Exploring the path of high-quality development of the tourism industry in the context of smart tourism

Literature and tourism industry is an industry formed on the basis of the integration of culture industry and tourism industry, and there are close links and differences with the traditional culture industry and tourism industry [1–2]. At present, the development of Chinese culture and tourism industry faces dilemmas, such as institutional mechanism with inertia, lack of effective aggregation, insufficient synergistic development of cultural resources, ineffective creation and publicity, lack of professional service team, insufficient support to promote cultural development, lack of tourism protection, insufficient power of characteristic excavation, and so on. In recent years, China attaches great importance to the integrated development of culture and tourism, and has made a series of important deployments in promoting the integrated development of cultural undertakings, cultural industries and tourism [3–6]. The marvelous combination of traditional tourism and culture is not only the urgent need for consumption upgrading on the demand side, but also a realistic way for supply-side reform. When the demand for tourism changes from the viewing of beautiful scenery to the common pursuit of a better life, traditional tourism resources such as landscapes, non-heritage arts and crafts, or need to be renewed and revitalized [7–9]. The introduction of new educational elements, fashion trends, science and technology and capital as a solid backing to support, like a “catalyst”, so that the tourism industry from high-speed development to high-quality integration of new kinetic energy [10–12]. With the continuous development of modern information technology, science and technology has become a new driving force for the integration and innovation of the culture and tourism industry, and the promotion of the culture and tourism industry to the intelligent transformation is an effective path to achieve high-quality development of the economy, so we should grasp the new wave of intelligent tourism, so that science and technology to promote the integration of high-quality development of culture and tourism [13–15].

The culture and tourism industry emphasizes the cultural value of tourism and the tourism value of the culture industry, and regards culture and tourism as two industries that are complementary and mutually beneficial. Using the cities of Bucharest and Paris as examples, literature [16] used benchmarking, SWOT analysis, and Pareto analysis to analyze civil monuments and architectural complexes, religious monuments and architectural complexes, festivals, personalities, tourism digitization, and cultural and educational institutions in both cities in order to identify ways to improve the promotion and capitalization of cultural tourism as well as to increase sustainability. Literature [17] explores the impact of digital business models on demand and supply in the cultural tourism industry and proposes an app-based service approach to identify, characterize, and analyze specific categories of cultural tourism digital products in order to determine the unique characteristics of digital business models, and the results of the study provide reference value for sustainable development of the cultural tourism industry. Literature [18] uses the city of Valletta as a case study to assess the potential for the development of urban heritage and cultural tourism in the Maltese tourism industry, pointing out that having an established form of tourism is extremely important for the development of cultural tourism in a tourist destination. Literature [19] points out the past deficiencies in heritage conservation and heritage tourism marketing in Lushan, China. A virtual tourism sub-system was constructed based on digital technologies such as tilted aerial photography technology, 3D laser scanning technology, 360-degree panoramic technology, etc. It can provide a visualization platform for tourists and managers to enhance the virtual experience of users on cultural landscape heritage tourism and promote cultural landscape tourism marketing. Literature [20] tries to apply “social big data” and user-generated content to the collaborative design of tourists’ experience, and verifies the feasibility and effectiveness of the method through example analysis, which can provide tourists with better interaction of tourism needs/services, and provide managers with better decision-making methods for cultural tourism planning, and thus promote the high-quality development of the cultural tourism industry. Literature [21] utilizes cutting-edge 3D scanning and modeling to innovate heritage preservation methods and in turn develop tourism experiences at heritage sites, and validates the effectiveness of the methodology using the example of a World War II U.S. Air Force Base in Charleville, Australia, which can provide tourists with memorable tourism experiences, lessons learned for research on the sustainable development of the cultural tourism industry, and significant economic and social contributions to the regional community.

Taking the cultural and tourism industry of Province A as the research object, the article firstly establishes measurement indicators for the level of high-quality development of cultural and tourism industry in 14 counties and cities of Province A, and uses entropy weight method and Moran’s I method for quantitative analysis. Then based on the selection principle of influencing factor indicators, the influencing factor indicators for the high-quality development of cultural and tourism industry are summarized. For the data from 2009 to 2023, a static panel model is established, and a robustness test is carried out. For the data of 2009 and 2023, the cross-sectional data regression model is established to analyze the factors affecting the high-quality development of cultural tourism industry in Province A respectively. Finally, based on the results of the study, the paths favoring the high-quality development of cultural tourism industry in Province A are proposed.

2

Measurement and analysis of the level of high-quality development of the cultural tourism industry in Province A

2.1

Construction of Measurement Indicator System

2.1.1

System of measurement indicators

In order to adequately measure the level of high-quality development of the cultural and tourism industry in the Yellow River Basin of Province A, five first-level indicators are constructed, including the level of supply, innovation capacity, structural coordination, green ecology and openness. Ten secondary indicators such as cultural and tourism enterprises, market innovation, urban and rural structure, and ecological environment quality. The number of travel agencies, public libraries, cultural centers, intelligent scenic spots, cultural and tourism project innovations, and other 35 tertiary indicators and other indicator systems. The indicator system for measuring the level of high-quality development of the cultural and tourism industry is shown in Table 1.

Table 1.

The development level measure system of the high quality of the wentravel industry

Primary indicator	Secondary indicator	Tertiary index
Supply level	venter	Number of travel agents; The number of star hotels; A number of scenic spots; The number of casinos
Supply level	Public service	The number of art performers; Number of public libraries; The number of cultural pavilions; Museums; Number of public toilets; National key cultural relics protection unit
Innovative ability	Market innovation	The number of innovation in the project; Number of smart spots
	Innovative environment	Human input
		Technology investment; The number of enterprises and technology centers at or above provincial level and the number of practical innovation enterprises; Patent number
		Capital input
Structural coordination	Urban and rural structure	The per capita disposable income of urban residents; The per capita disposable income of rural residents; Consumption expenditure per capita of urban residents; Rural residents per capita consumption expenditure
Structural coordination	Regional structure	The total number of tourists; Travel revenue
Green ecology	Ecological quality	Water quality; Air quality; Forest coverage; Green coverage product; Yellow River wetland area
Green ecology	Number of ecological spots	The number of nature reserves; Number of forest parks
Openness	Bring in	Number of inbound visitors; Travel foreign exchange income; Practical use of foreign capital; The number of communication activities of the host country
Openness	Get out	The number of exchanges activities in the participating countries

2.1.2

Data sources

The data mainly came from two sources: primary information was compiled from interview records of the Culture and Tourism Bureau of City A and culture and tourism enterprises. Secondary data come from China Culture and Tourism Development Statistical Bulletin (2023), Province A Tourism Statistical Yearbook (2023), Province A Statistical Yearbook (2023), Province A National Economy and Social Development Statistical Bulletin (2023), Prefecture and Municipality National Economy and Social Development Statistical Bulletin (2023), Local Municipal Government Work Report (2023), and Provincial Culture and Tourism Department Tourism Work Conference Report (2023), etc.

2.2

Methods of analysis

2.2.1

Entropy weight method

Entropy weight method is an evaluation method based on information entropy theory, which can effectively reduce the influence of subjective human factors and is widely used in comprehensive evaluation [22]. The calculation steps are as follows: 1)

Construct the index matrix. 1 $X_{i j} = {(x_{i j})}_{m \times n}$ where x_ij is the j rd indicator for the i nd city. m is the number of cities, and n is the evaluation indicator i = 1, 2, 3, 4, ⋯⋯, m. j = 1, 2, 3, 4, ⋯⋯, n.

2)

Data normalization processing.

The original data are normalized using the extreme value normalization method.

When x_i is a positive indicator: 2 $T_{i j} = (1 - a) + a \frac{x_{i j} - \min (x_{i j})}{\max (x_{i j}) - \min (x_{i j})}$

When x_i is a negative indicator: 3 $T_{i j} = (1 - a) + a \frac{\max (x_{i j}) - x_{i j}}{\max (x_{i j}) - \min (x_{i j})}$

Where T_ij is the standardized value of the indicator, max(x_ij) indicates the maximum value of its original value, min(x_ij) indicates the minimum value of its original value, and the value of a is in the range of 0-1, generally 0.9 [23].

3)

Normalization of indicators: 4 $P_{j} = \frac{T_{i j}}{\sum_{i = 1}^{m} T_{i j}}$

4)

Calculate the information entropy of each indicator e_j: 5 $e_{j} = - k \sum_{i = 1}^{n} (P_{j} \times \ln P_{j})$

Eqs. k > 0, k = 1/ln n, and e_j ≥ 0.

5)

Calculate the coefficient of variation of information entropy: 6 $d_{j} = 1 - e_{j}$

6)

Weighting formula for evaluation indicator j: 7 $w_{j} = \frac{d_{j}}{\sum_{j = 1}^{m} d_{j}}$

The weights of the evaluation index system for high-quality development of cultural industry are shown in Table 2.

7)

Calculate the high quality index of cultural industries in Province A: 8 $y_{i} = \sum_{j = 1}^{n} W_{j} \times T_{i j}$

Where y_i is the index of high-quality development of cultural industry, the larger y_i is, the higher the level of high-quality development of cultural industry is, and vice versa, the lower the level is.

Table 2.

The index system weight of the development evaluation index of cultural industry

System layer	System layer weight	Index layer	Index weight
Innovative development	0.1755	Number of patent authorization for cultural industry(x1)	0.0659
		The number of academic papers in the cultural industry(x2)	0.0603
		Number of national and provincial cultural industry demonstration garden (base) (x3)	0.0493
Coordinated development	0.1751	The above scale culture service sector revenue ratio(x4)	0.0784
		The ratio of the consumption expenditure per capita of urban and rural residents per capita(x5)	0.0161
		Personal expenditure per capita(x6)	0.0443
		Cultural and media spending is the general public budget expenditure ratio(x7)	0.0363
Green development	0.2745	The total amount of cultural enterprise assets above is (x8)	0.088
		Above scale cultural enterprise capital productivity (x9)	0.0426
		Above scale cultural enterprise labor productivity (x10)	0.0531
		National and provincial intangible cultural heritage (x11)	0.0417
		Number of national and provincial cultural relics protection units (x12)	0.0491
Open development	0.1748	International travel exchange income (x13)	0.088
		Culture and tourism (x14)	0.0544
		Culture and technology (x15)	0.0324
Shared development	0.2001	Urban residents per capita cultural and entertainment spending (x16)	0.0275
		Rural residents per capita cultural and entertainment spending (x17)	0.0299
		The average average public library is the amount of books (x18)	0.0423
		Every 10,000 people have museums, cultural pavilions, and library Numbers (x19)	0.0359
		The number of art performers per 10,000 people (x20)	0.0645

2.2.2

Global spatial autocorrelation test

Moran’s I index is calculated as: 9 $\begin{array}{l} I (d) = \frac{\sum_{i = 1}^{n} \sum_{i, j \neq 1}^{n} W_{i j} (X_{i} - \bar{X}) (X_{i} - \bar{X})}{S^{2} \sum_{i = 1}^{n} \sum_{j, j \neq 1}^{n} W_{i j}} \\ W h i c h S^{2} = \frac{1}{n} \sum_{i = 1}^{n} {(X_{i} - \bar{X})}^{2}, \bar{X} = \frac{1}{n} \sum_{i = 1}^{n} X_{i} \end{array}$ (9) where X_i is the observed value of the phenomenon attribute value on the regional unit i, and W_ij is the element of the spatial weight matrix. the value range of Moran’s I index is [–1,1], and the value of Moran’s I is greater than zero, which indicates that the global spatial autocorrelation is positive correlation, and less than zero is negative correlation, and the larger the absolute value of which, the larger the correlation degree. When I = 1, it indicates that there is a significant spatial positive correlation. When I = –1, there is a significant negative spatial correlation. And I = 0, global spatial significant uncorrelation [24].

The results of Moran’s I index can be tested using two assumptions of random distribution and approximate normal distribution, respectively, with the standardized formula of $Z (I) = \frac{I (d) - E (I)}{\sqrt{V A R (I)}}$ and the expected value of $E (I) = - \frac{1}{n - 1}$ . According to the different assumptions of the spatial data distribution, the formula for the variance calculation is respectively:

For normal distribution: 10 $V_{A R}^{n} (I) = \frac{n^{2} S_{1} - n S_{2} + 3 S_{0}^{2}}{S_{0}^{2} (n^{2} - 1)} - E (I)^{2}$

For a random distribution: 11 $V A R_{r} (I) = \frac{n [(n^{2} - 3 n + 3) S_{1} - n S_{2} + 3 S_{0}^{2}] - k_{2} [(n^{2} - n) S_{1} - 2 n S_{2} + 6 S_{0}^{2}]}{S_{0}^{2} (n - 1) (n - 2) (n - 3)} - E (I)^{2}$

2.3

Analysis of measurement results

2.3.1

Measurement results

According to the entropy weight method, the 14 counties (cities) in Province A are comprehensively evaluated on four level 1 indicators, namely, tourism supply level, industrial structure, industrial economic performance and effect, and industrial socio-cultural and environmental effect, and the results of measuring the level of high-quality development of cultural and tourism industry in Province A are shown in Table 3. The rankings in order are: a (0.9003), b (0.8786), c (0.8284), d (0.7937), e (0.7793), f (0.5789), g (0.5696), h (0.5386), i (0.5228), j (0.4572), k (0.463), l (0.4462), m (0.2708), n (0.2014).

Table 3.

The development level of the industry is the result of the horizontal measure

County (city)	Primary indicator					Comprehensive score	Ranking
County (city)	Innovative development	Coordinated development	Green development	Open development	Shared development	Comprehensive score	Ranking
a	0.9659	0.8658	0.9054	0.8717	0.9987	0.9003	1
b	0.8692	0.8881	0.8926	0.8811	0.689	0.8786	2
c	0.8361	0.757	0.8279	0.8822	0.854	0.8284	3
d	0.8659	0.7594	0.6796	0.8623	0.8711	0.7937	4
e	0.7011	0.8871	0.6753	0.8412	0.7796	0.7793	5
f	0.6643	0.5387	0.3609	0.7442	0.7234	0.5789	6
^g	0.536	0.6269	0.5845	0.537	0.4758	0.5696	7
h	0.4177	0.5564	0.5762	0.5734	0.8106	0.5386	8
i	0.5767	0.4738	0.4745	0.5923	0.7274	0.5228	9
j	0.3651	0.5329	0.5249	0.4365	0.4622	0.4572	10
k	0.483	0.409	0.4159	0.5226	0.325	0.463	11
l	0.4602	0.4007	0.4603	0.4688	0.48	0.4462	12
m	0.2672	0.2223	0.2695	0.3493	0.4797	0.2708	13
n	0.1656	0.1695	0.2525	0.2415	0.199	0.2014	14

2.3.2

Analysis of results

Taken together, city a has the highest overall score. The top-ranked counties and cities performed well in tourism supply, industrial structure, economic performance and effects, and socio-cultural and environmental effects, creating a comprehensive advantage. The lower ranked counties and cities, on the other hand, perform weakly in all of these key indicators, resulting in a low composite score.

In terms of tourism supply, cities a, d, b, and c have significant advantages and are located in the first tier. city a has a relatively balanced resource endowment, supporting facilities, service quality, and innovation capacity, while city d has better supporting facilities than resource endowment, service quality, and innovation capacity. Cities b and c are at the same level. Located in the second tier are cities e, f, i, and g. These cities are more balanced in terms of resource endowment, supporting facilities, and service quality, all of which are in the middle of the pack, and are generally weaker in terms of innovation capacity. In the third echelon are cities k, l, h, j and n, where resource endowment is better than facilities, service quality and innovation capacity.

In terms of industrial structure, those in the first echelon are e city, a city and b city. These three counties have an early start in the development of culture and tourism industry, superior transportation location, coupled with the rationalization and advanced industrial structure, development stability and development efficiency is more balanced, so it is better than other counties and cities in terms of industrial structure, but the degree of internationalization is lower. Located in the second echelon are c city, d city, g city, h city, f city, and j city. These six counties are better in terms of development stability and development efficiency than in terms of rationalization and advanced industrial structure, with a similarly low degree of internationalization.

In terms of industrial economic performance and effects, those in the first tier are cities a, b and c. These three counties all have a higher degree of industrial integration, and the cultural and tourism industry drives the agriculture, industry and service sectors, so they are better than other counties in terms of industrial economic performance and effect. Located in the second tier are city e, city g, city d, and city h. These four counties have better cultural and tourism industry coupling and cultural and tourism industry coordination than industry driving effect.

In terms of industrial socio-cultural and environmental effects, those in the first tier are city d, city a, city b and city d. Among them, city d is a large energy city with a developed economy, complete cultural infrastructure and a large number of hosts, so the socio-cultural effect is significantly better than the environmental effect. City a, city b and city e are more balanced in terms of socio-cultural and environmental benefits. Located in the second tier are city f, city c, city i, city h, and city k. Among these five counties, city f is more similar to city d, and the socio-cultural effect is significantly better than the environmental effect. City c is a large agricultural county, and the environmental effect is better than the socio-cultural effect due to the higher growth rate of carbon emissions from tourism, contribution of greening from tourism, level of environmental regulation, and decoupling index of carbon emissions from tourism.

2.4

Spatial autocorrelation of the high quality index of the cultural tourism industry

In order to deeply analyze the spatial agglomeration characteristics of the integration development level, this paper uses Stata16.0 software to calculate the global autocorrelation Moran’s I of 14 cities in the Yangtze River Economic Belt in the period of 2009-2023.The results of Moran’s I test are shown in Table 4.

Table 4.

Moran’s I test results

Year	Moran’s I	Z	P
2009	0.161	2.651	0.003
2010	0.15	2.452	0.008
2011	0.191	2.838	0.002
2012	0.186	2.87	0.003
2013	0.234	3.307	0
2014	0.191	2.811	0.002
2015	0.185	2.843	0.002
2016	0.195	2.897	0.001
2017	0.185	2.858	0.011
2018	0.178	2.737	0.006
2019	0.139	2.322	0.016
2020	0.103	1.979	0.031
2021	0.072	1.687	0.052
2022	0.11	2.02	0.025
2023	0.114	1.94	0.026

It can be seen from Table 4 that during the study period, under the premise of P<0.05, Moran’s I, and the Z values were all greater than 1.94 except for special years in 2020, which indicated that there was an obvious spatial correlation between the integration of cultural industry and tourism industry, and the spatial correlation showed the stage evolution characteristics of “enhancement-weakening-enhancement”. From 2009 to 2013, the value of Moran’s I fluctuated and increased, reflecting the positive correlation between the level of integrated development of cultural industry and tourism industry in province A, and the intensity of spatial agglomeration increased. From 2014 to 2021, the Moran’s I value was opposite to the first stage, showing a fluctuating and declining trend, indicating that the integration of cultural and tourism industries in province A was positively correlated at this time, but showed the characteristics of weakening spatial agglomeration. From 2022 to 2023, the fluctuation of Moran’s I increased, and the level of integration and development of Moran’s I was positively correlated, and the degree of spatial agglomeration was slightly enhanced.

3

Analysis of factors affecting the high-quality development of the cultural tourism industry in Province A

3.1

Factors affecting high-quality tourism development

3.1.1

Principles for the selection of influencing factors

1)

Scientific principle

The selection of impact factor indicators must follow the objective law of the development of things, and in the selection of indicators, we should read more relevant literature to see how other scholars select relevant impact factor indicators and refer to the research results of previous scholars. On the other hand, the indicators of influence factors must come from the official websites of government departments or data released by authoritative organizations to ensure that all indicators and research results are authoritative and reliable. The above elements together reflect the scientific principle of indicator selection.

2)

Principle of focus

There is a universal connection between things, and there are many factors that can influence the high-quality development of tourism, and there may be a certain correlation between the influencing factors. The principle of focus is to highlight the importance of the selected indicators, to ensure that the selected indicators are representative and can effectively analyze the mechanism of high-quality development of tourism.

3)

Principle of feasibility

The principle of feasibility means that when selecting indicators, the difficulty of data collection, whether there are missing values, whether the statistical caliber has changed, etc., otherwise it is not conducive to the analysis of the influencing factors in this chapter.

3.1.2

Variable Selection and Preprocessing

1)

Selection of variables

The content of this section selects the score of high-quality comprehensive development of tourism as the explanatory variable, combines the relevant literature and the principles of indicator selection listed in section 3.1.1, selects the level of science and technology, the level of the regional economy and the level of consumption as the influencing factors, i.e., explanatory variables, and adopts the internal expenditure of R&D funds, per capita GDP and per capita disposable income of the residents as the indicators to quantify these three influencing factors. As for the influence factor of government support, some literatures adopt the indicator of local government financial expenditure as a proportion of GDP to measure it, and this paper takes into account the current background of the integration of culture and tourism, and therefore innovatively chooses the culture and tourism utility fees in government expenditure to quantify the influence factor of government support.

The research of new economic geography on the theory of industrial spatial agglomeration shows that: the result of the confrontation between the attraction and repulsion force that exists between the manufacturers will ultimately decide whether the production activities are spatially transferred to the region of the agglomeration tendency or the dispersion tendency that is transferred out of the region. The actual utilization of foreign capital belongs to the index of discrete force in industrial transfer, and the volume of post and telecommunications business belongs to the index of attraction in industrial transfer, both of which have an important impact on the spatial agglomeration of the tourism industry, and therefore have a certain impact on the level of high-quality development of China’s tourism industry under the perspective of cultural and tourism integration. In this paper, the two influencing factors of the degree of openness and the level of communication are added, and the two indicators of the actual use of foreign capital and the volume of postal and telegraphic business are selected for measurement. The indicators of the influencing factors of high-quality development of tourism in the context of smart tourism are shown in Table 5. And each influencing factor is explained and analyzed as follows:

(1)

Technology level

Nowadays, the rise of “smart tourism”, people will choose more online booking, booking hotels, find the introduction of attractions and travel tips, etc., and the level of science and technology will affect the level of innovation in a city, in order to enhance the level of innovation, you need to have a certain level of science and technology support. If the development of science and technology is good, it can play a role in promoting the development of intelligentization and informationization of the city’s tourism industry, and then improve the construction of tourism information system. In this paper, we choose the indicator of internal expenditure of R&D funds to measure the level of scientific and technological development of provinces in China, which is denoted by x₁.

(2)

Government support

The coordination of culture and tourism as well as the development of travel agencies, libraries, cultural markets and other infrastructures can not be separated from the government’s support, culture and tourism fees can promote the healthy development of China’s culture and tourism, and the coordinated development of the tourism industry also plays a vital role, this paper uses the culture and tourism fees to measure the level of government support, expressed in x₂.

(3)

Regional economic level

The development of tourism in a region should be based on the national economy of the region, especially in the context of the integration of culture and tourism and the high- quality development of tourism, only with good economic development can we build up the tourism peripheral industries, such as culture and service, and then better promote the development of tourism, in this paper, we choose the per capita GDP to measure the economic foundation of China’s provinces, which is expressed by x₃.

(4)

Degree of openness

The attractiveness of a province to external resources can be a good reflection of the degree of openness of the region, an excellent open environment can not only drive the development of a regional economy, but also improve the visibility of the region, thus attracting many foreign tourists, tourism revenue and tourism quality development level will also be higher. In this paper, the actual use of foreign capital is chosen to measure the degree of openness of each province, which is denoted by x₄.

(5)

Consumption level

People’s living standards and purchasing power will affect people’s plans to travel, tour, and see performances, and will also have an impact on the degree of cultural and tourism integration and the level of high-quality development of tourism in a region. Disposable income per capita is widely recognized as an important factor in determining residents’ consumption expenditures, so the paper chooses disposable income per capita to measure the consumption level of residents in each province in China, which is expressed as x₅.

(6)

Communication Level

With the rapid development of communication technology, people’s access to information is accelerated and expanded, and the information channels for obtaining tourism information, cultural venues, and cultural performances are increasing, which will affect the development of cultural and tourism integration to some extent. This paper adopts the volume of postal and telecommunication business to measure the level of communication technology development in each province, and a larger volume of postal and telecommunication business means a higher level of communication technology, which is expressed by x₆.

2)

Data sources and pre-processing

All the data of independent variables come from China Statistical Yearbook, Statistical Yearbook and Statistical Bulletin of National Economic and Social Development. Due to the different units of the above explanatory variables and the large quantitative differences among the indicators, the explanatory variables are logarithmically processed before variable screening, and the processed explanatory variables are recorded as ln x₁, ln x₂, ln x₃, ln x₄, ln x₅ and ln x₆, respectively.The results of descriptive analysis of each variable are shown in Table 6.

Observation of Table 6 reveals that the standard deviation of variables ln x₄ and ln x₅ is 1.655 and 0.435 respectively, which is a large difference. The extreme deviation of variable ln x₁ is 6.093, while that of ln x₅ is only 2.265. Therefore, in order to eliminate the effect of outliers, all continuous variables were also subjected to 1% shrinkage.

Table 5.

Indicators of the development of high quality of tourism

Influencing Factor	Index Selection	Unit	Variable
Technology Level	Internal Expenses Of R&D Funds	Yuan	x₁
Government Support	Cultural And Tourism Fees	Yuan	x₂
Regional Economic Level	Regional GDP	Hundred Dollars	x₃
Openness	Actual Foreign Investment	Dollars	x₄
Consumption Level	Per Capita Disposable Income	Yuan	x₅
Communication Level	Postal Capacity	Hundred Dollars	x₆

Table 6.

Descriptive statistical analysis of variables

Variable	Mean	Standard Deviation	Aberration
ln x₁	14.652	1.328	6.093
ln x₂	12.036	0.655	3.735
ln x₃	9.691	0.888	4.545
ln x₄	3.678	1.655	9.101
ln x₅	9.879	0.435	2.265
ln x₆	6.625	1.043	5.827
y	0.226	0.129	0.527

3.2

Model design and correlation test

3.2.1

Modeling

In order to test the role of science and technology level, government support, regional economic level, openness, consumption level and communication level on the overall high-quality development of cultural and tourism industry in Province A and the four important regions (R1, R2, R3 and R4), a basic panel regression model is constructed based on the panel data of each state and city of Province A in the period from 2009 to 2023: 12 $\ln Q_{i t} = α + β_{1} x_{1} + β_{2} x_{2} + β_{3} x_{3} + β_{4} x_{4} + β_{5} x_{5} + β_{6} x_{6} + u_{i} + ε_{i t}$

Where: i is the region, t represents the year, Q_it is the i region, the quality index of economic development in the tth year, α is the intercept term, x₁, x₂, x₃, x₄, x₅, x₆ represent the test of the level of science and technology, the strength of government support, the level of the regional economy, the degree of openness, the level of consumption and the level of communication, β₁, β₂, β₃, β₄, β₅, β₆ is the regression coefficient of each explanatory variable, u_i is the regional effect, and ε_it is the random error term. In order to minimize data fluctuation, the data of each variable are logarithmically processed. 13 $\ln Q_{i t} = α + β_{1} \ln x_{1} + β_{2} \ln x_{2} + β_{3} \ln x_{3} + β_{4} \ln x_{4} + β_{5} \ln x_{5} + β_{6} \ln x_{6} + u_{i} + ε_{i t}$

3.2.2

Correlation test and multicollinearity test of variables

Firstly, the correlation of each variable is tested, and the correlation coefficient and VIF value are shown in Table 7. From the table, it can be seen that the absolute value of the correlation coefficient of each variable is less than 0.5, and the existence of the correlation of each variable basically does not affect the regression results, while the VIF value is less than 10, which further excludes the possibility of the existence of multiple covariance among the variables.

Table 7.

Correlation coefficient and VIF value

Correlation coefficient	Q	x₁	x₂	x₃	x₄	x₅	x₆
Q	1
x₁	0.404	1
x₂	0.304	0.155	1
x₃	0.573	0.137	0.301	1
x₄	0.014	0.082	0.058	0.311	1
x₅	0.335	0.178	0.299	0.056	0.13	1
x₆	0.404	0.155	0.301	0.295	0.596	0.029	1
VIF	8.668	6.306	7.255	5.621	5.654	7.488	6.828

3.2.3

Smoothness test of variables

Before regressing the panel data of Province A from 2009 to 2023, in order to avoid the problem of pseudo-regression and ensure the validity of the regression effect, this paper adopts the LLC test and the ADF test to carry out the unit root test for the explanatory variables and the explanatory variables. The results of unit root test are shown in Table 8. As can be seen from the table, all panel data variables reject the original hypothesis at 1% significant level, that is, there is no unit root in all panel data variables, and the variable series are smooth and can be regressed.

Table 8.

Unit root test results

variable	LLC test		Fisher-ADF test
variable	Statistic	p-vlaue	Statistic	p-vlaue
Q	-6.1418	0.000***	40.5855	0.000***
x₁	-5.5624	0.000***	85.0985	0.000***
x₂	-5.0835	0.000***	33.8155	0.000***
x₃	-4.3033	0.000***	58.4175	0.000***
x₄	-13.8104	0.000***	61.5195	0.000***
x₅	-16.2047	0.000***	145.4366	0.000***
x₆	-17.2311	0.000***	146.5213	0.000***

3.2.4

Regression analysis by region

In order to be able to draw a clearer picture of the differences in the degree of influence of each influencing factor on different regions of Province A, Province A is divided into four regions, R1, R2, R3 and R4, according to the regional coordinated development plan of Province A, and regressions are carried out for each of the four regions. The results of the subregional regression are shown in Table 9 (*p < 0.1, **p < 0.05, ***p < 0.01). Before regression, F test, BP test and Hausman test were conducted on the model respectively, and the test results of p-value were all 0. Therefore, R1, R2 and R3 were regressed by fixed regression model. And R4 includes only one city, which does not constitute a panel regression model, so the time series regression model is used to regress it. As can be seen from the table, there are differences in the role of the influence factors on the regions of Province A. The level of science and technology, government support influencing factors significantly promote the high-quality development of the economy in R1.

Table 9.

The partition domain returns

variable	whole	R1	R2	R3	R4
	FE	FE	FE	FE	Time series regression
	lnq	lnq	lnq	lnq	lnq
ln x₁	0.0783**	0.185**	0.0951**	0.422**	0.1736**
ln x₁	(0.068)	(0.16)	(0.042)	(1.79)	(0.2869)
ln x₂	0.0325**	0.2511	0.0831**	0.068**	0.1187**
ln x₂	(0.035)	(0.045)	(0.085)	(0.221)	(0.087)
ln x₃	0.0662***	0.1286	0.3376**	0.1496**	0.2748
ln x₃	(0.018)	(0.393)	(0.03)	(1.204)	(0.309)
ln x₄	0.0537***	0.1295**	0.1163**	0.149**	0.387**
ln x₄	(0.025)	(0.294)	(0.089)	(1.362)	(0.2291)
ln x₅	0.0583**	0.099**	0.267	0.3365	0.3845
ln x₅	(0.095)	(0.029)	(0.028)	(1.028)	(0.124)
In x₆	0.0517***	0.1145***	0.1163***	0.153***	0.377***
In x₆	(0.015)	(0.297)	(0.084)	(1.365)	(0.2381)
_cons	-0 46***	-0.343***	-0.6176***	-0.128***	-0.474***
_cons	0.107	0.047	0.039	-0.458	-0.298
N	151	38	55	34	11
r²	0.371	0.8155	0.4788	0.5911	0.7305

3.2.5

Robustness Tests

In order to further ensure the stability and reliability of the explanatory ability of the test model and the empirical results, this paper chooses to replace the explanatory variables for the model robustness test according to the experience of previous scholars’ empirical research. The consumption ratio of urban and rural residents and the level of urbanization development are both important indicators of the standard of living of residents, and both have a certain role in enhancing the high-quality development of the economy, so the explanatory variable of the urbanization level is replaced by the urban and rural residents’ consumption ratio to test the model. From the regression results of each model, the sign direction of the explanatory variables in the model is consistent with the benchmark regression model, and the significance is also relatively consistent, which can prove that the benchmark regression results are robust. The robustness tests are shown in Table 10 (* p < 0.1, ** p < 0.05, *** p < 0.01).

Table 10.

Robustness test

variable	whole	R1	R2	R3	R4
	FE	FE	FE	FE	Time series regression
	lnq	lnq	lnq	lnq	lnq
x₁	0.2242	0.1436	0.3506	0.148	0.582
x₁	(0.013)	(0.381)	(0.04)	(1.203)	(0.459)
x₂	0.1936	0.172	0.0063	0.087	0.214
x₂	(0.018)	(0.292)	(0.033)	(0.645)	(0.583)
x₃	0.1577	0.088	0.0233	0.42	0.6752
x₃	(0.079)	(0.128)	(0.044)	(1.736)	(0.2935)
x₄	0.0242	0.041	0.0123	0.067	0.7936
x₄	(0.019)	(0.038)	(0.079)	(1.207)	(0.168)
x₅	0.3071	0.2961	0.3653	0.745	0.1658
x₅	(0.07)	(0.046)	(0.021)	(0.969)	(0.44)
x₆	0.2282	0.1446	0.3596	0.152	0.585
x₆	(0.018)	(0.374)	(0.039)	(1.192)	(0.461)
_cons	-0.505	-0.536	-0.2622	-0.426	-0.278
_cons	(0.092)	(0.024)	(0.071)	(-0.338)	(-0.535)
N	151	38	55	34	11
r²	0.8569	0.4312	0.5521	0.6871	0.7215

4

Proposing a path for the high-quality development of the cultural and tourism industry

4.1

Deepening the new layout of the integrated development of culture and tourism

Province A is home to many historical and cultural celebrities, including Cangjie, the founder of Chinese characters; Jia SiFo, the agricultural sage who compiled “The Essential Art of Qi Min”; Fan Zhongyan, the governor of Qingzhou; Su Shi, the governor of Mizhou; and Zheng Banqiao, the magistrate of Weixian County. These historical cultures, together with the beautiful natural scenery of Province A, have shaped a unique humanistic temperament and created a unique urban “IP” for the province. At the same time, Province A is rich in tourism resources, and is uniquely equipped with the characteristics of “culture” and “tourism” integration. In order to give full play to this advantage, Province A can gather all kinds of tourism resources within the city, to create a deep linkage + cluster development of the deep integration of culture and tourism development model. For example, to explore the depth of the county and urban areas have their own unique cultural and tourism resources, and its geographical advantages, the development of industrial direction combined with the series, to create a variety of tourism projects and patterns. To build a new layout of cultural and tourism integration of cluster development, and to promote the convergence of tourism resources of counties and districts in Province A, forming a situation of “a hundred flowers blooming, full of colors in the spring”.

4.2

Expanding new forms of integrated development of culture and tourism

The tourism industry in provinces and cities A has gradually transformed and upgraded from a single cultural tourism model to a more diversified “pan-cultural tourism” to meet the escalating consumption needs of tourists. Widely implement the “cultural tourism +” plan, actively implement the “Double Ten Project” for the integrated development of cultural tourism, continuously expand new integration formats, and build a new pattern of integrated development of cultural tourism and multi-industry and multi-direction. For example, a county has played a key role in the innovation of the “tourism + rural revitalization” model, and has made a hermit village a representative location for characteristic rural tourism. Relying on these two industries, province and city A are also actively exploring the cross-border integration model of “tourism + agriculture” and “tourism + industry”. Qianquezhuang Village, Shuangyang Street, Economic Zone of Province A, uses advanced fruit and vegetable planting parks to actively develop characteristic leisure tourism and create an immersive experience of “tourism + sightseeing agriculture”. Based on farm tourism, the village has gradually expanded the cultural tourism industry of leisure farms, children’s cute pet paradises, fruit picking, agricultural experience, popular science demonstrations, rural folk customs, and red culture. According to statistics, more than 100,000 tourists visit Qianquezhuang Village every year, and the village has also become a well-known “model village” for rural revitalization in China.

This paper argues that the tourism industry in province A may wish to move towards a higher stage of participation and diversification. On the basis of the cross-border integration and development that has been formed, the cultural tourism industry in province and city A will continue to deepen and form diversified new business formats, such as “cultural tourism + health care”, “cultural tourism + research”, “cultural tourism + intangible cultural heritage”, “cultural tourism + sports”, “cultural tourism + new technology” and other innovative cross-border integration models.

These new innovative integration modes help to promote the upgrading of cultural tourism patterns and the transformation and development of industries in Province A and create unique and exciting tourism experiences and entertainment contents, which not only increase the participation and engagement of tourists, but also promote scientific and technological innovation and industrial upgrading in Province A, strengthen the market competitiveness and the level of economic development, and enhance the travelers’ experience of tourism.

4.3

Building new vehicles for integrated development of culture and tourism

Benefiting from the completion of a comprehensive well-off society, the tourism industry has ushered in a new era of mass tourism and a new station of regional tourism, and the concept of tourism has gradually changed, emphasizing the sharing of a better life and focusing on personal itinerary choices has become mainstream [25]. This means that the dominance of the tourism economy has shifted to the demand side of tourists, and this change in the background, by creating a new cultural and tourism carrier, it can make the high-quality cultural and tourism resources of A province and city reflect each other.

The Qingzhou City Night City project has attracted a large number of tourists and become a popular attraction, and the Qilu Sky Road in the hilly countryside has become a boutique route favored by self-driving tourists, etc., all of which are typical cases of successful attempts.A province and city have also made use of cultural heritage protection to enhance the thickness of the city’s heritage. By focusing on building a highland for cultural heritage protection and utilization, the Specialized Committee on New Year’s Paintings of the China Association for the Protection of Intangible Cultural Heritage has taken root in Province A, and Province A has been shortlisted as one of the three nationally preferred projects for the integrated development of intangible cultural heritage and tourism. At the same time, the number of museums in Province A has been increased substantially, with more than 10 new ones on record, totaling more than 70. In addition, the public welfare Civic and Political Science class of the museums in Province A and the city was selected as an advanced typical case of volunteer service in the national museums.

Utilizing cultural foreign exchange and cooperation to make cultural and tourism branding brighter. Province A cities have repeatedly attended as provincial representatives in intangible cultural heritage exhibitions organized by foreign countries such as Japan. In the process, the visibility of the cultural and tourism brand of Province A has been increasing. Through active cultural exchanges and cooperation with foreign countries, the doors of international exchanges have been opened, cultural mutual understanding and cooperation have been promoted, and the image and influence of Province A in the international arena have been enhanced.

To create a big stage for the deep integration and development of culture and tourism, you can construct a new platform for the integration and development of culture and tourism by tapping into high-quality resources and fully energizing the potential of culture and tourism integration. For example, to create similar cultural tourism venues such as Chaoran Terrace and Zheng Banqiao Memorial Hall, to create tourism products with special charms such as kites, painting and calligraphy, dinosaurs, red sorghum, etc., and to create unique nighttime cultural tourism business districts such as Qingzhou Ancient City, Shikwuyuan, and Fenghuidi. At the same time, expanding immersive experience tours, such as caravan campgrounds, ski resorts and other fashionable trendy places to play, or fashionable restaurants, city study rooms and other leisure spaces. Create a new carrier for the integrated development of culture and tourism, create a new trend of tourism with life, quality, temperature, feeling and sharing, satisfy the people’s desire for a better life, and allow tourism projects to incorporate traditional cultural elements, regional characteristics, and innovative experiences, attracting tourists to keep coming back to visit, experience, and hit the ground running.

5

Conclusion

With the development trend of cultural tourism industry gradually showing a good trend, the traditional way of development gradually unable to meet the diversified needs of the people, therefore, in order to comply with the inherent development trend of the industry, the exploration of high-quality development path of cultural tourism industry is imminent. The article puts forward the high-quality development path of cultural tourism industry in the background of intelligent tourism by analyzing the measurement of the high-quality development level of cultural tourism industry in province A and analyzing the factors affecting the high-quality development of cultural tourism industry.

By using Moran’s I method to study the spatiotemporal evolution pattern of the cultural tourism industry in 14 counties and cities in province A from 2009 to 2023, the results show that the cultural tourism industry in province A shows the stage evolution characteristics of “enhancement-weakening- enhancement” in terms of spatial correlation. That is, from 2009 to 2023, the level of integrated development of cultural industry and tourism industry in province A is positively correlated, and the intensity of spatial agglomeration increases. From 2014 to 2021, the integrated development of the cultural and tourism industry in province A was negatively correlated, and the spatial agglomeration was weakened. From 2022 to 2023, the level of integrated development of the cultural and tourism industry in province A is positively correlated, and the degree of spatial agglomeration is slightly enhanced.

Finally, based on the conclusions of the study, a development path is proposed for the high-quality development of the cultural and tourism industry in Province A: (1) deepening the new layout of cultural and tourism integration and development, (2) expanding the new industry of cultural and tourism integration and development, and (3) building a new carrier for cultural and tourism integration and development.

Lingua:: Inglese

Frequenza di pubblicazione:: 1 volte all'anno
Argomenti della rivista:: Scienze biologiche, Scienze della vita, altro, Matematica, Matematica applicata, Matematica generale, Fisica, Fisica, altro

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Exploring the path of high-quality development of the tourism industry in the context of smart tourism

Jianfei Xing

Xiao Li

Li Sun

Pubblicato online: 23 set 2025

Ricevuto: 21 gen 2025

Accettato: 10 mag 2025

DOI: https://doi.org/10.2478/amns-2025-0967

Parole chiaveEntropy power method, Regression analysis, Intelligent tourism, Cultural and tourism industry

© 2025 Jianfei Xing et al., published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Parole chiave
Entropy power method, Regression analysis, Intelligent tourism, Cultural and tourism industry