultural Heritage , 245–262.

Pencarelli, T. (2020). The digital revolution in the travel and tourism industry. Information Technology & Tourism, 22 (3), 455–476.

Phi, G. T., & Dredge, D. (2021). Collaborative tourism-making: An interdisciplinary review of co-creation and a future research agenda.  Critical Issues in Tourism Co-Creation , 5–20.

Pivoto, D. G., de Almeida, L. F., da Rosa Righi, R., Rodrigues, J. J., Lugli, A. B., & Alberti, A. M. (2021). Cyber-physical systems architectures for industrial internet of things applications in Industry 4.0: A literature review. Journal of Manufacturing Systems, 58 , 176–192.

Qiao, Y. (2020, March). The innovative exploration of culture and tourism industries in Shenzhen under industry convergence. In  4th International Conference on Culture, Education and Economic Development of Modern Society (ICCESE 2020)  (pp. 1308–1315). Atlantis Press.

Qu, R., Rhee, Z., Bae, S. J., & Lee, S. H. (2022). Analysis of industrial diversification level of economic development in rural areas using Herfindahl index and two-step clustering. Sustainability, 14 (11), 6733.

Rasoolimanesh, S. M., Ramakrishna, S., Hall, C. M., Esfandiar, K., & Seyfi, S. (2023). A systematic scoping review of sustainable tourism indicators in relation to the sustainable development goals. Journal of Sustainable Tourism, 31 (7), 1497–1517.

Reyes, S. R. C., Miyazaki, A., Yiu, E., & Saito, O. (2020). Enhancing sustainability in traditional agriculture: Indicators for monitoring the conservation of globally important agricultural heritage systems (GIAHS) in Japan. Sustainability, 12 (14), 5656.

Rodrigues, C., Liberato, D., & Esteves, E. (2022). The intangible cultural heritage as a creative tourism product. The masks of Trás-os-Montes. In  Advances in Tourism, Technology and Systems: Selected Papers from ICOTTS 2021, Volume 1  (pp. 595–609). Singapore: Springer Nature Singapore.

Roy, S. (2022). Trend and development in Indian tourism.  ATITHYA: A Journal of Hospitality ,  8 (2).

Saarinen, J., & Wall-Reinius, S. (2019). Enclaves in tourism: Producing and governing exclusive spaces for tourism. Tourism Geographies, 21 (5), 739–748.

Shahid, N. U., & Sheikh, N. J. (2021). Impact of big data on innovation, competitive advantage, productivity, and decision making: Literature review. Open Journal of Business and Management, 9 (02), 586.

Shi, T., Yang, S., Zhang, W., & Zhou, Q. (2020). Coupling coordination degree measurement and spatiotemporal heterogeneity between economic development and ecological environment––Empirical evidence from tropical and subtropical regions of China. Journal of Cleaner Production, 244 , 118739.

Shi, X., Li, L., Cheng, Z., & Liu, J. (2018). The impact of Internet plus on value chain of China’s manufacturing industry. Stud. Sci. Sci, 36 , 1384–1394.

Shuying, L., & Shu, F. (2017). Review of data analysis methods in measuring technology fusion and trend. Data Analysis and Knowledge Discovery, 1 (7), 2–12.

Sigala, M. (2020). Tourism and COVID-19: Impacts and implications for advancing and resetting industry and research. Journal of Business Research, 117 , 312–321.

Article   PubMed   PubMed Central   Google Scholar  

Song, M., & Li, H. (2019). Estimating the efficiency of a sustainable Chinese tourism industry using bootstrap technology rectification. Technological Forecasting and Social Change, 143 , 45–54.

Streimikiene, D., Svagzdiene, B., Jasinskas, E., & Simanavicius, A. (2021). Sustainable tourism development and competitiveness: The systematic literature review. Sustainable Development, 29 (1), 259–271.

Surya, B., Hernita, H., Salim, A., Suriani, S., Perwira, I., Yulia, Y., ... & Yunus, K. (2022). Travel-business stagnation and SME business turbulence in the tourism sector in the era of the COVID-19 pandemic.  Sustainability ,  14 (4), 2380

Szromek, A. R., Herman, K., & Naramski, M. (2021). Sustainable development of industrial heritage tourism–A case study of the Industrial Monuments Route in Poland. Tourism Management, 83 , 104252.

Tang, C., Yang, Y., Liu, Y., & Xiao, X. (2023). Comprehensive evaluation of the cultural inheritance level of tourism-oriented traditional villages: The example of Beijing. Tourism Management Perspectives, 48 , 101166.

Tiruvengadam, N., Beruvides, M. G., & Elizondo-Noriega, A. (2021). Comparison of total factor productivity and total productivity for their relationship attributes with profit. Engineering Management Journal, 33 (1), 57–74.

Tong, X., Tao, D., & Lifset, R. (2018). Varieties of business models for post-consumer recycling in China. Journal of Cleaner Production, 170 , 665–673.

Tortorella, G. L., Giglio, R., & Van Dun, D. H. (2019). Industry 4.0 adoption as a moderator of the impact of lean production practices on operational performance improvement.  International journal of operations & production management ,  39 (6/7/8), 860–886.

Tortorella, G., Sawhney, R., Jurburg, D., de Paula, I. C., Tlapa, D., & Thurer, M. (2021). Towards the proposition of a lean automation framework: Integrating industry 4.0 into lean production.  Journal of Manufacturing Technology Management ,  32 (3), 593–620.

Ungerman, O., Dedkova, J., & Gurinova, K. (2018). The impact of marketing innovation on the competitiveness of enterprises in the context of industry 4.0.  Journal of competitiveness ,  10 (2), 132.

Uyar, A., Kilic, M., Koseoglu, M. A., Kuzey, C., & Karaman, A. S. (2020). The link among board characteristics, corporate social responsibility performance, and financial performance: Evidence from the hospitality and tourism industry. Tourism Management Perspectives, 35 , 100714.

Valeri, M., & Baggio, R. (2022). Increasing the efficiency of knowledge transfer in an Italian tourism system: A network approach. Current Issues in Tourism, 25 (13), 2127–2142.

Wang, J., Huang, X., Gong, Z., & Cao, K. (2020a). Dynamic assessment of tourism carrying capacity and its impacts on tourism economic growth in urban tourism destinations in China. Journal of Destination Marketing & Management, 15 , 100383.

Wang, Y., Hong, A., Li, X., & Gao, J. (2020b). Marketing innovations during a global crisis: A study of China firms’ response to COVID-19. Journal of Business Research, 116 , 214–220.

Wanner, A., Pröbstl-Haider, U., & Feilhammer, M. (2021). The future of Alpine pastures–agricultural or tourism development? Experiences from the German Alps. Journal of Outdoor Recreation and Tourism, 35 , 100405.

Welling, J., Árnason, Þ, & Ólafsdóttir, R. (2020). Implications of climate change on nature-based tourism demand: A segmentation analysis of glacier site visitors in southeast Iceland. Sustainability, 12 (13), 5338.

Xia, B., Dong, S., Ba, D., Li, Y., Li, F., Liu, H., ... & Zhao, M. (2018). Research on the spatial differentiation and driving factors of tourism enterprises’ efficiency: Chinese scenic spots, travel agencies, and hotels.  Sustainability ,  10 (4), 901

Xu, H., Shen, Y., Wu, Y., & Xu, P. (2023). Total factor productivity and regional difference of tourism industry of listed companies. Management System Engineering, 2 (1), 6.

Yang, N., Hong, J., Wang, H., & Liu, Q. (2020). Global value chain, industrial agglomeration and innovation performance in developing countries: Insights from China’s manufacturing industries. Technology Analysis & Strategic Management, 32 (11), 1307–1321.

Yang, R. (2015). Study on the total factor productivity of Chinese manufacturing enterprises. Economic Research Journal, 2 , 61–74.

Yeon, J. I., Hwang, S., & Jun, B. (2023). Ports as catalysts: spillover effects of neighbouring ports on regional industrial diversification and economic resilience.  Regional Studies , 1–18.

Yousaf, U. B., Ullah, I., Wang, M., Junyan, L., & Rehman, A. U. (2022). Does board capital increase firm performance in the Chinese tourism industry? Corporate Governance: The International Journal of Business in Society, 22 (4), 653–679.

Yu, Y., Zhang, J. Z., Cao, Y., & Kazancoglu, Y. (2021). Intelligent transformation of the manufacturing industry for Industry 4.0: Seizing financial benefits from supply chain relationship capital through enterprise green management.  Technological Forecasting and Social Change ,  172 , 120999.

Yuanxu, L., & Yafei, H. (2021). A review and prospects of cross-boundary assemblage of EMNEs. Foreign Economics & Management, 43 (10), 85–102.

Zehrer, A., & Raich, F. (2010). Applying a lifecycle perspective to explain tourism network development. The Service Industries Journal, 30 (10), 1683–1705.

Zha, J., Yuan, W., Dai, J., Tan, T., & He, L. (2020). Eco-efficiency, eco-productivity and tourism growth in China: A non-convex metafrontier DEA-based decomposition model. Journal of Sustainable Tourism, 28 (5), 663–685.

Zhang, H., Lu, L., & Sun, F. (2021). Changing role of lifestyle in tourism entrepreneurship: Case study of naked retreats enterprise. Tourism Management, 84 , 104259.

Zhang, X., Guo, W., & Bashir, M. B. (2022). Inclusive green growth and development of the high-quality tourism industry in China: The dependence on imports. Sustainable Production and Consumption, 29 , 57–78.

Article   CAS   Google Scholar  

Zhang, Y., Li, X., & Min, Q. (2018). How to balance the relationship between conservation of Important Agricultural Heritage Systems (IAHS) and socio-economic development? A theoretical framework of sustainable industrial integration development. Journal of Cleaner Production, 204 , 553–563.

Zhao, J., Ji, G., Tian, Y., Chen, Y., & Wang, Z. (2018). Environmental vulnerability assessment for mainland China based on entropy method. Ecological Indicators, 91 , 410–422.

Zhao, Y., & Liu, B. (2020). The evolution and new trends of China’s tourism industry. National Accounting Review, 2 (4), 337–353.

Zhong-xu, Y. (2013). Dynamic effects and development tendency of coupling between rural tourism industry and big agriculture. Tourism Tribune, 28 , 80–87.

Zhou, P. (2019). The theory and practice of China’s tourism economy (1978–2017) . Springer.

Zhu, J., Yuan, X., Yuan, X., Liu, S., Guan, B., Sun, J., & Chen, H. (2021). Evaluating the sustainability of rural complex ecosystems during the development of traditional farming villages into tourism destinations: A diachronic emergy approach.  Journal of Rural Studies ,  86 , 473–484.s

Zhuang, X., Yao, Y., & Li, J. (2019). Sociocultural impacts of tourism on residents of world cultural heritage sites in China. Sustainability, 11 (3), 840.

Zhuang, Y., Yang, S., Razzaq, A., & Khan, Z. (2022). Environmental impact of infrastructure-led Chinese outward FDI, tourism development and technology innovation: A regional country analysis. Journal of Environmental Planning and Management, 66 (2), 367–399.

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Lu, Y. Transforming China’s Tourism Industry: The Impact of Industrial Integration on Quality, Performance, and Productivity. J Knowl Econ (2024). https://doi.org/10.1007/s13132-024-01852-w

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China Tourism and Hotel Industry Size & Share Analysis - Growth Trends & Forecasts (2024 - 2029)

The China Tourism and Hotel Industry Report is Segmented by Type Into Inbound Tourism and Outbound Tourism and by Product Into Chain Hotels and Independent Hotels. The Report Offers Market Size and Forecasts for China's Tourism and Hotel Industry in Value (USD) for all the Above Segments.

China Tourism and Hotel Market Size

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China Tourism and Hotel Market Analysis

The China Tourism And Hotel Market size is estimated at USD 385.07 billion in 2024, and is expected to reach USD 504.22 billion by 2029, growing at a CAGR of 5.53% during the forecast period (2024-2029).

China's tourism and hotel industry is a dynamic sector with significant growth potential driven by various aspects, such as rapid urbanization, rising disposable income, government support, and increasing inbound and domestic tourism. As the world's most populous country and a major tourist destination, China's tourism and hotel industry plays a crucial role in economic development and international engagement. China has experienced robust growth in both inbound and domestic tourism in recent years. The country's rich cultural heritage, diverse landscapes, modern infrastructure, and a growing number of attractions have attracted millions of tourists from around the world. Additionally, the Chinese government's efforts to promote tourism through policies, infrastructure development, and marketing campaigns have further boosted the industry's growth.

The hotel industry in China has also witnessed significant expansion and development to meet the growing demand from tourists and business travelers. Major international hotel chains and domestic hotel brands have expanded their presence in China, leading to increased competition and a wider range of accommodation options for travelers. The COVID-19 pandemic had a severe impact on China's tourism and hotel industry, leading to a sharp decline in international travel and hotel occupancy rates. However, the Chinese government's swift response and effective containment measures helped the country recover quickly compared to other countries.

China Tourism and Hotel Market Trends

Rising demand for hotels is driving the growth of the market.

The rising demand for hotels is a significant driver of growth within the tourism and hotel industry, exerting a profound impact on various aspects of the sector. As global travel continues to increase, driven by factors including growing disposable incomes, expanding middle-class populations, and growing interest in leisure and business travel, the demand for accommodation options, particularly hotels, experiences a corresponding surge.

This increased demand for hotels has several notable impacts on the tourism and hotel industry. Firstly, it drives investment in infrastructure, leading to the construction of new hotel properties and the expansion or renovation of existing ones. This influx of investment not only enhances the accommodation capacity but also improves the overall quality of hotel offerings, ranging from luxury resorts to budget-friendly accommodations catering to diverse traveler preferences.

Furthermore, the expanding hotel sector generates a ripple effect on the broader tourism ecosystem. Beyond accommodation, hotels often serve as hubs for tourism activities, offering amenities such as dining options, recreational facilities, conference spaces, and tour services. This integration of services creates a seamless and convenient experience for travelers, enhancing destination attractiveness and encouraging visitor spending on ancillary services and attractions.

Growing Internet Access and Online Testimonials Is Driving the Market

Growing Internet access and online testimonials have indeed emerged as key trends in the Chinese tourism and hotel market. China has witnessed a significant increase in internet penetration over the years, with over 70% of internet penetration. This high internet penetration has opened new opportunities for the tourism and hotel industry.

Online travel agencies (OTAs) and hotel booking platforms have gained immense popularity in China. Platforms like Ctrip (now Trip.com Group), Meituan-Dianping, and Alibaba's Fliggy have become go-to options for travelers to search, compare, and book hotels. These platforms provide a variety of options and competitive prices, making it convenient for travelers to find suitable accommodations.

Social media platforms, including WeChat, Weibo, and Douyin, play a crucial role in shaping travel trends and influencing consumer decisions. Travelers often seek advice, inspiration, and recommendations from their social media connections. Hotels and tourism businesses actively leverage social media channels to engage with potential customers, promote their offerings, and generate positive reviews.

China Tourism and Hotel Industry Overview

The hotel and tourism industries in China are inherently fragmented. Currently, a few firms control most of the industry in terms of brand awareness and market share. Several travel and tourism companies are providing a variety of inbound and international tourism packages to attract many customers and achieve a competitive advantage. Like this, major international hotel brands have extended to the nation to provide their services as a result of the huge population and volume of foreign visitors. eLong, Shanghai Jin Jiang International Hotels (Group) Co. Ltd, Fliggy, Trip.com Group Ltd, and Marriott International are a few of the leading companies in the market.

China Tourism and Hotel Market Leaders

Shanghai Jin Jiang International Hotels (Group) Co. Ltd

Marriott International

Trip.com Group Ltd

*Disclaimer: Major Players sorted in no particular order

China Tourism and Hotel Market News

• May 2023: IRIS, the provider of digital F&B and guest experience platforms, aimed to increase its market share across China’s growing hospitality market. The company made a new partnership with Asia-based hospitality technology reseller MYM, utilizing IRIS’s Chinese Azure cloud solution.
• October 2022: Wyndham Hotels and Resorts opened two hotels named Wyndham New Taipei Linkou and Wyndham Sun Moon Lake in partnership with Qingyu Property Co. Ltd and Lijing Enterprise Co. Ltd, respectively. The openings mark the first hotels for each brand in the China-Taiwan region.

China Tourism And Hotel Market Report - Table of Contents

1. INTRODUCTION

1.1 Study Assumptions and Market Definition

1.2 Scope of the Study

2. RESEARCH METHODOLOGY

3. EXECUTIVE SUMMARY

4. MARKET INSIGHTS AND DYNAMICS

4.1 Market Overview

4.2 Market Drivers

4.2.1 Cultural Heritage and Tourism Attractions Are Driving the Market

4.2.2 Increasing Domestic and International Tourism

4.3 Market Restraints

4.3.1 Language Barrier Is Restraining the Market

4.3.2 Seasonality and Regional Disparities

4.4 Market Opportunities

4.4.1 Eco-tourism Will Create Opportunities for New Entrants

4.4.2 Digital Transformation and Technology Integration

4.5 Insights on Government Initiatives Toward Tourism Sector in China

4.6 Insights on Government Regulations in the Hotel Industry of China

4.7 Industry Value Chain Analysis

4.8 Industry Attractiveness - Porter's Five Forces Analysis

4.8.1 Threat of New Entrants

4.8.2 Bargaining Power of Buyers

4.8.3 Bargaining Power of Suppliers

4.8.4 Threat of Substitutes

4.8.5 Intensity of Competitive Rivalry

4.9 Insights on Technological Innovations in the Tourism and Hotel Industry of China

4.10 Impact of COVID-19 on the Market

5. MARKET SEGMENTATION

5.1 By Type

5.1.1 Inbound Tourism

5.1.2 Outbound Tourism

5.2 By Product

5.2.1 Chain Hotels

5.2.2 Independent Hotels

6. COMPETITIVE LANDSCAPE

6.1 Market Concentration Overview

6.2 Company Profiles

6.2.1 eLong

6.2.2 Emei Shan Tourism Co. Ltd

6.2.3 Huangshan Tourism Development

6.2.4 Trip.com Group Ltd

6.2.5 Tuniu Corp.

6.2.6 Shanghai Jin Jiang International Hotels (Group) Co. Ltd

6.2.7 Marriott International

6.2.8 Huazhu Hotels Group Ltd

6.2.9 IHG Hotels

6.2.10 Shangri-la Hotels and Resorts

6.2.11 Zhejiang New Century Hotel Management Co. Ltd*

• *List Not Exhaustive

7. FUTURE MARKET TRENDS

8. DISCLAIMER AND ABOUT US

China Tourism and Hotel Industry Segmentation

The tourism and hotel industry encompasses businesses and services involved in providing accommodations, dining, and recreational activities for travelers, tourists, and visitors. It involves a wide range of businesses, comprising restaurants, bed & breakfasts, tour companies, motels, hotels, and travel agencies. China's tourism and hotel industry is segmented by type and product. By type, the market is segmented into inbound tourism and outbound tourism. By product, the market is segmented into chain hotels and independent hotels. The report offers market size and forecasts for China's tourism and hotel industry in value (USD) for all the above segments.

China Tourism And Hotel Market Research FAQs

How big is the china tourism and hotel market.

The China Tourism And Hotel Market size is expected to reach USD 385.07 billion in 2024 and grow at a CAGR of 5.53% to reach USD 504.22 billion by 2029.

What is the current China Tourism And Hotel Market size?

In 2024, the China Tourism And Hotel Market size is expected to reach USD 385.07 billion.

Who are the key players in China Tourism And Hotel Market?

eLong, Shanghai Jin Jiang International Hotels (Group) Co. Ltd, Marriott International, Fliggy and Trip.com Group Ltd are the major companies operating in the China Tourism And Hotel Market.

What years does this China Tourism And Hotel Market cover, and what was the market size in 2023?

In 2023, the China Tourism And Hotel Market size was estimated at USD 363.78 billion. The report covers the China Tourism And Hotel Market historical market size for years: 2020, 2021, 2022 and 2023. The report also forecasts the China Tourism And Hotel Market size for years: 2024, 2025, 2026, 2027, 2028 and 2029.

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China Tourism And Hotel Industry Report

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China Tourism Industry Overview

Domestic tourist, view china's domestic tourist from 1990 to 2023 in the chart:.

Domestic Tourist: Expenditure: per Capita

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Outbound Tourist

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Tourism Industry: Number of Employee

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Tourism Revenue: Domestic

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Tourism Revenue: Foreign Currency: Year to Date

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Travel Agency: Fixed Asset

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Travel Agency: Number of Employee

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Travel Agency: Number of Enterprise

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Travel Agency: Number of Enterprise: Domestic

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Travel Agency: Number of Enterprise: International

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Travel Agency: Profit

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Tourism to China Surged During First Seven Months of 2024

It’s been a good year for China’s tourism industry.

During the first seven months of 2024 the number of foreign visitors to the country reached 17.25 million, which is a record 129.9 percent increase year over year, according to news published by The State Council for The People’s Republic of China.

Government officials attribute the record growth to a series of entry policies that have made it easier for tourists to visit and explore China.

Meanwhile, a total of 341 million cross-border travels were recorded, up 62.34 percent from the same period last year.

That growth in visitation has translated into a similarly impressive increase in consumption. Thanks to foreign visitors China’s consumption topped 100 billion yuan, or about $14 billion U.S. 

The per capita daily average consumption volume was nearly 3,500 yuan, according to  Liu Haitao, deputy head of China’s National Immigration Administration (NIA), which held a press conference recently to announce the new tourism figures.

Additional data released by NIA included:

• 846,000 port visas were issued to foreign nationals who had ‘urgent needs’ to enter China but did not have enough time to apply for a visa at Chinese embassies or consulates abroad.
• The number of visas issued increased 183 percent year over year.

Foreign nationals visiting China have various avenues to submit applications for visas. They can submit applications in advance to port visa authorities by themselves or through inviting parties, or apply on site upon arrival at ports in China, according to Liu.

As for the policy changes that have been enacted in China, Liu explained that the country’s immigration authorities have “stepped up efforts to make customs clearance more convenient” while also working to ensure that “cross-border supply chains” are more efficient. All of this has apparently decreased the time for customs inspections and created priority avenues for major cargo flights.

When it comes to tourists, China has expanded its visa-free policies, relaxed visa application requirements and simplified procedures. Border checks have also been exempted for some transit passengers. In addition, mobile payment services have been made easier for foreign visitors.

More recently, in July, the NIA rolled out another new policy that allows individuals from countries that have diplomatic relations with China to visit the southern island province of Hainan visa-free for 144 hours. The visitors however, must be with tour groups that are registered in Hong Kong and Macao.

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China’s ecotourism industry: how sustainable traveling looks like today

• September 12, 2022
• Chinese tourise , Ecotourism in China , Green tourism in China , Tourism in China

Over the years, the tourism sector in China has undergone a significant transformation – keeping up with global trends in embracing sustainability and ecotourism in its development, especially as government initiatives are pushing for environmental beautification as well as conservation. 

Ecotourism is a form of sustainable tourism that focuses on the natural environment, cultural heritage, and community development. It encourages travelers to gain a deeper understanding of the cultures when visiting destinations and be responsible towards nature.

The Chinese outbound eco-travel market

As Chinese tourists become more eco-conscious, both when traveling domestically and overseas , businesses have also been working diligently to improve the quality of their ecotourism offerings. 

For example, Six Senses Qing Cheng Mountain resort (青城山六善酒)in Sichuan houses, an organic farm, uses only local produce, provides guests with reusable glass bottles ( saving 110, 922 plastic bottles in 2021 ), as well as only uses Teslas for airport transportation. 

Villa Finder , a rental company with thousands of private villas globally, also engages in green practices. Such as planting a tree for each booking, offsetting carbon footprint, as well as creating a dedicated collection for eco-conscious villas. Their initiatives are notably strong in Bali, with special sustainable listings like RedDoor Villa , as well as partnerships with local companies for recycling efforts.

Although Villa Finder does not currently operate in China, Khanh Tran, the Growth Manager of Villa Finder, observed that most bookings from China were for vacation villas in Bali. In fact. China was the top inbound market for Bali at 23% in 2019. On the other hand, most of the Chinese clientele of Villa Finder were from Hong Kong rather than Mainland China, with Koh Samui being the top destination for Hong Kong travelers.

Another luxury eco-resort located abroad in the Maldives, Gili Lankanfushi , also witnessed an influx of Chinese tourists in 2018. However, the Gili Lankanfushi team remarks that while all Chinese tourists are engaged with sustainability and environmental policies, Hong Kong tourists, in particular, seem to have a stronger “sustainable ethos” compared to tourists from mainland China based on their conversations. 

Domestic eco-travel in China and its social implications

Sustainability being a priority for Chinese tourists is even more apparent in domestic travel, which registered a huge market of six billion domestic trips in 2019 , and 2.92 trillion RMB of travel expenses in 2021. The government has continuously invested in local tourism development, having poured 155 billion USD into its tourism infrastructure in 2018 , which led to an $830 billion tourism industry pre-pandemic.

In 2021, China’s Zhejiang province produced an “ ecotourism map ” that listed 21 ecotourism sites for domestic travel in response to the growing Chinese demand for ecotourism products. This list included sites such as wetland parks, forests, nature reserves, villages, and cultural facilities in 11 cities. 

Shaanxi province also released a list of government development project s, boasting six ecotourism trails that center Northwest China’s signature red leaves, giant pandas, golden monkeys, and other natural attractions. China’s rural regions make full use of their geographical assets to attract visitors and facilitate green tourism. 

However, government support for green initiatives goes beyond just being environmentally friendly. It also serves to help promote cultural preservation and economic development for struggling ethnic minorities . China’s ecotourism is an effective method for economic growth and rural poverty reduction, addressing the problem of ethnic groups that fall behind in economic development due to living in mountain-locked regions. 

Cultural Consumerism in China

China is home to some of the world’s most beautiful mountains, and many of them are rich in cultural heritage. Tourists can visit China’s mountains and enjoy a scenic view while learning about its culture and history, but these mountain ranges often require cultural knowledge to appreciate or understand them fully.

For example, there are signs at the Yellow Mountain that describe a dreamy view known as the Xihai (West Sea), but there are no actual bodies of water in sight. As the West Sea refers to a myth where a hiker reached the Yellow Mountains peaks and saw a sea of clouds. While hikers can of course enjoy the Yellow Mountain view without a Chinese mythology class, knowing the cultural lore helps to create a more immersive touristic experience. 

Mountain spectacles: A new not-so-natural phenomenon

One way that China has connected its cultural heritage with the ecotourism industry is through mountain performances. Instead of traditional forms of ecotourism like scenic tours, nature walks, and reserve visits; cities such as Zhangjiajie and Guilin have created outdoor theatrical shows in their mountain landscapes that showcase the local cultures. 

One of the most popular mountain shows is Hunan Province’s Tianmen Fox Fairy ( 天门狐仙 ) show , with an initial production investment of one hundred million yuan. This huge production in the mountains includes a 10,000 square-meter glass steel stage, which can host 530 actors and stage crew. Some scenes of the show highlight the local Tujia minority’s traditions, like their “ hanging houses .” 

Another popular show called the ‘Impressions of Liu Sanjie’ (刘三姐) in Guilin reached over 300 million yuan in revenue in 2017. This mountain production enabled the local Zhuang villagers to become “farmer-performers,” working in production rather than traditional field work. These new jobs in the tourism industry allow them to earn up to 10,000 yuan annually (mostly during tourist season) , which is ten times higher than their former agricultural incomes. 

These new additions to China’s growing ecotourism industry show that traditional culture and heritage are important factors for development. Tourists are drawn not only to natural landscapes, but also to cultural assets. 

Popular ecotourism mountain destinations in China

One famous site is the Tianmen Mountain National Park, or Tianmenshan (天门山), located in Zhangjiajie City, Hunan Province. This mountain is famous for its natural beauty and was named one of the Seven Wonders of China by the Chinese government in 2011. It was officially designated as a UNESCO World Heritage Site in 1992 .

The mountain itself has three peaks: East Peak (Dongyue Feng), Central Peak (Zhongyue Feng), and West Peak (Xiyue Feng). The highest point on this mountain is at 2,160 meters above sea level.

Other tourist hotspots in China include:

• The Three Mountains of God
• The Jade Dragon Snow Mountain 

Key takeaways of China’s ecotourism industry

• Many companies in the tourism industry acknowledge that Chinese tourists are becoming more eco-conscious in their traveling preferences when going abroad.
• As government policies focus more on sustainability, domestic conservation, and nature development efforts, the average Chinese consumer is also more aware and interested in being eco-conscious.
• Government investment in ecotourism is growing in order to further economic development.
• Ecotourism is important for the preservation of minority culture, and the development of mountain-locked minority communities. 
• Mountain shows are a relatively new phenomenon that combines cultural assets with China’s natural landscape and has generated significant tourist revenue.

Author: Gloria Tsang

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County tourism carves niche in China

Convenient access, affordable hotels, travel policies, new experiences stoke expansion.

A niche tourism segment has emerged in China this summer with a focus on small counties, in spite of the fiercely competitive nature of the market.

Counties with convenient transportation and those that are close to scenic spots are expected to witness further growth in the tourism and accommodation markets, industry players said.

Hotel bookings in fourth- and fifth-tier cities and county-level areas grow the fastest among different tourist areas, according to Qunar, a Beijing-based online travel agency.

In particular, high-end hotel bookings in small cities have jumped nearly 50 percent year-on-year this summer, and the most sought-after counties are in western China that are relatively cooler, Qunar found.

"Libo, a scenic county in Guizhou province, Southwest China, renowned for its karst mountains, has seen hotel bookings surge more than seven times over last summer. The growth rate tops the list nationwide," said Xiao Peng, a Qunar researcher.

Besides Libo, some other counties in Guizhou, as well as counties in Qinghai and Gansu provinces, are most popular with local hotel bookings surging. The top five hotels with the highest booking volumes in Libo this summer are high-end hotels and hotel chains, with an average price of over 397 yuan ($55.5) per night, the travel agency said.

Meanwhile, Guizhou's scenic areas have issued policies that by the end of this year, domestic college students, middle-school students and primary school students, and some other groups such as teachers, police officers and medical staff, will get complimentary admission to all A-level scenic spots in the province. Some consumers said they chose to visit Guizhou mainly because of such preferential policies.

"More travelers have been obtaining travel-related information through livestreaming sessions online," said Qiao Chengwei, a director of domestic travel products at Tuniu Corp, a Nanjing, Jiangsu province-based online travel agency.

"Still, there exist some unreasonably low-priced products for certain travel groups, and there are various self-paying trips indirectly included in the travel itinerary. We suggest tourists choose legitimate tourism products offering guaranteed after-sales service."

For long-distance travelers, what is important is whether or not a chosen county-level destination is conveniently accessible.

Some 80 percent of the top 20 domestic counties that have seen the highest growth in hotel bookings are either directly accessible by air, high-speed railway trains or regular trains, or located within a two-hour drive from the nearest train station, Qunar said.

For instance, the high-speed railway train station of Libo is only 11 kilometers away from Xiaoqikong (seven small arches) scenic area in the county, making it convenient for travelers to visit the sightseeing spot.

In August 2023, the high-speed railway line connecting Libo and Guiyang, the provincial capital of Guizhou, began operations. It takes a mere 57 minutes now to reach the other city. The number of tourists that Libo receives has since grown significantly, making it one of the most popular tourist destinations in Guizhou.

Guanling, another county in Guizhou, boasts rich tourism resources and convenient transportation facilities. The Shanghai-Kunming high-speed railway line has a stop at the county, and the fastest high-speed railway train journey from Guiyang to Guanling takes only 47 minutes.

Fusong, a county surrounded by dense forest, is located in Northeast China's Jilin province, northwest of Changbai Mountain. The county operates Changbaishan Airport, which serves as China's first airport dedicated to forest tourism.

The airport has direct flights connecting Fusong with more than 20 domestic cities such as Beijing, Shanghai, Qingdao in Shandong province and Guangzhou in Guangdong province.

It is expected that by the end of 2025, the high-speed railway connecting Shenyang, the provincial capital of Liaoning province, and Changbaishan, will start operations. By then, passengers will need no more than about three hours in a high-speed train to travel from Beijing to Fusong.

"The level of tourism development of counties is highly correlated with the maturity of local infrastructure construction," said Cai Muzi, a Qunar researcher.

"The popularity of county-level tourism this year is expected to further accelerate the building of local infrastructure, which will then contribute to the growth of county-level tourism, thus shaping a virtuous cycle," Cai said.

She also said that an increasing number of hotel chains and midrange to high-end hotels are accelerating their expansion in fourth- and fifth-tier domestic cities and county-level markets to meet growing travel demand.

With hotels in many parts of the country lowering their prices this summer, accommodation prices in most counties remain flat compared with last summer, but higher than the levels seen in the summer of 2019, before the COVID-19 pandemic.

Prices of rooms in high-end hotels in counties, however, have edged up 15 percent on average over the 2019 level, Qunar data showed.

Unlike in major cities where a large number of luxury hotels compete with each other fiercely, urban residents prefer to book high-end hotels in counties, boosting demand, market insiders said. Travelers from Chengdu, Chongqing, Beijing, Shanghai and Guangzhou have topped the list of tourists visiting counties.

"The number of high-end hotels in counties is relatively small, and in some popular areas, supply even falls short of demand. This is influenced by the shift in people's travel habits, as more travelers are willing to venture into smaller cities to explore different landscapes and gain some unique experiences," Cai said.

"Besides, the number of parent-child trips have increased significantly in summer, and parents who take their children to counties often prefer to stay at better hotels."

A booming tourism market has fueled the demand for both the air and rail routes this summer. China is expecting 860 million railway passenger trips in July and August, averaging 13.87 million daily. The strong demand mainly comes from summer vacationers and family visitors, according to China State Railway Group Co Ltd, the country's railway operator.

From July 1 to Aug 31, China's air travel market is expected to continue its robust momentum and handle 133 million passenger trips via domestic and international flights, with the daily average reaching 2.15 million passenger trips, up 5 percent year-on-year and 10 percent over the summer of the pre-pandemic period in 2019, according to the Civil Aviation Administration of China.

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Change of travel and tourism industry's contribution to GDP in China 2012-2023

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• Number of tourism day visits in Great Britain 2011-2022
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• Average spend on domestic summer holidays in the United Kingdom (UK) 2011-2023
• Number of accommodation businesses in the UK 2008-2022
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• Attitudes towards traveling in the UK 2024
• Travel frequency for private purposes in the UK 2024
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• Share of Britons taking days of holiday 2019-2023, by number of days
• Share of Britons who did not take any holiday days 2019-2023, by gender
• Share of Britons who did not take any holiday days 2019-2023, by age
• Leading regions for summer staycations in the UK 2024
• Preferred methods to book the next overseas holiday in the UK October 2022, by age
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• Travel & Tourism market revenue growth in the UK 2020-2029, by segment
• Revenue forecast in selected countries in the Travel & Tourism market in 2024
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Travel behavior

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• Premium Statistic Share of Britons taking days of holiday 2019-2023, by number of days
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More foreign tourists are making the trip to China, spurred by relaxed visa restrictions

Beijing is hoping that foreign visitors can help lift the economy out of its slump . Since removing its COVID quarantine controls in early 2023, Chinese officials have tried to make it easier for tourists to come to the country and spend money. 

Around 17.3 million foreign visitors entered China between January and July, the National Immigration Administration said Monday, a 130% year-on-year increase. Foreign tourists are expected to contribute about 100 billion yuan ($14 billion) in direct spending this year, based on the amount foreign tourists spent per day last year (3,459 yuan, or $483).

Tourism would spur a sluggish Chinese economy, still struggling to grow at rates seen before the COVID pandemic.

Around 145 million tourists visited China in 2019, according to data from the Ministry of Culture and Tourism, of which 31.9 million came from locations other than Hong Kong, Macau, or Taiwan. Those 31.9 million tourists spent $77 billion.

The number of visitors to China collapsed during the COVID pandemic as Beijing imposed strict quarantine periods for anyone entering the country. China was also slower to reopen than many other countries, only fully lifting border restrictions in January 2023, and issuing foreign tourist visas a few months later. 

China unilaterally eased restrictions for visitors from a dozen European countries, letting them stay in the country for up to 15 days visa-free. Australia, New Zealand, Singapore, Malaysia, and Thailand are also in China’s visa-free entry scheme. 

Reservations from Southeast Asian visitors headed to China are “several times” higher than pre-pandemic levels, Klook CEO Ethan Lim said at a UBS investment conference in Hong Kong in late May. 

China has also made it easier for foreigners to make transactions in increasingly cashless mainland China. 

Since July 2023, foreign tourists have been able to link their foreign credit cards to Tencent’s WeChat and Ant Group’s Alipay, allowing them to more easily pay for goods and services during their travel. (Many merchants in China use local digital payment services—and some rarely accept cash.)

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• Published: 27 August 2024

Spatial patterns and their influencing factors for China’s catering industry

• Li Tian   ORCID: orcid.org/0000-0002-0410-1146 1 &
• Xiaoyan Shen 2  

Humanities and Social Sciences Communications volume  11 , Article number:  1090 ( 2024 ) Cite this article

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• Development studies

The catering industry plays an essential role in providing life services. Understanding its spatial patterns offers insights into the economic, cultural and spiritual image of a society. Especially in urban settings, the catering industry is often considered a key element for improving the competitiveness of cities. With rapid urbanization and economic growth in China, the catering industry has become the main driving force to stimulate China’s service sectors. In this study, we conducted a comprehensive spatial analysis to explore the distribution patterns by calculating kernel density and applying the geographically weighted regression (GWR) model with 4.49 million restaurants across 336 cities in China in 2020. The restaurants were categorized into four types: Chinese restaurants (CRs), Western restaurants (WRs), fast-food restaurants (FFRs), and dessert and drink restaurants (DDRs). We incorporated a diverse set of socio-economic indicators to explore potential causal influences, including population size and density, GDP, etc. Our study revealed a gradual decrease in restaurant density from southeast to northwest China, with high density observed in the Pearl River Economic Delta, Yangtze River Economic Delta, Chongqing, and Chengdu regions. In terms of the potential influencing factors, we observed that in west and southwest regions, density appeared to be more affected by GDP per unit area, total tourism revenue, disposable income per capita of urban residents, and total retail sales of social consumption. While in northeast areas, restaurant density was more affected by total retail sales of social consumption, GDP per unit area, number of urban population, and the proportion of tertiary industry in GDP. These insights serve as a direct scientific foundation for informing the strategic planning of different types of restaurants at municipal, provincial, and regional levels.

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Introduction.

The catering industry is an essential part of the service sector in cities. Its growth is a reflection of a city’s culture and vitality, and its prosperity would improve the service level and competitiveness of cities (Osimani and Clementi 2016 ). As a fundamental geographical unit within the catering industry, restaurants, along with their types and spatial distribution, have a direct impact on a city’s spatial structure, economy, culture, openness, and vibrancy. These attributes of restaurants serve as a critical reference for urban planning and business location (Dong et al. 2019 ), also for analyzing the development status of a city from the perspectives of economic geography, spatial geography, cuisine culture, and social problem (Schiff 2015 ; Lee et al. 2016 ; Lu et al. 2020 ), etc.

Economic development and urbanization increase the proportion of urban residents, which further boosts the development of catering industry as a basic service sector in cities. According to Maslow’s hierarchical theory of needs, people incline to pursue higher needs after satisfying the most basic physiological need for food provided (Maslow 1987 ). With an increase in income and consumption capacity, people begin to adjust their dining preferences based on the variety, price, and proximity of restaurants. Eckert and Vojnovic ( 2017 ) found that residents with lower incomes may be more influenced by nearby dining locations than residents with higher incomes. Prayag et al. ( 2012 ) used GIS techniques to assess the evolution of restaurant locations and found a clustered pattern within a short walking distance from the CBD and spillover effects emerging in the northern part of a city in Hamilton, New Zealand. Dong et al. ( 2019 ) found that the geographic concentration and the size of the population led to diversity in the type of restaurants. Mossay et al. ( 2022 ) found that an increase in the population density in U.K. city centers was associated with a decrease in the spatial dispersion of both top-and bottom-tier restaurants. The aggregation characteristics of restaurants often arise from the merging of diverse consumer groups (Kim et al. 2020 ; Zhang et al. 2021b ). Han et al. ( 2024 ) utilized the spatial co-location pattern mining to capture the spatial correlation of specific restaurant to determine the candidate location selection range. In addition, the distribution characteristics of restaurants with varied tastes (Minner and Shi 2017 ; Zhang et al. 2021a ;), the development of sweet food in specific cities (Chen et al. 2022 ; Li et al. 2022 ; Yen et al. 2020 ; Zhang et al. 2021b ; Walter et al. 2022 ; Wang et al. 2022 ), and the impact of catering industry on urban form (Shi et al. 2018 ; Shi et al. 2021 ) and urban population distribution have been extensively studied. (Xi et al. 2014 ; Xia et al. 2018 ; Li et al. 2022 ). In recent years, due to the globalization of economy and culture, food consumption is not only an economic activity, but also a cultural phenomenon (Zhang et al. 2021c ). Economic, social and cultural globalization has had a significant impact on food culture in all regions (Goto et al. 2014 ; Tricarico and Geissler 2017 ; Tian et al. 2021 ; Yigit 2022 ). What kind of distributional characteristics and relationships exist between local and foreign foods in different regions from the geographical perspective? How is reliable data obtained to explore the integration of foreign and local food cultures in the process of globalization? These challenges are generally encountered by researchers in the fields of geography and GIS.

Improved living standards encourage people to pursue spiritual and cultural needs to realize their societal, respect, or self-realization needs. Tourism can realize people’s third level of social needs and fifth level of self-realization needs. The catering industry, along with the accommodation industry, constitutes one of the three fundamental pillars of tourism. In the context of consumption upgrading, it has played a pivotal role in driving the development of tourism (Zhu et al. 2021 ; Li et al. 2022 ; Pilis et al. 2022 ). People from different regions exhibit diverse preferences on tastes, styles, and ingredients owing to differences in climate, economy, and culture. Then form distinct food culture spaces to a certain extent (Morgan and Sonnino 2010 ). These unique food culture spaces promote people’s pursuit of gastronomy through tourism. The locational sense and politics of food and the relationship between food and tourism have become popular topics (Cohen and Avieli 2004 ; Mak et al. 2012 ; Zeng et al. 2012 ; Floyd 2013 ; Boesveldt et al. 2018 ; Ray 2019 ; Zhong and Moon 2020 ).And local gastronomy has established itself as one of the key elements for the enhancement, sustainable and consolidation of tourist destinations (Zhang et al. 2022 ). For example, Jiménez-Beltrán et al. ( 2016 ) found that importance for local gastronomy to tourists through on the relation between the gastronomy, culture and tourism in city of Cordoba, Spain. And get the satisfaction of traveling by tasting the local gastronomy. Even one kind of food in a city can drive tourism for the whole city, like in China’s Chengdu gastronomy, Guangzhou gastronomy and so on.

A comprehensive study on the distribution of catering industry and its influencing factors for a certain country can reflect its economy, culture, openness and development vitality. As a multi-ethnic country, China is experiencing rapid urbanization and increasing openness to the outside world. Local and foreign restaurants are widely distributed in Chinese cities, and various catering culture exchanges and integration. The types and distributions of the catering industry differ not only between the east and west but also between the south and north of China. In this context, we are keen to investigate the spatial distribution characteristics of Chinese urban catering culture, as well as the distribution patterns and interrelationships between local and international restaurants in different regions. Additionally, we aim to explore the correlation between these distribution characteristics and regional economic development levels, individual consumption power, and population urbanization, with a focus on identifying the underlying driving factors. More specifically, we seek to uncover the hidden elements influencing the relationship between these factors. We assume that the number of urban restaurants is mainly affected by the level of economic development and the number of urban population. Furthermore, the types of restaurants, in addition to these two factors, are also affected by the degree of openness of a city, living habits of residents and other factors. The spatial distribution of restaurants is comprehensively affected by additional driving factors, such as the type of city (e.g., tourist cities). Now, the impact of these factors on the catering industry is still vague and lacks statistical hypothesis testing. This study will address this gap and provide guidance for the spatial layout of the catering industry, aiding local governments in strategic decision-making for the tertiary industry.

Data and methodology

Data sources and data processing.

With the advent of the Geospatial big data era, mounting confidences are built on the research of urban Geospatial entities. The electronic map point of interest (POI) has information on geographical identification, including name, category, latitude and longitude, which are critical for investigating the spatial distribution form and structure of the research object. For this study, POI data for restaurants in March 2020 was obtained from the AMAP database, consisting of over 6.09 million POIs ( https://lbs.amap.com/api/webservice ). The spatial pattern reflected by this data is not affected by the COVID-19 pandemic, and it can truly mirror the catering consumption situation in China. After thorough data screening and filtering, a total of 4.49 million efficient POI data points were obtained. Then POI data were classified into 4 first-level categories (Chinese restaurants, CRs; Western restaurants, WRs; fast-food restaurants, FFRs; dessert and drink restaurants, DDRs). The CRs represent the traditional cuisine, which are preferred by most Chinese taste and possess regional characteristics. The presence and distribution of WRs imply the openness, internationalization, and diversification of a specific area. FFRs can satisfy the fast-paced life needs of people, and are characterized as convenience and low prices. DDRs’ distribution density is affected by the regional diet culture and life pace. Based on their geographic coordinates, these restaurants are located in 336 cities across China. Due to data unavailability, this study excluded Taiwan, Hong Kong, and Macau regions.

To elucidate the spatial distribution pattern of restaurants and the driving forces, three types of influencing factors were included, i.e., population, economy, and social development. A total of ten socio-economic indicators were collected from the China Statistical Yearbook 2021, including urban population, population density, urbanization rate, GDP per capita, GDP per unit area, the proportion of tertiary industry added value in GDP, the proportion of non-agricultural industry added value in GDP, total retail sales of consumer goods, the per capita disposable income of urban households, and total tourism revenue (Table 1 ).

Methodology

To analyze the spatial distribution characteristics of national catering facilities, we employed the kernel density estimation method, and then adopted the global and local spatial autocorrelation analysis in the exploratory spatial data analysis method (Osullivan and Pawitan 1993 ; Anselin 1999 ; Messner et al. 1999 ) to comprehensively analyze the spatial agglomeration characteristics of national catering facilities of the 4.49 million efficient POI data points. The geographical weighted regression (GWR) model (Brunsdon et al. 1996 ) was used to explore the spatial distribution of restaurants and their influencing factors.

Kernel density estimation

The kernel density estimation method is one commonly used nonparametric density estimation technique, and it was used to explore the spatial clustering of the four types of restaurants (CRs, FFRs, WRs, and DDRs) in this study. The formula is as follows:

where \(f\left(x\right)\) is the distribution density function; \(n\) is the number of restaurants within the bandwidth; \(h(h\, >\, 0)\) is the bandwidth parameter, which reflects the free parameter of the size, \(i\) is each sub-area in the study area, \(K\left(\frac{x-{x}_{i}}{h}\right)\) is the kernel function, and \(\left(x-{x}_{i}\right)\) is the estimated distance from one restaurant to another in the study area. The kernel functions and bandwidth are the parameters that most strongly influence kernel density estimation. However, when the sample size is large enough, the selection of the kernel function has little influence on the estimation results. Bandwidth selection methods include the reference method (Karunamuni and Alberts 2005 ; Kim and Scott 2012 ) and least-squares cross-validation (Bowman 1984 ). This study employs a Gaussian kernel function and the reference method for bandwidth selection (Zhu et al. 2021 ). According to the size of the study area, the final search radius was set as 2 degrees, and it was implemented in the kernel density module of spatial analysis in ArcGIS.

Spatial data analysis method

The exploratory spatial data analysis was conducted to show the spatial agglomeration characteristics of the restaurants. It includes the global and local spatial autocorrelation analysis methods as formulas (2) and (3):

Global spatial autocorrelation analysis: it can quantified the correlation characteristics for restaurants between cities (Cliff and Ord 1981 ; Wrigley 1982 ) and it was calculated by Moran’s I index as formulas:

where \(I\) is the index of Moran, and the value range of Moran’s I is [−1, 1]. When the value is (0, 1], it expresses positive correlation, and the feature attribute tends to spatial agglomeration distribution. The value of [−1, 0) means the scattered distribution; 0 showed irrelevant distribution. n is the number of cities; \({x}_{i}\) and \({x}_{j}\) are the sweetness of the i and j cities, respectively; \(\bar{x}\) is the average value of the city’s sweetness; \({W}_{{ij}}\) is the spatial weight matrix, and it shows the spatial dependence between observation objects. It was calculated based on adjacency relationship weight matrix and based on distance relationship weight matrix. In different spatial matrix, the autocorrelation has significant spatial differences.

Local spatial autocorrelation analysis was conducted to measure the degree of difference between one city’s restaurants and other surrounding cities’ restaurants. Local Moran’s I index analysis as follows:

where the \({I}_{i}\) is Local Moran’s I index, and value is [−1, 1]. The value in (0, 1] shows the positive spatial relevance between one city’s restaurants with its neighboring cities’; [−1, 0) represents negative spatial relevance, and 0 represents no correlation. The specific spatial relationship contains four types: “High-High Correlation” (H-H), which means high-attribute areas are also surrounded by other high-attribute areas; “Low-Low Correlation” (L-L), which is the low-attribute region still surrounded by the low-attribute region. The \({x}_{i},\) \({x}_{j},\,{W}_{{ij}},\,\bar{x},\) are same as in (2). This analysis was completed in GeoDa.

Geographically weighted regression (GWR)

The GWR model is a statistical analysis model for parameter estimation. Compared with the global model, the GWR model can better reflect the heterogeneity of the spatial distribution of factors and has a better fitting effect on the change of factor space. The model structure as follows:

where \({y}_{i}\) is a dependent variable via a linear function of a set of \(p\) independent variables, \({x}_{1}\) , \(\,{x}_{2}\) , \(\ldots \ldots ,{x}_{p}\) ; \({\beta }_{0}\left({u}_{i},{v}_{i}\right)\) is the regression coefficient at point \(i\) , which indicates the degree of influence of the independent variable on the dependent variable; \(\left({u}_{i},{v}_{i}\right)\) is the geographic center coordinate of the \(i\) sample point; \({\beta }_{k}\left({u}_{i},{v}_{i}\right)\) is the sample value of the continuous function, \({\beta }_{k}(u,v)\) in the sample space at point \(i\) ; \({x}_{{ik}}\) represents the value of the independent variable \({x}_{k}\) at point \(i\) ; and \(\varepsilon\) is the normal distribution function with constant variance, which represents the random error term.

First, the ordinary least square (OLS) regression method was applied to test the relationship between independent variables and dependent variables (Supplementary Table S1 ). Based on the OLS results, a geographically weighted evaluation model of restaurant space grid was constructed by selecting the index that passed the model test (Supplementary Table S2 ). Then, the weights in the GWR model are set as a function of the distance from a certain observation point to other observation points, where the accuracy of the model is largely affected by the bandwidth. Methods for determining the bandwidth include the Akaike information criterion (AIC) and the Cross-Validation (CV) method. As the AIC method takes into account the differences among different models with different degrees of freedom, we adopted the Gaussian function to determine the weights and the AIC method to determine the optimal bandwidth in the calculation. Then we analyzed the driving factors affecting the spatial distribution of the four types of restaurants, and all the data were processed in ArcGIS.

Number and spatial distribution of the restaurants

For the 4.49 million restaurants, CRs, FFRs, DDRs, and WRs account for 67%, 21%, 10%, and 2% of the total, respectively. Spatially, the total number of restaurants showed a gradually reducing trend from southeast to northwest and the pattern is similar to that of the population. The cities with more than 5000 restaurants were predominantly located in the southeast (Fig. 1a ). Additionally, a notable cluster of restaurants was observed in the western region, particularly in Chongqing and Chengdu, where an unusually high density of restaurants was observed (Fig. 1a ).

a The urban restaurants; b the urban population. *In this paper, all maps are produced according to the Chinese standard map - Audit No GS(2020)4619, with no alterations to the base map.

Then 336 cities were divided into eight groups according to their hosting total number of restaurants (Table 2 ). The cities home to over 100,000 restaurants were Chongqing, Chengdu, and Guangzhou, and these three cities host 7.29% (327,169) of the total restaurants in this study. Chongqing city is the municipality directly under the central government, while Chengdu is the provincial capital. These two cities are the gateway cities of southwest China and they also exhibit strong economic development potential. In addition, these cities are leisure and tourism cities, whose wide variety of cuisines and rich food culture attract a large number of tourists. Guangzhou is the provincial capital of Guangdong Province, which focuses on processing industry and represents rich food culture. The number of cities hosting (50,000–100,000) restaurants was ten, accounting for 15.60% (701,596) of the total restaurants. These cities include Shenzhen, Shanghai, Dongguan, Beijing, Xi’an, Suzhou, Zhengzhou, Wuhan, Hangzhou, and Tianjin. These cities are mainly the municipality directly under the central government, or the provincial capital, which are characterized as strong economic development, a large size of urban area and population. The cities home a total of restaurants (30,000–50,000) were 24, accounting for 19.95% (897,250) of the total restaurants. These cities are relatively underdeveloped provincial capital cities, including Jinan, Changchun, Foshan, and some of them exhibit solid economic development as third-tier cities in China. The cities with (20,000–30,000) restaurants were 19, accounting for 459,495 (10.22%) of the total restaurants, they were distributed in densely populated areas.

Eighty cities (23.81%) had (10,000–20,000) restaurants and the total number of restaurants in these cities was 1,087,972 (24.19%). Most of these cities are located in the central-eastern part with a lower level of economic development and some of them are provincial capitals, such as Lanzhou, Yinchuan and Urumqi in the west. One hundred and three cities (30.65%) had (5000–10,000) restaurants, with their total of 752,935 (16.74%). These cities are primarily prefecture-level cities in the mid- and southwest, where economic development is in relatively low level and population density is also low.

Eighty-two cities (24.40%) had (1000–5000) restaurants, with a total of 263,574 (5.86%) restaurants. These cities are mainly distributed in the northeast, western and southwestern regions in China, and they are less-developed areas with sparse population. Moreover, the per capita income was also low in these regions, with low spending power. Finally, 15 cities (4.4.6%) had less than 1000 restaurants, mainly located in Xinjiang, Tibet, and Hainan, with only 6720 (0.15%) restaurants altogether. These cities have a sparse population, under-developed tourism industry, and insufficient consumption capacity of urban residents. In general, the distribution and the number of restaurants in Chinese cities had obvious hierarchical and spatial heterogeneity, and the pattern was decreasing from the east to the west.

Kernel density estimation of the four types of restaurants

The kernel density estimation of the four types of restaurants revealed that the high-density agglomeration of CRs is concentrated in the Pearl River Economic Delta and Yangtze River Economic Delta (Fig. 2a ), including cities such as Guangzhou, Shenzhen, Dongguan, Zhongshan, Shanghai, Hangzhou, and Suzhou. Other dense agglomeration areas were in the Beijing-Tianjin-Hebei region, the Chengdu-Chongqing economic circle, central Henan province, western Shandong province, and other isolated regions, such as Xi’an, Wuhan, and Changsha, etc. (Fig. 2a ).

a CRs: Chinese restaurants; b WRs: Western restaurants; c FFRs: fast-food restaurants; d DDRs: dessert and drink restaurants.

For the WRs, the high-density agglomeration areas were clustered in the Pearl River Economic Delta and Yangtze River Economic Delta regions, as well as in the Beijing-Tianjin-Hebei region, Chengdu-Chongqing economic circle, central Henan province, Eastern Hubei, Southeastern Fujian, and Dalian in Liaoning province (Fig. 2b ). These areas are characterized by dense populations and robust economic development. The three provinces (Heilongjiang, Liaoning and Jilin Provinces) in the northeast of China, and Shandong Peninsula are home to a higher proportion of Japanese and Korean restaurants, which is related to their close distance to, convenient transportation to and more trade opportunities with Japan and South Korea.

FFRs cater to the fast-paced lifestyle needs of the population. Following the rapid economic development in China, this type of restaurant has become widely distributed (Fig. 2c ). The high-density agglomeration areas were primarily situated in the Pearl River Economic Delta, Yangtze River Economic Delta, Beijing-Tianjin-Hebei region, central and western Shandong, central Henan, the Liao-dong Peninsula, Xi’an, and Guangzhou cities, the Chengdu-Chongqing economic circle, the middle reaches of the Yangtze River, and Fujian Province (Fig. 2c ).

DDRs are more affected by people’s diet habits, climate, urban tourism and leisure characteristics. For example, in the Chengdu-Chongqing economic circle, the eating habits and leisure life style have fostered hot spots for DDRs (Fig. 2d ). For the Pearl River Economic Delta and Shanghai area, the climate and economic environment are suitable for growing DDRs. The other relatively high-density agglomeration of DDRs can be found in Beijing, which has a large population, and needs more varied food and beverage services. In addition, medium-density of DDRs was diffused around the high-density agglomeration areas.

Spatial correlation of four types of restaurants

The global spatial autocorrelation analysis revealed that the Moran’s I value for the total restaurants was 0.185, with a standardized Z-statistic of 13.071. This result passed the significant test at the 0.01 level of significance ( P  = 0.000) (Table 3 ), indicating a significant positive correlation within China’s catering industry. For four types of restaurants, the Moran’s I were also greater than 0 and p-value was less than 0.01. It indicated that the four types of restaurants in each city was not spatially independent and showed a certain degree of clustering. Therefore, it is necessary and feasible to use the GWR model to analyze the influencing factors on the distribution of restaurants.

For local spatial autocorrelation analysis, we utilized the Queen matrix of spatial weights to define the neighborhood and bandwidth of the moving window. For the four types of restaurants, the result showed an agglomeration pattern (Fig. 3 ). In the 366 cities, the four types of restaurants were primarily observed in the form of High-High (in the eastern coastal cities) and low-low (in the western areas) agglomeration. The Low-High agglomeration was distributed around the High-High agglomeration areas, where were some middle size cities. For the High-Low characteristic of agglomeration, the WRs were the Nanning, Kunming, Lanzhou, Shenyang, and Karamay cities; the CRs were Chengdu, Chongqing, Guiyang, Kunming, Nanning, Changsha, Wuhan, and Xi’an; the FFRs were Shenyang, Harbin, Lanzhou, Chengdu, Kunming, Nanning, Changsha, and Karamay; the DDRs were Shenyang, Changchun, Harbin, Lanzhou and Kunming. Overall, the spatial distribution pattern of the four types of restaurants exhibits both autocorrelation and heterogeneity, with this pattern being more pronounced in the clustering of cities with fewer restaurants.

a CRs; b WRs; c FFRs; and d DDRs.

Correlation analysis of the GWR model for restaurants

At first, we utilized the OLS model to analyze the correlations between the 10 influencing factors and distribution of the four types of restaurants. The values of VIF<5 and the tolerance <1 indicate that there was nonlinearity between the dependent variables and the independent variables. For the CRs showed nonlinearity with four variables (Urban population, GDP per unit area, Total tourism revenue, Total retail sales of social consumption). For the WRs, six variables were identified (Urban population, GDP per capita, GDP per unit area, Proportion of tertiary industry added value in GDP, Total retail sales of social consumption, Per capita disposable income of urban residents). For FFRs, five variables were identified (Urban population, GDP per unit area, Urbanization rate, Total retail sales of social consumption, Per capita disposable income of urban residents). For DDRs, three variables were identified (Urban population, GDP per unit area, Total tourism revenue) (Supplementary Table S1 ). Then based on the correlation analysis by OLS, we compared the model fitting efficiencies between the GWR and the OLS models. The result showed the GWR has advantages in terms of both R 2 -value and adjusted R 2 value (Supplementary Table S2 in Appendix A). The statistical analysis on the regression coefficients in GWR model showed significant difference among the four types of restaurants (Supplementary Table S3 ).

In the GWR model, for the CRs with the four factors (Urban population, GDP per unit area, Total tourism revenue, Total retail sales of social consumption), showed a positive correlation 98.81% for study and 1.19% a negative correlation with the urban population (Fig. 4a ), and the negative correlation was concentrated in Xinjiang and Tibet; with the GDP per unit area showed a positive correlation in 85.12% of the study areas, and focused in Xinjiang, eastern Gansu, and Heilongjiang (Fig. 4b ); with the total tourism revenue factor showed a positive correlation in 80.95% of the cities, main distributed in Xinjiang, most of Tibet, eastern Gansu, Ningxia, Shaanxi, and southwest regions (Fig. 4c ), and the negative correlations concentrated in the north and southeast; with the total retail sales of social consumption factor were positive and negative in 96.43% and 3.57% of the study area, respectively (Fig. 4d ), and the high-value areas were concentrated in Tibet, western Gansu, Sichuan, Yunnan and the three northeastern provinces, where economic development and social goods purchasing power are at low levels.

a Urban population; b GDP per unit area; c Total tourism revenue; d Total retail sales of social consumption.

For the WRs with the six factors(Urban population, GDP per capita, GDP per unit area, Proportion of tertiary industry added value in GDP, Total retail sales of social consumption, Per capita disposable income of urban residents) showed strong positive correlation and high spatial heterogeneity. The proportion of tertiary industry in GDP and the Urban population factors all showed weakening trend from east to west, especially for the proportion of tertiary industry in GDP factor in Xinjiang (Fig. 5a, b ); On the contrary, the total retail sales of social consumption factor showed a weakening trend from west to east (Fig. 5c ); the GDP per unit area showed a weakening trend from northwest to southeast (Fig. 5d ). However, the GDP per capita factor showed weakening from southeast to northwest (Fig. 5f ); For per capita disposable income of urban residents’ factor, showed a weaken trend from southwest to the border region was displayed (Fig. 5e ). Strong correlations were identified in Yunnan, Guangxi, and Guizhou provinces.

a The Proportion of tertiary industry added value in GDP; b urban population; c total retail sales of social consumption; d GDP per unit area; e disposable income per capita of urban residents; f GDP per capita.

For the FFRs with the five factors (urban population, GDP per unit area, Urbanization rate, total retail sales of social consumption, per capita disposable income of urban residents) were positive in all cities, but they also showed strong spatial heterogeneity (Fig. 6 ). For example, urbanization rate showed a gradual weakening trend from southeast to northwest, and their strong correlation occurred in the Pearl River Economic Delta (Fig. 6a ). Urban population showed a weakening trend from the middle to the borders (Fig. 6b ). Strong correlations showed in Heilongjiang, northeastern Inner Mongolia, Shaanxi, Ningxia, Gansu, Sichuan, Chongqing, Yunnan, Guizhou and Guangxi regions. For the total retail sales of social consumption, the correlation was weakened from inland to the border, except for the border of Inner Mongolia (Fig. 6c ); The GDP per unit area factor was weakening from northwest to southeast (Fig. 6d ). Interestingly, the per capita disposable income of urban residents showed a weakening pattern from southwest and northeast to the center (Fig. 6e ) such as Yunnan, and Sichuan in the southwest and Xinjiang total area.

a Urbanization rate; b Urban population; c Total retail sales of social consumption; d GDP per unit area; e disposable income per capita of urban residents.

The DDRs showed strong correlations with three factors (urban population, GDP per unit area, total tourism revenue). Spatially, the urban population factor showed a weakening pattern from south to north, except in Xinjiang and Tibet (Fig. 7a ), and strong correlations occurred in the Pearl River Economic Delta, Guangxi, Guizhou, and Yunnan. In contrast, the negative correlations were located in the western region; For the GDP per unit area factor, 97.02% of the cities showed a positive correlation and 2.98% showed a negative correlation (Fig. 7b ), the strong correlation occurred typically in Tibet (except Nagqu), Sichuan, Yunnan. Meanwhile, the weak correlations were located in the eastern region and Yangtze River Economic Delta; For the total tourism revenue factor showed strong correlation in the west, such as east of Tibet, Qinghai, Gansu, and Ningxia, etc (Fig. 7c ), where tourism resources are abundant.

a Urban population; b GDP per unit area; c total tourism revenue.

Discussion and conclusion

Food serves as the fundamental material to meet human physiological needs. The quantity and spatial distribution of restaurants reflect human needs and behaviors (Nathan et al. 2012 ). Increasing urbanization would facilitate development of catering industry. Then how urbanization affects the total number and their spatial distribution pattern of restaurants need mounting attentions (Zhang et al. 2021a ; Tu et al. 2020 ; Zhu et al. 2021 ; Lan and Tseng 2018 ; Xia et al. 2018 ; Wu et al. 2017 ; Zhou et al. 2015 ).

In this study, based on actual distribution point data in 2019, the restaurants were categorized into four types (Chinese restaurants (CRs), Western restaurants (WRs), fast-food restaurants (FFRs), and dessert and drink restaurant (DDRs)), and comprehensively analyzed their number, spatial distribution, and influencing factors. The result showed that over 78.86% of the included cities were home to (1000–20,000) restaurants. Spatially, the total number of restaurants decreases from southeast to northwest in China, but Chengdu-Chongqing economic circle in the southwest host a large number of restaurants. One noteworthy point is that the restaurants are mostly concentrated in core region of Chengdu-Chongqing circle, while a low proportion are distributed in the surrounding cities. Along the Yangtze River and Pearl rivers economic belts, restaurants are concentrated not only in major cities such as Wuhan, Nanjing, Hangzhou, Shanghai, Guangzhou and Shenzhen but also evenly distributed in surrounding smaller cities, this indicates that major cities exert a strong influence on their surrounding areas.

The local spatial autocorrelation analysis on the four types of restaurants reveals a High-High agglomeration in the eastern coastal cities, and low-low agglomeration in west regions. The dominant factors in shaping this spatial differentiation model are the diverse regional advantages and economic development levels of coastal and inland areas. WRs are primarily concentrated in eastern coastal urban agglomerations and major cities, showcasing a high level of concentration; As China’s urbanization continues to progress and the fast-paced lifestyle demands increase, the rapidly growing FFR, encompassing honor brand fast food restaurants and casual dining establishments, experienced significant growth across all regions of China. The distribution pattern of DDR was greatly influenced by people’s dietary preferences, climate conditions, as well as urban tourism and leisure characteristics. It main distributed in the Chengdu-Chongqing economic circle, the Pearl River Delta region, Shanghai area with favorable climate and economic environment, along with densely populated Beijing.

The analysis results of the factors influencing the spatial differentiation of these four types of restaurants indicate that both urban population and GDP per unit land area affected the spatial pattern of the four types of restaurants. Additionally, WRs was closely associated with the tertiary industry; FFR exhibited a strong correlation with urbanization rate; DDRS, encompassing dessert shops, cold drinks and coffee shops, were contingent upon the income and purchasing power of urban residents; CRs, represented by restaurants and hot pot restaurants, were the most widely distributed, and its influencing factors were more complex. Based on spatial correlation analysis of the GWR model, the number of restaurants in central and eastern China was mainly regulated by urban population, GDP per capita, urbanization rate, and proportion of tertiary industry. For west and northwest regions, the main factors included GDP per unit area, total tourism revenue, per capita disposable income of urban residents, and total retail sales of social consumption. In northeast, the primary influencing factors were the total retail sales of social consumption, GDP per unit area, urban population, and Proportion of tertiary industry added value in GDP.

The catering industry serves as a barometer for domestic demand in the context of consumption upgrading. China has the number of restaurants about 9 times than the United States, but the total revenue from restaurants is similar between the two countries. China’s per capita food consumption is less than 1/5 of the United States ( https://baijiahao.baidu.com/s?id=1688700104778489233&wfr=spider&for=pc ). The comparison between the two countries implies that the increasing urban population and total retail sales of social consumption will further boost China’s catering industry.

The rapid advancement of information technology and the continuous enhancement of Internet map data are accompanied by the generation and exploration of big data as well as the new paradigms. This study reveals the space and driving factors of Chinese food culture with big data and catering service POIs with millions of categories. Big data thinking and methods based on data-intensive empirical research improve the accuracy of relevant research results and provide a new perspective for other types of cultural space-related research. However, China is a big country and its land is home to many cultures and histories. In addition to the factors discussed in this article, other factors such as topography, climate, and other natural factors may play a key role in regulating the distribution and type of restaurants. In the future, we will dig deep into the internal correlation of data and show the coupling characteristics between Chinese food, culture, economy and politics from a geographical perspective.

Data availability

The datasets generated during and/or analyzed during the current study -the access to the analyzed videos and the data analysis grid- are available through the following link: https://doi.org/10.7910/DVN/47AV71 .

Anselin L (1999) Interactive techniques and exploratory spatial data analysis. In: Goodchild MF, Maguire DJ, David W (eds) Geographical Information systems, principles, technical issues, management issues and applications Paul A Longley, John Wiley & Sons, pp 253–266

Boesveldt SN, Bobowski K, McCrickerd I, Maître C, Sulmont-Rossé C, Forde G (2018) The changing role of the senses in food choice and food intake across the lifespan. Food Qual Prefer 68:80–89. https://doi.org/10.1016/j.foodqual.2018.02.004

Article   Google Scholar  

Bowman AW (1984) An Alternative Method of Cross-Validation for the Smoothing of Density Estimates. Biometrika 71:353–360

Article   MathSciNet   Google Scholar  

Brunsdon C, Fotheringham AS, Charlton ME (1996) Geographically weighted regression: A method for exploring spatial nonstationarity. Geogr Anal 28:281–298. https://doi.org/10.1111/j.1538-4632.1996.tb00936.x

Chen YB, Yin GW, Hou YM (2022) Street centrality and vitality of a healthy catering industry: A case study of Jinan, China. Front Public Health 10 https://doi.org/10.3389/fpubh.2022.1032668

Cliff AD, Ord J (1981) Spatial processes, models and applications. Pion, London

Google Scholar  

Cohen E, Avieli N (2004) Food in tourism-Attraction and impediment. Ann Tour Res 31:755–778. https://doi.org/10.1016/j.annals.2004.02.003

Article   CAS   Google Scholar  

Dong L, Ratti C, Zheng SQ (2019) Predicting neighborhoods’ socioeconomic attributes using restaurant data. Prc Natl Acad. Sci. USA 116:15447–15452. https://doi.org/10.1073/pnas.1903064116

Article   ADS   CAS   Google Scholar  

Eckert J, Vojnovic I (2017) Fast food landscapes: Exploring restaurant choice and travel behavior for residents living in lower eastside Detroit neighborhoods. Appl Geogr 89:41–51. https://doi.org/10.1016/j.apgeog.2017.09.011

Floyd VD (2013) Food, culture and politics. Food Cult Soc 32(1):165–186

Goto K, Ominami C, Song CY, Murayama N, Wolff C (2014) Globalization, localization and food culture: perceived roles of social and cultural capitals in healthy child feeding practices in Japan. Glob Health Promot 21:50–58. https://doi.org/10.1177/1757975913511133

Article   PubMed   Google Scholar  

Han SH, Chen LL, Su ZP, Gupta S and Sivarajah U (2024) Identifying a good business location using prescriptive analytics: Restaurant location recommendation based on spatial data mining. J Bus Res 179. https://doi.org/10.1016/j.jbusres.2024.114691

Jiménez-Beltrán FJ, López-Guzmán T, Santa Cruz FG (2016) Analysis of the Relationship between Tourism and Food Culture. Sustain Basel 8 https://doi.org/10.3390/su8050418

Karunamuni RJ, Alberts T (2005) A generalized reflection method of boundary correction in kernel density estimation. Can J Stat 33:497–509. https://doi.org/10.1002/cjs.5550330403

Kim J, Jang S, Kang S, Kim S (2020) Why are hotel room prices different? Exploring spatially varying relationships between room price and hotel attributes. J Bus Res 107:118–129. https://doi.org/10.1016/j.jbusres.2018.09.006

Kim J, Scott CD (2012) Robust Kernel Density Estimation. J Mach Learn Res 13:2529–2565

MathSciNet   Google Scholar  

Lan SL, Tseng ML (2018) Coordinated Development of Metropolitan Logistics and Economy Toward Sustainability. Comput Econ. 52:1113–1138. https://doi.org/10.1007/s10614-017-9788-z

Lee C, Hallak R, Sardeshmukh SR (2016) Drivers of success in independent restaurants: A study of the Australian restaurant sector. J Hosp Tour Manag 29:99–111. https://doi.org/10.1016/j.jhtm.2016.06.003

Li YQ, Xiong J, Ma WQ, Ma HM, Yuan Z (2022) Environmental decoupling, eco-efficiency improvement, and industrial network optimization: Insights from 44 sectors in China. J Clean Prod 376. https://doi.org/10.1016/j.jclepro.2022.134374

Lu CY, Pang M, Zhang Y, Li HJ, Lu CP, Tang XL, Cheng W (2020) Mapping Urban Spatial Structure Based on POI (Point of Interest) Data: A Case Study of the Central City of Lanzhou, China. ISPRS Int Geo Inf 9. https://doi.org/10.3390/ijgi9020092

Mak AHN, Lumbers M, Eves A (2012) Globalisation and Food Consumption in Tourism. Ann. Tour. Res 39:171–196. https://doi.org/10.1016/j.annals.2011.05.010

Maslow AH (1987) Motivation and personality, 3rd ed. Earson Education, Delhi, India

Messner SF, Anselin L, Baller RD, Hawkins DF, Deane G, Tolnay SE (1999) The spatial patterning of county homicide rates: An application of exploratory spatial data analysis. J Quant Criminol 15:423–450. https://doi.org/10.1023/A:1007544208712

Minner JS, Shi X (2017) Churn and change along commercial strips: Spatial analysis of patterns in remodelling activity and landscapes of local business. Urban Stud. 54:3655–3680. https://doi.org/10.1177/0042098016684274

Morgan K, Sonnino R (2010) The urban foodscape: world cities and the new food equation. Camb J Reg Econ Soc 3:209–224. https://doi.org/10.1093/cjres/rsq007

Mossay P, Shin JK, Smrkolj G (2022) Quality Differentiation and Spatial Clustering among Restaurants*. Int J Ind Organ 80. https://doi.org/10.1016/j.ijindorg.2021.102799

Nathan A et al. (2012) Access to commercial destinations within the neighbourhood and walking among Australian older adults. Int J Behav Nutr Phy 9. https://doi.org/10.1186/1479-5868-9-133

Osimani A, Clementi F (2016) The catering industry as a source of campylobacteriosis in Europe-A review. Int J Hosp Manag 54:68–74. https://doi.org/10.1016/j.ijhm.2016.01.006

Osullivan F, Pawitan Y (1993) Multidimensional Density-Estimation by Tomography. J R Stat Soc B Met 55:509–521

Pilis W, Kabus D, Miciula I (2022) Food Services in the Tourism Industry in Terms of Customer Service Management: The Case of Poland. Sustain Basel 14:65. https://doi.org/10.3390/su14116510

Prayag G, Landre M, Ryan C (2012) Restaurant location in Hamilton, New Zealand: clustering patterns from 1996 to 2008. Int J Contemp Hosp M 24:430–450. https://doi.org/10.1108/09596111211217897

Ray K (2019) ASFS presidential address 2018: Towards an epistemology of pleasure and a post-liberal politics of joy. Food Cult Soc 22:3–8. https://doi.org/10.1080/15528014.2018.1547068

Schiff N (2015) Cities and product variety: evidence from restaurants. J Econ Geogr 15:1085–1123. https://doi.org/10.1093/jeg/lbu040

Shi K, Tang Y, Bai S, Li E, Chen D (2018) Innovation diffusion hypothesis or efficiency hypothesis: spatial penetration of online-to-offline e-commerce in China based on Meituan.com. Geogr Res 37(4):783–796. (In Chinese)

Shi YS, Tao TH, Cao XY, Pei XW (2021) The association between spatial attributes and neighborhood characteristics based on Meituan take-out data: Evidence from shanghai business circles. J Retail Consum Serv 58. https://doi.org/10.1016/j.jretconser.2020.102302

Tian C, Luan WX, Li SJ, Xue YA, Jin XM (2021) Spatial imbalance of Chinese seafood restaurants and its relationship with socioeconomic factors. Ocean Coast Manage 211. https://doi.org/10.1016/j.ocecoaman.2021.105764

Tricarico L, Geissler JB (2017) The food territory: Cultural identity as local facilitator in the gastronomy sector, the case of Lyon.City. Territory Architecture 4(1):1–9

Tu JC, Lin KC, Chen HY (2020) Investigating the Relationship between the Third Places and the Level of Happiness for Seniors in Taiwan. Int J Env Res Pub He 17. https://doi.org/10.3390/ijerph17041172

Walter U, Muller DK, Langvall A (2022) The spatial distribution of gourmet restaurants Scand. J Hosp Tour 22:285–302. https://doi.org/10.1080/15022250.2022.2112412

Wang WW, Wang S, Chen H, Liu LJ, Fu TL, Yang YX (2022) Analysis of the Characteristics and Spatial Pattern of the Catering Industry in the Four Central Cities of the Yangtze River Delta. ISPRS Int Geo Inf 11:321. https://doi.org/10.3390/ijgi11060321

Wrigley N (1982) Spatial Processes - Models and Applications - Cliff, Ad, Ord, Jk. Geogr J 148:383–385. https://doi.org/10.2307/633177

Wu Y et al. (2017) The impact of urbanization on the community food environment in China. Asia Pac J Clin Nutr 26:504–513. https://doi.org/10.6133/apjcn.032016.09

Article   ADS   PubMed   Google Scholar  

Xi G, Zhen F, Wang X, Qing X (2014) Study on the influencing factors and spatial characteristics of residents’ online consumption in Nanjing. Geogr Res 33(2):284–295. (In Chinese)

Xia L, Liu Y, Liu G (2018) Spatial distribution pattern and influencing factors of catering industry in prefecture level and above cities in China: an empirical study based on the Dianping.com data. Econ Geogr 38(5):133–141. (In Chinese)

Yen BTH, Mulley C, Burke M, Tseng WC (2020) Parking and restaurant business: Differences in business perceptions and customer travel behaviour in Brisbane, Queensland, Australia. Land Use Policy 92. https://doi.org/10.1016/j.landusepol.2019.01.021

Yigit S (2022) Is it possible to get to know a culture through cooking classes? Tourists experiences of cooking classes in Istanbul. Int J Gastron Food S 28 https://doi.org/10.1016/j.ijgfs.2022.100527

Zeng GJ, Go F, de Vries HJ (2012) Paradox of authenticity versus standardization: Expansion strategies of restaurant groups in China. Int J Hosp Manag 31(4):1090–1100. https://doi.org/10.1016/j.ijhm.2012.01.006

Zhang HP, Zhou XX, Tang GA, Xiong LY, Dong KX (2021a) Mining spatial patterns of food culture in China using restaurant POI data. T Gis 25:579–601. https://doi.org/10.1111/tgis.12682

Zhang HY, Sun MP, Zhang H, Zhang L, Wang ZL (2021b) Spatial Distribution and Influencing Factors of Chinese Time-Honored Catering Brands in the Five Northwestern Provinces. Sustain Basel 13. https://doi.org/10.3390/su13073755

Zhang SN, Li YQ, Liu CH, Ruan WQ (2021c) Reconstruction of the relationship between traditional and emerging restaurant brand and customer WOM. Int J Hosp Manag 94. https://doi.org/10.1016/j.ijhm.2021.102879

Zhang X, Guo W, Bashir MB (2022) Inclusive green growth and development of the high-quality tourism industry in China: The dependence on imports. Sustain Prod Consum 29:57–78. https://doi.org/10.1016/j.spc.2021.09.023

Zhong YP, Moon HC (2020) What Drives Customer Satisfaction, Loyalty, and Happiness in Fast-Food Restaurants in China? Perceived Price, Service Quality, Food Quality, Physical Environment Quality, and the Moderating Role of Gender. Foods 9. https://doi.org/10.3390/foods9040460

Zhou ZT et al. (2015) Effective Sampling of Points of Interests on Maps Based on Road Networks. Lect Notes Comput Sci 9098:563–566. https://doi.org/10.1007/978-3-319-21042-1_63

Zhu WB, Chen YF, Zhao J, Wu BB (2021) Impacts of household income on beef at-home consumption: Evidence from urban China. J Integr Agr 20:1701–1715. https://doi.org/10.1016/S2095-3119(20)63582-1

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Acknowledgements

We would like to thank Xiliang Liu, School of Computer, Beijing University of Technology, He was of great help in data processing, and other colleagues for their support and help in this study. Without their timely help, this study would not have been finished successfully.This work was supported by the second Tibetan Plateau Scientific Expedition Program (2019QZKK0608).

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Li Tian is responsible for conceptualization, methodology, formal analysis, data curation, visualization, funding acquisition, writing-original draft, writing – review & editing. Xiaoyan Shen is responsible for data curation, methodology, writing-original draft, supervision.

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Tian, L., Shen, X. Spatial patterns and their influencing factors for China’s catering industry. Humanit Soc Sci Commun 11 , 1090 (2024). https://doi.org/10.1057/s41599-024-03580-7

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DOI : https://doi.org/10.1057/s41599-024-03580-7

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